STRATEGIES IN SEISMIC INFERENCE OF SUPERGRANULAR FLOWS ON THE SUN

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Bhattacharya, Jishnu; Hanasoge, Shravan M. [Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Mumbai-400005 (India)

2016-08-01

Observations of the solar surface reveal the presence of flows with length scales of around 35 Mm, commonly referred to as supergranules. Inferring the subsurface flow profile of supergranules from measurements of the surface and photospheric wavefield is an important challenge faced by helioseismology. Traditionally, the inverse problem has been approached by studying the linear response of seismic waves in a horizontally translationally invariant background to the presence of the supergranule; following an iterative approach that does not depend on horizontal translational invariance might perform better, since the misfit can be analyzed post iterations. In this work, we construct synthetic observations using a reference supergranule and invert for the flow profile using surface measurements of travel times of waves belonging to modal ridges f (surface gravity) and p {sub 1} through p {sub 7} (acoustic). We study the extent to which individual modes and their combinations contribute to infer the flow. We show that this method of nonlinear iterative inversion tends to underestimate the flow velocities, as well as inferring a shallower flow profile, with significant deviations from the reference supergranule near the surface. We carry out a similar analysis for a sound-speed perturbation and find that analogous near-surface deviations persist, although the iterations converge faster and more accurately. We conclude that a better approach to inversion would be to expand the supergranule profile in an appropriate basis, thereby reducing the number of parameters being inverted for and appropriately regularizing them.

Methodology for the inference of gene function from phenotype data.

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Ascensao, Joao A; Dolan, Mary E; Hill, David P; Blake, Judith A

2014-12-12

Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures. We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function. We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes. We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and

A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

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Santra, Tapesh, E-mail: tapesh.santra@ucd.ie [Systems Biology Ireland, University College Dublin, Dublin (Ireland)

2014-05-20

Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

International Nuclear Information System (INIS)

Santra, Tapesh

2014-01-01

Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.

Evaluation of artificial time series microarray data for dynamic gene regulatory network inference.

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Xenitidis, P; Seimenis, I; Kakolyris, S; Adamopoulos, A

2017-08-07

High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process. We examined the effect of various factors on the inference performance such as the network size, the presence of noise in microarray data, and the network sparseness. We used a system theory approach and examined the relationship between the pole placement of the inferred system and the inference performance. We examined the relationship between the inference performance in the time domain and the true system parameter identification. Simulation results indicated that time separation and the percentage of directly triggered genes are crucial factors. Also, network sparseness, the triggering function type and noise in input data affect the inference performance. When two factors were simultaneously varied, it was found that variation of one parameter significantly affects the dynamic response of the other. Crucial factors were also examined using a real GRN and acquired results confirmed simulation findings with artificial data. Different initial conditions were also used as an alternative triggering approach. Relevant results confirmed that the number of datasets constitutes the most significant parameter with regard to the inference performance. Copyright © 2017 Elsevier

Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

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Xiaobo Guo

Full Text Available Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs. It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC curve and the precision-recall (PR curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

STRIDE: Species Tree Root Inference from Gene Duplication Events.

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Emms, David M; Kelly, Steven

2017-12-01

The correct interpretation of any phylogenetic tree is dependent on that tree being correctly rooted. We present STRIDE, a fast, effective, and outgroup-free method for identification of gene duplication events and species tree root inference in large-scale molecular phylogenetic analyses. STRIDE identifies sets of well-supported in-group gene duplication events from a set of unrooted gene trees, and analyses these events to infer a probability distribution over an unrooted species tree for the location of its root. We show that STRIDE correctly identifies the root of the species tree in multiple large-scale molecular phylogenetic data sets spanning a wide range of timescales and taxonomic groups. We demonstrate that the novel probability model implemented in STRIDE can accurately represent the ambiguity in species tree root assignment for data sets where information is limited. Furthermore, application of STRIDE to outgroup-free inference of the origin of the eukaryotic tree resulted in a root probability distribution that provides additional support for leading hypotheses for the origin of the eukaryotes. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Inferring the gene network underlying the branching of tomato inflorescence.

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Laura Astola

Full Text Available The architecture of tomato inflorescence strongly affects flower production and subsequent crop yield. To understand the genetic activities involved, insight into the underlying network of genes that initiate and control the sympodial growth in the tomato is essential. In this paper, we show how the structure of this network can be derived from available data of the expressions of the involved genes. Our approach starts from employing biological expert knowledge to select the most probable gene candidates behind branching behavior. To find how these genes interact, we develop a stepwise procedure for computational inference of the network structure. Our data consists of expression levels from primary shoot meristems, measured at different developmental stages on three different genotypes of tomato. With the network inferred by our algorithm, we can explain the dynamics corresponding to all three genotypes simultaneously, despite their apparent dissimilarities. We also correctly predict the chronological order of expression peaks for the main hubs in the network. Based on the inferred network, using optimal experimental design criteria, we are able to suggest an informative set of experiments for further investigation of the mechanisms underlying branching behavior.

Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information.

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Jia, Bin; Wang, Xiaodong

2013-12-17

: The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type filter for inferring the structure of the gene regulatory network. In this paper, an inference framework based on point-based Gaussian approximation filters that can exploit the prior information is developed to solve the gene regulatory network inference problem. Different point-based Gaussian approximation filters, including the unscented Kalman filter (UKF), the third-degree cubature Kalman filter (CKF3), and the fifth-degree cubature Kalman filter (CKF5) are employed. Several types of network prior information, including the existing network structure information, sparsity assumption, and the range constraint of parameters, are considered, and the corresponding filters incorporating the prior information are developed. Experiments on a synthetic network of eight genes and the yeast protein synthesis network of five genes are carried out to demonstrate the performance of the proposed framework. The results show that the proposed methods provide more accurate inference results than existing methods, such as the EKF and the traditional UKF.

Does gene flow constrain adaptive divergence or vice versa? A test using ecomorphology and sexual isolation in Timema cristinae walking-sticks.

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Nosil, P; Crespi, B J

2004-01-01

Population differentiation often reflects a balance between divergent natural selection and the opportunity for homogenizing gene flow to erode the effects of selection. However, during ecological speciation, trait divergence results in reproductive isolation and becomes a cause, rather than a consequence, of reductions in gene flow. To assess both the causes and the reproductive consequences of morphological differentiation, we examined morphological divergence and sexual isolation among 17 populations of Timema cristinae walking-sticks. Individuals from populations adapted to using Adenostoma as a host plant tended to exhibit smaller overall body size, wide heads, and short legs relative to individuals using Ceonothus as a host. However, there was also significant variation in morphology among populations within host-plant species. Mean trait values for each single population could be reliably predicted based upon host-plant used and the potential for homogenizing gene flow, inferred from the size of the neighboring population using the alternate host and mitochondrial DNA estimates of gene flow. Morphology did not influence the probability of copulation in between-population mating trials. Thus, morphological divergence is facilitated by reductions in gene flow, but does not cause reductions in gene flow via the evolution of sexual isolation. Combined with rearing data indicating that size and shape have a partial genetic basis, evidence for parallel origins of the host-associated forms, and inferences from functional morphology, these results indicate that morphological divergence in T. cristinae reflects a balance between the effects of host-specific natural selection and gene flow. Our findings illustrate how data on mating preferences can help determine the causal associations between trait divergence and levels of gene flow.

Evolutionary Inference across Eukaryotes Identifies Specific Pressures Favoring Mitochondrial Gene Retention.

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Johnston, Iain G; Williams, Ben P

2016-02-24

Since their endosymbiotic origin, mitochondria have lost most of their genes. Although many selective mechanisms underlying the evolution of mitochondrial genomes have been proposed, a data-driven exploration of these hypotheses is lacking, and a quantitatively supported consensus remains absent. We developed HyperTraPS, a methodology coupling stochastic modeling with Bayesian inference, to identify the ordering of evolutionary events and suggest their causes. Using 2015 complete mitochondrial genomes, we inferred evolutionary trajectories of mtDNA gene loss across the eukaryotic tree of life. We find that proteins comprising the structural cores of the electron transport chain are preferentially encoded within mitochondrial genomes across eukaryotes. A combination of high GC content and high protein hydrophobicity is required to explain patterns of mtDNA gene retention; a model that accounts for these selective pressures can also predict the success of artificial gene transfer experiments in vivo. This work provides a general method for data-driven inference of the ordering of evolutionary and progressive events, here identifying the distinct features shaping mitochondrial genomes of present-day species. Copyright © 2016 Elsevier Inc. All rights reserved.

Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

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Xiaodong Cai

Full Text Available Integrating genetic perturbations with gene expression data not only improves accuracy of regulatory network topology inference, but also enables learning of causal regulatory relations between genes. Although a number of methods have been developed to integrate both types of data, the desiderata of efficient and powerful algorithms still remains. In this paper, sparse structural equation models (SEMs are employed to integrate both gene expression data and cis-expression quantitative trait loci (cis-eQTL, for modeling gene regulatory networks in accordance with biological evidence about genes regulating or being regulated by a small number of genes. A systematic inference method named sparsity-aware maximum likelihood (SML is developed for SEM estimation. Using simulated directed acyclic or cyclic networks, the SML performance is compared with that of two state-of-the-art algorithms: the adaptive Lasso (AL based scheme, and the QTL-directed dependency graph (QDG method. Computer simulations demonstrate that the novel SML algorithm offers significantly better performance than the AL-based and QDG algorithms across all sample sizes from 100 to 1,000, in terms of detection power and false discovery rate, in all the cases tested that include acyclic or cyclic networks of 10, 30 and 300 genes. The SML method is further applied to infer a network of 39 human genes that are related to the immune function and are chosen to have a reliable eQTL per gene. The resulting network consists of 9 genes and 13 edges. Most of the edges represent interactions reasonably expected from experimental evidence, while the remaining may just indicate the emergence of new interactions. The sparse SEM and efficient SML algorithm provide an effective means of exploiting both gene expression and perturbation data to infer gene regulatory networks. An open-source computer program implementing the SML algorithm is freely available upon request.

Structural influence of gene networks on their inference: analysis of C3NET

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Emmert-Streib Frank

2011-06-01

Full Text Available Abstract Background The availability of large-scale high-throughput data possesses considerable challenges toward their functional analysis. For this reason gene network inference methods gained considerable interest. However, our current knowledge, especially about the influence of the structure of a gene network on its inference, is limited. Results In this paper we present a comprehensive investigation of the structural influence of gene networks on the inferential characteristics of C3NET - a recently introduced gene network inference algorithm. We employ local as well as global performance metrics in combination with an ensemble approach. The results from our numerical study for various biological and synthetic network structures and simulation conditions, also comparing C3NET with other inference algorithms, lead a multitude of theoretical and practical insights into the working behavior of C3NET. In addition, in order to facilitate the practical usage of C3NET we provide an user-friendly R package, called c3net, and describe its functionality. It is available from https://r-forge.r-project.org/projects/c3net and from the CRAN package repository. Conclusions The availability of gene network inference algorithms with known inferential properties opens a new era of large-scale screening experiments that could be equally beneficial for basic biological and biomedical research with auspicious prospects. The availability of our easy to use software package c3net may contribute to the popularization of such methods. Reviewers This article was reviewed by Lev Klebanov, Joel Bader and Yuriy Gusev.

Inferring gene networks from discrete expression data

KAUST Repository

Zhang, L.

2013-07-18

The modeling of gene networks from transcriptional expression data is an important tool in biomedical research to reveal signaling pathways and to identify treatment targets. Current gene network modeling is primarily based on the use of Gaussian graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which generate counts of mRNAtranscripts in cell samples.We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution.We restrict the gene network structures to decomposable graphs and derive the graphs by selecting the covariance matrix of the Gaussian distribution with the hyper-inverse Wishart priors. Furthermore, we incorporate prior network models based on gene ontology information, which avails existing biological information on the genes of interest. We conduct simulation studies to examine the performance of our discrete graphical model and apply the method to two real datasets for gene network inference. © The Author 2013. Published by Oxford University Press. All rights reserved.

Comparison of evolutionary algorithms in gene regulatory network model inference.

LENUS (Irish Health Repository)

2010-01-01

ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

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Lee, Wei-Po; Hsiao, Yu-Ting; Hwang, Wei-Che

2014-01-16

To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

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Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

A novel mutual information-based Boolean network inference method from time-series gene expression data.

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Shohag Barman

Full Text Available Inferring a gene regulatory network from time-series gene expression data in systems biology is a challenging problem. Many methods have been suggested, most of which have a scalability limitation due to the combinatorial cost of searching a regulatory set of genes. In addition, they have focused on the accurate inference of a network structure only. Therefore, there is a pressing need to develop a network inference method to search regulatory genes efficiently and to predict the network dynamics accurately.In this study, we employed a Boolean network model with a restricted update rule scheme to capture coarse-grained dynamics, and propose a novel mutual information-based Boolean network inference (MIBNI method. Given time-series gene expression data as an input, the method first identifies a set of initial regulatory genes using mutual information-based feature selection, and then improves the dynamics prediction accuracy by iteratively swapping a pair of genes between sets of the selected regulatory genes and the other genes. Through extensive simulations with artificial datasets, MIBNI showed consistently better performance than six well-known existing methods, REVEAL, Best-Fit, RelNet, CST, CLR, and BIBN in terms of both structural and dynamics prediction accuracy. We further tested the proposed method with two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network, and also observed better results using MIBNI compared to the six other methods.Taken together, MIBNI is a promising tool for predicting both the structure and the dynamics of a gene regulatory network.

Human synthetic lethal inference as potential anti-cancer target gene detection

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Solé Ricard V

2009-12-01

Full Text Available Abstract Background Two genes are called synthetic lethal (SL if mutation of either alone is not lethal, but mutation of both leads to death or a significant decrease in organism's fitness. The detection of SL gene pairs constitutes a promising alternative for anti-cancer therapy. As cancer cells exhibit a large number of mutations, the identification of these mutated genes' SL partners may provide specific anti-cancer drug candidates, with minor perturbations to the healthy cells. Since existent SL data is mainly restricted to yeast screenings, the road towards human SL candidates is limited to inference methods. Results In the present work, we use phylogenetic analysis and database manipulation (BioGRID for interactions, Ensembl and NCBI for homology, Gene Ontology for GO attributes in order to reconstruct the phylogenetically-inferred SL gene network for human. In addition, available data on cancer mutated genes (COSMIC and Cancer Gene Census databases as well as on existent approved drugs (DrugBank database supports our selection of cancer-therapy candidates. Conclusions Our work provides a complementary alternative to the current methods for drug discovering and gene target identification in anti-cancer research. Novel SL screening analysis and the use of highly curated databases would contribute to improve the results of this methodology.

Predictive minimum description length principle approach to inferring gene regulatory networks.

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Chaitankar, Vijender; Zhang, Chaoyang; Ghosh, Preetam; Gong, Ping; Perkins, Edward J; Deng, Youping

2011-01-01

Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold that defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we propose a new inference algorithm that incorporates mutual information (MI), conditional mutual information (CMI), and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm is evaluated using both synthetic time series data sets and a biological time series data set (Saccharomyces cerevisiae). The results show that the proposed algorithm produced fewer false edges and significantly improved the precision when compared to existing MDL algorithm.

Sex-biased gene flow in spectacled eiders (Anatidae): Inferences from molecular markers with contrasting modes of inheritance

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Scribner, Kim T.; Petersen, Margaret R.; Fields, Raymond L.; Talbot, Sandra L.; Pearce, John M.; Chesser, Ronald K.

2001-01-01

Genetic markers that differ in mode of inheritance and rate of evolution (a sex-linked Z-specific microsatellite locus, five biparentally inherited microsatellite loci, and maternally inherited mitochondrial [mtDNA] sequences) were used to evaluate the degree of spatial genetic structuring at macro- and microgeographic scales, among breeding regions and local nesting populations within each region, respectively, for a migratory sea duck species, the spectacled eider (Somateria fisheri). Disjunct and declining breeding populations coupled with sex-specific differences in seasonal migratory patterns and life history provide a series of hypotheses regarding rates and directionality of gene flow among breeding populations from the Indigirka River Delta, Russia, and the North Slope and Yukon-Kuskokwim Delta, Alaska. The degree of differentiation in mtDNA haplotype frequency among breeding regions and populations within regions was high (ϕCT = 0.189, P 0.05; biparentally inherited microsatellites: mean θ = 0.001, P > 0.05) than was observed for mtDNA. Using models explicitly designed for uniparental and biparentally inherited genes, estimates of spatial divergence based on nuclear and mtDNA data together with elements of the species' breeding ecology were used to estimate effective population size and degree of male and female gene flow. Differences in the magnitude and spatial patterns of gene correlations for maternally inherited and nuclear genes revealed that females exhibit greater natal philopatry than do males. Estimates of generational female and male rates of gene flow among breeding regions differed markedly (3.67 × 10−4 and 1.28 × 10−2, respectively). Effective population size for mtDNA was estimated to be at least three times lower than that for biparental genes (30,671 and 101,528, respectively). Large disparities in population sizes among breeding areas greatly reduces the proportion of total genetic variance captured by dispersal, which may

A novel gene network inference algorithm using predictive minimum description length approach.

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Chaitankar, Vijender; Ghosh, Preetam; Perkins, Edward J; Gong, Ping; Deng, Youping; Zhang, Chaoyang

2010-05-28

Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold which defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we proposed a new inference algorithm which incorporated mutual information (MI), conditional mutual information (CMI) and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm was evaluated using both synthetic time series data sets and a biological time series data set for the yeast Saccharomyces cerevisiae. The benchmark quantities precision and recall were used as performance measures. The results show that the proposed algorithm produced less false edges and significantly improved the precision, as compared to the existing algorithm. For further analysis the performance of the algorithms was observed over different sizes of data. We have proposed a new algorithm that implements the PMDL principle for inferring gene regulatory networks from time series DNA microarray data that eliminates the need of a fine tuning parameter. The evaluation results obtained from both synthetic and actual biological data sets show that the

Inference of gene regulatory networks from time series by Tsallis entropy

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de Oliveira Evaldo A

2011-05-01

Full Text Available Abstract Background The inference of gene regulatory networks (GRNs from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information, a new criterion function is here proposed. Results In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 �

A comparative study of covariance selection models for the inference of gene regulatory networks.

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Stifanelli, Patrizia F; Creanza, Teresa M; Anglani, Roberto; Liuzzi, Vania C; Mukherjee, Sayan; Schena, Francesco P; Ancona, Nicola

2013-10-01

The inference, or 'reverse-engineering', of gene regulatory networks from expression data and the description of the complex dependency structures among genes are open issues in modern molecular biology. In this paper we compared three regularized methods of covariance selection for the inference of gene regulatory networks, developed to circumvent the problems raising when the number of observations n is smaller than the number of genes p. The examined approaches provided three alternative estimates of the inverse covariance matrix: (a) the 'PINV' method is based on the Moore-Penrose pseudoinverse, (b) the 'RCM' method performs correlation between regression residuals and (c) 'ℓ(2C)' method maximizes a properly regularized log-likelihood function. Our extensive simulation studies showed that ℓ(2C) outperformed the other two methods having the most predictive partial correlation estimates and the highest values of sensitivity to infer conditional dependencies between genes even when a few number of observations was available. The application of this method for inferring gene networks of the isoprenoid biosynthesis pathways in Arabidopsis thaliana allowed to enlighten a negative partial correlation coefficient between the two hubs in the two isoprenoid pathways and, more importantly, provided an evidence of cross-talk between genes in the plastidial and the cytosolic pathways. When applied to gene expression data relative to a signature of HRAS oncogene in human cell cultures, the method revealed 9 genes (p-value<0.0005) directly interacting with HRAS, sharing the same Ras-responsive binding site for the transcription factor RREB1. This result suggests that the transcriptional activation of these genes is mediated by a common transcription factor downstream of Ras signaling. Software implementing the methods in the form of Matlab scripts are available at: http://users.ba.cnr.it/issia/iesina18/CovSelModelsCodes.zip. Copyright © 2013 The Authors. Published by

GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods.

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Schaffter, Thomas; Marbach, Daniel; Floreano, Dario

2011-08-15

Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international Dialogue for Reverse Engineering Assessments and Methods (DREAM) competition with three network inference challenges (DREAM3, DREAM4 and DREAM5). GNW is available at http://gnw.sourceforge.net along with its Java source code, user manual and supporting data. Supplementary data are available at Bioinformatics online. dario.floreano@epfl.ch.

Inferring Phylogenetic Networks from Gene Order Data

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Alexey Anatolievich Morozov

2013-01-01

Full Text Available Existing algorithms allow us to infer phylogenetic networks from sequences (DNA, protein or binary, sets of trees, and distance matrices, but there are no methods to build them using the gene order data as an input. Here we describe several methods to build split networks from the gene order data, perform simulation studies, and use our methods for analyzing and interpreting different real gene order datasets. All proposed methods are based on intermediate data, which can be generated from genome structures under study and used as an input for network construction algorithms. Three intermediates are used: set of jackknife trees, distance matrix, and binary encoding. According to simulations and case studies, the best intermediates are jackknife trees and distance matrix (when used with Neighbor-Net algorithm. Binary encoding can also be useful, but only when the methods mentioned above cannot be used.

Doing ecohydrology backward: Inferring wetland flow and hydroperiod from landscape patterns

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Acharya, Subodh; Kaplan, David A.; Jawitz, James W.; Cohen, Matthew J.

2017-07-01

Human alterations to hydrology have globally impacted wetland ecosystems. Preventing or reversing these impacts is a principal focus of restoration efforts. However, restoration effectiveness is often hampered by limited information on historical landscape properties and hydrologic regime. To help address this gap, we developed a novel statistical approach for inferring flows and inundation frequency (i.e., hydroperiod, HP) in wetlands where changes in spatial vegetation and geomorphic patterns have occurred due to hydrologic alteration. We developed an analytical expression for HP as a transformation of the landscape-scale stage-discharge relationship. We applied this model to the Everglades "ridge-slough" (RS) landscape, a patterned, lotic peatland in southern Florida that has been drastically degraded by compartmentalization, drainage, and flow diversions. The new method reliably estimated flow and HP for a range of RS landscape patterns. Crucially, ridge-patch anisotropy and elevation above sloughs were strong drivers of flow-HP relationships. Increasing ridge heights markedly increased flow required to achieve sufficient HP to support peat accretion. Indeed, ridge heights inferred from historical accounts would require boundary flows 3-4 times greater than today, which agrees with restoration flow estimates from more complex, spatially distributed models. While observed loss of patch anisotropy allows HP targets to be met with lower flows, such landscapes likely fail to support other ecological functions. This work helps inform restoration flows required to restore stable ridge-slough patterning and positive peat accretion in this degraded ecosystem, and, more broadly, provides tools for exploring interactions between landscape and hydrology in lotic wetlands and floodplains.

Inferring causal genomic alterations in breast cancer using gene expression data

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2011-01-01

Background One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies. Results We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments. Conclusions To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data. PMID:21806811

Inferring the functional effect of gene expression changes in signaling pathways

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Sebastián-León, Patricia; Carbonell, José; Salavert, Francisco; Sanchez, Rubén; Medina, Ignacio; Dopazo, Joaquín

2013-01-01

Signaling pathways constitute a valuable source of information that allows interpreting the way in which alterations in gene activities affect to particular cell functionalities. There are web tools available that allow viewing and editing pathways, as well as representing experimental data on them. However, few methods aimed to identify the signaling circuits, within a pathway, associated to the biological problem studied exist and none of them provide a convenient graphical web interface. We present PATHiWAYS, a web-based signaling pathway visualization system that infers changes in signaling that affect cell functionality from the measurements of gene expression values in typical expression microarray case–control experiments. A simple probabilistic model of the pathway is used to estimate the probabilities for signal transmission from any receptor to any final effector molecule (taking into account the pathway topology) using for this the individual probabilities of gene product presence/absence inferred from gene expression values. Significant changes in these probabilities allow linking different cell functionalities triggered by the pathway to the biological problem studied. PATHiWAYS is available at: http://pathiways.babelomics.org/. PMID:23748960

Inferring species trees from incongruent multi-copy gene trees using the Robinson-Foulds distance

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2013-01-01

Background Constructing species trees from multi-copy gene trees remains a challenging problem in phylogenetics. One difficulty is that the underlying genes can be incongruent due to evolutionary processes such as gene duplication and loss, deep coalescence, or lateral gene transfer. Gene tree estimation errors may further exacerbate the difficulties of species tree estimation. Results We present a new approach for inferring species trees from incongruent multi-copy gene trees that is based on a generalization of the Robinson-Foulds (RF) distance measure to multi-labeled trees (mul-trees). We prove that it is NP-hard to compute the RF distance between two mul-trees; however, it is easy to calculate this distance between a mul-tree and a singly-labeled species tree. Motivated by this, we formulate the RF problem for mul-trees (MulRF) as follows: Given a collection of multi-copy gene trees, find a singly-labeled species tree that minimizes the total RF distance from the input mul-trees. We develop and implement a fast SPR-based heuristic algorithm for the NP-hard MulRF problem. We compare the performance of the MulRF method (available at http://genome.cs.iastate.edu/CBL/MulRF/) with several gene tree parsimony approaches using gene tree simulations that incorporate gene tree error, gene duplications and losses, and/or lateral transfer. The MulRF method produces more accurate species trees than gene tree parsimony approaches. We also demonstrate that the MulRF method infers in minutes a credible plant species tree from a collection of nearly 2,000 gene trees. Conclusions Our new phylogenetic inference method, based on a generalized RF distance, makes it possible to quickly estimate species trees from large genomic data sets. Since the MulRF method, unlike gene tree parsimony, is based on a generic tree distance measure, it is appealing for analyses of genomic data sets, in which many processes such as deep coalescence, recombination, gene duplication and losses as

Speciation with gene flow in whiptail lizards from a Neotropical xeric biome.

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Oliveira, Eliana F; Gehara, Marcelo; São-Pedro, Vinícius A; Chen, Xin; Myers, Edward A; Burbrink, Frank T; Mesquita, Daniel O; Garda, Adrian A; Colli, Guarino R; Rodrigues, Miguel T; Arias, Federico J; Zaher, Hussam; Santos, Rodrigo M L; Costa, Gabriel C

2015-12-01

Two main hypotheses have been proposed to explain the diversification of the Caatinga biota. The riverine barrier hypothesis (RBH) claims that the São Francisco River (SFR) is a major biogeographic barrier to gene flow. The Pleistocene climatic fluctuation hypothesis (PCH) states that gene flow, geographic genetic structure and demographic signatures on endemic Caatinga taxa were influenced by Quaternary climate fluctuation cycles. Herein, we analyse genetic diversity and structure, phylogeographic history, and diversification of a widespread Caatinga lizard (Cnemidophorus ocellifer) based on large geographical sampling for multiple loci to test the predictions derived from the RBH and PCH. We inferred two well-delimited lineages (Northeast and Southwest) that have diverged along the Cerrado-Caatinga border during the Mid-Late Miocene (6-14 Ma) despite the presence of gene flow. We reject both major hypotheses proposed to explain diversification in the Caatinga. Surprisingly, our results revealed a striking complex diversification pattern where the Northeast lineage originated as a founder effect from a few individuals located along the edge of the Southwest lineage that eventually expanded throughout the Caatinga. The Southwest lineage is more diverse, older and associated with the Cerrado-Caatinga boundaries. Finally, we suggest that C. ocellifer from the Caatinga is composed of two distinct species. Our data support speciation in the presence of gene flow and highlight the role of environmental gradients in the diversification process. © 2015 John Wiley & Sons Ltd.

State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

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Tuqyah Abdullah Al Qazlan

2015-01-01

Full Text Available To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.

Spurious correlations and inference in landscape genetics

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Samuel A. Cushman; Erin L. Landguth

2010-01-01

Reliable interpretation of landscape genetic analyses depends on statistical methods that have high power to identify the correct process driving gene flow while rejecting incorrect alternative hypotheses. Little is known about statistical power and inference in individual-based landscape genetics. Our objective was to evaluate the power of causalmodelling with partial...

Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions.

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Yinyin Yuan

Full Text Available Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/.

Artificial neural network inference (ANNI: a study on gene-gene interaction for biomarkers in childhood sarcomas.

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Dong Ling Tong

Full Text Available OBJECTIVE: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI. METHOD: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. RESULTS: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS; FCGRT and OLFM1 in Ewing's sarcoma (EWS suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. CONCLUSIONS: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas.

Learning a Markov Logic network for supervised gene regulatory network inference.

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Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

2013-09-12

Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

Large-scale modeling of condition-specific gene regulatory networks by information integration and inference.

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Ellwanger, Daniel Christian; Leonhardt, Jörn Florian; Mewes, Hans-Werner

2014-12-01

Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η(2) (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

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Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

2009-01-01

Abstract Background Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori assumptions about the interactions, which all simulate the observed patterns. It is important to analyze the properties of the circuits. Findings We have analyzed the simulated gene expression ...

Correlating Information Contents of Gene Ontology Terms to Infer Semantic Similarity of Gene Products

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Mingxin Gan

2014-01-01

Full Text Available Successful applications of the gene ontology to the inference of functional relationships between gene products in recent years have raised the need for computational methods to automatically calculate semantic similarity between gene products based on semantic similarity of gene ontology terms. Nevertheless, existing methods, though having been widely used in a variety of applications, may significantly overestimate semantic similarity between genes that are actually not functionally related, thereby yielding misleading results in applications. To overcome this limitation, we propose to represent a gene product as a vector that is composed of information contents of gene ontology terms annotated for the gene product, and we suggest calculating similarity between two gene products as the relatedness of their corresponding vectors using three measures: Pearson’s correlation coefficient, cosine similarity, and the Jaccard index. We focus on the biological process domain of the gene ontology and annotations of yeast proteins to study the effectiveness of the proposed measures. Results show that semantic similarity scores calculated using the proposed measures are more consistent with known biological knowledge than those derived using a list of existing methods, suggesting the effectiveness of our method in characterizing functional relationships between gene products.

Forest corridors maintain historical gene flow in a tiger metapopulation in the highlands of central India.

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Sharma, Sandeep; Dutta, Trishna; Maldonado, Jesús E; Wood, Thomas C; Panwar, Hemendra Singh; Seidensticker, John

2013-09-22

Understanding the patterns of gene flow of an endangered species metapopulation occupying a fragmented habitat is crucial for landscape-level conservation planning and devising effective conservation strategies. Tigers (Panthera tigris) are globally endangered and their populations are highly fragmented and exist in a few isolated metapopulations across their range. We used multi-locus genotypic data from 273 individual tigers (Panthera tigris tigris) from four tiger populations of the Satpura-Maikal landscape of central India to determine whether the corridors in this landscape are functional. This 45 000 km(2) landscape contains 17% of India's tiger population and 12% of its tiger habitat. We applied Bayesian and coalescent-based analyses to estimate contemporary and historical gene flow among these populations and to infer their evolutionary history. We found that the tiger metapopulation in central India has high rates of historical and contemporary gene flow. The tests for population history reveal that tigers populated central India about 10 000 years ago. Their population subdivision began about 1000 years ago and accelerated about 200 years ago owing to habitat fragmentation, leading to four spatially separated populations. These four populations have been in migration-drift equilibrium maintained by high gene flow. We found the highest rates of contemporary gene flow in populations that are connected by forest corridors. This information is highly relevant to conservation practitioners and policy makers, because deforestation, road widening and mining are imminent threats to these corridors.

Inferring Drosophila gap gene regulatory network: Pattern analysis of simulated gene expression profiles and stability analysis

NARCIS (Netherlands)

Fomekong-Nanfack, Y.; Postma, M.; Kaandorp, J.A.

2009-01-01

Background: Inference of gene regulatory networks (GRNs) requires accurate data, a method to simulate the expression patterns and an efficient optimization algorithm to estimate the unknown parameters. Using this approach it is possible to obtain alternative circuits without making any a priori

Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood.

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Wu, Yufeng

2012-03-01

Incomplete lineage sorting can cause incongruence between the phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. In this article, I present a new coalescent-based algorithm for species tree inference with maximum likelihood. I first describe an improved method for computing the probability of a gene tree topology given a species tree, which is much faster than an existing algorithm by Degnan and Salter (2005). Based on this method, I develop a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. This algorithm searches for the best species tree by starting from initial species trees and performing heuristic search to obtain better trees with higher likelihood. This algorithm, called STELLS (which stands for Species Tree InfErence with Likelihood for Lineage Sorting), has been implemented in a program that is downloadable from the author's web page. The simulation results show that the STELLS algorithm is more accurate than an existing maximum likelihood method for many datasets, especially when there is noise in gene trees. I also show that the STELLS algorithm is efficient and can be applied to real biological datasets. © 2011 The Author. Evolution© 2011 The Society for the Study of Evolution.

A Meta-Analysis of Multiple Matched Copy Number and Transcriptomics Data Sets for Inferring Gene Regulatory Relationships

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Newton, Richard; Wernisch, Lorenz

2014-01-01

Inferring gene regulatory relationships from observational data is challenging. Manipulation and intervention is often required to unravel causal relationships unambiguously. However, gene copy number changes, as they frequently occur in cancer cells, might be considered natural manipulation experiments on gene expression. An increasing number of data sets on matched array comparative genomic hybridisation and transcriptomics experiments from a variety of cancer pathologies are becoming publicly available. Here we explore the potential of a meta-analysis of thirty such data sets. The aim of our analysis was to assess the potential of in silico inference of trans-acting gene regulatory relationships from this type of data. We found sufficient correlation signal in the data to infer gene regulatory relationships, with interesting similarities between data sets. A number of genes had highly correlated copy number and expression changes in many of the data sets and we present predicted potential trans-acted regulatory relationships for each of these genes. The study also investigates to what extent heterogeneity between cell types and between pathologies determines the number of statistically significant predictions available from a meta-analysis of experiments. PMID:25148247

Pathogenomic inference of virulence-associated genes in Leptospira interrogans.

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Lehmann, Jason S; Fouts, Derrick E; Haft, Daniel H; Cannella, Anthony P; Ricaldi, Jessica N; Brinkac, Lauren; Harkins, Derek; Durkin, Scott; Sanka, Ravi; Sutton, Granger; Moreno, Angelo; Vinetz, Joseph M; Matthias, Michael A

2013-01-01

Leptospirosis is a globally important, neglected zoonotic infection caused by spirochetes of the genus Leptospira. Since genetic transformation remains technically limited for pathogenic Leptospira, a systems biology pathogenomic approach was used to infer leptospiral virulence genes by whole genome comparison of culture-attenuated Leptospira interrogans serovar Lai with its virulent, isogenic parent. Among the 11 pathogen-specific protein-coding genes in which non-synonymous mutations were found, a putative soluble adenylate cyclase with host cell cAMP-elevating activity, and two members of a previously unstudied ∼15 member paralogous gene family of unknown function were identified. This gene family was also uniquely found in the alpha-proteobacteria Bartonella bacilliformis and Bartonella australis that are geographically restricted to the Andes and Australia, respectively. How the pathogenic Leptospira and these two Bartonella species came to share this expanded gene family remains an evolutionary mystery. In vivo expression analyses demonstrated up-regulation of 10/11 Leptospira genes identified in the attenuation screen, and profound in vivo, tissue-specific up-regulation by members of the paralogous gene family, suggesting a direct role in virulence and host-pathogen interactions. The pathogenomic experimental design here is generalizable as a functional systems biology approach to studying bacterial pathogenesis and virulence and should encourage similar experimental studies of other pathogens.

An integrative approach to inferring biologically meaningful gene modules

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Wang Kai

2011-07-01

Full Text Available Abstract Background The ability to construct biologically meaningful gene networks and modules is critical for contemporary systems biology. Though recent studies have demonstrated the power of using gene modules to shed light on the functioning of complex biological systems, most modules in these networks have shown little association with meaningful biological function. We have devised a method which directly incorporates gene ontology (GO annotation in construction of gene modules in order to gain better functional association. Results We have devised a method, Semantic Similarity-Integrated approach for Modularization (SSIM that integrates various gene-gene pairwise similarity values, including information obtained from gene expression, protein-protein interactions and GO annotations, in the construction of modules using affinity propagation clustering. We demonstrated the performance of the proposed method using data from two complex biological responses: 1. the osmotic shock response in Saccharomyces cerevisiae, and 2. the prion-induced pathogenic mouse model. In comparison with two previously reported algorithms, modules identified by SSIM showed significantly stronger association with biological functions. Conclusions The incorporation of semantic similarity based on GO annotation with gene expression and protein-protein interaction data can greatly enhance the functional relevance of inferred gene modules. In addition, the SSIM approach can also reveal the hierarchical structure of gene modules to gain a broader functional view of the biological system. Hence, the proposed method can facilitate comprehensive and in-depth analysis of high throughput experimental data at the gene network level.

Large-scale inference of gene function through phylogenetic annotation of Gene Ontology terms: case study of the apoptosis and autophagy cellular processes.

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Feuermann, Marc; Gaudet, Pascale; Mi, Huaiyu; Lewis, Suzanna E; Thomas, Paul D

2016-01-01

We previously reported a paradigm for large-scale phylogenomic analysis of gene families that takes advantage of the large corpus of experimentally supported Gene Ontology (GO) annotations. This 'GO Phylogenetic Annotation' approach integrates GO annotations from evolutionarily related genes across ∼100 different organisms in the context of a gene family tree, in which curators build an explicit model of the evolution of gene functions. GO Phylogenetic Annotation models the gain and loss of functions in a gene family tree, which is used to infer the functions of uncharacterized (or incompletely characterized) gene products, even for human proteins that are relatively well studied. Here, we report our results from applying this paradigm to two well-characterized cellular processes, apoptosis and autophagy. This revealed several important observations with respect to GO annotations and how they can be used for function inference. Notably, we applied only a small fraction of the experimentally supported GO annotations to infer function in other family members. The majority of other annotations describe indirect effects, phenotypes or results from high throughput experiments. In addition, we show here how feedback from phylogenetic annotation leads to significant improvements in the PANTHER trees, the GO annotations and GO itself. Thus GO phylogenetic annotation both increases the quantity and improves the accuracy of the GO annotations provided to the research community. We expect these phylogenetically based annotations to be of broad use in gene enrichment analysis as well as other applications of GO annotations.Database URL: http://amigo.geneontology.org/amigo. © The Author(s) 2016. Published by Oxford University Press.

Algorithms for MDC-based multi-locus phylogeny inference: beyond rooted binary gene trees on single alleles.

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Yu, Yun; Warnow, Tandy; Nakhleh, Luay

2011-11-01

One of the criteria for inferring a species tree from a collection of gene trees, when gene tree incongruence is assumed to be due to incomplete lineage sorting (ILS), is Minimize Deep Coalescence (MDC). Exact algorithms for inferring the species tree from rooted, binary trees under MDC were recently introduced. Nevertheless, in phylogenetic analyses of biological data sets, estimated gene trees may differ from true gene trees, be incompletely resolved, and not necessarily rooted. In this article, we propose new MDC formulations for the cases where the gene trees are unrooted/binary, rooted/non-binary, and unrooted/non-binary. Further, we prove structural theorems that allow us to extend the algorithms for the rooted/binary gene tree case to these cases in a straightforward manner. In addition, we devise MDC-based algorithms for cases when multiple alleles per species may be sampled. We study the performance of these methods in coalescent-based computer simulations.

Pathogenomic inference of virulence-associated genes in Leptospira interrogans.

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Jason S Lehmann

Full Text Available Leptospirosis is a globally important, neglected zoonotic infection caused by spirochetes of the genus Leptospira. Since genetic transformation remains technically limited for pathogenic Leptospira, a systems biology pathogenomic approach was used to infer leptospiral virulence genes by whole genome comparison of culture-attenuated Leptospira interrogans serovar Lai with its virulent, isogenic parent. Among the 11 pathogen-specific protein-coding genes in which non-synonymous mutations were found, a putative soluble adenylate cyclase with host cell cAMP-elevating activity, and two members of a previously unstudied ∼15 member paralogous gene family of unknown function were identified. This gene family was also uniquely found in the alpha-proteobacteria Bartonella bacilliformis and Bartonella australis that are geographically restricted to the Andes and Australia, respectively. How the pathogenic Leptospira and these two Bartonella species came to share this expanded gene family remains an evolutionary mystery. In vivo expression analyses demonstrated up-regulation of 10/11 Leptospira genes identified in the attenuation screen, and profound in vivo, tissue-specific up-regulation by members of the paralogous gene family, suggesting a direct role in virulence and host-pathogen interactions. The pathogenomic experimental design here is generalizable as a functional systems biology approach to studying bacterial pathogenesis and virulence and should encourage similar experimental studies of other pathogens.

GRN2SBML: automated encoding and annotation of inferred gene regulatory networks complying with SBML.

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Vlaic, Sebastian; Hoffmann, Bianca; Kupfer, Peter; Weber, Michael; Dräger, Andreas

2013-09-01

GRN2SBML automatically encodes gene regulatory networks derived from several inference tools in systems biology markup language. Providing a graphical user interface, the networks can be annotated via the simple object access protocol (SOAP)-based application programming interface of BioMart Central Portal and minimum information required in the annotation of models registry. Additionally, we provide an R-package, which processes the output of supported inference algorithms and automatically passes all required parameters to GRN2SBML. Therefore, GRN2SBML closes a gap in the processing pipeline between the inference of gene regulatory networks and their subsequent analysis, visualization and storage. GRN2SBML is freely available under the GNU Public License version 3 and can be downloaded from http://www.hki-jena.de/index.php/0/2/490. General information on GRN2SBML, examples and tutorials are available at the tool's web page.

CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.

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Gillani, Zeeshan; Akash, Muhammad Sajid Hamid; Rahaman, M D Matiur; Chen, Ming

2014-11-30

Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. For network with nodes (SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .

A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

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Xiangyun Xiao

Full Text Available The reconstruction of gene regulatory networks (GRNs from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM, experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

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Xiao, Xiangyun; Zhang, Wei; Zou, Xiufen

2015-01-01

The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM), experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

A Genome-Scale Investigation of How Sequence, Function, and Tree-Based Gene Properties Influence Phylogenetic Inference.

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Shen, Xing-Xing; Salichos, Leonidas; Rokas, Antonis

2016-09-02

Molecular phylogenetic inference is inherently dependent on choices in both methodology and data. Many insightful studies have shown how choices in methodology, such as the model of sequence evolution or optimality criterion used, can strongly influence inference. In contrast, much less is known about the impact of choices in the properties of the data, typically genes, on phylogenetic inference. We investigated the relationships between 52 gene properties (24 sequence-based, 19 function-based, and 9 tree-based) with each other and with three measures of phylogenetic signal in two assembled data sets of 2,832 yeast and 2,002 mammalian genes. We found that most gene properties, such as evolutionary rate (measured through the percent average of pairwise identity across taxa) and total tree length, were highly correlated with each other. Similarly, several gene properties, such as gene alignment length, Guanine-Cytosine content, and the proportion of tree distance on internal branches divided by relative composition variability (treeness/RCV), were strongly correlated with phylogenetic signal. Analysis of partial correlations between gene properties and phylogenetic signal in which gene evolutionary rate and alignment length were simultaneously controlled, showed similar patterns of correlations, albeit weaker in strength. Examination of the relative importance of each gene property on phylogenetic signal identified gene alignment length, alongside with number of parsimony-informative sites and variable sites, as the most important predictors. Interestingly, the subsets of gene properties that optimally predicted phylogenetic signal differed considerably across our three phylogenetic measures and two data sets; however, gene alignment length and RCV were consistently included as predictors of all three phylogenetic measures in both yeasts and mammals. These results suggest that a handful of sequence-based gene properties are reliable predictors of phylogenetic signal

Inferences of the deep solar meridional flow

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Böning, Vincent G. A.

2017-10-01

Understanding the solar meridional flow is important for uncovering the origin of the solar activity cycle. Yet, recent helioseismic estimates of this flow have come to conflicting conclusions in deeper layers of the solar interior, i.e., at depths below about 0.9 solar radii. The aim of this thesis is to contribute to a better understanding of the deep solar meridional flow. Time-distance helioseismology is the major method for investigating this flow. In this method, travel times of waves propagating between pairs of locations on the solar surface are measured. Until now, the travel-time measurements have been modeled using the ray approximation, which assumes that waves travel along infinitely thin ray paths between these locations. In contrast, the scattering of the full wave field in the solar interior due to the flow is modeled in first order by the Born approximation. It is in general a more accurate model of the physics in the solar interior. In a first step, an existing model for calculating the sensitivity of travel-time measurements to solar interior flows using the Born approximation is extended from Cartesian to spherical geometry. The results are succesfully compared to the Cartesian ones and are tested for self-consistency. In a second step, the newly developed model is validated using an existing numerical simulation of linear wave propagation in the Sun. An inversion of artificial travel times for meridional flow shows excellent agreement for noiseless data and reproduces many features in the input flow profile in the case of noisy data. Finally, the new method is used to infer the deep meridional flow. I used Global Oscillation Network Group (GONG) data that were earlier analyzed using the ray approximation and I employed the same Substractive Optimized Local Averaging (SOLA) inversion technique as in the earlier study. Using an existing formula for the covariance of travel-time measurements, it is shown that the assumption of uncorrelated errors

Evolutionary history of the third chromosome gene arrangements of Drosophila pseudoobscura inferred from inversion breakpoints.

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Wallace, Andre G; Detweiler, Don; Schaeffer, Stephen W

2011-08-01

The third chromosome of Drosophila pseudoobscura is polymorphic for numerous gene arrangements that form classical clines in North America. The polytene salivary chromosomes isolated from natural populations revealed changes in gene order that allowed the different gene arrangements to be linked together by paracentric inversions representing one of the first cases where genetic data were used to construct a phylogeny. Although the inversion phylogeny can be used to determine the relationships among the gene arrangements, the cytogenetic data are unable to infer the ancestral arrangement or the age of the different chromosome types. These are both important properties if one is to infer the evolutionary forces responsible for the spread and maintenance of the chromosomes. Here, we employ the nucleotide sequences of 18 regions distributed across the third chromosome in 80-100 D. pseudoobscura strains to test whether five gene arrangements are of unique or multiple origin, what the ancestral arrangement was, and what are the ages of the different arrangements. Each strain carried one of six commonly found gene arrangements and the sequences were used to infer their evolutionary relationships. Breakpoint regions in the center of the chromosome supported monophyly of the gene arrangements, whereas regions at the ends of the chromosome gave phylogenies that provided less support for monophyly of the chromosomes either because the individual markers did not have enough phylogenetically informative sites or genetic exchange scrambled information among the gene arrangements. A data set where the genetic markers were concatenated strongly supported a unique origin of the different gene arrangements. The inversion polymorphism of D. pseudoobscura is estimated to be about a million years old. We have also shown that the generated phylogeny is consistent with the cytological phylogeny of this species. In addition, the data presented here support hypothetical as the ancestral

cDREM: inferring dynamic combinatorial gene regulation.

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Wise, Aaron; Bar-Joseph, Ziv

2015-04-01

Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.

Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.

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Gong, Wuming; Koyano-Nakagawa, Naoko; Li, Tongbin; Garry, Daniel J

2015-03-07

Decoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions. Therefore, there is a pressing need to develop a systems approach to integrate these data from individual studies and infer the dynamic regulatory networks in an unbiased fashion. We developed a two-step strategy to integrate data from (1) temporal RNA-seq, (2) temporal histone modification ChIP-seq, (3) transcription factor (TF) ChIP-seq and (4) gene perturbation experiments to reconstruct the dynamic network during heart development. First, we trained a logistic regression model to predict the probability (LR score) of any base being bound by 543 TFs with known positional weight matrices. Second, four dimensions of data were combined using a time-varying dynamic Bayesian network model to infer the dynamic networks at four developmental stages in the mouse [mouse embryonic stem cells (ESCs), mesoderm (MES), cardiac progenitors (CP) and cardiomyocytes (CM)]. Our method not only infers the time-varying networks between different stages of heart development, but it also identifies the TF binding sites associated with promoter or enhancers of downstream genes. The LR scores of experimentally verified ESCs and heart enhancers were significantly higher than random regions (p network inference model identified a region with an elevated LR score approximately -9400 bp upstream of the transcriptional start site of Nkx2-5, which overlapped with a previously reported enhancer region (-9435 to -8922 bp). TFs such as Tead1, Gata4, Msx2, and Tgif1 were predicted to bind to this region and participate in the regulation of Nkx2-5 gene expression. Our model also predicted the key regulatory networks for the ESC-MES, MES-CP and CP

Detecting dynamic causal inference in nonlinear two-phase fracture flow

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Faybishenko, Boris

2017-08-01

Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.

Phylogeny Inference of Closely Related Bacterial Genomes: Combining the Features of Both Overlapping Genes and Collinear Genomic Regions

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Zhang, Yan-Cong; Lin, Kui

2015-01-01

Overlapping genes (OGs) represent one type of widespread genomic feature in bacterial genomes and have been used as rare genomic markers in phylogeny inference of closely related bacterial species. However, the inference may experience a decrease in performance for phylogenomic analysis of too closely or too distantly related genomes. Another drawback of OGs as phylogenetic markers is that they usually take little account of the effects of genomic rearrangement on the similarity estimation, such as intra-chromosome/genome translocations, horizontal gene transfer, and gene losses. To explore such effects on the accuracy of phylogeny reconstruction, we combine phylogenetic signals of OGs with collinear genomic regions, here called locally collinear blocks (LCBs). By putting these together, we refine our previous metric of pairwise similarity between two closely related bacterial genomes. As a case study, we used this new method to reconstruct the phylogenies of 88 Enterobacteriale genomes of the class Gammaproteobacteria. Our results demonstrated that the topological accuracy of the inferred phylogeny was improved when both OGs and LCBs were simultaneously considered, suggesting that combining these two phylogenetic markers may reduce, to some extent, the influence of gene loss on phylogeny inference. Such phylogenomic studies, we believe, will help us to explore a more effective approach to increasing the robustness of phylogeny reconstruction of closely related bacterial organisms. PMID:26715828

A canonical correlation analysis-based dynamic bayesian network prior to infer gene regulatory networks from multiple types of biological data.

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Baur, Brittany; Bozdag, Serdar

2015-04-01

One of the challenging and important computational problems in systems biology is to infer gene regulatory networks (GRNs) of biological systems. Several methods that exploit gene expression data have been developed to tackle this problem. In this study, we propose the use of copy number and DNA methylation data to infer GRNs. We developed an algorithm that scores regulatory interactions between genes based on canonical correlation analysis. In this algorithm, copy number or DNA methylation variables are treated as potential regulator variables, and expression variables are treated as potential target variables. We first validated that the canonical correlation analysis method is able to infer true interactions in high accuracy. We showed that the use of DNA methylation or copy number datasets leads to improved inference over steady-state expression. Our results also showed that epigenetic and structural information could be used to infer directionality of regulatory interactions. Additional improvements in GRN inference can be gleaned from incorporating the result in an informative prior in a dynamic Bayesian algorithm. This is the first study that incorporates copy number and DNA methylation into an informative prior in dynamic Bayesian framework. By closely examining top-scoring interactions with different sources of epigenetic or structural information, we also identified potential novel regulatory interactions.

Evolutionary inference across eukaryotes identifies specific pressures favoring mitochondrial gene retention

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Williams, Ben; Johnston, Iain

2016-01-01

Since their endosymbiotic origin, mitochondria have lost most of their genes. Although many selective mechanisms underlying the evolution of mitochondrial genomes have been proposed, a data-driven exploration of these hypotheses is lacking, and a quantitatively supported consensus remains absent. We developed HyperTraPS, a methodology coupling stochastic modelling with Bayesian inference, to identify the ordering of evolutionary events and suggest their causes. Using 2015 complete mitochondri...

Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks.

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Wu, Mengmeng; Lin, Zhixiang; Ma, Shining; Chen, Ting; Jiang, Rui; Wong, Wing Hung

2017-12-01

Although genome-wide association studies (GWAS) have successfully identified thousands of genomic loci associated with hundreds of complex traits in the past decade, the debate about such problems as missing heritability and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and anticipated genetic data. Towards this goal, gene-level integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advantages as straightforward interpretation, less multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET (Simultaneously Inference of GeNEs and Tissues) to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype-associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in finding both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the prevention, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies

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Hero Alfred

2010-11-01

Full Text Available Abstract Background Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP, the Indian Buffet Process (IBP, and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB analysis. Results Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV, Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD, closely related non-Bayesian approaches. Conclusions Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

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Chen, Bo; Chen, Minhua; Paisley, John; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S; Hero, Alfred; Lucas, Joseph; Dunson, David; Carin, Lawrence

2010-11-09

Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

ERC analysis: web-based inference of gene function via evolutionary rate covariation.

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Wolfe, Nicholas W; Clark, Nathan L

2015-12-01

The recent explosion of comparative genomics data presents an unprecedented opportunity to construct gene networks via the evolutionary rate covariation (ERC) signature. ERC is used to identify genes that experienced similar evolutionary histories, and thereby draws functional associations between them. The ERC Analysis website allows researchers to exploit genome-wide datasets to infer novel genes in any biological function and to explore deep evolutionary connections between distinct pathways and complexes. The website provides five analytical methods, graphical output, statistical support and access to an increasing number of taxonomic groups. Analyses and data at http://csb.pitt.edu/erc_analysis/ nclark@pitt.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Phylogenetic relationships and timing of diversification in gonorynchiform fishes inferred using nuclear gene DNA sequences (Teleostei: Ostariophysi).

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Near, Thomas J; Dornburg, Alex; Friedman, Matt

2014-11-01

The Gonorynchiformes are the sister lineage of the species-rich Otophysi and provide important insights into the diversification of ostariophysan fishes. Phylogenies of gonorynchiforms inferred using morphological characters and mtDNA gene sequences provide differing resolutions with regard to the sister lineage of all other gonorynchiforms (Chanos vs. Gonorynchus) and support for monophyly of the two miniaturized lineages Cromeria and Grasseichthys. In this study the phylogeny and divergence times of gonorynchiforms are investigated with DNA sequences sampled from nine nuclear genes and a published morphological character matrix. Bayesian phylogenetic analyses reveal substantial congruence among individual gene trees with inferences from eight genes placing Gonorynchus as the sister lineage to all other gonorynchiforms. Seven gene trees resolve Cromeria and Grasseichthys as a clade, supporting previous inferences using morphological characters. Phylogenies resulting from either concatenating the nuclear genes, performing a multispecies coalescent species tree analysis, or combining the morphological and nuclear gene DNA sequences resolve Gonorynchus as the living sister lineage of all other gonorynchiforms, strongly support the monophyly of Cromeria and Grasseichthys, and resolve a clade containing Parakneria, Cromeria, and Grasseichthys. The morphological dataset, which includes 13 gonorynchiform fossil taxa that range in age from Early Cretaceous to Eocene, was analyzed in combination with DNA sequences from the nine nuclear genes and a relaxed molecular clock to estimate times of evolutionary divergence. This "tip dating" strategy accommodates uncertainty in the phylogenetic resolution of fossil taxa that provide calibration information in the relaxed molecular clock analysis. The estimated age of the most recent common ancestor (MRCA) of living gonorynchiforms is slightly older than estimates from previous node dating efforts, but the molecular tip dating

Platform dependence of inference on gene-wise and gene-set involvement in human lung development

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Kho Alvin T

2009-06-01

Full Text Available Abstract Background With the recent development of microarray technologies, the comparability of gene expression data obtained from different platforms poses an important problem. We evaluated two widely used platforms, Affymetrix U133 Plus 2.0 and the Illumina HumanRef-8 v2 Expression Bead Chips, for comparability in a biological system in which changes may be subtle, namely fetal lung tissue as a function of gestational age. Results We performed the comparison via sequence-based probe matching between the two platforms. "Significance grouping" was defined as a measure of comparability. Using both expression correlation and significance grouping as measures of comparability, we demonstrated that despite overall cross-platform differences at the single gene level, increased correlation between the two platforms was found in genes with higher expression level, higher probe overlap, and lower p-value. We also demonstrated that biological function as determined via KEGG pathways or GO categories is more consistent across platforms than single gene analysis. Conclusion We conclude that while the comparability of the platforms at the single gene level may be increased by increasing sample size, they are highly comparable ontologically even for subtle differences in a relatively small sample size. Biologically relevant inference should therefore be reproducible across laboratories using different platforms.

Optimal structural inference of signaling pathways from unordered and overlapping gene sets.

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Acharya, Lipi R; Judeh, Thair; Wang, Guangdi; Zhu, Dongxiao

2012-02-15

A plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures. We propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a 'search and score' network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and

On the relation between gene flow theory and genetic gain

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Woolliams John A

2000-01-01

Full Text Available Abstract In conventional gene flow theory the rate of genetic gain is calculated as the summed products of genetic selection differential and asymptotic proportion of genes deriving from sex-age groups. Recent studies have shown that asymptotic proportions of genes predicted from conventional gene flow theory may deviate considerably from true proportions. However, the rate of genetic gain predicted from conventional gene flow theory was accurate. The current note shows that the connection between asymptotic proportions of genes and rate of genetic gain that is embodied in conventional gene flow theory is invalid, even though genetic gain may be predicted correctly from it.

Extensive error in the number of genes inferred from draft genome assemblies.

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James F Denton

2014-12-01

Full Text Available Current sequencing methods produce large amounts of data, but genome assemblies based on these data are often woefully incomplete. These incomplete and error-filled assemblies result in many annotation errors, especially in the number of genes present in a genome. In this paper we investigate the magnitude of the problem, both in terms of total gene number and the number of copies of genes in specific families. To do this, we compare multiple draft assemblies against higher-quality versions of the same genomes, using several new assemblies of the chicken genome based on both traditional and next-generation sequencing technologies, as well as published draft assemblies of chimpanzee. We find that upwards of 40% of all gene families are inferred to have the wrong number of genes in draft assemblies, and that these incorrect assemblies both add and subtract genes. Using simulated genome assemblies of Drosophila melanogaster, we find that the major cause of increased gene numbers in draft genomes is the fragmentation of genes onto multiple individual contigs. Finally, we demonstrate the usefulness of RNA-Seq in improving the gene annotation of draft assemblies, largely by connecting genes that have been fragmented in the assembly process.

Inferring transcriptional gene regulation network of starch metabolism in Arabidopsis thaliana leaves using graphical Gaussian model

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Ingkasuwan Papapit

2012-08-01

Full Text Available Abstract Background Starch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM. Results Time-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF. A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090, which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene. The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070 and constans-like (COL: At2g21320, were identified as positive regulators of starch synthase 4 (SS4: At4g18240. The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines. Conclusions In this study, we utilized a systematic approach of microarray

Highly restricted gene flow and deep evolutionary lineages in the giant clam Tridacna maxima

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Nuryanto, A.; Kochzius, M.

2009-09-01

The tropical Indo-West Pacific is the biogeographic region with the highest diversity of marine shallow water species, with its centre in the Indo-Malay Archipelago. However, due to its high endemism, the Red Sea is also considered as an important centre of evolution. Currently, not much is known about exchange among the Red Sea, Indian Ocean and West Pacific, as well as connectivity within the Indo-Malay Archipelago, even though such information is important to illuminate ecological and evolutionary processes that shape marine biodiversity in these regions. In addition, the inference of connectivity among populations is important for conservation. This study aims to test the hypothesis that the Indo-Malay Archipelago and the Red Sea are important centres of evolution by studying the genetic population structure of the giant clam Tridacna maxima. This study is based on a 484-bp fragment of the cytochrome c oxidase I gene from 211 individuals collected at 14 localities in the Indo-West Pacific to infer lineage diversification and gene flow as a measure for connectivity. The analysis showed a significant genetic differentiation among sample sites in the Indo-West Pacific (Φst = 0.74, P < 0.001) and across the Indo-Malay Archipelago (Φst = 0.72, P < 0.001), indicating restricted gene flow. Hierarchical AMOVA revealed the highest fixation index (Φct = 0.8, P < 0.001) when sample sites were assigned to the following regions: (1) Red Sea, (2) Indian Ocean and Java Sea, (3) Indonesian throughflow and seas in the East of Sulawesi, and (4) Western Pacific. Geological history as well as oceanography are important factors that shape the genetic structure of T. maxima in the Indo-Malay Archipelago and Red Sea. The observed deep evolutionary lineages might include cryptic species and this result supports the notion that the Indo-Malay Archipelago and the Red Sea are important centres of evolution.

NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

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Bellot, Pau; Olsen, Catharina; Salembier, Philippe; Oliveras-Vergés, Albert; Meyer, Patrick E

2015-09-29

In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

Paternity analysis of pollen-mediated gene flow for Fraxinus excelsior L. in a chronically fragmented landscape.

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Bacles, C F E; Ennos, R A

2008-10-01

Paternity analysis based on microsatellite marker genotyping was used to infer contemporary genetic connectivity by pollen of three population remnants of the wind-pollinated, wind-dispersed tree Fraxinus excelsior, in a deforested Scottish landscape. By deterministically accounting for genotyping error and comparing a range of assignment methods, individual-based paternity assignments were used to derive population-level estimates of gene flow. Pollen immigration into a 300 ha landscape represents between 43 and 68% of effective pollination, mostly depending on assignment method. Individual male reproductive success is unequal, with 31 of 48 trees fertilizing one seed or more, but only three trees fertilizing more than ten seeds. Spatial analysis suggests a fat-tailed pollen dispersal curve with 85% of detected pollination occurring within 100 m, and 15% spreading between 300 and 1900 m from the source. Identification of immigrating pollen sourced from two neighbouring remnants indicates further effective dispersal at 2900 m. Pollen exchange among remnants is driven by population size rather than geographic distance, with larger remnants acting predominantly as pollen donors, and smaller remnants as pollen recipients. Enhanced wind dispersal of pollen in a barren landscape ensures that the seed produced within the catchment includes genetic material from a wide geographic area. However, gene flow estimates based on analysis of non-dispersed seeds were shown to underestimate realized gene immigration into the remnants by a factor of two suggesting that predictive landscape conservation requires integrated estimates of post-recruitment gene flow occurring via both pollen and seed.

Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks

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Alina Sîrbu

2015-05-01

Full Text Available Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions. Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks.

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Sîrbu, Alina; Crane, Martin; Ruskin, Heather J

2015-05-14

Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

Phylogenetic relationships of Hemiptera inferred from mitochondrial and nuclear genes.

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Song, Nan; Li, Hu; Cai, Wanzhi; Yan, Fengming; Wang, Jianyun; Song, Fan

2016-11-01

Here, we reconstructed the Hemiptera phylogeny based on the expanded mitochondrial protein-coding genes and the nuclear 18S rRNA gene, separately. The differential rates of change across lineages may associate with long-branch attraction (LBA) effect and result in conflicting estimates of phylogeny from different types of data. To reduce the potential effects of systematic biases on inferences of topology, various data coding schemes, site removal method, and different algorithms were utilized in phylogenetic reconstruction. We show that the outgroups Phthiraptera, Thysanoptera, and the ingroup Sternorrhyncha share similar base composition, and exhibit "long branches" relative to other hemipterans. Thus, the long-branch attraction between these groups is suspected to cause the failure of recovering Hemiptera under the homogeneous model. In contrast, a monophyletic Hemiptera is supported when heterogeneous model is utilized in the analysis. Although higher level phylogenetic relationships within Hemiptera remain to be answered, consensus between analyses is beginning to converge on a stable phylogeny.

Allopatric speciation despite historical gene flow: Divergence and hybridization in Carex furva and C. lucennoiberica (Cyperaceae) inferred from plastid and nuclear RAD-seq data.

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Maguilla, Enrique; Escudero, Marcial; Hipp, Andrew L; Luceño, Modesto

2017-10-01

Gene flow among incipient species can act as a creative or destructive force in the speciation process, generating variation on which natural selection can act while, potentially, undermining population divergence. The flowering plant genus Carex exhibits a rapid and relatively recent radiation with many species limits still unclear. This is the case with the Iberian Peninsula (Spain and Portugal)-endemic C. lucennoiberica, which lay unrecognized within Carex furva until its recent description as a new species. In this study, we test how these species were impacted by interspecific gene flow during speciation. We sampled the full range of distribution of C. furva (15 individuals sampled) and C. lucennoiberica (88 individuals), sequenced two cpDNA regions (atpI-atpH, psbA-trnH) and performed genomic sequencing of 45,100 SNPs using restriction site-associated DNA sequencing (RAD-seq). We utilized a set of partitioned D-statistic tests and demographic analyses to study the degree and direction of introgression. Additionally, we modelled species distributions to reconstruct changes in range distribution during glacial and interglacial periods. Plastid, nuclear and morphological data strongly support divergence between species with subsequent gene flow. Combined with species distribution modelling, these data support a scenario of allopatry leading to species divergence, followed by secondary contact and gene flow due to long-distance dispersal and/or range expansions and contractions in response to Quaternary glacial cycles. We conclude that this is a case of allopatric speciation despite historical secondary contacts, which could have temporally influenced the speciation process, contributing to the knowledge of forces that are driving or counteracting speciation. © 2017 John Wiley & Sons Ltd.

BPhyOG: An interactive server for genome-wide inference of bacterial phylogenies based on overlapping genes

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Lin Kui

2007-07-01

Full Text Available Abstract Background Overlapping genes (OGs in bacterial genomes are pairs of adjacent genes of which the coding sequences overlap partly or entirely. With the rapid accumulation of sequence data, many OGs in bacterial genomes have now been identified. Indeed, these might prove a consistent feature across all microbial genomes. Our previous work suggests that OGs can be considered as robust markers at the whole genome level for the construction of phylogenies. An online, interactive web server for inferring phylogenies is needed for biologists to analyze phylogenetic relationships among a set of bacterial genomes of interest. Description BPhyOG is an online interactive server for reconstructing the phylogenies of completely sequenced bacterial genomes on the basis of their shared overlapping genes. It provides two tree-reconstruction methods: Neighbor Joining (NJ and Unweighted Pair-Group Method using Arithmetic averages (UPGMA. Users can apply the desired method to generate phylogenetic trees, which are based on an evolutionary distance matrix for the selected genomes. The distance between two genomes is defined by the normalized number of their shared OG pairs. BPhyOG also allows users to browse the OGs that were used to infer the phylogenetic relationships. It provides detailed annotation for each OG pair and the features of the component genes through hyperlinks. Users can also retrieve each of the homologous OG pairs that have been determined among 177 genomes. It is a useful tool for analyzing the tree of life and overlapping genes from a genomic standpoint. Conclusion BPhyOG is a useful interactive web server for genome-wide inference of any potential evolutionary relationship among the genomes selected by users. It currently includes 177 completely sequenced bacterial genomes containing 79,855 OG pairs, the annotation and homologous OG pairs of which are integrated comprehensively. The reliability of phylogenies complemented by

A new isolation with migration model along complete genomes infers very different divergence processes among closely related great ape species.

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Thomas Mailund

Full Text Available We present a hidden Markov model (HMM for inferring gradual isolation between two populations during speciation, modelled as a time interval with restricted gene flow. The HMM describes the history of adjacent nucleotides in two genomic sequences, such that the nucleotides can be separated by recombination, can migrate between populations, or can coalesce at variable time points, all dependent on the parameters of the model, which are the effective population sizes, splitting times, recombination rate, and migration rate. We show by extensive simulations that the HMM can accurately infer all parameters except the recombination rate, which is biased downwards. Inference is robust to variation in the mutation rate and the recombination rate over the sequence and also robust to unknown phase of genomes unless they are very closely related. We provide a test for whether divergence is gradual or instantaneous, and we apply the model to three key divergence processes in great apes: (a the bonobo and common chimpanzee, (b the eastern and western gorilla, and (c the Sumatran and Bornean orang-utan. We find that the bonobo and chimpanzee appear to have undergone a clear split, whereas the divergence processes of the gorilla and orang-utan species occurred over several hundred thousands years with gene flow stopping quite recently. We also apply the model to the Homo/Pan speciation event and find that the most likely scenario involves an extended period of gene flow during speciation.

River Stream-Flow and Zayanderoud Reservoir Operation Modeling Using the Fuzzy Inference System

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Saeed Jamali

2007-12-01

Full Text Available The Zayanderoud basin is located in the central plateau of Iran. As a result of population increase and agricultural and industrial developments, water demand on this basin has increased extensively. Given the importance of reservoir operation in water resource and management studies, the performance of fuzzy inference system (FIS for Zayanderoud reservoir operation is investigated in this paper. The model of operation consists of two parts. In the first part, the seasonal river stream-flow is forecasted using the fuzzy rule-based system. The southern oscillated index, rain, snow, and discharge are inputs of the model and the seasonal river stream-flow its output. In the second part, the operation model is constructed. The amount of releases is first optimized by a nonlinear optimization model and then the rule curves are extracted using the fuzzy inference system. This model operates on an "if-then" principle, where the "if" is a vector of fuzzy permits and "then" is the fuzzy result. The reservoir storage capacity, inflow, demand, and year condition factor are used as permits. Monthly release is taken as the consequence. The Zayanderoud basin is investigated as a case study. Different performance indices such as reliability, resiliency, and vulnerability are calculated. According to results, FIS works more effectively than the traditional reservoir operation methods such as standard operation policy (SOP or linear regression.

Intraspecific relationship within the genus convolvulus l. inferred by rbcl gene using different phylogenetic approaches

International Nuclear Information System (INIS)

Kausar, S.; Qamarunnisa, S.

2016-01-01

A molecular systematics analysis was conducted using sequence data of chloroplast rbcL gene for the genus Convolvulus L., by distance and character based phylogenetic methods. Fifteen representative members from genus Convolvulus L., were included as in group whereas two members from a sister family Solanaceae were taken as out group to root the tree. Intraspecific relationships within Convolvulus were inferred by distance matrix, maximum parsimony and bayesian analysis. Transition/transversion ratio was also calculated and it was revealed that in the investigated Convolvulus species, transitional changes were more prevalent in rbcL gene. The nature of rbcL gene in the present study was observed to be conserved, as it does not show major variations between examined species. Distance matrix represented the minimal genetic variations between some species (C. glomeratus and C. pyrrhotrichus), thus exhibiting them as close relatives. The result of parsimonious and bayesian analysis revealed almost similar clades however maximum parsimony based tree was unable to establish relationship between some Convolvulus species. The bayesian inference method was found to be the method of choice for establishing intraspecific associations between Convolvulus species using rbcL data as it clearly defined the connections supported by posterior probability values. (author)

Sex-biased gene flow among elk in the greater Yellowstone ecosystem

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Hand, Brian K.; Chen, Shanyuan; Anderson, Neil; Beja-Pereira, Albano; Cross, Paul C.; Ebinger, Michael R.; Edwards, Hank; Garrott, Robert A.; Kardos, Marty D.; Kauffman, Matthew J.; Landguth, Erin L.; Middleton, Arthur; Scurlock, Brandon M.; White, P.J.; Zager, Pete; Schwartz, Michael K.; Luikart, Gordon

2014-01-01

We quantified patterns of population genetic structure to help understand gene flow among elk populations across the Greater Yellowstone Ecosystem. We sequenced 596 base pairs of the mitochondrial control region of 380 elk from eight populations. Analysis revealed high mitochondrial DNA variation within populations, averaging 13.0 haplotypes with high mean gene diversity (0.85). The genetic differentiation among populations for mitochondrial DNA was relatively high (FST  =  0.161; P  =  0.001) compared to genetic differentiation for nuclear microsatellite data (FST  =  0.002; P  =  0.332), which suggested relatively low female gene flow among populations. The estimated ratio of male to female gene flow (mm/mf  =  46) was among the highest we have seen reported for large mammals. Genetic distance (for mitochondrial DNA pairwise FST) was not significantly correlated with geographic (Euclidean) distance between populations (Mantel's r  =  0.274, P  =  0.168). Large mitochondrial DNA genetic distances (e.g., FST > 0.2) between some of the geographically closest populations (<65 km) suggested behavioral factors and/or landscape features might shape female gene flow patterns. Given the strong sex-biased gene flow, future research and conservation efforts should consider the sexes separately when modeling corridors of gene flow or predicting spread of maternally transmitted diseases. The growing availability of genetic data to compare male vs. female gene flow provides many exciting opportunities to explore the magnitude, causes, and implications of sex-biased gene flow likely to occur in many species.

Patterns of gene flow define species of thermophilic Archaea.

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Hinsby Cadillo-Quiroz

2012-02-01

Full Text Available Despite a growing appreciation of their vast diversity in nature, mechanisms of speciation are poorly understood in Bacteria and Archaea. Here we use high-throughput genome sequencing to identify ongoing speciation in the thermoacidophilic Archaeon Sulfolobus islandicus. Patterns of homologous gene flow among genomes of 12 strains from a single hot spring in Kamchatka, Russia, demonstrate higher levels of gene flow within than between two persistent, coexisting groups, demonstrating that these microorganisms fit the biological species concept. Furthermore, rates of gene flow between two species are decreasing over time in a manner consistent with incipient speciation. Unlike other microorganisms investigated, we do not observe a relationship between genetic divergence and frequency of recombination along a chromosome, or other physical mechanisms that would reduce gene flow between lineages. Each species has its own genetic island encoding unique physiological functions and a unique growth phenotype that may be indicative of ecological specialization. Genetic differentiation between these coexisting groups occurs in large genomic "continents," indicating the topology of genomic divergence during speciation is not uniform and is not associated with a single locus under strong diversifying selection. These data support a model where species do not require physical barriers to gene flow but are maintained by ecological differentiation.

Patterns of gene flow define species of thermophilic Archaea.

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Cadillo-Quiroz, Hinsby; Didelot, Xavier; Held, Nicole L; Herrera, Alfa; Darling, Aaron; Reno, Michael L; Krause, David J; Whitaker, Rachel J

2012-02-01

Despite a growing appreciation of their vast diversity in nature, mechanisms of speciation are poorly understood in Bacteria and Archaea. Here we use high-throughput genome sequencing to identify ongoing speciation in the thermoacidophilic Archaeon Sulfolobus islandicus. Patterns of homologous gene flow among genomes of 12 strains from a single hot spring in Kamchatka, Russia, demonstrate higher levels of gene flow within than between two persistent, coexisting groups, demonstrating that these microorganisms fit the biological species concept. Furthermore, rates of gene flow between two species are decreasing over time in a manner consistent with incipient speciation. Unlike other microorganisms investigated, we do not observe a relationship between genetic divergence and frequency of recombination along a chromosome, or other physical mechanisms that would reduce gene flow between lineages. Each species has its own genetic island encoding unique physiological functions and a unique growth phenotype that may be indicative of ecological specialization. Genetic differentiation between these coexisting groups occurs in large genomic "continents," indicating the topology of genomic divergence during speciation is not uniform and is not associated with a single locus under strong diversifying selection. These data support a model where species do not require physical barriers to gene flow but are maintained by ecological differentiation.

[Phylogeny of protostome moulting animals (Ecdysozoa) inferred from 18 and 28S rRNA gene sequences].

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Petrov, N B; Vladychenskaia, N S

2005-01-01

Reliability of reconstruction of phylogenetic relationships within a group of protostome moulting animals was evaluated by means of comparison of 18 and 28S rRNA gene sequences sets both taken separately and combined. Reliability of reconstructions was evaluated by values of the bootstrap support of major phylogenetic tree nodes and by degree of congruence of phylogenetic trees inferred by various methods. By both criteria, phylogenetic trees reconstructed from the combined 18 and 28S rRNA gene sequences were better than those inferred from 18 and 28S sequences taken separately. Results obtained are consistent with phylogenetic hypothesis separating protostome animals into two major clades, moulting Ecdysozoa (Priapulida + Kinorhyncha, Nematoda + Nematomorpha, Onychophora + Tardigrada, Myriapoda + Chelicerata, Crustacea + Hexapoda) and unmoulting Lophotrochozoa (Plathelminthes, Nemertini, Annelida, Mollusca, Echiura, Sipuncula). Clade Cephalorhyncha does not include nematomorphs (Nematomorpha). Conclusion was taken that it is necessary to use combined 18 and 28S data in phylogenetic studies.

Dinoflagellate phylogeny as inferred from heat shock protein 90 and ribosomal gene sequences.

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Mona Hoppenrath

2010-10-01

Full Text Available Interrelationships among dinoflagellates in molecular phylogenies are largely unresolved, especially in the deepest branches. Ribosomal DNA (rDNA sequences provide phylogenetic signals only at the tips of the dinoflagellate tree. Two reasons for the poor resolution of deep dinoflagellate relationships using rDNA sequences are (1 most sites are relatively conserved and (2 there are different evolutionary rates among sites in different lineages. Therefore, alternative molecular markers are required to address the deeper phylogenetic relationships among dinoflagellates. Preliminary evidence indicates that the heat shock protein 90 gene (Hsp90 will provide an informative marker, mainly because this gene is relatively long and appears to have relatively uniform rates of evolution in different lineages.We more than doubled the previous dataset of Hsp90 sequences from dinoflagellates by generating additional sequences from 17 different species, representing seven different orders. In order to concatenate the Hsp90 data with rDNA sequences, we supplemented the Hsp90 sequences with three new SSU rDNA sequences and five new LSU rDNA sequences. The new Hsp90 sequences were generated, in part, from four additional heterotrophic dinoflagellates and the type species for six different genera. Molecular phylogenetic analyses resulted in a paraphyletic assemblage near the base of the dinoflagellate tree consisting of only athecate species. However, Noctiluca was never part of this assemblage and branched in a position that was nested within other lineages of dinokaryotes. The phylogenetic trees inferred from Hsp90 sequences were consistent with trees inferred from rDNA sequences in that the backbone of the dinoflagellate clade was largely unresolved.The sequence conservation in both Hsp90 and rDNA sequences and the poor resolution of the deepest nodes suggests that dinoflagellates reflect an explosive radiation in morphological diversity in their recent

Classification of natural circulation two-phase flow patterns using fuzzy inference on image analysis

International Nuclear Information System (INIS)

Mesquita, R.N. de; Masotti, P.H.F.; Penha, R.M.L.; Andrade, D.A.; Sabundjian, G.; Torres, W.M.

2012-01-01

Highlights: ► A fuzzy classification system for two-phase flow instability patterns is developed. ► Flow patterns are classified based on images of natural circulation experiments. ► Fuzzy inference is optimized to use single grayscale profiles as input. - Abstract: Two-phase flow on natural circulation phenomenon has been an important theme on recent studies related to nuclear reactor designs. The accuracy of heat transfer estimation has been improved with new models that require precise prediction of pattern transitions of flow. In this work, visualization of natural circulation cycles is used to study two-phase flow patterns associated with phase transients and static instabilities of flow. A Fuzzy Flow-type Classification System (FFCS) was developed to classify these patterns based only on image extracted features. Image acquisition and temperature measurements were simultaneously done. Experiments in natural circulation facility were adjusted to generate a series of characteristic two-phase flow instability periodic cycles. The facility is composed of a loop of glass tubes, a heat source using electrical heaters, a cold source using a helicoidal heat exchanger, a visualization section and thermocouples positioned over different loop sections. The instability cyclic period is estimated based on temperature measurements associated with the detection of a flow transition image pattern. FFCS shows good results provided that adequate image acquisition parameters and pre-processing adjustments are used.

Speciation and gene flow between snails of opposite chirality.

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Angus Davison

2005-09-01

Full Text Available Left-right asymmetry in snails is intriguing because individuals of opposite chirality are either unable to mate or can only mate with difficulty, so could be reproductively isolated from each other. We have therefore investigated chiral evolution in the Japanese land snail genus Euhadra to understand whether changes in chirality have promoted speciation. In particular, we aimed to understand the effect of the maternal inheritance of chirality on reproductive isolation and gene flow. We found that the mitochondrial DNA phylogeny of Euhadra is consistent with a single, relatively ancient evolution of sinistral species and suggests either recent "single-gene speciation" or gene flow between chiral morphs that are unable to mate. To clarify the conditions under which new chiral morphs might evolve and whether single-gene speciation can occur, we developed a mathematical model that is relevant to any maternal-effect gene. The model shows that reproductive character displacement can promote the evolution of new chiral morphs, tending to counteract the positive frequency-dependent selection that would otherwise drive the more common chiral morph to fixation. This therefore suggests a general mechanism as to how chiral variation arises in snails. In populations that contain both chiral morphs, two different situations are then possible. In the first, gene flow is substantial between morphs even without interchiral mating, because of the maternal inheritance of chirality. In the second, reproductive isolation is possible but unstable, and will also lead to gene flow if intrachiral matings occasionally produce offspring with the opposite chirality. Together, the results imply that speciation by chiral reversal is only meaningful in the context of a complex biogeographical process, and so must usually involve other factors. In order to understand the roles of reproductive character displacement and gene flow in the chiral evolution of Euhadra, it will be

The evolutionary history of bears is characterized by gene flow across species

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Kumar, Vikas; Lammers, Fritjof; Bidon, Tobias; Pfenninger, Markus; Kolter, Lydia; Nilsson, Maria A.; Janke, Axel

2017-01-01

Bears are iconic mammals with a complex evolutionary history. Natural bear hybrids and studies of few nuclear genes indicate that gene flow among bears may be more common than expected and not limited to polar and brown bears. Here we present a genome analysis of the bear family with representatives of all living species. Phylogenomic analyses of 869 mega base pairs divided into 18,621 genome fragments yielded a well-resolved coalescent species tree despite signals for extensive gene flow across species. However, genome analyses using different statistical methods show that gene flow is not limited to closely related species pairs. Strong ancestral gene flow between the Asiatic black bear and the ancestor to polar, brown and American black bear explains uncertainties in reconstructing the bear phylogeny. Gene flow across the bear clade may be mediated by intermediate species such as the geographically wide-spread brown bears leading to large amounts of phylogenetic conflict. Genome-scale analyses lead to a more complete understanding of complex evolutionary processes. Evidence for extensive inter-specific gene flow, found also in other animal species, necessitates shifting the attention from speciation processes achieving genome-wide reproductive isolation to the selective processes that maintain species divergence in the face of gene flow. PMID:28422140

The evolutionary history of bears is characterized by gene flow across species.

Science.gov (United States)

Kumar, Vikas; Lammers, Fritjof; Bidon, Tobias; Pfenninger, Markus; Kolter, Lydia; Nilsson, Maria A; Janke, Axel

2017-04-19

Bears are iconic mammals with a complex evolutionary history. Natural bear hybrids and studies of few nuclear genes indicate that gene flow among bears may be more common than expected and not limited to polar and brown bears. Here we present a genome analysis of the bear family with representatives of all living species. Phylogenomic analyses of 869 mega base pairs divided into 18,621 genome fragments yielded a well-resolved coalescent species tree despite signals for extensive gene flow across species. However, genome analyses using different statistical methods show that gene flow is not limited to closely related species pairs. Strong ancestral gene flow between the Asiatic black bear and the ancestor to polar, brown and American black bear explains uncertainties in reconstructing the bear phylogeny. Gene flow across the bear clade may be mediated by intermediate species such as the geographically wide-spread brown bears leading to large amounts of phylogenetic conflict. Genome-scale analyses lead to a more complete understanding of complex evolutionary processes. Evidence for extensive inter-specific gene flow, found also in other animal species, necessitates shifting the attention from speciation processes achieving genome-wide reproductive isolation to the selective processes that maintain species divergence in the face of gene flow.

Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database.

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Allan Peter Davis

Full Text Available Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/ manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO file from NCBI Gene. By integrating these GO-gene annotations with CTD's gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and

Inferring common cognitive mechanisms from brain blood-flow lateralisation data obtained with functional transcranial Doppler ultrasound.

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Georg eMeyer

2014-06-01

Full Text Available Current neuroimaging techniques with high spatial resolution constrain participant motion so that many natural tasks cannot be carried out. The aim of this paper is to show how a time-locked correlation-analysis of cerebral blood flow velocity (CBFV lateralisation data, obtained with functional TransCranial Doppler (fTCD ultrasound, can be used to infer cerebral activation patterns across tasks. In a first experiment we demonstrate that the proposed analysis method results in data that are comparable with the standard Lateralisation Index (LI for within-task comparisons of CBFV patterns, recorded during cued word generation (CWG at two difficulty levels.In the main experiment we demonstrate that the proposed analysis method shows correlated blood-flow patterns for two different cognitive tasks that are known to draw on common brain areas, CWG and Music Synthesis. We show that CBFV patterns for Music and CWG are correlated only for participants with prior musical training.CBFV patterns for tasks that draw on distinct brain areas, the Tower of London and CWG, are not correlated.The proposed methodology extends conventional fTCD analysis by including temporal information in the analysis of cerebral blood-flow patterns to provide a robust, non-invasive method to infer whether common brain areas are used in different cognitive tasks. It complements conventional high resolution imaging techniques.

Polyphyly and gene flow between non-sibling Heliconius species

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Jiggins Chris D

2006-04-01

Full Text Available Abstract Background The view that gene flow between related animal species is rare and evolutionarily unimportant largely antedates sensitive molecular techniques. Here we use DNA sequencing to investigate a pair of morphologically and ecologically divergent, non-sibling butterfly species, Heliconius cydno and H. melpomene (Lepidoptera: Nymphalidae, whose distributions overlap in Central and Northwestern South America. Results In these taxa, we sequenced 30–45 haplotypes per locus of a mitochondrial region containing the genes for cytochrome oxidase subunits I and II (CoI/CoII, and intron-spanning fragments of three unlinked nuclear loci: triose-phosphate isomerase (Tpi, mannose-6-phosphate isomerase (Mpi and cubitus interruptus (Ci genes. A fifth gene, dopa decarboxylase (Ddc produced sequence data likely to be from different duplicate loci in some of the taxa, and so was excluded. Mitochondrial and Tpi genealogies are consistent with reciprocal monophyly, whereas sympatric populations of the species in Panama share identical or similar Mpi and Ci haplotypes, giving rise to genealogical polyphyly at the species level despite evidence for rapid sequence divergence at these genes between geographic races of H. melpomene. Conclusion Recent transfer of Mpi haplotypes between species is strongly supported, but there is no evidence for introgression at the other three loci. Our results demonstrate that the boundaries between animal species can remain selectively porous to gene flow long after speciation, and that introgression, even between non-sibling species, can be an important factor in animal evolution. Interspecific gene flow is demonstrated here for the first time in Heliconius and may provide a route for the transfer of switch-gene adaptations for Müllerian mimicry. The results also forcefully demonstrate how reliance on a single locus may give an erroneous picture of the overall genealogical history of speciation and gene flow.

Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources

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van Hijum Sacha AFT

2008-10-01

Full Text Available Abstract Background Despite a plethora of functional genomic efforts, the function of many genes in sequenced genomes remains unknown. The increasing amount of microarray data for many species allows employing the guilt-by-association principle to predict function on a large scale: genes exhibiting similar expression patterns are more likely to participate in shared biological processes. Results We developed Prosecutor, an application that enables researchers to rapidly infer gene function based on available gene expression data and functional annotations. Our parameter-free functional prediction method uses a sensitive algorithm to achieve a high association rate of linking genes with unknown function to annotated genes. Furthermore, Prosecutor utilizes additional biological information such as genomic context and known regulatory mechanisms that are specific for prokaryotes. We analyzed publicly available transcriptome data sets and used literature sources to validate putative functions suggested by Prosecutor. We supply the complete results of our analysis for 11 prokaryotic organisms on a dedicated website. Conclusion The Prosecutor software and supplementary datasets available at http://www.prosecutor.nl allow researchers working on any of the analyzed organisms to quickly identify the putative functions of their genes of interest. A de novo analysis allows new organisms to be studied.

Cortical information flow during inferences of agency

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Myrthel eDogge

2014-08-01

Full Text Available Building on the recent finding that agency experiences do not merely rely on sensorimotor information but also on cognitive cues, this exploratory study uses electroencephalographic recordings to examine functional connectivity during agency inference processing in a setting where action and outcome are independent. Participants completed a computerized task in which they pressed a button followed by one of two color words (red or blue and rated their experienced agency over producing the color. Before executing the action, a matching or mismatching color word was pre-activated by explicitly instructing participants to produce the color (goal condition or by briefly presenting the color word (prime condition. In both conditions, experienced agency was higher in matching versus mismatching trials. Furthermore, increased electroencephalography (EEG-based connectivity strength was observed between parietal and frontal nodes and within the (prefrontal cortex when color-outcomes matched with goals and participants reported high agency. This pattern of increased connectivity was not identified in trials where outcomes were pre-activated through primes. These results suggest that different connections are involved in the experience and in the loss of agency, as well as in inferences of agency resulting from different types of pre-activation. Moreover, the findings provide novel support for the involvement of a fronto-parietal network in agency inferences.

Ecological Divergence and the Origins of Intrinsic Postmating Isolation with Gene Flow

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Aneil F. Agrawal

2011-01-01

Full Text Available The evolution of intrinsic postmating isolation has received much attention, both historically and in recent studies of speciation genes. Intrinsic isolation often stems from between-locus genetic incompatibilities, where alleles that function well within species are incompatible with one another when brought together in the genome of a hybrid. It can be difficult for such incompatibilities to originate when populations diverge with gene flow, because deleterious genotypic combinations will be created and then purged by selection. However, it has been argued that if genes underlying incompatibilities are themselves subject to divergent selection, then they might overcome gene flow to diverge between populations, resulting in the origin of incompatibilities. Nonetheless, there has been little explicit mathematical exploration of such scenarios for the origin of intrinsic incompatibilities during ecological speciation with gene flow. Here we explore theoretical models for the origin of intrinsic isolation where genes subject to divergent natural selection also affect intrinsic isolation, either directly or via linkage disequilibrium with other loci. Such genes indeed overcome gene flow, diverge between populations, and thus result in the evolution of intrinsic isolation. We also examine barriers to neutral gene flow. Surprisingly, we find that intrinsic isolation sometimes weakens this barrier, by impeding differentiation via ecologically based divergent selection.

Urban landscape genomics identifies fine-scale gene flow patterns in an avian invasive.

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Low, G W; Chattopadhyay, B; Garg, K M; Irestedt, M; Ericson, Pgp; Yap, G; Tang, Q; Wu, S; Rheindt, F E

2018-01-01

Invasive species exert a serious impact on native fauna and flora and have been the target of many eradication and management efforts worldwide. However, a lack of data on population structure and history, exacerbated by the recency of many species introductions, limits the efficiency with which such species can be kept at bay. In this study we generated a novel genome of high assembly quality and genotyped 4735 genome-wide single nucleotide polymorphic (SNP) markers from 78 individuals of an invasive population of the Javan Myna Acridotheres javanicus across the island of Singapore. We inferred limited population subdivision at a micro-geographic level, a genetic patch size (~13-14 km) indicative of a pronounced dispersal ability, and barely an increase in effective population size since introduction despite an increase of four to five orders of magnitude in actual population size, suggesting that low population-genetic diversity following a bottleneck has not impeded establishment success. Landscape genomic analyses identified urban features, such as low-rise neighborhoods, that constitute pronounced barriers to gene flow. Based on our data, we consider an approach targeting the complete eradication of Javan Mynas across Singapore to be unfeasible. Instead, a mixed approach of localized mitigation measures taking into account urban geographic features and planning policy may be the most promising avenue to reducing the adverse impacts of this urban pest. Our study demonstrates how genomic methods can directly inform the management and control of invasive species, even in geographically limited datasets with high gene flow rates.

Gene network inference and biochemical assessment delineates GPCR pathways and CREB targets in small intestinal neuroendocrine neoplasia.

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Ignat Drozdov

Full Text Available Small intestinal (SI neuroendocrine tumors (NET are increasing in incidence, however little is known about their biology. High throughput techniques such as inference of gene regulatory networks from microarray experiments can objectively define signaling machinery in this disease. Genome-wide co-expression analysis was used to infer gene relevance network in SI-NETs. The network was confirmed to be non-random, scale-free, and highly modular. Functional analysis of gene co-expression modules revealed processes including 'Nervous system development', 'Immune response', and 'Cell-cycle'. Importantly, gene network topology and differential expression analysis identified over-expression of the GPCR signaling regulators, the cAMP synthetase, ADCY2, and the protein kinase A, PRKAR1A. Seven CREB response element (CRE transcripts associated with proliferation and secretion: BEX1, BICD1, CHGB, CPE, GABRB3, SCG2 and SCG3 as well as ADCY2 and PRKAR1A were measured in an independent SI dataset (n = 10 NETs; n = 8 normal preparations. All were up-regulated (p<0.035 with the exception of SCG3 which was not differently expressed. Forskolin (a direct cAMP activator, 10(-5 M significantly stimulated transcription of pCREB and 3/7 CREB targets, isoproterenol (a selective ß-adrenergic receptor agonist and cAMP activator, 10(-5 M stimulated pCREB and 4/7 targets while BIM-53061 (a dopamine D(2 and Serotonin [5-HT(2] receptor agonist, 10(-6 M stimulated 100% of targets as well as pCREB; CRE transcription correlated with the levels of cAMP accumulation and PKA activity; BIM-53061 stimulated the highest levels of cAMP and PKA (2.8-fold and 2.5-fold vs. 1.8-2-fold for isoproterenol and forskolin. Gene network inference and graph topology analysis in SI NETs suggests that SI NETs express neural GPCRs that activate different CRE targets associated with proliferation and secretion. In vitro studies, in a model NET cell system, confirmed that transcriptional

Inferring common cognitive mechanisms from brain blood-flow lateralization data: a new methodology for fTCD analysis.

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Meyer, Georg F; Spray, Amy; Fairlie, Jo E; Uomini, Natalie T

2014-01-01

Current neuroimaging techniques with high spatial resolution constrain participant motion so that many natural tasks cannot be carried out. The aim of this paper is to show how a time-locked correlation-analysis of cerebral blood flow velocity (CBFV) lateralization data, obtained with functional TransCranial Doppler (fTCD) ultrasound, can be used to infer cerebral activation patterns across tasks. In a first experiment we demonstrate that the proposed analysis method results in data that are comparable with the standard Lateralization Index (LI) for within-task comparisons of CBFV patterns, recorded during cued word generation (CWG) at two difficulty levels. In the main experiment we demonstrate that the proposed analysis method shows correlated blood-flow patterns for two different cognitive tasks that are known to draw on common brain areas, CWG, and Music Synthesis. We show that CBFV patterns for Music and CWG are correlated only for participants with prior musical training. CBFV patterns for tasks that draw on distinct brain areas, the Tower of London and CWG, are not correlated. The proposed methodology extends conventional fTCD analysis by including temporal information in the analysis of cerebral blood-flow patterns to provide a robust, non-invasive method to infer whether common brain areas are used in different cognitive tasks. It complements conventional high resolution imaging techniques.

Inferring metabolic states in uncharacterized environments using gene-expression measurements.

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Sergio Rossell

Full Text Available The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of

Phylogenetic Relationships of Pseudorasbora, Pseudopungtungia, and Pungtungia (Teleostei; Cypriniformes; Gobioninae Inferred from Multiple Nuclear Gene Sequences

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Keun-Yong Kim

2013-01-01

Full Text Available Gobionine species belonging to the genera Pseudorasbora, Pseudopungtungia, and Pungtungia (Teleostei; Cypriniformes; Cyprinidae have been heavily studied because of problems on taxonomy, threats of extinction, invasion, and human health. Nucleotide sequences of three nuclear genes, that is, recombination activating protein gene 1 (rag1, recombination activating gene 2 (rag2, and early growth response 1 gene (egr1, from Pseudorasbora, Pseudopungtungia, and Pungtungia species residing in China, Japan, and Korea, were analyzed to elucidate their intergeneric and interspecific phylogenetic relationships. In the phylogenetic tree inferred from their multiple gene sequences, Pseudorasbora, Pseudopungtungia and Pungtungia species ramified into three phylogenetically distinct clades; the “tenuicorpa” clade composed of Pseudopungtungia tenuicorpa, the “parva” clade composed of all Pseudorasbora species/subspecies, and the “herzi” clade composed of Pseudopungtungia nigra, and Pungtungia herzi. The genus Pseudorasbora was recovered as monophyletic, while the genus Pseudopungtungia was recovered as polyphyletic. Our phylogenetic result implies the unstable taxonomic status of the genus Pseudopungtungia.

Assessment of network inference methods: how to cope with an underdetermined problem.

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Caroline Siegenthaler

Full Text Available The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.

A conceptual framework that links pollinator foraging behavior to gene flow

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In insect-pollinated crops such as alfalfa, a better understanding of how pollinator foraging behavior affects gene flow could lead to the development of management strategies to reduce gene flow and facilitate the coexistence of distinct seed-production markets. Here, we introduce a conceptual fram...

Cortical information flow during inferences of agency

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Dogge, Myrthel; Hofman, Dennis; Boersma, Maria; Dijkerman, H Chris; Aarts, Henk

2014-01-01

Building on the recent finding that agency experiences do not merely rely on sensorimotor information but also on cognitive cues, this exploratory study uses electroencephalographic recordings to examine functional connectivity during agency inference processing in a setting where action and outcome

Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.

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Richard R Stein

2015-07-01

Full Text Available Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.

Long-distance gene flow and adaptation of forest trees to rapid climate change

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Kremer, Antoine; Ronce, Ophélie; Robledo-Arnuncio, Juan J; Guillaume, Frédéric; Bohrer, Gil; Nathan, Ran; Bridle, Jon R; Gomulkiewicz, Richard; Klein, Etienne K; Ritland, Kermit; Kuparinen, Anna; Gerber, Sophie; Schueler, Silvio

2012-01-01

Forest trees are the dominant species in many parts of the world and predicting how they might respond to climate change is a vital global concern. Trees are capable of long-distance gene flow, which can promote adaptive evolution in novel environments by increasing genetic variation for fitness. It is unclear, however, if this can compensate for maladaptive effects of gene flow and for the long-generation times of trees. We critically review data on the extent of long-distance gene flow and summarise theory that allows us to predict evolutionary responses of trees to climate change. Estimates of long-distance gene flow based both on direct observations and on genetic methods provide evidence that genes can move over spatial scales larger than habitat shifts predicted under climate change within one generation. Both theoretical and empirical data suggest that the positive effects of gene flow on adaptation may dominate in many instances. The balance of positive to negative consequences of gene flow may, however, differ for leading edge, core and rear sections of forest distributions. We propose future experimental and theoretical research that would better integrate dispersal biology with evolutionary quantitative genetics and improve predictions of tree responses to climate change. PMID:22372546

Phylogeny of the Celastraceae inferred from phytochrome B gene sequence and morphology.

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Simmons, M P; Clevinger, C C; Savolainen, V; Archer, R H; Mathews, S; Doyle, J J

2001-02-01

Phylogenetic relationships within Celastraceae were inferred using a simultaneous analysis of 61 morphological characters and 1123 base pairs of phytochrome B exon 1 from the nuclear genome. No gaps were inferred, and the gene tree topology suggests that the primers were specific to a single locus that did not duplicate among the lineages sampled. This region of phytochrome B was most useful for examining relationships among closely related genera. Fifty-one species from 38 genera of Celastraceae were sampled. The Celastraceae sensu lato (including Hippocrateaceae) were resolved as a monophyletic group. Loesener's subfamilies and tribes of Celastraceae were not supported. The Hippocrateaceae were resolved as a monophyletic group nested within a paraphyletic Celastraceae sensu stricto. Goupia was resolved as more closely related to Euphorbiaceae, Corynocarpaceae, and Linaceae than to Celastraceae. Plagiopteron (Flacourtiaceae) was resolved as the sister group of Hippocrateoideae. Brexia (Brexiaceae) was resolved as closely related to Elaeodendron and Pleurostylia. Canotia was resolved as the sister group of Acanthothamnus within Celastraceae. Perrottetia and Mortonia were resolved as the sister group of the rest of the Celastraceae. Siphonodon was resolved as a derived member of Celastraceae. Maytenus was resolved as three disparate groups, suggesting that this large genus needs to be recircumscribed.

SWPhylo - A Novel Tool for Phylogenomic Inferences by Comparison of Oligonucleotide Patterns and Integration of Genome-Based and Gene-Based Phylogenetic Trees.

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Yu, Xiaoyu; Reva, Oleg N

2018-01-01

Modern phylogenetic studies may benefit from the analysis of complete genome sequences of various microorganisms. Evolutionary inferences based on genome-scale analysis are believed to be more accurate than the gene-based alternative. However, the computational complexity of current phylogenomic procedures, inappropriateness of standard phylogenetic tools to process genome-wide data, and lack of reliable substitution models which correlates with alignment-free phylogenomic approaches deter microbiologists from using these opportunities. For example, the super-matrix and super-tree approaches of phylogenomics use multiple integrated genomic loci or individual gene-based trees to infer an overall consensus tree. However, these approaches potentially multiply errors of gene annotation and sequence alignment not mentioning the computational complexity and laboriousness of the methods. In this article, we demonstrate that the annotation- and alignment-free comparison of genome-wide tetranucleotide frequencies, termed oligonucleotide usage patterns (OUPs), allowed a fast and reliable inference of phylogenetic trees. These were congruent to the corresponding whole genome super-matrix trees in terms of tree topology when compared with other known approaches including 16S ribosomal RNA and GyrA protein sequence comparison, complete genome-based MAUVE, and CVTree methods. A Web-based program to perform the alignment-free OUP-based phylogenomic inferences was implemented at http://swphylo.bi.up.ac.za/. Applicability of the tool was tested on different taxa from subspecies to intergeneric levels. Distinguishing between closely related taxonomic units may be enforced by providing the program with alignments of marker protein sequences, eg, GyrA.

Integrative inference of population history in the Ibero-Maghrebian endemic Pleurodeles waltl (Salamandridae).

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Gutiérrez-Rodríguez, Jorge; Barbosa, A Márcia; Martínez-Solano, Íñigo

2017-07-01

Inference of population histories from the molecular signatures of past demographic processes is challenging, but recent methodological advances in species distribution models and their integration in time-calibrated phylogeographic studies allow detailed reconstruction of complex biogeographic scenarios. We apply an integrative approach to infer the evolutionary history of the Iberian ribbed newt (Pleurodeles waltl), an Ibero-Maghrebian endemic with populations north and south of the Strait of Gibraltar. We analyzed an extensive multilocus dataset (mitochondrial and nuclear DNA sequences and ten polymorphic microsatellite loci) and found a deep east-west phylogeographic break in Iberian populations dating back to the Plio-Pleistocene. This break is inferred to result from vicariance associated with the formation of the Guadalquivir river basin. In contrast with previous studies, North African populations showed exclusive mtDNA haplotypes, and formed a monophyletic clade within the Eastern Iberian lineage in the mtDNA genealogy. On the other hand, microsatellites failed to recover Moroccan populations as a differentiated genetic cluster. This is interpreted to result from post-divergence gene flow based on the results of IMA2 and Migrate analyses. Thus, Moroccan populations would have originated after overseas dispersal from the Iberian Peninsula in the Pleistocene, with subsequent gene flow in more recent times, implying at least two trans-marine dispersal events. We modeled the distribution of the species and of each lineage, and projected these models back in time to infer climatically favourable areas during the mid-Holocene, the last glacial maximum (LGM) and the last interglacial (LIG), to reconstruct more recent population dynamics. We found minor differences in climatic favourability across lineages, suggesting intraspecific niche conservatism. Genetic diversity was significantly correlated with the intersection of environmental favourability in the LIG and

Inferring Phylogenetic Networks Using PhyloNet.

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Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

2018-07-01

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

Functional networks inference from rule-based machine learning models.

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Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

Geography, assortative mating, and the effects of sexual selection on speciation with gene flow.

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Servedio, Maria R

2016-01-01

Theoretical and empirical research on the evolution of reproductive isolation have both indicated that the effects of sexual selection on speciation with gene flow are quite complex. As part of this special issue on the contributions of women to basic and applied evolutionary biology, I discuss my work on this question in the context of a broader assessment of the patterns of sexual selection that lead to, versus inhibit, the speciation process, as derived from theoretical research. In particular, I focus on how two factors, the geographic context of speciation and the mechanism leading to assortative mating, interact to alter the effect that sexual selection through mate choice has on speciation. I concentrate on two geographic contexts: sympatry and secondary contact between two geographically separated populations that are exchanging migrants and two mechanisms of assortative mating: phenotype matching and separate preferences and traits. I show that both of these factors must be considered for the effects of sexual selection on speciation to be inferred.

Study of gene flow from GM cotton (Gossypium hirsutum) varieties in El Espinal (Tolima, Colombia)

International Nuclear Information System (INIS)

Rache Cardenal, Leidy Yanira; Mora Oberlaender, Julian; Chaparro Giraldo, Alejandro

2013-01-01

In 2009, 4088 hectares of genetically modified (GM) cotton were planted in Tolima (Colombia), however there is some uncertainty about containment measures needed to prevent the flow of pollen and seed from regulated GM fields into adjacent fields. In this study, the gene flow from GM cotton varieties to conventional or feral cotton plants via seed and pollen was evaluated. ImmunostripTM, PCR and ELISA assays were used to detect gene flow. Fifty six refuges, 27 fields with conventional cotton and four feral individuals of the enterprise Remolinos Inc. located in El Espinal (Tolima) were analyzed in the first half of 2010. The results indicated seed mediated gene flow in 45 refuges (80.4 %) and 26 fields with conventional cotton (96 %), besides pollen mediated gene flow in one field with conventional cotton and nine refuges. All fields cultivated with conventional cotton showed gene flow from GM cotton. Two refuges and two feral individuals did not reveal gene flow from GM cotton.

A new fast method for inferring multiple consensus trees using k-medoids.

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Tahiri, Nadia; Willems, Matthieu; Makarenkov, Vladimir

2018-04-05

Gene trees carry important information about specific evolutionary patterns which characterize the evolution of the corresponding gene families. However, a reliable species consensus tree cannot be inferred from a multiple sequence alignment of a single gene family or from the concatenation of alignments corresponding to gene families having different evolutionary histories. These evolutionary histories can be quite different due to horizontal transfer events or to ancient gene duplications which cause the emergence of paralogs within a genome. Many methods have been proposed to infer a single consensus tree from a collection of gene trees. Still, the application of these tree merging methods can lead to the loss of specific evolutionary patterns which characterize some gene families or some groups of gene families. Thus, the problem of inferring multiple consensus trees from a given set of gene trees becomes relevant. We describe a new fast method for inferring multiple consensus trees from a given set of phylogenetic trees (i.e. additive trees or X-trees) defined on the same set of species (i.e. objects or taxa). The traditional consensus approach yields a single consensus tree. We use the popular k-medoids partitioning algorithm to divide a given set of trees into several clusters of trees. We propose novel versions of the well-known Silhouette and Caliński-Harabasz cluster validity indices that are adapted for tree clustering with k-medoids. The efficiency of the new method was assessed using both synthetic and real data, such as a well-known phylogenetic dataset consisting of 47 gene trees inferred for 14 archaeal organisms. The method described here allows inference of multiple consensus trees from a given set of gene trees. It can be used to identify groups of gene trees having similar intragroup and different intergroup evolutionary histories. The main advantage of our method is that it is much faster than the existing tree clustering approaches, while

Algorithms for MDC-Based Multi-locus Phylogeny Inference

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Yu, Yun; Warnow, Tandy; Nakhleh, Luay

One of the criteria for inferring a species tree from a collection of gene trees, when gene tree incongruence is assumed to be due to incomplete lineage sorting (ILS), is minimize deep coalescence, or MDC. Exact algorithms for inferring the species tree from rooted, binary trees under MDC were recently introduced. Nevertheless, in phylogenetic analyses of biological data sets, estimated gene trees may differ from true gene trees, be incompletely resolved, and not necessarily rooted. In this paper, we propose new MDC formulations for the cases where the gene trees are unrooted/binary, rooted/non-binary, and unrooted/non-binary. Further, we prove structural theorems that allow us to extend the algorithms for the rooted/binary gene tree case to these cases in a straightforward manner. Finally, we study the performance of these methods in coalescent-based computer simulations.

Bayesian Inference of High-Dimensional Dynamical Ocean Models

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Lin, J.; Lermusiaux, P. F. J.; Lolla, S. V. T.; Gupta, A.; Haley, P. J., Jr.

2015-12-01

This presentation addresses a holistic set of challenges in high-dimension ocean Bayesian nonlinear estimation: i) predict the probability distribution functions (pdfs) of large nonlinear dynamical systems using stochastic partial differential equations (PDEs); ii) assimilate data using Bayes' law with these pdfs; iii) predict the future data that optimally reduce uncertainties; and (iv) rank the known and learn the new model formulations themselves. Overall, we allow the joint inference of the state, equations, geometry, boundary conditions and initial conditions of dynamical models. Examples are provided for time-dependent fluid and ocean flows, including cavity, double-gyre and Strait flows with jets and eddies. The Bayesian model inference, based on limited observations, is illustrated first by the estimation of obstacle shapes and positions in fluid flows. Next, the Bayesian inference of biogeochemical reaction equations and of their states and parameters is presented, illustrating how PDE-based machine learning can rigorously guide the selection and discovery of complex ecosystem models. Finally, the inference of multiscale bottom gravity current dynamics is illustrated, motivated in part by classic overflows and dense water formation sites and their relevance to climate monitoring and dynamics. This is joint work with our MSEAS group at MIT.

Interspecific and interploidal gene flow in Central European Arabidopsis (Brassicaceae

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Jørgensen Marte H

2011-11-01

Full Text Available Abstract Background Effects of polyploidisation on gene flow between natural populations are little known. Central European diploid and tetraploid populations of Arabidopsis arenosa and A. lyrata are here used to study interspecific and interploidal gene flow, using a combination of nuclear and plastid markers. Results Ploidal levels were confirmed by flow cytometry. Network analyses clearly separated diploids according to species. Tetraploids and diploids were highly intermingled within species, and some tetraploids intermingled with the other species, as well. Isolation with migration analyses suggested interspecific introgression from tetraploid A. arenosa to tetraploid A. lyrata and vice versa, and some interploidal gene flow, which was unidirectional from diploid to tetraploid in A. arenosa and bidirectional in A. lyrata. Conclusions Interspecific genetic isolation at diploid level combined with introgression at tetraploid level indicates that polyploidy may buffer against negative consequences of interspecific hybridisation. The role of introgression in polyploid systems may, however, differ between plant species, and even within the small genus Arabidopsis, we find very different evolutionary fates when it comes to introgression.

SWPhylo – A Novel Tool for Phylogenomic Inferences by Comparison of Oligonucleotide Patterns and Integration of Genome-Based and Gene-Based Phylogenetic Trees

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Yu, Xiaoyu; Reva, Oleg N

2018-01-01

Modern phylogenetic studies may benefit from the analysis of complete genome sequences of various microorganisms. Evolutionary inferences based on genome-scale analysis are believed to be more accurate than the gene-based alternative. However, the computational complexity of current phylogenomic procedures, inappropriateness of standard phylogenetic tools to process genome-wide data, and lack of reliable substitution models which correlates with alignment-free phylogenomic approaches deter microbiologists from using these opportunities. For example, the super-matrix and super-tree approaches of phylogenomics use multiple integrated genomic loci or individual gene-based trees to infer an overall consensus tree. However, these approaches potentially multiply errors of gene annotation and sequence alignment not mentioning the computational complexity and laboriousness of the methods. In this article, we demonstrate that the annotation- and alignment-free comparison of genome-wide tetranucleotide frequencies, termed oligonucleotide usage patterns (OUPs), allowed a fast and reliable inference of phylogenetic trees. These were congruent to the corresponding whole genome super-matrix trees in terms of tree topology when compared with other known approaches including 16S ribosomal RNA and GyrA protein sequence comparison, complete genome-based MAUVE, and CVTree methods. A Web-based program to perform the alignment-free OUP-based phylogenomic inferences was implemented at http://swphylo.bi.up.ac.za/. Applicability of the tool was tested on different taxa from subspecies to intergeneric levels. Distinguishing between closely related taxonomic units may be enforced by providing the program with alignments of marker protein sequences, eg, GyrA. PMID:29511354

Impact of Bee Species and Plant Density on Alfalfa Pollination and Potential for Gene Flow

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Johanne Brunet

2010-01-01

Full Text Available In outcrossing crops like alfalfa, various bee species can contribute to pollination and gene flow in seed production fields. With the increasing use of transgenic crops, it becomes important to determine the role of these distinct pollinators on alfalfa pollination and gene flow. The current study examines the relative contribution of honeybees, three bumble bee species, and three solitary bee species to pollination and gene flow in alfalfa. Two wild solitary bee species and one wild bumble bee species were best at tripping flowers, while the two managed pollinators commonly used in alfalfa seed production, honeybees and leaf cutting bees, had the lowest tripping rate. Honeybees had the greatest potential for gene flow and risk of transgene escape relative to the other pollinators. For honeybees, gene flow and risk of transgene escape were not affected by plant density although for the three bumble bee species gene flow and risk of transgene escape were the greatest in high-density fields.

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

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Meyer Patrick

2007-01-01

Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

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Patrick E. Meyer

2007-06-01

Full Text Available The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR, an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.

Inferring contemporary levels of gene flow and demographic history in a local population of the leaf beetle Gonioctena olivacea from mitochondrial DNA sequence variation.

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Mardulyn, Patrick; Milinkovitch, Michel C

2005-05-01

We have studied mitochondrial DNA variation in a local population of the leaf beetle species Gonioctena olivacea, to check whether its apparent low dispersal behaviour affects its pattern of genetic variation at a small geographical scale. We have sampled 10 populations of G. olivacea within a rectangle of 5 x 2 km in the Belgian Ardennes, as well as five populations located approximately along a straight line of 30 km and separated by distances of 3-12 km. For each sampled individual (8-19 per population), a fragment of the mtDNA control region was polymerase chain reaction-amplified and sequenced. Sequence data were analysed to test whether significant genetic differentiation could be detected among populations separated by such relatively short distances. The reconstructed genealogy of the mitochondrial haplotypes was also used to investigate the demographic history of these populations. Computer simulations of the evolution of populations were conducted to assess the minimum amount of gene flow that is necessary to explain the observed pattern of variation in the samples. Results show that migration among populations included in the rectangle of 5 x 2 km is substantial, and probably involves the occurrence of dispersal flights. This appears difficult to reconcile with the results of a previous ecological field study that concluded that most of this species dispersal occurs by walking. While sufficient migration to homogenize genetic diversity occurs among populations separated by distances of a few hundred metres to a few kilometres, distances greater than 5 km results in contrast in strong differentiation among populations, suggesting that migration is drastically reduced on such distances. Finally, the results of coalescent simulations suggest that the star-like genealogy inferred from the mtDNA sequence data is fully compatible with a past demographic expansion. However, a metapopulation structure alone (without the need to invoke a population expansion

Divergence and gene flow in the globally distributed blue-winged ducks

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Nelson, Joel; Wilson, Robert E.; McCracken, Kevin G.; Cumming, Graeme; Joseph, Leo; Guay, Patrick-Jean; Peters, Jeffrey

2017-01-01

The ability to disperse over long distances can result in a high propensity for colonizing new geographic regions, including uninhabited continents, and lead to lineage diversification via allopatric speciation. However, high vagility can also result in gene flow between otherwise allopatric populations, and in some cases, parapatric or divergence-with-gene-flow models might be more applicable to widely distributed lineages. Here, we use five nuclear introns and the mitochondrial control region along with Bayesian models of isolation with migration to examine divergence, gene flow, and phylogenetic relationships within a cosmopolitan lineage comprising six species, the blue-winged ducks (genus Anas), which inhabit all continents except Antarctica. We found two primary sub-lineages, the globally-distributed shoveler group and the New World blue-winged/cinnamon teal group. The blue-winged/cinnamon sub-lineage is composed of sister taxa from North America and South America, and taxa with parapatric distributions are characterized by low to moderate levels of gene flow. In contrast, our data support strict allopatry for most comparisons within the shovelers. However, we found evidence of gene flow from the migratory, Holarctic northern shoveler (A. clypeata) and the more sedentary, African Cape shoveler (A. smithii) into the Australasian shoveler (A. rhynchotis), although we could not reject strict allopatry. Given the diverse mechanisms of speciation within this complex, the shovelers and blue-winged/cinnamon teals can serve as an effective model system for examining how the genome diverges under different evolutionary processes and how genetic variation is partitioned among highly dispersive taxa.

Phylogeny of Celastraceae tribe Euonymeae inferred from morphological characters and nuclear and plastid genes.

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Simmons, Mark P; McKenna, Miles J; Bacon, Christine D; Yakobson, Kendra; Cappa, Jennifer J; Archer, Robert H; Ford, Andrew J

2012-01-01

The phylogeny of Celastraceae tribe Euonymeae (≈ 230 species in eight genera in both the Old and New Worlds) was inferred using morphological characters together with plastid (matK, trnL-F) and nuclear (ITS and 26S rDNA) genes. Tribe Euonymeae has been defined as those genera of Celastraceae with generally opposite leaves, isomerous carpels, loculicidally dehiscent capsules, and arillate seeds (except Microtropis). Euonymus is the most diverse (129 species) and widely cultivated genus in the tribe. We infer that tribe Euonymeae consists of at least six separate lineages within Celastraceae and that a revised natural classification of the family is needed. Microtropis and Quetzalia are inferred to be distinct sister groups that together are sister to Zinowiewia. The endangered Monimopetalum chinense is an isolated and early derived lineage of Celastraceae that represents an important component of phylogenetic diversity within the family. Hedraianthera is sister to Brassiantha, and we describe a second species (Brassiantha hedraiantheroides A.J. Ford) that represents the first reported occurrence of this genus in Australia. Euonymus globularis, from eastern Australia, is sister to Menepetalum, which is endemic to New Caledonia, and we erect a new genus (Dinghoua R.H. Archer) for it. The Madagascan species of Euonymus are sister to Pleurostylia and recognized as a distinct genus (Astrocassine ined.). Glyptopetalum, Torralbasia, and Xylonymus are all closely related to Euonymus sensu stricto and are questionably distinct from it. Current intrageneric classifications of Euonymus are not completely natural and require revision. Copyright © 2011 Elsevier Inc. All rights reserved.

Bayesian assignment of gene ontology terms to gene expression experiments

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Sykacek, P.

2012-01-01

Motivation: Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment. Results: This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways. Availability: Source code under GPL license is available from the author. Contact: peter.sykacek@boku.ac.at PMID:22962488

Bayesian assignment of gene ontology terms to gene expression experiments.

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Sykacek, P

2012-09-15

Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment. This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways. Source code under GPL license is available from the author. peter.sykacek@boku.ac.at.

Gene flow among wild and domesticated almond species: insights from chloroplast and nuclear markers

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Delplancke, Malou; Alvarez, Nadir; Espíndola, Anahí; Joly, Hélène; Benoit, Laure; Brouck, Elise; Arrigo, Nils

2012-01-01

Hybridization has played a central role in the evolutionary history of domesticated plants. Notably, several breeding programs relying on gene introgression from the wild compartment have been performed in fruit tree species within the genus Prunus but few studies investigated spontaneous gene flow among wild and domesticated Prunus species. Consequently, a comprehensive understanding of genetic relationships and levels of gene flow between domesticated and wild Prunus species is needed. Combining nuclear and chloroplastic microsatellites, we investigated the gene flow and hybridization among two key almond tree species, the cultivated Prunus dulcis and one of the most widespread wild relative Prunus orientalis in the Fertile Crescent. We detected high genetic diversity levels in both species along with substantial and symmetric gene flow between the domesticated P. dulcis and the wild P. orientalis. These results were discussed in light of the cultivated species diversity, by outlining the frequent spontaneous genetic contributions of wild species to the domesticated compartment. In addition, crop-to-wild gene flow suggests that ad hoc transgene containment strategies would be required if genetically modified cultivars were introduced in the northwestern Mediterranean. PMID:25568053

Ancestry dynamics in a South American population: The impact of gene flow and preferential mating.

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Hedrick, Philip W

2017-07-01

European ancestry in many populations in Latin America at autosomal loci is often higher than that from X-linked loci indicating more European male ancestry and more Amerindian female ancestry. Generally, this has been attributed to more European male gene flow but could also result from an advantage to European mating or reproductive success. Population genetic models were developed to investigate the dynamics of gene flow and mating or reproductive success. Using estimates of autosomal and X-chromosome European ancestry, the amount of male gene flow or mating or reproductive advantage for Europeans, or those with European ancestry, was estimated. In a population from Antioquia, Colombia with an estimated 79% European autosomal ancestry and an estimated 69% European X-chromosome ancestry, about 15% male gene flow from Europe or about 20% mating or reproductive advantage of Europeans over Amerindians resulted in these levels of European ancestry in the contemporary population. Combinations of gene flow and mating advantage were nearly additive in their impact. Gene flow, mating advantage, or a combination of both factors, are consistent with observed levels of European ancestry in a Latin American population. This approach provides a general methodology to determine the levels of gene flow and mating differences that can explain the observed contemporary differences in ancestry from autosomes and X-chromosomes. © 2017 Wiley Periodicals, Inc.

Gene flow in genetically modified wheat.

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Silvan Rieben

Full Text Available Understanding gene flow in genetically modified (GM crops is critical to answering questions regarding risk-assessment and the coexistence of GM and non-GM crops. In two field experiments, we tested whether rates of cross-pollination differed between GM and non-GM lines of the predominantly self-pollinating wheat Triticum aestivum. In the first experiment, outcrossing was studied within the field by planting "phytometers" of one line into stands of another line. In the second experiment, outcrossing was studied over distances of 0.5-2.5 m from a central patch of pollen donors to adjacent patches of pollen recipients. Cross-pollination and outcrossing was detected when offspring of a pollen recipient without a particular transgene contained this transgene in heterozygous condition. The GM lines had been produced from the varieties Bobwhite or Frisal and contained Pm3b or chitinase/glucanase transgenes, respectively, in homozygous condition. These transgenes increase plant resistance against pathogenic fungi. Although the overall outcrossing rate in the first experiment was only 3.4%, Bobwhite GM lines containing the Pm3b transgene were six times more likely than non-GM control lines to produce outcrossed offspring. There was additional variation in outcrossing rate among the four GM-lines, presumably due to the different transgene insertion events. Among the pollen donors, the Frisal GM line expressing a chitinase transgene caused more outcrossing than the GM line expressing both a chitinase and a glucanase transgene. In the second experiment, outcrossing after cross-pollination declined from 0.7-0.03% over the test distances of 0.5-2.5 m. Our results suggest that pollen-mediated gene flow between GM and non-GM wheat might only be a concern if it occurs within fields, e.g. due to seed contamination. Methodologically our study demonstrates that outcrossing rates between transgenic and other lines within crops can be assessed using a phytometer

Gene flow in genetically modified wheat.

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Rieben, Silvan; Kalinina, Olena; Schmid, Bernhard; Zeller, Simon L

2011-01-01

Understanding gene flow in genetically modified (GM) crops is critical to answering questions regarding risk-assessment and the coexistence of GM and non-GM crops. In two field experiments, we tested whether rates of cross-pollination differed between GM and non-GM lines of the predominantly self-pollinating wheat Triticum aestivum. In the first experiment, outcrossing was studied within the field by planting "phytometers" of one line into stands of another line. In the second experiment, outcrossing was studied over distances of 0.5-2.5 m from a central patch of pollen donors to adjacent patches of pollen recipients. Cross-pollination and outcrossing was detected when offspring of a pollen recipient without a particular transgene contained this transgene in heterozygous condition. The GM lines had been produced from the varieties Bobwhite or Frisal and contained Pm3b or chitinase/glucanase transgenes, respectively, in homozygous condition. These transgenes increase plant resistance against pathogenic fungi. Although the overall outcrossing rate in the first experiment was only 3.4%, Bobwhite GM lines containing the Pm3b transgene were six times more likely than non-GM control lines to produce outcrossed offspring. There was additional variation in outcrossing rate among the four GM-lines, presumably due to the different transgene insertion events. Among the pollen donors, the Frisal GM line expressing a chitinase transgene caused more outcrossing than the GM line expressing both a chitinase and a glucanase transgene. In the second experiment, outcrossing after cross-pollination declined from 0.7-0.03% over the test distances of 0.5-2.5 m. Our results suggest that pollen-mediated gene flow between GM and non-GM wheat might only be a concern if it occurs within fields, e.g. due to seed contamination. Methodologically our study demonstrates that outcrossing rates between transgenic and other lines within crops can be assessed using a phytometer approach and that gene-flow

STUDY OF GENE FLOW FROM GM COTTON (Gossypium hirsutum VARIETIES IN “EL ESPINAL” (TOLIMA, COLOMBIA.

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Alejandro Chaparro Giraldo

2013-09-01

Full Text Available In 2009, 4088 hectares of genetically modified (GM cotton were planted in Tolima (Colombia, however there is some uncertainty about containment measures needed to prevent the flow of pollen and seed from regulated GM fields into adjacent fields. In this study, the gene flow from GM cotton varieties to conventional or feral cotton plants via seed and pollen was evaluated. ImmunostripTM, PCR and ELISA assays were used to detect gene flow. Fifty six refuges, 27 fields with conventional cotton and four feral individuals of the enterprise “Remolinos Inc.” located in El Espinal (Tolima were analyzed in the first half of 2010. The results indicated seeds mediated gene flow in 45 refuges (80,4 % and 26 fields with conventional cotton (96 %, besides a pollen mediated gene flow in one field with conventional cotton and nine refuges. All fields cultivated with conventional cotton showed gene flow from GM cotton. Two refuges and two feral individuals did not reveal gene flow from GM cotton.

Gene flow analysis method, the D-statistic, is robust in a wide parameter space.

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Zheng, Yichen; Janke, Axel

2018-01-08

We evaluated the sensitivity of the D-statistic, a parsimony-like method widely used to detect gene flow between closely related species. This method has been applied to a variety of taxa with a wide range of divergence times. However, its parameter space and thus its applicability to a wide taxonomic range has not been systematically studied. Divergence time, population size, time of gene flow, distance of outgroup and number of loci were examined in a sensitivity analysis. The sensitivity study shows that the primary determinant of the D-statistic is the relative population size, i.e. the population size scaled by the number of generations since divergence. This is consistent with the fact that the main confounding factor in gene flow detection is incomplete lineage sorting by diluting the signal. The sensitivity of the D-statistic is also affected by the direction of gene flow, size and number of loci. In addition, we examined the ability of the f-statistics, [Formula: see text] and [Formula: see text], to estimate the fraction of a genome affected by gene flow; while these statistics are difficult to implement to practical questions in biology due to lack of knowledge of when the gene flow happened, they can be used to compare datasets with identical or similar demographic background. The D-statistic, as a method to detect gene flow, is robust against a wide range of genetic distances (divergence times) but it is sensitive to population size. The D-statistic should only be applied with critical reservation to taxa where population sizes are large relative to branch lengths in generations.

Pleistocene land bridges act as semipermeable agents of avian gene flow in Wallacea.

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Garg, Kritika M; Chattopadhyay, Balaji; Wilton, Peter R; Malia Prawiradilaga, Dewi; Rheindt, Frank E

2018-08-01

Cyclical periods of global cooling have been important drivers of biotic differentiation throughout the Quaternary. Ice age-induced sea level fluctuations can lead to changing patterns of land connections, both facilitating and disrupting gene flow. In this study, we test if species with differing life histories are differentially affected by Quaternary land connections. We used genome-wide SNPs in combination with mitochondrial gene sequences to analyse levels of divergence and gene flow between two songbird complexes across two Wallacean islands that have been repeatedly connected during glaciations. Although the two bird complexes are similar in ecological attributes, the forest and edge-inhabiting golden whistler Pachycephala pectoralis is comparatively flexible in its diet and niche requirements as compared to the henna-tailed jungle-flycatcher Cyornis colonus, which is largely restricted to the forest interior. Using population-genomic and coalescent approaches, we estimated levels of gene flow, population differentiation and divergence time between the two island populations. We observed higher levels of differentiation, an approximately two to four times deeper divergence time and near-zero levels of gene flow between the two island populations of the more forest-dependent henna-tailed jungle-flycatcher as compared to the more generalist golden whistler. Our results suggest that Quaternary land bridges act as semipermeable agents of gene flow in Wallacea, allowing only certain taxa to connect between islands while others remain isolated. Quaternary land bridges do not accommodate all terrestrial species equally, differing in suitability according to life history and species biology. More generalist species are likely to use Quaternary land connections as a conduit for gene flow between islands whereas island populations of more specialist species may continue to be reproductively isolated even during periods of Quaternary land bridges. Copyright © 2018

Inference of purifying and positive selection in three subspecies of chimpanzees (Pan troglodytes) from exome sequencing

DEFF Research Database (Denmark)

Bataillon, Thomas; Duan, Jinjie; Hvilsom, Christina

2015-01-01

of recent gene flow from Western into Eastern chimpanzees. The striking contrast in X-linked vs. autosomal polymorphism and divergence previously reported in Central chimpanzees is also found in Eastern and Western chimpanzees. We show that the direction of selection (DoS) statistic exhibits a strong non......-monotonic relationship with the strength of purifying selection S, making it inappropriate for estimating S. We instead use counts in synonymous vs. non-synonymous frequency classes to infer the distribution of S coefficients acting on non-synonymous mutations in each subspecies. The strength of purifying selection we...... infer is congruent with the differences in effective sizes of each subspecies: Central chimpanzees are undergoing the strongest purifying selection followed by Eastern and Western chimpanzees. Coding indels show stronger selection against indels changing the reading frame than observed in human...

Cryptic species? Patterns of maternal and paternal gene flow in eight neotropical bats.

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Elizabeth L Clare

Full Text Available Levels of sequence divergence at mitochondrial loci are frequently used in phylogeographic analysis and species delimitation though single marker systems cannot assess bi-parental gene flow. In this investigation I compare the phylogeographic patterns revealed through the maternally inherited mitochondrial COI region and the paternally inherited 7(th intron region of the Dby gene on the Y-chromosome in eight common Neotropical bat species. These species are diverse and include members of two families from the feeding guilds of sanguivores, nectarivores, frugivores, carnivores and insectivores. In each case, the currently recognized taxon is comprised of distinct, substantially divergent intraspecific mitochondrial lineages suggesting cryptic species complexes. In Chrotopterus auritus, and Saccopteryx bilineata I observed congruent patterns of divergence in both genetic regions suggesting a cessation of gene flow between intraspecific groups. This evidence supports the existence of cryptic species complexes which meet the criteria of the genetic species concept. In Glossophaga soricina two intraspecific groups with largely sympatric South American ranges show evidence for incomplete lineage sorting or frequent hybridization while a third group with a Central American distribution appears to diverge congruently at both loci suggesting speciation. Within Desmodus rotundus and Trachops cirrhosus the paternally inherited region was monomorphic and thus does not support or refute the potential for cryptic speciation. In Uroderma bilobatum, Micronycteris megalotis and Platyrrhinus helleri the gene regions show conflicting patterns of divergence and I cannot exclude ongoing gene flow between intraspecific groups. This analysis provides a comprehensive comparison across taxa and employs both maternally and paternally inherited gene regions to validate patterns of gene flow. I present evidence for previously unrecognized species meeting the criteria of

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

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Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

2012-01-01

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.

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Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A; Kellis, Manolis

2012-07-01

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

Introgression in the Drosophila subobscura--D. Madeirensis sister species: evidence of gene flow in nuclear genes despite mitochondrial differentiation.

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Herrig, Danielle K; Modrick, Alec J; Brud, Evgeny; Llopart, Ana

2014-03-01

Species hybridization, and thus the potential for gene flow, was once viewed as reproductive mistake. However, recent analysis based on large datasets and newly developed models suggest that gene exchange is not as rare as originally suspected. To investigate the history and speciation of the closely related species Drosophila subobscura, D. madeirensis, and D. guanche, we obtained polymorphism and divergence data for 26 regions throughout the genome, including the Y chromosome and mitochondrial DNA. We found that the D. subobscura X/autosome ratio of silent nucleotide diversity is significantly smaller than the 0.75 expected under neutrality. This pattern, if held genomewide, may reflect a faster accumulation of beneficial mutations on the X chromosome than on autosomes. We also detected evidence of gene flow in autosomal regions, while sex chromosomes remain distinct. This is consistent with the large X effect on hybrid male sterility seen in this system and the presence of two X chromosome inversions fixed between species. Overall, our data conform to chromosomal speciation models in which rearrangements are proposed to serve as gene flow barriers. Contrary to other observations in Drosophila, the mitochondrial genome appears resilient to gene flow in the presence of nuclear exchange. © 2013 The Authors. Evolution published by Wiley Periodicals, Inc. on behalf of The Society for the Study of Evolution.

Data identification for improving gene network inference using computational algebra.

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Dimitrova, Elena; Stigler, Brandilyn

2014-11-01

Identification of models of gene regulatory networks is sensitive to the amount of data used as input. Considering the substantial costs in conducting experiments, it is of value to have an estimate of the amount of data required to infer the network structure. To minimize wasted resources, it is also beneficial to know which data are necessary to identify the network. Knowledge of the data and knowledge of the terms in polynomial models are often required a priori in model identification. In applications, it is unlikely that the structure of a polynomial model will be known, which may force data sets to be unnecessarily large in order to identify a model. Furthermore, none of the known results provides any strategy for constructing data sets to uniquely identify a model. We provide a specialization of an existing criterion for deciding when a set of data points identifies a minimal polynomial model when its monomial terms have been specified. Then, we relax the requirement of the knowledge of the monomials and present results for model identification given only the data. Finally, we present a method for constructing data sets that identify minimal polynomial models.

Pollen-mediated gene flow in flax (Linum usitatissimum L.): can genetically engineered and organic flax coexist?

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Jhala, A J; Bhatt, H; Topinka, K; Hall, L M

2011-04-01

Coexistence allows growers and consumers the choice of producing or purchasing conventional or organic crops with known standards for adventitious presence of genetically engineered (GE) seed. Flax (Linum usitatissimum L.) is multipurpose oilseed crop in which product diversity and utility could be enhanced for industrial, nutraceutical and pharmaceutical markets through genetic engineering. If GE flax were released commercially, pollen-mediated gene flow will determine in part whether GE flax could coexist without compromising other markets. As a part of pre-commercialization risk assessment, we quantified pollen-mediated gene flow between two cultivars of flax. Field experiments were conducted at four locations during 2006 and 2007 in western Canada using a concentric donor (20 × 20 m) receptor (120 × 120 m) design. Gene flow was detected through the xenia effect of dominant alleles of high α-linolenic acid (ALA; 18:3(cisΔ9,12,15)) to the low ALA trait. Seeds were harvested from the pollen recipient plots up to a distance of 50 m in eight directions from the pollen donor. High ALA seeds were identified using a thiobarbituric acid test and served as a marker for gene flow. Binomial distribution and power analysis were used to predict the minimum number of seeds statistically required to detect the frequency of gene flow at specific α (confidence interval) and power (1-β) values. As a result of the low frequency of gene flow, approximately 4 million seeds were screened to derive accurate quantification. Frequency of gene flow was highest near the source: averaging 0.0185 at 0.1 m but declined rapidly with distance, 0.0013 and 0.00003 at 3 and 35 m, respectively. Gene flow was reduced to 50% (O₅₀) and 90% (O₉₀) between 0.85 to 2.64 m, and 5.68 to 17.56 m, respectively. No gene flow was detected at any site or year > 35 m distance from the pollen source, suggesting that frequency of gene flow was ≤ 0.00003 (P = 0.95). Although it is not possible to

Impact of flows on ion temperatures inferred from neutron spectra in asymmetrically driven OMEGA DT implosions

Science.gov (United States)

Gatu Johnson, M.; Frenje, J.; Lahmann, B.; Seguin, F.; Petrasso, R.; Appelbe, B.; Chittenden, J.; Walsh, C.; Delettrez, J.; Igumenshchev, I.; Knauer, J. P.; Glebov, V. Yu.; Forrest, C.; Grimble, W.; Marshall, F.; Michel, T.; Stoeckl, C.; Haines, B. M.; Zylstra, A. B.

2017-10-01

Ion temperatures (Tion) in Inertial Confinement Fusion (ICF) experiments have traditionally been inferred from the broadening of primary neutron spectra. Directional motion (flow) of the fuel at burn, expected to arise due to asymmetries imposed by e.g. engineering features or drive non-uniformity, also impacts broadening and may lead to artificially inflated ``Tion'' values. Flow due to low-mode asymmetries is expected to give rise to line-of-sight variations in measured Tion, as observed in OMEGA cryogenic DT implosions but not in similar experiments at the NIF. In this presentation, we report on OMEGA experiments with intentional drive asymmetry designed for testing the ability to accurately predict and measure line-of-sight differences in apparent Tion due to low-mode asymmetry-seeded flows. The measurements are contrasted to CHIMERA, RAGE and ASTER simulations, providing insight into implosion dynamics and the relative importance of laser drive non-uniformity, stalk and offset as sources of asymmetry. The results highlight the complexity of hot-spot dynamics, which is a problem that must be mastered to achieve ICF ignition. This work was supported in part by the U.S. DOE, NLUF and LLE.

Targeted Enrichment of Large Gene Families for Phylogenetic Inference: Phylogeny and Molecular Evolution of Photosynthesis Genes in the Portullugo Clade (Caryophyllales).

Science.gov (United States)

Moore, Abigail J; Vos, Jurriaan M De; Hancock, Lillian P; Goolsby, Eric; Edwards, Erika J

2018-05-01

Hybrid enrichment is an increasingly popular approach for obtaining hundreds of loci for phylogenetic analysis across many taxa quickly and cheaply. The genes targeted for sequencing are typically single-copy loci, which facilitate a more straightforward sequence assembly and homology assignment process. However, this approach limits the inclusion of most genes of functional interest, which often belong to multi-gene families. Here, we demonstrate the feasibility of including large gene families in hybrid enrichment protocols for phylogeny reconstruction and subsequent analyses of molecular evolution, using a new set of bait sequences designed for the "portullugo" (Caryophyllales), a moderately sized lineage of flowering plants (~ 2200 species) that includes the cacti and harbors many evolutionary transitions to C$_{\\mathrm{4}}$ and CAM photosynthesis. Including multi-gene families allowed us to simultaneously infer a robust phylogeny and construct a dense sampling of sequences for a major enzyme of C$_{\\mathrm{4}}$ and CAM photosynthesis, which revealed the accumulation of adaptive amino acid substitutions associated with C$_{\\mathrm{4}}$ and CAM origins in particular paralogs. Our final set of matrices for phylogenetic analyses included 75-218 loci across 74 taxa, with ~ 50% matrix completeness across data sets. Phylogenetic resolution was greatly improved across the tree, at both shallow and deep levels. Concatenation and coalescent-based approaches both resolve the sister lineage of the cacti with strong support: Anacampserotaceae $+$ Portulacaceae, two lineages of mostly diminutive succulent herbs of warm, arid regions. In spite of this congruence, BUCKy concordance analyses demonstrated strong and conflicting signals across gene trees. Our results add to the growing number of examples illustrating the complexity of phylogenetic signals in genomic-scale data.

Intercontinental gene flow among western arctic populations of Lesser Snow Geese

Science.gov (United States)

Shorey, Rainy I.; Scribner, Kim T.; Kanefsky, Jeannette; Samuel, Michael D.; Libants, Scot V.

2011-01-01

Quantifying the spatial genetic structure of highly vagile species of birds is important in predicting their degree of population demographic and genetic independence during changing environmental conditions, and in assessing their abundance and distribution. In the western Arctic, Lesser Snow Geese (Chen caerulescens caerulescens) provide an example useful for evaluating spatial population genetic structure and the relative contribution of male and female philopatry to breeding and wintering locales. We analyzed biparentally inherited microsatellite loci and maternally inherited mtDNA sequences from geese breeding at Wrangel Island (Russia) and Banks Island (Canada) to estimate gene flow among populations whose geographic overlap during breeding and winter differ. Significant differences in the frequencies of mtDNA haplotypes contrast with the homogeneity of allele frequencies for microsatellite loci. Coalescence simulations revealed high variability and asymmetry between males and females in rates and direction of gene flow between populations. Our results highlight the importance of wintering areas to demographic independence and spatial genetic structure of these populations. Male-mediated gene flow among the populations on northern Wrangel Island, southern Wrangel Island, and Banks Island has been substantial. A high rate of female-mediated gene flow from southern Wrangel Island to Banks Island suggests that population exchange can be achieved when populations winter in a common area. Conversely, when birds from different breeding populations do not share a common wintering area, the probability of population exchange is likely to be dramatically reduced.

Gene flow from transgenic common beans expressing the bar gene.

Science.gov (United States)

Faria, Josias C; Carneiro, Geraldo E S; Aragão, Francisco J L

2010-01-01

Gene flow is a common phenomenon even in self-pollinated plant species. With the advent of genetically modified plants this subject has become of the utmost importance due to the need for controlling the spread of transgenes. This study was conducted to determine the occurrence and intensity of outcrossing in transgenic common beans. In order to evaluate the outcross rates, four experiments were conducted in Santo Antonio de Goiás (GO, Brazil) and one in Londrina (PR, Brazil), using transgenic cultivars resistant to the herbicide glufosinate ammonium and their conventional counterparts as recipients of the transgene. Experiments with cv. Olathe Pinto and the transgenic line Olathe M1/4 were conducted in a completely randomized design with ten replications for three years in one location, whereas the experiments with cv. Pérola and the transgenic line Pérola M1/4 were conducted at two locations for one year, with the transgenic cultivar surrounded on all sides by the conventional counterpart. The outcross occurred at a negligible rate of 0.00741% in cv. Pérola, while none was observed (0.0%) in cv. Olathe Pinto. The frequency of gene flow was cultivar dependent and most of the observed outcross was within 2.5 m from the edge of the pollen source. Index terms: Phaseolus vulgaris, outcross, glufosinate ammonium.

Phylogenetic relationships within Echinococcus and Taenia tapeworms (Cestoda: Taeniidae): an inference from nuclear protein-coding genes.

Science.gov (United States)

Knapp, Jenny; Nakao, Minoru; Yanagida, Tetsuya; Okamoto, Munehiro; Saarma, Urmas; Lavikainen, Antti; Ito, Akira

2011-12-01

The family Taeniidae of tapeworms is composed of two genera, Echinococcus and Taenia, which obligately parasitize mammals including humans. Inferring phylogeny via molecular markers is the only way to trace back their evolutionary histories. However, molecular dating approaches are lacking so far. Here we established new markers from nuclear protein-coding genes for RNA polymerase II second largest subunit (rpb2), phosphoenolpyruvate carboxykinase (pepck) and DNA polymerase delta (pold). Bayesian inference and maximum likelihood analyses of the concatenated gene sequences allowed us to reconstruct phylogenetic trees for taeniid parasites. The tree topologies clearly demonstrated that Taenia is paraphyletic and that the clade of Echinococcus oligarthrus and Echinococcusvogeli is sister to all other members of Echinococcus. Both species are endemic in Central and South America, and their definitive hosts originated from carnivores that immigrated from North America after the formation of the Panamanian land bridge about 3 million years ago (Ma). A time-calibrated phylogeny was estimated by a Bayesian relaxed-clock method based on the assumption that the most recent common ancestor of E. oligarthrus and E. vogeli existed during the late Pliocene (3.0 Ma). The results suggest that a clade of Taenia including human-pathogenic species diversified primarily in the late Miocene (11.2 Ma), whereas Echinococcus started to diversify later, in the end of the Miocene (5.8 Ma). Close genetic relationships among the members of Echinococcus imply that the genus is a young group in which speciation and global radiation occurred rapidly. Copyright © 2011 Elsevier Inc. All rights reserved.

Effect of selective logging on genetic diversity and gene flow in Cariniana legalis sampled from a cacao agroforestry system.

Science.gov (United States)

Leal, J B; Santos, R P; Gaiotto, F A

2014-01-28

The fragments of the Atlantic Forest of southern Bahia have a long history of intense logging and selective cutting. Some tree species, such as jequitibá rosa (Cariniana legalis), have experienced a reduction in their populations with respect to both area and density. To evaluate the possible effects of selective logging on genetic diversity, gene flow, and spatial genetic structure, 51 C. legalis individuals were sampled, representing the total remaining population from the cacao agroforestry system. A total of 120 alleles were observed from the 11 microsatellite loci analyzed. The average observed heterozygosity (0.486) was less than the expected heterozygosity (0.721), indicating a loss of genetic diversity in this population. A high fixation index (FIS = 0.325) was found, which is possibly due to a reduction in population size, resulting in increased mating among relatives. The maximum (1055 m) and minimum (0.095 m) distances traveled by pollen or seeds were inferred based on paternity tests. We found 36.84% of unique parents among all sampled seedlings. The progenitors of the remaining seedlings (63.16%) were most likely out of the sampled area. Positive and significant spatial genetic structure was identified in this population among classes 10 to 30 m away with an average coancestry coefficient between pairs of individuals of 0.12. These results suggest that the agroforestry system of cacao cultivation is contributing to maintaining levels of diversity and gene flow in the studied population, thus minimizing the effects of selective logging.

A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data

Science.gov (United States)

Gallopin, Mélina; Rau, Andrea; Jaffrézic, Florence

2013-01-01

Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data. PMID:24147011

High rates of gene flow by pollen and seed in oak populations across Europe

NARCIS (Netherlands)

Gerber, S.; Chadoeuf, J.; Gugerli, F.; Lascoux, M.; Buiteveld, J.; Cottrell, J.; Dounavi, A.; Fineschi, S.; Forrest, L.; Fogelqvist, J.; Goicoechea, P.G.; Jensen, J.S.; Salvini, D.; Vendramin, G.G.; Kremer, A.

2014-01-01

Gene flow is a key factor in the evolution of species, influencing effective population size, hybridisation and local adaptation. We analysed local gene flow in eight stands of white oak (mostly Quercus petraea and Q. robur, but also Q. pubescens and Q. faginea) distributed across Europe. Adult

Adaptive divergence with gene flow in incipient speciation of Miscanthus floridulus / sinensis complex (Poaceae)

KAUST Repository

Huang, Chao-Li; Ho, Chuan-Wen; Chiang, Yu-Chung; Shigemoto, Yasumasa; Hsu, Tsai-Wen; Hwang, Chi-Chuan; Ge, Xue-Jun; Chen, Charles; Wu, Tai-Han; Chou, Chang-Hung; Huang, Hao-Jen; Gojobori, Takashi; Osada, Naoki; Chiang, Tzen-Yuh

2014-01-01

Young incipient species provide ideal materials for untangling the process of ecological speciation in the presence of gene flow. The Miscanthus floridulus/sinensis complex exhibits diverse phenotypic and ecological differences despite recent divergence (approximately 1.59million years ago). To elucidate the process of genetic differentiation during early stages of ecological speciation, we analyzed genomic divergence in the Miscanthus complex using 72 randomly selected genes from a newly assembled transcriptome. In this study, rampant gene flow was detected between species, estimated as M=3.36x10(-9) to 1.20x10(-6), resulting in contradicting phylogenies across loci. Nevertheless, beast analyses revealed the species identity and the effects of extrinsic cohesive forces that counteracted the non-stop introgression. As expected, early in speciation with gene flow, only 3-13 loci were highly diverged; two to five outliers (approximately 2.78-6.94% of the genome) were characterized by strong linkage disequilibrium, and asymmetrically distributed among ecotypes, indicating footprints of diversifying selection. In conclusion, ecological speciation of incipient species of Miscanthus probably followed the parapatric model, whereas allopatric speciation cannot be completely ruled out, especially between the geographically isolated northern and southern M.sinensis, for which no significant gene flow across oceanic barriers was detected. Divergence between local ecotypes in early-stage speciation began at a few genomic regions under the influence of natural selection and divergence hitchhiking that overcame gene flow.

Adaptive divergence with gene flow in incipient speciation of Miscanthus floridulus / sinensis complex (Poaceae)

KAUST Repository

Huang, Chao-Li

2014-11-11

Young incipient species provide ideal materials for untangling the process of ecological speciation in the presence of gene flow. The Miscanthus floridulus/sinensis complex exhibits diverse phenotypic and ecological differences despite recent divergence (approximately 1.59million years ago). To elucidate the process of genetic differentiation during early stages of ecological speciation, we analyzed genomic divergence in the Miscanthus complex using 72 randomly selected genes from a newly assembled transcriptome. In this study, rampant gene flow was detected between species, estimated as M=3.36x10(-9) to 1.20x10(-6), resulting in contradicting phylogenies across loci. Nevertheless, beast analyses revealed the species identity and the effects of extrinsic cohesive forces that counteracted the non-stop introgression. As expected, early in speciation with gene flow, only 3-13 loci were highly diverged; two to five outliers (approximately 2.78-6.94% of the genome) were characterized by strong linkage disequilibrium, and asymmetrically distributed among ecotypes, indicating footprints of diversifying selection. In conclusion, ecological speciation of incipient species of Miscanthus probably followed the parapatric model, whereas allopatric speciation cannot be completely ruled out, especially between the geographically isolated northern and southern M.sinensis, for which no significant gene flow across oceanic barriers was detected. Divergence between local ecotypes in early-stage speciation began at a few genomic regions under the influence of natural selection and divergence hitchhiking that overcame gene flow.

Illegal gene flow from transgenic creeping bentgrass: the saga continues.

Science.gov (United States)

Snow, Allison A

2012-10-01

Ecologists have paid close attention to environmental effects that fitness-enhancing transgenes might have following crop-to-wild gene flow (e.g. Snow et al. 2003). For some crops, gene flow also can lead to legal problems,especially when government agencies have not approved transgenic events for unrestricted environmental release.Creeping bentgrass (Agrostis stolonifera), a common turf grass used in golf courses, is the focus of both areas of concern. In 2002, prior to expected deregulation (still pending), The Scotts Company planted creeping bentgrass with transgenic resistance to the herbicide glyphosate,also known as RoundUp, on 162 ha in a designated control area in central Oregon (Fig. 1).Despite efforts to restrict gene flow, wind-dispersed pollen carried transgenes to florets of local A. stolonifera and A. gigantea as far as 14 km away, and to sentinel plants placed as far as 21 km away (Watrud et al. 2004).Then, in August 2003, a strong wind event moved transgenic seeds from wind rows of cut bentgrass into nearby areas. The company’s efforts to kill all transgenic survivors in the area failed: feral glyphosate-resistant populations of A. stolonifera were found by Reichman et al.(2006), and 62% of 585 bentgrass plants had the telltale CP4 EPSPS transgene in 2006 (Zapiola et al. 2008; Fig. 2).Now, in this issue, the story gets even more interesting as Zapiola & Mallory-Smith (2012) describe a transgenic,intergeneric hybrid produced on a feral, transgenic creeping bentgrass plant that received pollen from Polypogon monspeliensis (rabbitfoot grass). Their finding raises a host of new questions about the prevalence and fitness of intergeneric hybrids, as well as how to evaluate the full extent of gene flow from transgenic crops.

Learning gene regulatory networks from gene expression data using weighted consensus

KAUST Repository

Fujii, Chisato; Kuwahara, Hiroyuki; Yu, Ge; Guo, Lili; Gao, Xin

2016-01-01

An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

Learning gene regulatory networks from gene expression data using weighted consensus

KAUST Repository

Fujii, Chisato

2016-08-25

An accurate determination of the network structure of gene regulatory systems from high-throughput gene expression data is an essential yet challenging step in studying how the expression of endogenous genes is controlled through a complex interaction of gene products and DNA. While numerous methods have been proposed to infer the structure of gene regulatory networks, none of them seem to work consistently over different data sets with high accuracy. A recent study to compare gene network inference methods showed that an average-ranking-based consensus method consistently performs well under various settings. Here, we propose a linear programming-based consensus method for the inference of gene regulatory networks. Unlike the average-ranking-based one, which treats the contribution of each individual method equally, our new consensus method assigns a weight to each method based on its credibility. As a case study, we applied the proposed consensus method on synthetic and real microarray data sets, and compared its performance to that of the average-ranking-based consensus and individual inference methods. Our results show that our weighted consensus method achieves superior performance over the unweighted one, suggesting that assigning weights to different individual methods rather than giving them equal weights improves the accuracy. © 2016 Elsevier B.V.

Spatial and temporal assessment of pollen- and seed-mediated gene flow from genetically engineered plum Prunus domestica.

Directory of Open Access Journals (Sweden)

Ralph Scorza

Full Text Available Pollen flow from a 0.46 ha plot of genetically engineered (GE Prunus domestica located in West Virginia, USA was evaluated from 2000-2010. Sentinel plum trees were planted at distances ranging from 132 to 854 m from the center of the GE orchard. Plots of mixed plum varieties and seedlings were located at 384, 484 and 998 m from the GE plot. Bee hives (Apis mellifera were dispersed between the GE plum plot and the pollen flow monitoring sites. Pollen-mediated gene flow from out of the GE plum plot to non-GE plums under the study conditions was low, only occurring at all in 4 of 11 years and then in only 0.31% of the 12,116 seeds analyzed. When it occurred, gene flow, calculated as the number of GUS positive embryos/total embryos sampled, ranged from 0.215% at 132 m from the center of the GE plum plot (28 m from the nearest GE plum tree to 0.033-0.017% at longer distances (384-998 m. Based on the percentage of GUS positive seeds per individual sampled tree the range was 0.4% to 12%. Within the GE field plot, gene flow ranged from 4.9 to 39%. Gene flow was related to distance and environmental conditions. A single year sample from a sentinel plot 132 m from the center of the GE plot accounted for 65% of the total 11-year gene flow. Spatial modeling indicated that gene flow dramatically decreased at distances over 400 m from the GE plot. Air temperature and rainfall were, respectively, positively and negatively correlated with gene flow, reflecting the effects of weather conditions on insect pollinator activity. Seed-mediated gene flow was not detected. These results support the feasibility of coexistence of GE and non-GE plum orchards.

Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method.

Science.gov (United States)

Grassi, Angela; Di Camillo, Barbara; Ciccarese, Francesco; Agnusdei, Valentina; Zanovello, Paola; Amadori, Alberto; Finesso, Lorenzo; Indraccolo, Stefano; Toffolo, Gianna Maria

2016-03-12

Inference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. A challenging task for many researchers working in the field of systems biology is to build up an experiment with a limited budget and produce a dataset suitable to reconstruct putative regulatory modules worth of biological validation. Here, we focus on small-scale gene expression screens and we introduce a novel experimental set-up and a customized method of analysis to make inference on regulatory modules starting from genetic perturbation data, e.g. knockdown and overexpression data. To illustrate the utility of our strategy, it was applied to produce and analyze a dataset of quantitative real-time RT-PCR data, in which interferon-α (IFN-α) transcriptional response in endothelial cells is investigated by RNA silencing of two candidate IFN-α modulators, STAT1 and IFIH1. A putative regulatory module was reconstructed by our method, revealing an intriguing feed-forward loop, in which STAT1 regulates IFIH1 and they both negatively regulate IFNAR1. STAT1 regulation on IFNAR1 was object of experimental validation at the protein level. Detailed description of the experimental set-up and of the analysis procedure is reported, with the intent to be of inspiration for other scientists who want to realize similar experiments to reconstruct gene regulatory modules starting from perturbations of possible regulators. Application of our approach to the study of IFN-α transcriptional response modulators in endothelial cells has led to many interesting novel findings and new biological hypotheses worth of validation.

Interspecific gene flow and maintenance of species integrity in oaks

Directory of Open Access Journals (Sweden)

Oliver Gailing

2014-07-01

Full Text Available Oak species show a wide variation in morphological and physiological characters, and species boundaries between closely related species are often not clear-cut. Still, despite frequent interspecific gene flow, oaks maintain distinct morphological and physiological adaptations. In sympatric stands, spatial distribution of species with different ecological requirements is not random but constrained by soil and other microenvironmental factors. Pre-zygotic isolation (e.g. cross incompatibilities, asynchrony in flowering, pollen competition and post-zygotic isolation (divergent selection contribute to the maintenance of species integrity in sympatric oak stands. The antagonistic effects of interspecific gene flow and divergent selection are reflected in the low genetic differentiation between hybridizing oak species at most genomic regions interspersed by regions with signatures of divergent selection (outlier regions. In the near future, the availability of high-density genetic linkage maps anchored to scaffolds of a sequenced Q. robur genome will allow to characterize the underlying genes in these outlier regions and their putative role in reproductive isolation between species. Reciprocal transplant experiments of seedlings between parental environments can be used to characterize selection on outlier genes. High transferability of gene-based markers will enable comparative outlier screens in different oak species.

Inference of Transcriptional Network for Pluripotency in Mouse Embryonic Stem Cells

International Nuclear Information System (INIS)

Aburatani, S

2015-01-01

In embryonic stem cells, various transcription factors (TFs) maintain pluripotency. To gain insights into the regulatory system controlling pluripotency, I inferred the regulatory relationships between the TFs expressed in ES cells. In this study, I applied a method based on structural equation modeling (SEM), combined with factor analysis, to 649 expression profiles of 19 TF genes measured in mouse Embryonic Stem Cells (ESCs). The factor analysis identified 19 TF genes that were regulated by several unmeasured factors. Since the known cell reprogramming TF genes (Pou5f1, Sox2 and Nanog) are regulated by different factors, each estimated factor is considered to be an input for signal transduction to control pluripotency in mouse ESCs. In the inferred network model, TF proteins were also arranged as unmeasured factors that control other TFs. The interpretation of the inferred network model revealed the regulatory mechanism for controlling pluripotency in ES cells

A Network Inference Workflow Applied to Virulence-Related Processes in Salmonella typhimurium

Energy Technology Data Exchange (ETDEWEB)

Taylor, Ronald C.; Singhal, Mudita; Weller, Jennifer B.; Khoshnevis, Saeed; Shi, Liang; McDermott, Jason E.

2009-04-20

Inference of the structure of mRNA transcriptional regulatory networks, protein regulatory or interaction networks, and protein activation/inactivation-based signal transduction networks are critical tasks in systems biology. In this article we discuss a workflow for the reconstruction of parts of the transcriptional regulatory network of the pathogenic bacterium Salmonella typhimurium based on the information contained in sets of microarray gene expression data now available for that organism, and describe our results obtained by following this workflow. The primary tool is one of the network inference algorithms deployed in the Software Environment for BIological Network Inference (SEBINI). Specifically, we selected the algorithm called Context Likelihood of Relatedness (CLR), which uses the mutual information contained in the gene expression data to infer regulatory connections. The associated analysis pipeline automatically stores the inferred edges from the CLR runs within SEBINI and, upon request, transfers the inferred edges into either Cytoscape or the plug-in Collective Analysis of Biological of Biological Interaction Networks (CABIN) tool for further post-analysis of the inferred regulatory edges. The following article presents the outcome of this workflow, as well as the protocols followed for microarray data collection, data cleansing, and network inference. Our analysis revealed several interesting interactions, functional groups, metabolic pathways, and regulons in S. typhimurium.

Time and flow-dependent changes in the p27(kip1) gene network drive maladaptive vascular remodeling.

Science.gov (United States)

DeSart, Kenneth M; Butler, Khayree; O'Malley, Kerri A; Jiang, Zhihua; Berceli, Scott A

2015-11-01

Although clinical studies have identified that a single nucleotide polymorphism in the p27(kip1) gene is associated with success or failure after vein bypass grafting, the underlying mechanisms for this difference are not well defined. Using a high-throughput approach in a flow-dependent vein graft model, we explored the differences in p27(kip1)-related genes that drive the enhanced hyperplastic response under low-flow conditions. Bilateral rabbit carotid artery interposition grafts with jugular vein were placed with a unilateral distal outflow branch ligation to create differential flow states. Grafts were harvested at 2 hours and at 1, 3, 7, 14, and 28 days after implantation, measured for neointimal area, and assayed for cell proliferation. Whole-vessel messenger RNA was isolated and analyzed using an Affymetrix (Santa Clara, Calif) gene array platform. Ingenuity Pathway Analysis (Ingenuity, Redwood City, Calif) was used to identify the gene networks surrounding p27(kip1). This gene set was then analyzed for temporal expression changes after graft placement and for differential expression in the alternate flow conditions. Outflow branch ligation resulted in an eightfold difference in mean flow rates throughout the 28-day perfusion period (P Flow reduction led to a robust hyperplastic response, resulting in a significant increase in intimal area by 7 days (0.13 ± 0.04 mm(2) vs 0.014 ± 0.006 mm(2); P flow grafts demonstrated a burst of actively dividing intimal cells (36.4 ± 9.4 cells/mm(2) vs 11.5 ± 1.9 cells/mm(2); P = .04). Sixty-five unique genes within the microarray were identified as components of the p27(kip1) network. At a false discovery rate of 0.05, 26 genes demonstrated significant temporal changes, and two dominant patterns of expression were identified. Class comparison analysis identified differential expression of 11 genes at 2 hours and seven genes and 14 days between the high-flow and low-flow grafts (P flow and shear stress result in

dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data.

Science.gov (United States)

Huynh-Thu, Vân Anh; Geurts, Pierre

2018-02-21

The elucidation of gene regulatory networks is one of the major challenges of systems biology. Measurements about genes that are exploited by network inference methods are typically available either in the form of steady-state expression vectors or time series expression data. In our previous work, we proposed the GENIE3 method that exploits variable importance scores derived from Random forests to identify the regulators of each target gene. This method provided state-of-the-art performance on several benchmark datasets, but it could however not specifically be applied to time series expression data. We propose here an adaptation of the GENIE3 method, called dynamical GENIE3 (dynGENIE3), for handling both time series and steady-state expression data. The proposed method is evaluated extensively on the artificial DREAM4 benchmarks and on three real time series expression datasets. Although dynGENIE3 does not systematically yield the best performance on each and every network, it is competitive with diverse methods from the literature, while preserving the main advantages of GENIE3 in terms of scalability.

Inferring regulatory networks from expression data using tree-based methods.

Directory of Open Access Journals (Sweden)

Vân Anh Huynh-Thu

2010-09-01

Full Text Available One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene is predicted from the expression patterns of all the other genes (input genes, using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.

Applying gene flow science to environmental policy needs: a boundary work perspective.

Science.gov (United States)

Ridley, Caroline E; Alexander, Laurie C

2016-08-01

One application of gene flow science is the policy arena. In this article, we describe two examples in which the topic of gene flow has entered into the U.S. national environmental policymaking process: regulation of genetically engineered crops and clarification of the jurisdictional scope of the Clean Water Act. We summarize both current scientific understanding and the legal context within which gene flow science has relevance. We also discuss the process by which scientific knowledge has been synthesized and communicated to decision-makers in these two contexts utilizing the concept of 'boundary work'. Boundary organizations, the work they engage in to bridge the worlds of science, policy, and practice, and the boundary objects they produce to translate scientific knowledge existed in both examples. However, the specific activities and attributes of the objects produced varied based on the needs of the decision-makers. We close with suggestions for how scientists can contribute to or engage in boundary work with policymakers.

Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

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Ji Wei

2010-10-01

Full Text Available Abstract Background Microarray data discretization is a basic preprocess for many algorithms of gene regulatory network inference. Some common discretization methods in informatics are used to discretize microarray data. Selection of the discretization method is often arbitrary and no systematic comparison of different discretization has been conducted, in the context of gene regulatory network inference from time series gene expression data. Results In this study, we propose a new discretization method "bikmeans", and compare its performance with four other widely-used discretization methods using different datasets, modeling algorithms and number of intervals. Sensitivities, specificities and total accuracies were calculated and statistical analysis was carried out. Bikmeans method always gave high total accuracies. Conclusions Our results indicate that proper discretization methods can consistently improve gene regulatory network inference independent of network modeling algorithms and datasets. Our new method, bikmeans, resulted in significant better total accuracies than other methods.

Speciation with gene flow in equids despite extensive chromosomal plasticity.

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Jónsson, Hákon; Schubert, Mikkel; Seguin-Orlando, Andaine; Ginolhac, Aurélien; Petersen, Lillian; Fumagalli, Matteo; Albrechtsen, Anders; Petersen, Bent; Korneliussen, Thorfinn S; Vilstrup, Julia T; Lear, Teri; Myka, Jennifer Leigh; Lundquist, Judith; Miller, Donald C; Alfarhan, Ahmed H; Alquraishi, Saleh A; Al-Rasheid, Khaled A S; Stagegaard, Julia; Strauss, Günter; Bertelsen, Mads Frost; Sicheritz-Ponten, Thomas; Antczak, Douglas F; Bailey, Ernest; Nielsen, Rasmus; Willerslev, Eske; Orlando, Ludovic

2014-12-30

Horses, asses, and zebras belong to a single genus, Equus, which emerged 4.0-4.5 Mya. Although the equine fossil record represents a textbook example of evolution, the succession of events that gave rise to the diversity of species existing today remains unclear. Here we present six genomes from each living species of asses and zebras. This completes the set of genomes available for all extant species in the genus, which was hitherto represented only by the horse and the domestic donkey. In addition, we used a museum specimen to characterize the genome of the quagga zebra, which was driven to extinction in the early 1900s. We scan the genomes for lineage-specific adaptations and identify 48 genes that have evolved under positive selection and are involved in olfaction, immune response, development, locomotion, and behavior. Our extensive genome dataset reveals a highly dynamic demographic history with synchronous expansions and collapses on different continents during the last 400 ky after major climatic events. We show that the earliest speciation occurred with gene flow in Northern America, and that the ancestor of present-day asses and zebras dispersed into the Old World 2.1-3.4 Mya. Strikingly, we also find evidence for gene flow involving three contemporary equine species despite chromosomal numbers varying from 16 pairs to 31 pairs. These findings challenge the claim that the accumulation of chromosomal rearrangements drive complete reproductive isolation, and promote equids as a fundamental model for understanding the interplay between chromosomal structure, gene flow, and, ultimately, speciation.

High gene flow in epiphytic ferns despite habitat loss and fragmentation.

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Winkler, Manuela; Koch, Marcus; Hietz, Peter

2011-01-01

Tropical montane forests suffer from increasing fragmentation and replacement by other types of land-use such as coffee plantations. These processes are known to affect gene flow and genetic structure of plant populations. Epiphytes are particularly vulnerable because they depend on their supporting trees for their entire life-cycle. We compared population genetic structure and genetic diversity derived from AFLP markers of two epiphytic fern species differing in their ability to colonize secondary habitats. One species, Pleopeltis crassinervata , is a successful colonizer of shade trees and isolated trees whereas the other species, Polypodium rhodopleuron , is restricted to forests with anthropogenic separation leading to significant isolation between populations. By far most genetic variation was distributed within rather than among populations in both species, and a genetic admixture analysis did not reveal any clustering. Gene flow exceeded by far the benchmark of one migrant per generation to prevent genetic divergence between populations in both species. Though populations are threatened by habitat loss, long-distance dispersal is likely to support gene flow even between distant populations, which efficiently delays genetic isolation. Consequently, populations may rather be threatened by ecological consequences of habitat loss and fragmentation.

Performance of single and concatenated sets of mitochondrial genes at inferring metazoan relationships relative to full mitogenome data.

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Justin C Havird

Full Text Available Mitochondrial (mt genes are some of the most popular and widely-utilized genetic loci in phylogenetic studies of metazoan taxa. However, their linked nature has raised questions on whether using the entire mitogenome for phylogenetics is overkill (at best or pseudoreplication (at worst. Moreover, no studies have addressed the comparative phylogenetic utility of mitochondrial genes across individual lineages within the entire Metazoa. To comment on the phylogenetic utility of individual mt genes as well as concatenated subsets of genes, we analyzed mitogenomic data from 1865 metazoan taxa in 372 separate lineages spanning genera to subphyla. Specifically, phylogenies inferred from these datasets were statistically compared to ones generated from all 13 mt protein-coding (PC genes (i.e., the "supergene" set to determine which single genes performed "best" at, and the minimum number of genes required to, recover the "supergene" topology. Surprisingly, the popular marker COX1 performed poorest, while ND5, ND4, and ND2 were most likely to reproduce the "supergene" topology. Averaged across all lineages, the longest ∼2 mt PC genes were sufficient to recreate the "supergene" topology, although this average increased to ∼5 genes for datasets with 40 or more taxa. Furthermore, concatenation of the three "best" performing mt PC genes outperformed that of the three longest mt PC genes (i.e, ND5, COX1, and ND4. Taken together, while not all mt PC genes are equally interchangeable in phylogenetic studies of the metazoans, some subset can serve as a proxy for the 13 mt PC genes. However, the exact number and identity of these genes is specific to the lineage in question and cannot be applied indiscriminately across the Metazoa.

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.

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Ghanat Bari, Mehrab; Ung, Choong Yong; Zhang, Cheng; Zhu, Shizhen; Li, Hu

2017-08-01

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 10 8 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

Gene flow of common ash (Fraxinus excelsior L. in a fragmented landscape.

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Devrim Semizer-Cuming

Full Text Available Gene flow dynamics of common ash (Fraxinus excelsior L. is affected by several human activities in Central Europe, including habitat fragmentation, agroforestry expansion, controlled and uncontrolled transfer of reproductive material, and a recently introduced emerging infectious disease, ash dieback, caused by Hymenoscyphus fraxineus. Habitat fragmentation may alter genetic connectivity and effective population size, leading to loss of genetic diversity and increased inbreeding in ash populations. Gene flow from cultivated trees in landscapes close to their native counterparts may also influence the adaptability of future generations. The devastating effects of ash dieback have already been observed in both natural and managed populations in continental Europe. However, potential long-term effects of genetic bottlenecks depend on gene flow across fragmented landscapes. For this reason, we studied the genetic connectivity of ash trees in an isolated forest patch of a fragmented landscape in Rösenbeck, Germany. We applied two approaches to parentage analysis to estimate gene flow patterns at the study site. We specifically investigated the presence of background pollination at the landscape level and the degree of genetic isolation between native and cultivated trees. Local meteorological data was utilized to understand the effect of wind on the pollen and seed dispersal patterns. Gender information of the adult trees was considered for calculating the dispersal distances. We found that the majority of the studied seeds (55-64% and seedlings (75-98% in the forest patch were fathered and mothered by the trees within the same patch. However, we determined a considerable amount of pollen flow (26-45% from outside of the study site, representing background pollination at the landscape level. Limited pollen flow was observed from neighbouring cultivated trees (2%. Both pollen and seeds were dispersed in all directions in accordance with the local

Gene flow of common ash (Fraxinus excelsior L.) in a fragmented landscape.

Science.gov (United States)

Semizer-Cuming, Devrim; Kjær, Erik Dahl; Finkeldey, Reiner

2017-01-01

Gene flow dynamics of common ash (Fraxinus excelsior L.) is affected by several human activities in Central Europe, including habitat fragmentation, agroforestry expansion, controlled and uncontrolled transfer of reproductive material, and a recently introduced emerging infectious disease, ash dieback, caused by Hymenoscyphus fraxineus. Habitat fragmentation may alter genetic connectivity and effective population size, leading to loss of genetic diversity and increased inbreeding in ash populations. Gene flow from cultivated trees in landscapes close to their native counterparts may also influence the adaptability of future generations. The devastating effects of ash dieback have already been observed in both natural and managed populations in continental Europe. However, potential long-term effects of genetic bottlenecks depend on gene flow across fragmented landscapes. For this reason, we studied the genetic connectivity of ash trees in an isolated forest patch of a fragmented landscape in Rösenbeck, Germany. We applied two approaches to parentage analysis to estimate gene flow patterns at the study site. We specifically investigated the presence of background pollination at the landscape level and the degree of genetic isolation between native and cultivated trees. Local meteorological data was utilized to understand the effect of wind on the pollen and seed dispersal patterns. Gender information of the adult trees was considered for calculating the dispersal distances. We found that the majority of the studied seeds (55-64%) and seedlings (75-98%) in the forest patch were fathered and mothered by the trees within the same patch. However, we determined a considerable amount of pollen flow (26-45%) from outside of the study site, representing background pollination at the landscape level. Limited pollen flow was observed from neighbouring cultivated trees (2%). Both pollen and seeds were dispersed in all directions in accordance with the local wind directions

Illustration interface of accident progression in PWR by quick inference based on multilevel flow models

International Nuclear Information System (INIS)

Yoshikawa, H.; Ouyang, J.; Niwa, Y.

2006-01-01

In this paper, a new accident inference method is proposed by using a goal and function oriented modeling method called Multilevel Flow Model focusing on explaining the causal-consequence relations and the objective of automatic action in the accident of nuclear power plant. Users can easily grasp how the various plant parameters will behave and how the various safety facilities will be activated sequentially to cope with the accident until the nuclear power plants are settled into safety state, i.e., shutdown state. The applicability of the developed method was validated by the conduction of internet-based 'view' experiment to the voluntary respondents, and in the future, further elaboration of interface design and the further introduction of instruction contents will be developed to make it become the usable CAI system. (authors)

Inferring duplications, losses, transfers and incomplete lineage sorting with nonbinary species trees.

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Stolzer, Maureen; Lai, Han; Xu, Minli; Sathaye, Deepa; Vernot, Benjamin; Durand, Dannie

2012-09-15

Gene duplication (D), transfer (T), loss (L) and incomplete lineage sorting (I) are crucial to the evolution of gene families and the emergence of novel functions. The history of these events can be inferred via comparison of gene and species trees, a process called reconciliation, yet current reconciliation algorithms model only a subset of these evolutionary processes. We present an algorithm to reconcile a binary gene tree with a nonbinary species tree under a DTLI parsimony criterion. This is the first reconciliation algorithm to capture all four evolutionary processes driving tree incongruence and the first to reconcile non-binary species trees with a transfer model. Our algorithm infers all optimal solutions and reports complete, temporally feasible event histories, giving the gene and species lineages in which each event occurred. It is fixed-parameter tractable, with polytime complexity when the maximum species outdegree is fixed. Application of our algorithms to prokaryotic and eukaryotic data show that use of an incomplete event model has substantial impact on the events inferred and resulting biological conclusions. Our algorithms have been implemented in Notung, a freely available phylogenetic reconciliation software package, available at http://www.cs.cmu.edu/~durand/Notung. mstolzer@andrew.cmu.edu.

Population genetic inference from personal genome data: impact of ancestry and admixture on human genomic variation.

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Kidd, Jeffrey M; Gravel, Simon; Byrnes, Jake; Moreno-Estrada, Andres; Musharoff, Shaila; Bryc, Katarzyna; Degenhardt, Jeremiah D; Brisbin, Abra; Sheth, Vrunda; Chen, Rong; McLaughlin, Stephen F; Peckham, Heather E; Omberg, Larsson; Bormann Chung, Christina A; Stanley, Sarah; Pearlstein, Kevin; Levandowsky, Elizabeth; Acevedo-Acevedo, Suehelay; Auton, Adam; Keinan, Alon; Acuña-Alonzo, Victor; Barquera-Lozano, Rodrigo; Canizales-Quinteros, Samuel; Eng, Celeste; Burchard, Esteban G; Russell, Archie; Reynolds, Andy; Clark, Andrew G; Reese, Martin G; Lincoln, Stephen E; Butte, Atul J; De La Vega, Francisco M; Bustamante, Carlos D

2012-10-05

Full sequencing of individual human genomes has greatly expanded our understanding of human genetic variation and population history. Here, we present a systematic analysis of 50 human genomes from 11 diverse global populations sequenced at high coverage. Our sample includes 12 individuals who have admixed ancestry and who have varying degrees of recent (within the last 500 years) African, Native American, and European ancestry. We found over 21 million single-nucleotide variants that contribute to a 1.75-fold range in nucleotide heterozygosity across diverse human genomes. This heterozygosity ranged from a high of one heterozygous site per kilobase in west African genomes to a low of 0.57 heterozygous sites per kilobase in segments inferred to have diploid Native American ancestry from the genomes of Mexican and Puerto Rican individuals. We show evidence of all three continental ancestries in the genomes of Mexican, Puerto Rican, and African American populations, and the genome-wide statistics are highly consistent across individuals from a population once ancestry proportions have been accounted for. Using a generalized linear model, we identified subtle variations across populations in the proportion of neutral versus deleterious variation and found that genome-wide statistics vary in admixed populations even once ancestry proportions have been factored in. We further infer that multiple periods of gene flow shaped the diversity of admixed populations in the Americas-70% of the European ancestry in today's African Americans dates back to European gene flow happening only 7-8 generations ago. Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Genetics, Gene Flow, and Glaciation: The Case of the South American Limpet Nacella mytilina.

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Claudio A González-Wevar

Full Text Available Glacial episodes of the Quaternary, and particularly the Last Glacial Maximum (LGM drastically altered the distribution of the Southern-Hemisphere biota, principally at higher latitudes. The irregular coastline of Patagonia expanding for more than 84.000 km constitutes a remarkable area to evaluate the effect of Quaternary landscape and seascape shifts over the demography of near-shore marine benthic organisms. Few studies describing the biogeographic responses of marine species to the LGM have been conducted in Patagonia, but existing data from coastal marine species have demonstrated marked genetic signatures of post-LGM recolonization and expansion. The kelp-dweller limpet Nacella mytilina is broadly distributed along the southern tip of South America and at the Falkland/Malvinas Islands. Considering its distribution, abundance, and narrow bathymetry, N. mytilina represents an appropriate model to infer how historical and contemporary processes affected the distribution of intraspecific genetic diversity and structure along the southern tip of South America. At the same time, it will be possible to determine how life history traits and the ecology of the species are responsible for the current pattern of gene flow and connectivity across the study area. We conducted phylogeographic and demographic inference analyses in N. mytilina from 12 localities along Pacific Patagonia (PP and one population from the Falkland/Malvinas Islands (FI. Analyses of the mitochondrial gene COI in 300 individuals of N. mytilina revealed low levels of genetic polymorphism and the absence of genetic differentiation along PP. In contrast, FI showed a strong and significant differentiation from Pacific Patagonian populations. Higher levels of genetic diversity were also recorded in the FI population, together with a more expanded genealogy supporting the hypothesis of glacial persistence of the species in these islands. Haplotype genealogy, and mismatch analyses in

Genetics, Gene Flow, and Glaciation: The Case of the South American Limpet Nacella mytilina

Science.gov (United States)

González-Wevar, Claudio A.; Rosenfeld, Sebastián; Segovia, Nicolás I.; Hüne, Mathias; Gérard, Karin; Ojeda, Jaime; Mansilla, Andrés; Brickle, Paul; Díaz, Angie; Poulin, Elie

2016-01-01

Glacial episodes of the Quaternary, and particularly the Last Glacial Maximum (LGM) drastically altered the distribution of the Southern-Hemisphere biota, principally at higher latitudes. The irregular coastline of Patagonia expanding for more than 84.000 km constitutes a remarkable area to evaluate the effect of Quaternary landscape and seascape shifts over the demography of near-shore marine benthic organisms. Few studies describing the biogeographic responses of marine species to the LGM have been conducted in Patagonia, but existing data from coastal marine species have demonstrated marked genetic signatures of post-LGM recolonization and expansion. The kelp-dweller limpet Nacella mytilina is broadly distributed along the southern tip of South America and at the Falkland/Malvinas Islands. Considering its distribution, abundance, and narrow bathymetry, N. mytilina represents an appropriate model to infer how historical and contemporary processes affected the distribution of intraspecific genetic diversity and structure along the southern tip of South America. At the same time, it will be possible to determine how life history traits and the ecology of the species are responsible for the current pattern of gene flow and connectivity across the study area. We conducted phylogeographic and demographic inference analyses in N. mytilina from 12 localities along Pacific Patagonia (PP) and one population from the Falkland/Malvinas Islands (FI). Analyses of the mitochondrial gene COI in 300 individuals of N. mytilina revealed low levels of genetic polymorphism and the absence of genetic differentiation along PP. In contrast, FI showed a strong and significant differentiation from Pacific Patagonian populations. Higher levels of genetic diversity were also recorded in the FI population, together with a more expanded genealogy supporting the hypothesis of glacial persistence of the species in these islands. Haplotype genealogy, and mismatch analyses in the FI population

ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information.

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Lachmann, Alexander; Giorgi, Federico M; Lopez, Gonzalo; Califano, Andrea

2016-07-15

The accurate reconstruction of gene regulatory networks from large scale molecular profile datasets represents one of the grand challenges of Systems Biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective tools to accomplish this goal. However, the initial Fixed Bandwidth (FB) implementation is both inefficient and unable to deal with sample sets providing largely uneven coverage of the probability density space. Here, we present a completely new implementation of the algorithm, based on an Adaptive Partitioning strategy (AP) for estimating the Mutual Information. The new AP implementation (ARACNe-AP) achieves a dramatic improvement in computational performance (200× on average) over the previous methodology, while preserving the Mutual Information estimator and the Network inference accuracy of the original algorithm. Given that the previous version of ARACNe is extremely demanding, the new version of the algorithm will allow even researchers with modest computational resources to build complex regulatory networks from hundreds of gene expression profiles. A JAVA cross-platform command line executable of ARACNe, together with all source code and a detailed usage guide are freely available on Sourceforge (http://sourceforge.net/projects/aracne-ap). JAVA version 8 or higher is required. califano@c2b2.columbia.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Genetic diversity and gene flow revealed by microsatellite DNA ...

African Journals Online (AJOL)

Dacryodes edulis is a multipurpose tree integrated in the cropping system of Central African region still dominated by subsistence agriculture. Some populations grown are wild which can provide information on the domestication process, and could also represent a potential source of gene flow. Leaves samples for DNA ...

Gene flow among established Puerto Rican populations of the exotic tree species, Albizia lebbeck.

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Dunphy, B K; Hamrick, J L

2005-04-01

We estimate gene flow and patterns of genetic diversity in Albizia lebbeck, an invasive leguminous tree in the dry forest of southwestern Puerto Rico. Genetic diversity estimates calculated for 10 populations of 24 trees each indicated that these populations may have been formed from multiple introductions. The presence of unique genotypes in the northernmost populations suggests that novel genotypes are still immigrating into the area. This combination of individuals from disparate locations led to high estimates of genetic diversity (He = 0.266, P = 0.67). Indirect estimates of gene flow indicate that only 0.69 migrants per generation move between populations, suggesting that genetic diversity within populations should decrease due to genetic drift. Since migration-drift equilibrium was not found, however, this estimate needs to be viewed with caution. The regular production of pods in this outcrossing species (tm = 0.979) indicates that sufficient outcross pollen is received to insure successful reproduction. Direct estimates of gene flow indicate that between 44 and 100% of pollen received by trees in four small stands of trees (n < 11) was foreign. The role of gene flow in facilitating the spread of this invasive plant species is discussed.

Improving catchment discharge predictions by inferring flow route contributions from a nested-scale monitoring and model setup

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van der Velde, Y.; Rozemeijer, J. C.; de Rooij, G. H.; van Geer, F. C.; Torfs, P. J. J. F.; de Louw, P. G. B.

2011-03-01

Identifying effective measures to reduce nutrient loads of headwaters in lowland catchments requires a thorough understanding of flow routes of water and nutrients. In this paper we assess the value of nested-scale discharge and groundwater level measurements for the estimation of flow route volumes and for predictions of catchment discharge. In order to relate field-site measurements to the catchment-scale an upscaling approach is introduced that assumes that scale differences in flow route fluxes originate from differences in the relationship between groundwater storage and the spatial structure of the groundwater table. This relationship is characterized by the Groundwater Depth Distribution (GDD) curve that relates spatial variation in groundwater depths to the average groundwater depth. The GDD-curve was measured for a single field site (0.009 km2) and simple process descriptions were applied to relate groundwater levels to flow route discharges. This parsimonious model could accurately describe observed storage, tube drain discharge, overland flow and groundwater flow simultaneously with Nash-Sutcliff coefficients exceeding 0.8. A probabilistic Monte Carlo approach was applied to upscale field-site measurements to catchment scales by inferring scale-specific GDD-curves from the hydrographs of two nested catchments (0.4 and 6.5 km2). The estimated contribution of tube drain effluent (a dominant source for nitrates) decreased with increasing scale from 76-79% at the field-site to 34-61% and 25-50% for both catchment scales. These results were validated by demonstrating that a model conditioned on nested-scale measurements improves simulations of nitrate loads and predictions of extreme discharges during validation periods compared to a model that was conditioned on catchment discharge only.

DOSE RESPONSE FROM HIGH THROUGHPUT GENE EXPRESSION STUDIES AND THE INFLUENCE OF TIME AND CELL LINE ON INFERRED MODE OF ACTION BY ONTOLOGIC ENRICHMENT (SOT)

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Gene expression with ontologic enrichment and connectivity mapping tools is widely used to infer modes of action (MOA) for therapeutic drugs. Despite progress in high-throughput (HT) genomic systems, strategies suitable to identify industrial chemical MOA are needed. The L1000 is...

Beekeeping practices and geographic distance, not land use, drive gene flow across tropical bees.

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Jaffé, Rodolfo; Pope, Nathaniel; Acosta, André L; Alves, Denise A; Arias, Maria C; De la Rúa, Pilar; Francisco, Flávio O; Giannini, Tereza C; González-Chaves, Adrian; Imperatriz-Fonseca, Vera L; Tavares, Mara G; Jha, Shalene; Carvalheiro, Luísa G

2016-11-01

Across the globe, wild bees are threatened by ongoing natural habitat loss, risking the maintenance of plant biodiversity and agricultural production. Despite the ecological and economic importance of wild bees and the fact that several species are now managed for pollination services worldwide, little is known about how land use and beekeeping practices jointly influence gene flow. Using stingless bees as a model system, containing wild and managed species that are presumed to be particularly susceptible to habitat degradation, here we examine the main drivers of tropical bee gene flow. We employ a novel landscape genetic approach to analyse data from 135 populations of 17 stingless bee species distributed across diverse tropical biomes within the Americas. Our work has important methodological implications, as we illustrate how a maximum-likelihood approach can be applied in a meta-analysis framework to account for multiple factors, and weight estimates by sample size. In contrast to previously held beliefs, gene flow was not related to body size or deforestation, and isolation by geographic distance (IBD) was significantly affected by management, with managed species exhibiting a weaker IBD than wild ones. Our study thus reveals the critical importance of beekeeping practices in shaping the patterns of genetic differentiation across bee species. Additionally, our results show that many stingless bee species maintain high gene flow across heterogeneous landscapes. We suggest that future efforts to preserve wild tropical bees should focus on regulating beekeeping practices to maintain natural gene flow and enhancing pollinator-friendly habitats, prioritizing species showing a limited dispersal ability. © 2016 John Wiley & Sons Ltd.

Inferring gene expression dynamics via functional regression analysis

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Leng Xiaoyan

2008-01-01

Full Text Available Abstract Background Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other. Results We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles. Conclusion Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.

Spread of a new parasitic B chromosome variant is facilitated by high gene flow.

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María Inmaculada Manrique-Poyato

Full Text Available The B24 chromosome variant emerged several decades ago in a Spanish population of the grasshopper Eyprepocnemis plorans and is currently reaching adjacent populations. Here we report, for the first time, how a parasitic B chromosome (a strictly vertically transmitted parasite expands its geographical range aided by high gene flow in the host species. For six years we analyzed B frequency in several populations to the east and west of the original population and found extensive spatial variation, but only a slight temporal trend. The highest B24 frequency was found in its original population (Torrox and it decreased closer to both the eastern and the western populations. The analysis of Inter Simple Sequence Repeat (ISSR markers showed the existence of a low but significant degree of population subdivision, as well as significant isolation by distance (IBD. Pairwise Nem estimates suggested the existence of high gene flow between the four populations located in the Torrox area, with higher values towards the east. No significant barriers to gene flow were found among these four populations, and we conclude that high gene flow is facilitating B24 diffusion both eastward and westward, with minor role for B24 drive due to the arrival of drive suppressor genes which are also frequent in the donor population.

Stratigraphy, sedimentology and inferred flow dynamics from the July 2015 block-and-ash flow deposits at Volcán de Colima, Mexico

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Macorps, Elodie; Charbonnier, Sylvain J.; Varley, Nick R.; Capra, Lucia; Atlas, Zachary; Cabré, Josep

2018-01-01

The July 2015 block-and-ash flow (BAF) events represent the first documented series of large-volume and long-runout BAFs generated from sustained dome collapses at Volcán de Colima. This eruption is particularly exceptional at this volcano due to (1) the large volume of BAF material emplaced (0.0077 ± 0.001 km3), (2) the long runout reached by the associated BAFs (max. 10 km), and (3) the short period ( 18 h) over which two main long-sustained dome collapse events occurred (on 10 and 11 July, respectively). Stratigraphy and sedimentology of the 2015 BAF deposits exposed in the southern flank of the volcano based on lithofacies description, grain size measurements and clast componentry allowed the recognition of three main deposit facies (i.e., valley-confined, overbank and ash-cloud surge deposits). Correlations and lithofacies variations inside three main flow units from both the valley-confined and overbank deposits left from the emplacement of the second series of BAFs on 11 July provide detailed information about: (1) the distribution, volumes and sedimentological characteristics of the different units; (2) flow parameters (i.e., velocity and dynamic pressure) and mobility metrics as inferred from associated deposits; and (3) changes in the dynamics of the different flows and their material during emplacement. These data were coupled with geomorphic analyses to assess the role of the topography in controlling the behaviour and impacts of the successive BAF pulses on the volcano flanks. Finally, these findings are used to propose a conceptual model for transport and deposition mechanisms of the July 2015 BAFs at Volcán de Colima. In this model, deposition occurs by rapid stepwise aggradation of successive BAF pulses. Flow confinement in a narrow and sinuous channel enhance the mobility and runout of individual channelized BAF pulses. When these conditions occur, the progressive valley infilling from successive sustained dome-collapse events promote the

AN UNUSUAL PATTERN OF GENE FLOW BETWEEN THE TWO SOCIAL FORMS OF THE FIRE ANT SOLENOPSIS INVICTA.

Science.gov (United States)

Ross, Kenneth G; Shoemaker, D DeWayne

1993-10-01

Uncertainty over the role of shifts in social behavior in the process of speciation in social insects has stimulated interest in determining the extent of gene flow between conspecific populations differing in colony social organization. Allele and genotype frequencies at 12 neutral polymorphic protein markers, as well as the numbers of alleles at the sex-determining locus (loci), are shown here to be consistent with significant ongoing gene flow between two geographically adjacent populations of Solenopsis invicta that differ in colony queen number. Data from a thirteenth protein marker that is under strong differential selection in the two social forms confirm that such gene flow occurs. Data from this selected locus, combined with knowledge of the reproductive biology of the two social forms, further suggest that interform gene flow is largely unidirectional and mediated through males only. This unusual pattern of gene flow results from the influence of the unique social enviroments of the two forms on the behavior of workers and on the reproductive physiology of sexuals. © 1993 The Society for the Study of Evolution.

Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

Directory of Open Access Journals (Sweden)

Wang Woei-Fuh

2008-03-01

Full Text Available Abstract Background With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality. Results Motivated by inferring transcriptional compensation (TC interactions in yeast, a stepwise structural equation modeling algorithm (SSEM is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC SSEM works best. SSEM with Bayesian information criterion (BIC results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1 small groups of genes that are synthetic sick or lethal (SSL to SGS1, and (2 a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1, SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2 are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted. Conclusion SSEM is a useful tool for inferring genetic networks, and the

System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max.

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Yungang Xu

Full Text Available Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN, a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max, due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs, in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional

Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds

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Neelam Redekar

2017-11-01

Full Text Available A dominant loss of function mutation in myo-inositol phosphate synthase (MIPS gene and recessive loss of function mutations in two multidrug resistant protein type-ABC transporter genes not only reduce the seed phytic acid levels in soybean, but also affect the pathways associated with seed development, ultimately resulting in low emergence. To understand the regulatory mechanisms and identify key genes that intervene in the seed development process in low phytic acid crops, we performed computational inference of gene regulatory networks in low and normal phytic acid soybeans using a time course transcriptomic data and multiple network inference algorithms. We identified a set of putative candidate transcription factors and their regulatory interactions with genes that have functions in myo-inositol biosynthesis, auxin-ABA signaling, and seed dormancy. We evaluated the performance of our unsupervised network inference method by comparing the predicted regulatory network with published regulatory interactions in Arabidopsis. Some contrasting regulatory interactions were observed in low phytic acid mutants compared to non-mutant lines. These findings provide important hypotheses on expression regulation of myo-inositol metabolism and phytohormone signaling in developing low phytic acid soybeans. The computational pipeline used for unsupervised network learning in this study is provided as open source software and is freely available at https://lilabatvt.github.io/LPANetwork/.

Monte Carlo simulation of OLS and linear mixed model inference of phenotypic effects on gene expression.

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Walker, Jeffrey A

2016-01-01

Self-contained tests estimate and test the association between a phenotype and mean expression level in a gene set defined a priori . Many self-contained gene set analysis methods have been developed but the performance of these methods for phenotypes that are continuous rather than discrete and with multiple nuisance covariates has not been well studied. Here, I use Monte Carlo simulation to evaluate the performance of both novel and previously published (and readily available via R) methods for inferring effects of a continuous predictor on mean expression in the presence of nuisance covariates. The motivating data are a high-profile dataset which was used to show opposing effects of hedonic and eudaimonic well-being (or happiness) on the mean expression level of a set of genes that has been correlated with social adversity (the CTRA gene set). The original analysis of these data used a linear model (GLS) of fixed effects with correlated error to infer effects of Hedonia and Eudaimonia on mean CTRA expression. The standardized effects of Hedonia and Eudaimonia on CTRA gene set expression estimated by GLS were compared to estimates using multivariate (OLS) linear models and generalized estimating equation (GEE) models. The OLS estimates were tested using O'Brien's OLS test, Anderson's permutation [Formula: see text]-test, two permutation F -tests (including GlobalAncova), and a rotation z -test (Roast). The GEE estimates were tested using a Wald test with robust standard errors. The performance (Type I, II, S, and M errors) of all tests was investigated using a Monte Carlo simulation of data explicitly modeled on the re-analyzed dataset. GLS estimates are inconsistent between data sets, and, in each dataset, at least one coefficient is large and highly statistically significant. By contrast, effects estimated by OLS or GEE are very small, especially relative to the standard errors. Bootstrap and permutation GLS distributions suggest that the GLS results in

Monte Carlo simulation of OLS and linear mixed model inference of phenotypic effects on gene expression

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Jeffrey A. Walker

2016-10-01

Full Text Available Background Self-contained tests estimate and test the association between a phenotype and mean expression level in a gene set defined a priori. Many self-contained gene set analysis methods have been developed but the performance of these methods for phenotypes that are continuous rather than discrete and with multiple nuisance covariates has not been well studied. Here, I use Monte Carlo simulation to evaluate the performance of both novel and previously published (and readily available via R methods for inferring effects of a continuous predictor on mean expression in the presence of nuisance covariates. The motivating data are a high-profile dataset which was used to show opposing effects of hedonic and eudaimonic well-being (or happiness on the mean expression level of a set of genes that has been correlated with social adversity (the CTRA gene set. The original analysis of these data used a linear model (GLS of fixed effects with correlated error to infer effects of Hedonia and Eudaimonia on mean CTRA expression. Methods The standardized effects of Hedonia and Eudaimonia on CTRA gene set expression estimated by GLS were compared to estimates using multivariate (OLS linear models and generalized estimating equation (GEE models. The OLS estimates were tested using O’Brien’s OLS test, Anderson’s permutation ${r}_{F}^{2}$ r F 2 -test, two permutation F-tests (including GlobalAncova, and a rotation z-test (Roast. The GEE estimates were tested using a Wald test with robust standard errors. The performance (Type I, II, S, and M errors of all tests was investigated using a Monte Carlo simulation of data explicitly modeled on the re-analyzed dataset. Results GLS estimates are inconsistent between data sets, and, in each dataset, at least one coefficient is large and highly statistically significant. By contrast, effects estimated by OLS or GEE are very small, especially relative to the standard errors. Bootstrap and permutation GLS

Inferring genetic interactions from comparative fitness data.

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Crona, Kristina; Gavryushkin, Alex; Greene, Devin; Beerenwinkel, Niko

2017-12-20

Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax , the fungus Aspergillus niger , and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.

Structure, evolution and functional inference on the Mildew Locus O (MLO) gene family in three cultivated Cucurbitaceae spp.

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Iovieno, Paolo; Andolfo, Giuseppe; Schiavulli, Adalgisa; Catalano, Domenico; Ricciardi, Luigi; Frusciante, Luigi; Ercolano, Maria Raffaella; Pavan, Stefano

2015-12-29

The powdery mildew disease affects thousands of plant species and arguably represents the major fungal threat for many Cucurbitaceae crops, including melon (Cucumis melo L.), watermelon (Citrullus lanatus L.) and zucchini (Cucurbita pepo L.). Several studies revealed that specific members of the Mildew Locus O (MLO) gene family act as powdery mildew susceptibility factors. Indeed, their inactivation, as the result of gene knock-out or knock-down, is associated with a peculiar form of resistance, referred to as mlo resistance. We exploited recently available genomic information to provide a comprehensive overview of the MLO gene family in Cucurbitaceae. We report the identification of 16 MLO homologs in C. melo, 14 in C. lanatus and 18 in C. pepo genomes. Bioinformatic treatment of data allowed phylogenetic inference and the prediction of several ortholog pairs and groups. Comparison with functionally characterized MLO genes and, in C. lanatus, gene expression analysis, resulted in the detection of candidate powdery mildew susceptibility factors. We identified a series of conserved amino acid residues and motifs that are likely to play a major role for the function of MLO proteins. Finally, we performed a codon-based evolutionary analysis indicating a general high level of purifying selection in the three Cucurbitaceae MLO gene families, and the occurrence of regions under diversifying selection in candidate susceptibility factors. Results of this study may help to address further biological questions concerning the evolution and function of MLO genes. Moreover, data reported here could be conveniently used by breeding research, aiming to select powdery mildew resistant cultivars in Cucurbitaceae.

The evolutionary history of ferns inferred from 25 low-copy nuclear genes.

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Rothfels, Carl J; Li, Fay-Wei; Sigel, Erin M; Huiet, Layne; Larsson, Anders; Burge, Dylan O; Ruhsam, Markus; Deyholos, Michael; Soltis, Douglas E; Stewart, C Neal; Shaw, Shane W; Pokorny, Lisa; Chen, Tao; dePamphilis, Claude; DeGironimo, Lisa; Chen, Li; Wei, Xiaofeng; Sun, Xiao; Korall, Petra; Stevenson, Dennis W; Graham, Sean W; Wong, Gane K-S; Pryer, Kathleen M

2015-07-01

• Understanding fern (monilophyte) phylogeny and its evolutionary timescale is critical for broad investigations of the evolution of land plants, and for providing the point of comparison necessary for studying the evolution of the fern sister group, seed plants. Molecular phylogenetic investigations have revolutionized our understanding of fern phylogeny, however, to date, these studies have relied almost exclusively on plastid data.• Here we take a curated phylogenomics approach to infer the first broad fern phylogeny from multiple nuclear loci, by combining broad taxon sampling (73 ferns and 12 outgroup species) with focused character sampling (25 loci comprising 35877 bp), along with rigorous alignment, orthology inference and model selection.• Our phylogeny corroborates some earlier inferences and provides novel insights; in particular, we find strong support for Equisetales as sister to the rest of ferns, Marattiales as sister to leptosporangiate ferns, and Dennstaedtiaceae as sister to the eupolypods. Our divergence-time analyses reveal that divergences among the extant fern orders all occurred prior to ∼200 MYA. Finally, our species-tree inferences are congruent with analyses of concatenated data, but generally with lower support. Those cases where species-tree support values are higher than expected involve relationships that have been supported by smaller plastid datasets, suggesting that deep coalescence may be reducing support from the concatenated nuclear data.• Our study demonstrates the utility of a curated phylogenomics approach to inferring fern phylogeny, and highlights the need to consider underlying data characteristics, along with data quantity, in phylogenetic studies. © 2015 Botanical Society of America, Inc.

The genetic assimilation in language borrowing inferred from Jing People.

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Huang, Xiufeng; Zhou, Qinghui; Bin, Xiaoyun; Lai, Shu; Lin, Chaowen; Hu, Rong; Xiao, Jiashun; Luo, Dajun; Li, Yingxiang; Wei, Lan-Hai; Yeh, Hui-Yuan; Chen, Gang; Wang, Chuan-Chao

2018-02-28

The Jing people are a recognized ethnic group in Guangxi, southwest China, who are the immigrants from Vietnam during the 16th century. They speak Vietnamese but with lots of language borrowings from Cantonese, Zhuang, and Mandarin. However, it's unclear if there is large-scale gene flow from surrounding populations into Jing people during their language change due to the very limited genetic information of this population. We collected blood samples from 37 Jing and 3 Han Chinese individuals from Wanwei, Shanxin, and Wutou islands in Guangxi and genotyped about 600,000 genome-wide single nucleotide polymorphisms (SNPs). We used Principal Component Analysis (PCA), ADMIXTURE analysis, f statistics, qpWave and qpAdm to infer the population genetic structure and admixture. Our data revealed that the Jing people are genetically similar to the populations in southwest China and mainland Southeast Asia. But compared with Vietnamese, they show significant evidence of gene flow from surrounding East Asians. The admixture proportion is estimated to be around 35-42% in different Jing groups using southern Han Chinese as a proxy. The majority of the paternal lineages of Jing people are most likely from surrounding East Asians. We conclude that the formation and language change of present-day Jing people have involved genetic assimilation of surrounding East Asian populations. The language borrowing, in this case, is not only a cultural phenomenon but has involved demic diffusion. © 2018 Wiley Periodicals, Inc.

Improving catchment discharge predictions by inferring flow route contributions from a nested-scale monitoring and model setup

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Y. van der Velde

2011-03-01

Full Text Available Identifying effective measures to reduce nutrient loads of headwaters in lowland catchments requires a thorough understanding of flow routes of water and nutrients. In this paper we assess the value of nested-scale discharge and groundwater level measurements for the estimation of flow route volumes and for predictions of catchment discharge. In order to relate field-site measurements to the catchment-scale an upscaling approach is introduced that assumes that scale differences in flow route fluxes originate from differences in the relationship between groundwater storage and the spatial structure of the groundwater table. This relationship is characterized by the Groundwater Depth Distribution (GDD curve that relates spatial variation in groundwater depths to the average groundwater depth. The GDD-curve was measured for a single field site (0.009 km² and simple process descriptions were applied to relate groundwater levels to flow route discharges. This parsimonious model could accurately describe observed storage, tube drain discharge, overland flow and groundwater flow simultaneously with Nash-Sutcliff coefficients exceeding 0.8. A probabilistic Monte Carlo approach was applied to upscale field-site measurements to catchment scales by inferring scale-specific GDD-curves from the hydrographs of two nested catchments (0.4 and 6.5 km². The estimated contribution of tube drain effluent (a dominant source for nitrates decreased with increasing scale from 76–79% at the field-site to 34–61% and 25–50% for both catchment scales. These results were validated by demonstrating that a model conditioned on nested-scale measurements improves simulations of nitrate loads and predictions of extreme discharges during validation periods compared to a model that was conditioned on catchment discharge only.

Consequences of gene flow between oilseed rape (Brassica napus) and its relatives.

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Liu, Yongbo; Wei, Wei; Ma, Keping; Li, Junsheng; Liang, Yuyong; Darmency, Henri

2013-10-01

Numerous studies have focused on the probability of occurrence of gene flow between transgenic crops and their wild relatives and the likelihood of transgene escape, which should be assessed before the commercial release of transgenic crops. This review paper focuses on this issue for oilseed rape, Brassica napus L., a species that produces huge numbers of pollen grains and seeds. We analyze separately the distinct steps of gene flow: (1) pollen and seeds as vectors of gene flow; (2) spontaneous hybridization; (3) hybrid behavior, fitness cost due to hybridization and mechanisms of introgression; (4) and fitness benefit due to transgenes (e.g. herbicide resistance and Bt toxin). Some physical, biological and molecular means of transgene containment are also described. Although hybrids and first generation progeny are difficult to identify in fields and non-crop habitats, the literature shows that transgenes could readily introgress into Brassica rapa, Brassica juncea and Brassica oleracea, while introgression is expected to be rare with Brassica nigra, Hirschfeldia incana and Raphanus raphanistrum. The hybrids grow well but produce less seed than their wild parent. The difference declines with increasing generations. However, there is large uncertainty about the evolution of chromosome numbers and recombination, and many parameters of life history traits of hybrids and progeny are not determined with satisfactory confidence to build generic models capable to really cover the wide diversity of situations. We show that more studies are needed to strengthen and organize biological knowledge, which is a necessary prerequisite for model simulations to assess the practical and evolutionary outputs of introgression, and to provide guidelines for gene flow management. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Landscape genetics as a tool for conservation planning: predicting the effects of landscape change on gene flow.

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van Strien, Maarten J; Keller, Daniela; Holderegger, Rolf; Ghazoul, Jaboury; Kienast, Felix; Bolliger, Janine

2014-03-01

For conservation managers, it is important to know whether landscape changes lead to increasing or decreasing gene flow. Although the discipline of landscape genetics assesses the influence of landscape elements on gene flow, no studies have yet used landscape-genetic models to predict gene flow resulting from landscape change. A species that has already been severely affected by landscape change is the large marsh grasshopper (Stethophyma grossum), which inhabits moist areas in fragmented agricultural landscapes in Switzerland. From transects drawn between all population pairs within maximum dispersal distance (landscape composition as well as some measures of habitat configuration. Additionally, a complete sampling of all populations in our study area allowed incorporating measures of population topology. These measures together with the landscape metrics formed the predictor variables in linear models with gene flow as response variable (F(ST) and mean pairwise assignment probability). With a modified leave-one-out cross-validation approach, we selected the model with the highest predictive accuracy. With this model, we predicted gene flow under several landscape-change scenarios, which simulated construction, rezoning or restoration projects, and the establishment of a new population. For some landscape-change scenarios, significant increase or decrease in gene flow was predicted, while for others little change was forecast. Furthermore, we found that the measures of population topology strongly increase model fit in landscape genetic analysis. This study demonstrates the use of predictive landscape-genetic models in conservation and landscape planning.

Genetic interaction motif finding by expectation maximization – a novel statistical model for inferring gene modules from synthetic lethality

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Ye Ping

2005-12-01

Full Text Available Abstract Background Synthetic lethality experiments identify pairs of genes with complementary function. More direct functional associations (for example greater probability of membership in a single protein complex may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets. Results We have developed Genetic Interaction Motif Finding (GIMF, an algorithm for unsupervised motif discovery from synthetic lethal interaction data. Interaction motifs are characterized by position weight matrices and optimized through expectation maximization. Given a seed gene, GIMF performs a nonlinear transform on the input genetic interaction data and automatically assigns genes to the motif or non-motif category. We demonstrate the capacity to extract known and novel pathways for Saccharomyces cerevisiae (budding yeast. Annotations suggested for several uncharacterized genes are supported by recent experimental evidence. GIMF is efficient in computation, requires no training and automatically down-weights promiscuous genes with high degrees. Conclusion GIMF effectively identifies pathways from synthetic lethality data with several unique features. It is mostly suitable for building gene modules around seed genes. Optimal choice of one single model parameter allows construction of gene networks with different levels of confidence. The impact of hub genes the generic probabilistic framework of GIMF may be used to group other types of biological entities such as proteins based on stochastic motifs. Analysis of the strongest motifs discovered by the algorithm indicates that synthetic lethal interactions are depleted between genes within a motif, suggesting that synthetic

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

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Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

2017-10-06

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Recent long-distance transgene flow into wild populations conforms to historical patterns of gene flow in cotton (Gossypium hirsutum) at its centre of origin.

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Wegier, A; Piñeyro-Nelson, A; Alarcón, J; Gálvez-Mariscal, A; Alvarez-Buylla, E R; Piñero, D

2011-10-01

Over 95% of the currently cultivated cotton was domesticated from Gossypium hirsutum, which originated and diversified in Mexico. Demographic and genetic studies of this species at its centre of origin and diversification are lacking, although they are critical for cotton conservation and breeding. We investigated the actual and potential distribution of wild cotton populations, as well as the contribution of historical and recent gene flow in shaping cotton genetic diversity and structure. We evaluated historical gene flow using chloroplast microsatellites and recent gene flow through the assessment of transgene presence in wild cotton populations, exploiting the fact that genetically modified cotton has been planted in the North of Mexico since 1996. Assessment of geographic structure through Bayesian spatial analysis, BAPS and Genetic Algorithm for Rule-set Production (GARP), suggests that G. hirsutum seems to conform to a metapopulation scheme, with eight distinct metapopulations. Despite evidence for long-distance gene flow, genetic variation among the metapopulations of G. hirsutum is high (He = 0.894 ± 0.01). We identified 46 different haplotypes, 78% of which are unique to a particular metapopulation, in contrast to a single haplotype detected in cotton cultivars. Recent gene flow was also detected (m = 66/270 = 0.24), with four out of eight metapopulations having transgenes. We discuss the implications of the data presented here with respect to the conservation and future breeding of cotton populations and genetic diversity at its centre of crop origin. © 2011 Blackwell Publishing Ltd.

Causal inference in biology networks with integrated belief propagation.

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Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

Gene flow from North Africa contributes to differential human genetic diversity in southern Europe

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Botigué, Laura R.; Henn, Brenna M.; Gravel, Simon; Maples, Brian K.; Gignoux, Christopher R.; Corona, Erik; Atzmon, Gil; Burns, Edward; Ostrer, Harry; Flores, Carlos; Bertranpetit, Jaume; Comas, David; Bustamante, Carlos D.

2013-01-01

Human genetic diversity in southern Europe is higher than in other regions of the continent. This difference has been attributed to postglacial expansions, the demic diffusion of agriculture from the Near East, and gene flow from Africa. Using SNP data from 2,099 individuals in 43 populations, we show that estimates of recent shared ancestry between Europe and Africa are substantially increased when gene flow from North Africans, rather than Sub-Saharan Africans, is considered. The gradient of North African ancestry accounts for previous observations of low levels of sharing with Sub-Saharan Africa and is independent of recent gene flow from the Near East. The source of genetic diversity in southern Europe has important biomedical implications; we find that most disease risk alleles from genome-wide association studies follow expected patterns of divergence between Europe and North Africa, with the principal exception of multiple sclerosis. PMID:23733930

Sorting through the chaff, nDNA gene trees for phylogenetic inference and hybrid identification of annual sunflowers (Helianthus sect. Helianthus).

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Moody, Michael L; Rieseberg, Loren H

2012-07-01

The annual sunflowers (Helianthus sect. Helianthus) present a formidable challenge for phylogenetic inference because of ancient hybrid speciation, recent introgression, and suspected issues with deep coalescence. Here we analyze sequence data from 11 nuclear DNA (nDNA) genes for multiple genotypes of species within the section to (1) reconstruct the phylogeny of this group, (2) explore the utility of nDNA gene trees for detecting hybrid speciation and introgression; and (3) test an empirical method of hybrid identification based on the phylogenetic congruence of nDNA gene trees from tightly linked genes. We uncovered considerable topological heterogeneity among gene trees with or without three previously identified hybrid species included in the analyses, as well as a general lack of reciprocal monophyly of species. Nonetheless, partitioned Bayesian analyses provided strong support for the reciprocal monophyly of all species except H. annuus (0.89 PP), the most widespread and abundant annual sunflower. Previous hypotheses of relationships among taxa were generally strongly supported (1.0 PP), except among taxa typically associated with H. annuus, apparently due to the paraphyly of the latter in all gene trees. While the individual nDNA gene trees provided a useful means for detecting recent hybridization, identification of ancient hybridization was problematic for all ancient hybrid species, even when linkage was considered. We discuss biological factors that affect the efficacy of phylogenetic methods for hybrid identification.

Phylogenetic inference of Coxiella burnetii by 16S rRNA gene sequencing.

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Heather P McLaughlin

Full Text Available Coxiella burnetii is a human pathogen that causes the serious zoonotic disease Q fever. It is ubiquitous in the environment and due to its wide host range, long-range dispersal potential and classification as a bioterrorism agent, this microorganism is considered an HHS Select Agent. In the event of an outbreak or intentional release, laboratory strain typing methods can contribute to epidemiological investigations, law enforcement investigation and the public health response by providing critical information about the relatedness between C. burnetii isolates collected from different sources. Laboratory cultivation of C. burnetii is both time-consuming and challenging. Availability of strain collections is often limited and while several strain typing methods have been described over the years, a true gold-standard method is still elusive. Building upon epidemiological knowledge from limited, historical strain collections and typing data is essential to more accurately infer C. burnetii phylogeny. Harmonization of auspicious high-resolution laboratory typing techniques is critical to support epidemiological and law enforcement investigation. The single nucleotide polymorphism (SNP -based genotyping approach offers simplicity, rapidity and robustness. Herein, we demonstrate SNPs identified within 16S rRNA gene sequences can differentiate C. burnetii strains. Using this method, 55 isolates were assigned to six groups based on six polymorphisms. These 16S rRNA SNP-based genotyping results were largely congruent with those obtained by analyzing restriction-endonuclease (RE-digested DNA separated by SDS-PAGE and by the high-resolution approach based on SNPs within multispacer sequence typing (MST loci. The SNPs identified within the 16S rRNA gene can be used as targets for the development of additional SNP-based genotyping assays for C. burnetii.

Lineage divergence and historical gene flow in the Chinese horseshoe bat (Rhinolophus sinicus.

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Xiuguang Mao

Full Text Available Closely related taxa living in sympatry provide good opportunities to investigate the origin of barriers to gene flow as well as the extent of reproductive isolation. The only two recognized subspecies of the Chinese rufous horseshoe bat Rhinolophus sinicus are characterized by unusual relative distributions in which R. s. septentrionalis is restricted to a small area within the much wider range of its sister taxon R. s. sinicus. To determine the history of lineage divergence and gene flow between these taxa, we applied phylogenetic, demographic and coalescent analyses to multi-locus datasets. MtDNA gene genealogies and microsatellite-based clustering together revealed three divergent lineages of sinicus, corresponding to Central China, East China and the offshore Hainan Island. However, the central lineage of sinicus showed a closer relationship with septentrionalis than with other lineages of R. s. sinicus, in contrary to morphological data. Paraphyly of sinicus could result from either past asymmetric mtDNA introgression between these two taxa, or could suggest septentrionalis evolved in situ from its more widespread sister subspecies. To test between these hypotheses, we applied coalescent-based phylogenetic reconstruction and Approximate Bayesian Computation (ABC. We found that septentrionalis is likely to be the ancestral taxon and therefore a recent origin of this subspecies can be ruled out. On the other hand, we found a clear signature of asymmetric mtDNA gene flow from septentrionalis into central populations of sinicus yet no nuclear gene flow, thus strongly pointing to historical mtDNA introgression. We suggest that the observed deeply divergent lineages within R. sinicus probably evolved in isolation in separate Pleistocene refugia, although their close phylogeographic correspondence with distinct eco-environmental zones suggests that divergent selection might also have promoted broad patterns of population genetic structure.

Use of joint-growth directions and rock textures to infer thermal regimes during solidification of basaltic lava flows

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Degraff, James M.; Long, Philip E.; Aydin, Atilla

1989-09-01

Thermal contraction joints form in the upper and lower solidifying crusts of basaltic lava flows and grow toward the interior as the crusts thicken. Lava flows are thus divided by vertical joints that, by changes in joint spacing and form, define horizontal intraflow layers known as tiers. Entablatures are tiers with joint spacings less than about 40 cm, whereas colonnades have larger joint spacings. We use structural and petrographic methods to infer heat-transfer processes and to constrain environmental conditions that produce these contrasting tiers. Joint-surface morphology indicates overall joint-growth direction and thus identifies the level in a flow where the upper and lower crusts met. Rock texture provides information on relative cooling rates in the tiers of a flow. Lava flows without entablature have textures that develop by relatively slow cooling, and two joint sets that usually meet near their middles, which indicate mostly conductive cooling. Entablature-bearing flows have two main joint sets that meet well below their middles, and textures that indicate fast cooling of entablatures and slow cooling of colonnades. Entablatures always occur in the upper joint sets and sometimes alternate several times with colonnades. Solidification times of entablature-bearing flows, constrained by lower joint-set thicknesses, are much less than those predicted by a purely conductive cooling model. These results are best explained by a cooling model based on conductive heat transfer near a flow base and water-steam convection in the upper part of an entablature-bearing flow. Calculated solidification rates in the upper parts of such flows exceed that of the upper crust of Kilauea Iki lava lake, where water-steam convection is documented. Use of the solidification rates in an available model of water-steam convection yields permeability values that agree with measured values for fractured crystalline rock. We conclude, therefore, that an entablature forms when part

Evidence for gene flow between two sympatric mealybug species (Insecta; Coccoidea; Pseudococcidae.

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Hofit Kol-Maimon

Full Text Available Occurrence of inter-species hybrids in natural populations might be evidence of gene flow between species. In the present study we found evidence of gene flow between two sympatric, genetically related scale insect species--the citrus mealybug Planococcus citri (Risso and the vine mealybug Planococcus ficus (Signoret. These species can be distinguished by morphological, behavioral, and molecular traits. We employed the sex pheromones of the two respective species to study their different patterns of male attraction. We also used nuclear ITS2 (internal transcribed spacer 2 and mitochondrial COI (Cytochrome c oxidase sub unit 1 DNA sequences to characterize populations of the two species, in order to demonstrate the outcome of a possible gene flow between feral populations of the two species. Our results showed attraction to P. ficus pheromones of all tested populations of P. citri males but not vice versa. Furthermore, ITS2 sequences revealed the presence of 'hybrid females' among P. citri populations but not among those of P. ficus. 'hybrid females' from P. citri populations identified as P. citri females according to COI sequences. We offer two hypotheses for these results. 1 The occurrence of phenotypic and genotypic traits of P. ficus in P. citri populations may be attributed to both ancient and contemporary gene flow between their populations; and 2 we cannot rule out that an ancient sympatric speciation by which P. ficus emerged from P. citri might have led to the present situation of shared traits between these species. In light of these findings we also discuss the origin of the studied species and the importance of the pherotype phenomenon as a tool with which to study genetic relationships between congener scale insects.

Genetic clusters and sex-biased gene flow in a unicolonial Formica ant

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Chapuisat Michel

2009-03-01

Full Text Available Abstract Background Animal societies are diverse, ranging from small family-based groups to extraordinarily large social networks in which many unrelated individuals interact. At the extreme of this continuum, some ant species form unicolonial populations in which workers and queens can move among multiple interconnected nests without eliciting aggression. Although unicoloniality has been mostly studied in invasive ants, it also occurs in some native non-invasive species. Unicoloniality is commonly associated with very high queen number, which may result in levels of relatedness among nestmates being so low as to raise the question of the maintenance of altruism by kin selection in such systems. However, the actual relatedness among cooperating individuals critically depends on effective dispersal and the ensuing pattern of genetic structuring. In order to better understand the evolution of unicoloniality in native non-invasive ants, we investigated the fine-scale population genetic structure and gene flow in three unicolonial populations of the wood ant F. paralugubris. Results The analysis of geo-referenced microsatellite genotypes and mitochondrial haplotypes revealed the presence of cryptic clusters of genetically-differentiated nests in the three populations of F. paralugubris. Because of this spatial genetic heterogeneity, members of the same clusters were moderately but significantly related. The comparison of nuclear (microsatellite and mitochondrial differentiation indicated that effective gene flow was male-biased in all populations. Conclusion The three unicolonial populations exhibited male-biased and mostly local gene flow. The high number of queens per nest, exchanges among neighbouring nests and restricted long-distance gene flow resulted in large clusters of genetically similar nests. The positive relatedness among clustermates suggests that kin selection may still contribute to the maintenance of altruism in unicolonial

The population genomics of begomoviruses: global scale population structure and gene flow

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Prasanna HC

2010-09-01

Full Text Available Abstract Background The rapidly growing availability of diverse full genome sequences from across the world is increasing the feasibility of studying the large-scale population processes that underly observable pattern of virus diversity. In particular, characterizing the genetic structure of virus populations could potentially reveal much about how factors such as geographical distributions, host ranges and gene flow between populations combine to produce the discontinuous patterns of genetic diversity that we perceive as distinct virus species. Among the richest and most diverse full genome datasets that are available is that for the dicotyledonous plant infecting genus, Begomovirus, in the Family Geminiviridae. The begomoviruses all share the same whitefly vector, are highly recombinogenic and are distributed throughout tropical and subtropical regions where they seriously threaten the food security of the world's poorest people. Results We focus here on using a model-based population genetic approach to identify the genetically distinct sub-populations within the global begomovirus meta-population. We demonstrate the existence of at least seven major sub-populations that can further be sub-divided into as many as thirty four significantly differentiated and genetically cohesive minor sub-populations. Using the population structure framework revealed in the present study, we further explored the extent of gene flow and recombination between genetic populations. Conclusions Although geographical barriers are apparently the most significant underlying cause of the seven major population sub-divisions, within the framework of these sub-divisions, we explore patterns of gene flow to reveal that both host range differences and genetic barriers to recombination have probably been major contributors to the minor population sub-divisions that we have identified. We believe that the global Begomovirus population structure revealed here could

Delimiting Coalescence Genes (C-Genes) in Phylogenomic Data Sets.

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Springer, Mark S; Gatesy, John

2018-02-26

coalescence methods have emerged as a popular alternative for inferring species trees with large genomic datasets, because these methods explicitly account for incomplete lineage sorting. However, statistical consistency of summary coalescence methods is not guaranteed unless several model assumptions are true, including the critical assumption that recombination occurs freely among but not within coalescence genes (c-genes), which are the fundamental units of analysis for these methods. Each c-gene has a single branching history, and large sets of these independent gene histories should be the input for genome-scale coalescence estimates of phylogeny. By contrast, numerous studies have reported the results of coalescence analyses in which complete protein-coding sequences are treated as c-genes even though exons for these loci can span more than a megabase of DNA. Empirical estimates of recombination breakpoints suggest that c-genes may be much shorter, especially when large clades with many species are the focus of analysis. Although this idea has been challenged recently in the literature, the inverse relationship between c-gene size and increased taxon sampling in a dataset-the 'recombination ratchet'-is a fundamental property of c-genes. For taxonomic groups characterized by genes with long intron sequences, complete protein-coding sequences are likely not valid c-genes and are inappropriate units of analysis for summary coalescence methods unless they occur in recombination deserts that are devoid of incomplete lineage sorting (ILS). Finally, it has been argued that coalescence methods are robust when the no-recombination within loci assumption is violated, but recombination must matter at some scale because ILS, a by-product of recombination, is the raison d'etre for coalescence methods. That is, extensive recombination is required to yield the large number of independently segregating c-genes used to infer a species tree. If coalescent methods are powerful

Statistical inference of the generation probability of T-cell receptors from sequence repertoires.

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Murugan, Anand; Mora, Thierry; Walczak, Aleksandra M; Callan, Curtis G

2012-10-02

Stochastic rearrangement of germline V-, D-, and J-genes to create variable coding sequence for certain cell surface receptors is at the origin of immune system diversity. This process, known as "VDJ recombination", is implemented via a series of stochastic molecular events involving gene choices and random nucleotide insertions between, and deletions from, genes. We use large sequence repertoires of the variable CDR3 region of human CD4+ T-cell receptor beta chains to infer the statistical properties of these basic biochemical events. Because any given CDR3 sequence can be produced in multiple ways, the probability distribution of hidden recombination events cannot be inferred directly from the observed sequences; we therefore develop a maximum likelihood inference method to achieve this end. To separate the properties of the molecular rearrangement mechanism from the effects of selection, we focus on nonproductive CDR3 sequences in T-cell DNA. We infer the joint distribution of the various generative events that occur when a new T-cell receptor gene is created. We find a rich picture of correlation (and absence thereof), providing insight into the molecular mechanisms involved. The generative event statistics are consistent between individuals, suggesting a universal biochemical process. Our probabilistic model predicts the generation probability of any specific CDR3 sequence by the primitive recombination process, allowing us to quantify the potential diversity of the T-cell repertoire and to understand why some sequences are shared between individuals. We argue that the use of formal statistical inference methods, of the kind presented in this paper, will be essential for quantitative understanding of the generation and evolution of diversity in the adaptive immune system.

LASSIM-A network inference toolbox for genome-wide mechanistic modeling.

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Rasmus Magnusson

2017-06-01

Full Text Available Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM, which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE for gene regulatory networks (GRNs. LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naïve Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models

Study of male–mediated gene flow across a hybrid zone in the common shrew (Sorex araneus using Y chromosome

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Andrei V. Polyakov

2017-06-01

Full Text Available Despite many studies, the impact of chromosome rearrangements on gene flow between chromosome races of the common shrew (Sorex araneus Linnaeus, 1758 remains unclear. Interracial hybrids form meiotic chromosome complexes that are associated with reduced fertility. Nevertheless comprehensive investigations of autosomal and mitochondrial markers revealed weak or no barrier to gene flow between chromosomally divergent populations. In a narrow zone of contact between the Novosibirsk and Tomsk races hybrids are produced with extraordinarily complex configurations at meiosis I. Microsatellite markers have not revealed any barrier to gene flow, but the phenotypic differentiation between races is greater than may be expected if gene flow was unrestricted. To explore this contradiction we analyzed the distribution of the Y chromosome SNP markers within this hybrid zone. The Y chromosome variants in combination with race specific autosome complements allow backcrosses to be distinguished and their proportion among individuals within the hybrid zone to be evaluated. The balanced ratio of the Y variants observed among the pure race individuals as well as backcrosses reveals no male mediated barrier to gene flow. The impact of reproductive unfitness of backcrosses on gene flow is discussed as a possible mechanism of the preservation of race-specific morphology within the hybrid zone.

An analysis pipeline for the inference of protein-protein interaction networks

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Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason M.; Cannon, William R.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. H.; Pelletier, Dale A.; Schmoyer, Denise A.; Wiley, H. S.

2009-12-01

An analysis pipeline has been created for deployment of a novel algorithm, the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro), for use in the reconstruction of protein-protein interaction networks. We have combined the Software Environment for BIological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data, and the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources, to allow interactions computed by BEPro to be stored, visualized, and further analyzed. Incorporating BEPro into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of mass spectrometry bait-prey experiment data. SEBINI demo site: https://www.emsl.pnl.gov /SEBINI/ Contact: ronald.taylor@pnl.gov. BEPro is available at http://www.pnl.gov/statistics/BEPro3/index.htm. Contact: ds.daly@pnl.gov. CABIN is available at http://www.sysbio.org/dataresources/cabin.stm. Contact: mudita.singhal@pnl.gov.

Integrating gene flow, crop biology, and farm management in on-farm conservation of avocado (Persea americana, Lauraceae).

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Birnbaum, Kenneth; Desalle, Rob; Peters, Charles M; Benfey, Philip N

2003-11-01

Maintaining crop diversity on farms where cultivars can evolve is a conservation goal, but few tools are available to assess the long-term maintenance of genetic diversity on farms. One important issue for on-farm conservation is gene flow from crops with a narrow genetic base into related populations that are genetically diverse. In a case study of avocado (Persea americana var. americana) in one of its centers of diversity (San Jerónimo, Costa Rica), we used 10 DNA microsatellite markers in a parentage analysis to estimate gene flow from commercialized varieties into a traditional crop population. Five commercialized genotypes comprised nearly 40% of orchard trees, but they contributed only about 14.5% of the gametes to the youngest cohort of trees. Although commercialized varieties and the diverse population were often planted on the same farm, planting patterns appeared to keep the two types of trees separated on small scales, possibly explaining the limited gene flow. In a simulation that combined gene flow estimates, crop biology, and graft tree management, loss of allelic diversity was less than 10% over 150 yr, and selection was effective in retaining desirable alleles in the diverse subpopulation. Simulations also showed that, in addition to gene flow, managing the genetic makeup and life history traits of the invasive commercialized varieties could have a significant impact on genetic diversity in the target population. The results support the feasibility of on-farm crop conservation, but simulations also showed that higher levels of gene flow could lead to severe losses of genetic diversity even if farmers continue to plant diverse varieties.

TIMP2 gene polymorphism as a potential tool to infer Brazilian population origin

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da Silva RA

2013-12-01

Full Text Available Rodrigo Augusto da Silva,1 André Luis Shinohara,2 Denise Carleto Andia,1 Ariadne Letra,3 Regina Célia Peres,1 Ana Paula de Souza11Department of Morphology, Piracicaba Dental School, State University of Campinas, 2Oral Biology Program, Bauru Dental School, State University of São Paulo, São Paulo, Brazil; 3Department of Endodontics and Center for Craniofacial Research, School of Dentistry, University of Texas Health Science Center, Houston, TX, USAAbstract: Single nucleotide polymorphisms are genome variations that can be used as population-specific markers to infer genetic background and population origin. The Brazilian population is highly admixed due to immigration from several other populations. In particular, the state of São Paulo is recognized for the presence of Japanese individuals who seem likely to have contributed to a substantial proportion of ancestry in the modern Brazilian population. In the present study, we analyzed allele and genotype frequencies and associations of the –418G>C (rs8179090 single nucleotide polymorphism in the TIMP2 gene promoter in Brazilian and Japanese subjects, as well as in Japanese descendants from southeastern Brazil. The allele and genotype frequency analyses among groups demonstrated statistical significance (PC single nucleotide polymorphism of the TIMP2 gene, have a high probability of being Japanese or Japanese descendants. In addition to other genetic polymorphisms, the −418G>C TIMP2 polymorphism could be a population marker to assist in predicting Japanese ancestry, both in Japanese individuals and in admixed populations.Keywords: Brazilian, Japanese, polymorphism, allele, TIMP2

High rates of gene flow by pollen and seed in oak populations across Europe.

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Sophie Gerber

Full Text Available Gene flow is a key factor in the evolution of species, influencing effective population size, hybridisation and local adaptation. We analysed local gene flow in eight stands of white oak (mostly Quercus petraea and Q. robur, but also Q. pubescens and Q. faginea distributed across Europe. Adult trees within a given area in each stand were exhaustively sampled (range [239, 754], mean 423, mapped, and acorns were collected ([17,147], 51 from several mother trees ([3], [47], 23. Seedlings ([65,387], 178 were harvested and geo-referenced in six of the eight stands. Genetic information was obtained from screening distinct molecular markers spread across the genome, genotyping each tree, acorn or seedling. All samples were thus genotyped at 5-8 nuclear microsatellite loci. Fathers/parents were assigned to acorns and seedlings using likelihood methods. Mating success of male and female parents, pollen and seed dispersal curves, and also hybridisation rates were estimated in each stand and compared on a continental scale. On average, the percentage of the wind-borne pollen from outside the stand was 60%, with large variation among stands (21-88%. Mean seed immigration into the stand was 40%, a high value for oaks that are generally considered to have limited seed dispersal. However, this estimate varied greatly among stands (20-66%. Gene flow was mostly intraspecific, with large variation, as some trees and stands showed particularly high rates of hybridisation. Our results show that mating success was unevenly distributed among trees. The high levels of gene flow suggest that geographically remote oak stands are unlikely to be genetically isolated, questioning the static definition of gene reserves and seed stands.

Ploidy levels among species in the 'Oxalis tuberosa alliance' as inferred by flow cytometry.

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Emshwiller, Eve

2002-06-01

The 'Oxalis tuberosa alliance' is a group of Andean Oxalis species allied to the Andean tuber crop O. tuberosa Molina (Oxalidaceae), commonly known as 'oca'. As part of a larger project studying the origins of polyploidy and domestication of cultivated oca, flow cytometry was used to survey DNA ploidy levels among Bolivian and Peruvian accessions of alliance members. In addition, this study provided a first assessment of C-values in the alliance by estimating nuclear DNA contents of these accessions using chicken erythrocytes as internal standard. Ten Bolivian accessions of cultivated O. tuberosa were confirmed to be octoploid, with a mean nuclear DNA content of approx. 3.6 pg/2C. Two Peruvian wild Oxalis species, O. phaeotricha and O. picchensis, were inferred to be tetraploid (both with approx. 1.67 pg/2C), the latter being one of the putative progenitors of O. tuberosa identified by chloroplast-expressed glutamine synthetase data in prior work. The remaining accessions (from 78 populations provisionally identified as 35 species) were DNA diploid, with nuclear DNA contents varying from 0.79 to 1.34 pg/2C.

Morphological differentiation despite gene flow in an endangered grasshopper.

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Dowle, Eddy J; Morgan-Richards, Mary; Trewick, Steven A

2014-10-16

Gene flow is traditionally considered a limitation to speciation because selection is required to counter the homogenising effect of allele exchange. Here we report on two sympatric short-horned grasshoppers species in the South Island of New Zealand; one (Sigaus australis) widespread and the other (Sigaus childi) a narrow endemic. Of the 79 putatively neutral markers (mtDNA, microsatellite loci, ITS sequences and RAD-seq SNPs) all but one marker we examined showed extensive allele sharing, and similar or identical allele frequencies in the two species where they co-occur. We found no genetic evidence of deviation from random mating in the region of sympatry. However, analysis of morphological and geometric traits revealed no evidence of morphological introgression. Based on phenotype the two species are clearly distinct, but their genotypes thus far reveal no divergence. The best explanation for this is that some loci associated with the distinguishing morphological characters are under strong selection, but exchange of neutral loci is occurring freely between the two species. Although it is easier to define species as requiring a barrier between them, a dynamic model that accommodates gene flow is a biologically more reasonable explanation for these grasshoppers.

Asian wild rice is a hybrid swarm with extensive gene flow and feralization from domesticated rice.

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Wang, Hongru; Vieira, Filipe G; Crawford, Jacob E; Chu, Chengcai; Nielsen, Rasmus

2017-06-01

The domestication history of rice remains controversial, with multiple studies reaching different conclusions regarding its origin(s). These studies have generally assumed that populations of living wild rice, O. rufipogon , are descendants of the ancestral population that gave rise to domesticated rice, but relatively little attention has been paid to the origins and history of wild rice itself. Here, we investigate the genetic ancestry of wild rice by analyzing a diverse panel of rice genomes consisting of 203 domesticated and 435 wild rice accessions. We show that most modern wild rice is heavily admixed with domesticated rice through both pollen- and seed-mediated gene flow. In fact, much presumed wild rice may simply represent different stages of feralized domesticated rice. In line with this hypothesis, many presumed wild rice varieties show remnants of the effects of selective sweeps in previously identified domestication genes, as well as evidence of recent selection in flowering genes possibly associated with the feralization process. Furthermore, there is a distinct geographical pattern of gene flow from aus , indica , and japonica varieties into colocated wild rice. We also show that admixture from aus and indica is more recent than gene flow from japonica , possibly consistent with an earlier spread of japonica varieties. We argue that wild rice populations should be considered a hybrid swarm, connected to domesticated rice by continuous and extensive gene flow. © 2017 Wang et al.; Published by Cold Spring Harbor Laboratory Press.

Sex− and species−biased gene flow in a spotted eagle hybrid zone

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Väli Ülo

2011-04-01

Full Text Available Abstract Background Recent theoretical and empirical work points toward a significant role for sex-chromosome linked genes in the evolution of traits that induce reproductive isolation and for traits that evolve under influence of sexual selection. Empirical studies including recently diverged (Pleistocene, short-lived avian species pairs with short generation times have found that introgression occurs on the autosomes but not on the Z-chromosome. Here we study genetic differentiation and gene flow in the long-lived greater spotted eagle (Aquila clanga and lesser spotted eagle (A. pomarina, two species with comparatively long generation times. Results Our data suggest that there is a directional bias in migration rates between hybridizing spotted eagles in eastern Europe. We find that a model including post divergence gene flow fits our data best for both autosomal and Z-chromosome linked loci but, for the Z-chromosome, the rate is reduced in the direction from A. pomarina to A. clanga. Conclusions The fact that some introgression still occurs on the Z-chromosome between these species suggests that the differentiation process is in a more premature phase in our study system than in previously studied avian species pairs and that could be explained by a shorter divergence time and/or a longer average generation time in the spotted eagles. The results are in agreement with field observations and provide further insight into the role of sex-linked loci for the build-up of barriers to gene flow among diverging populations and species.

Gene flow for Echinococcus granulosus metapopulations determined by mitochondrial sequences: A reliable approach for reflecting epidemiological drift of parasite among neighboring countries.

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Mahami-Oskouei, Mahmoud; Kaseb-Yazdanparast, Azam; Spotin, Adel; Shahbazi, Abbas; Adibpour, Mohammad; Ahmadpour, Ehsan; Ghabouli-Mehrabani, Nader

2016-12-01

In genetic diversity and population structure of Echinococcus granulosus, the gene flow can illustrate how the Echinococcus isolates have epidemiologically drifted among endemic neighboring countries. 51 isolates of hydatid cysts were collected from human, dog, cattle and sheep in northwest Iran, where placed co-border with Turkey. DNA samples were extracted, amplified and subjected to sequence analysis of NADH dehydrogenase subunit 1 (nad1) and cytochrome oxidase subunit 1 (cox1) genes. As well, sequences of Echinococcus at east to the southeast regions of Turkey were retrieved from GenBank database for the cox1 gene. The confirmed isolates were grouped as G1 (n = 74) and G3 (n = 6) genotypes. 31 unique haplotypes were identified inferred by the analyzed sequences of cox1 among two distinct populations. A parsimonious network of the sequence haplotypes displayed star-like features in the overall population containing TUR1, IR15 and IR22 as the most common haplotypes. According to AMOVA test, the high value of haplotype diversity (0.94758-0.98901) of E. granulosus was reflected the total genetic variability within populations while nucleotide diversity was low (0.00727-0.01046) in Iranian and Turkish metapopulations. Neutrality indices of the cox1 were shown negative values (-15.078 to -10.057) in Echinococcus populations which indicating a significant divergence from neutrality. A pairwise fixation index (Fst) as a degree of gene flow was partially high value for all populations (0.151). The statistically Fst value indicates that E. granulosus sensu stricto (G1-G3) are genetically moderate differentiated among Iranian and Turkish isolates. The occurrence of TUR1 and IR15 elucidate that there is possibly the dawn of domestication due to transfer of alleles between populations through the diffusion of stock raising or anthropogenic movements. To evaluate the hypothetical evolutionary scenario, further exploration is necessitated to analyze isolates from

Intrinsic incompatibilities evolving as a by-product of divergent ecological selection: Considering them in empirical studies on divergence with gene flow.

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Kulmuni, J; Westram, A M

2017-06-01

The possibility of intrinsic barriers to gene flow is often neglected in empirical research on local adaptation and speciation with gene flow, for example when interpreting patterns observed in genome scans. However, we draw attention to the fact that, even with gene flow, divergent ecological selection may generate intrinsic barriers involving both ecologically selected and other interacting loci. Mechanistically, the link between the two types of barriers may be generated by genes that have multiple functions (i.e., pleiotropy), and/or by gene interaction networks. Because most genes function in complex networks, and their evolution is not independent of other genes, changes evolving in response to ecological selection can generate intrinsic barriers as a by-product. A crucial question is to what extent such by-product barriers contribute to divergence and speciation-that is whether they stably reduce gene flow. We discuss under which conditions by-product barriers may increase isolation. However, we also highlight that, depending on the conditions (e.g., the amount of gene flow and the strength of selection acting on the intrinsic vs. the ecological barrier component), the intrinsic incompatibility may actually destabilize barriers to gene flow. In practice, intrinsic barriers generated as a by-product of divergent ecological selection may generate peaks in genome scans that cannot easily be interpreted. We argue that empirical studies on divergence with gene flow should consider the possibility of both ecological and intrinsic barriers. Future progress will likely come from work combining population genomic studies, experiments quantifying fitness and molecular studies on protein function and interactions. © 2017 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

An insight into subterranean flow proposition around Alleppey mudbank coastal sector, Kerala, India: Inferences from the subsurface profiles of ground penetrating radar

Digital Repository Service at National Institute of Oceanography (India)

Loveson, V.J.; Dubey, R.; DineshKumar, P.K.; Nigam, R.; Naqvi, S.W.A.

-1 Author Version: Environ. Earth Sci., vol.75(20); 2016; no.1361 doi:10.1007/s12665-016-6172-6 An insight into subterranean flow proposition around Alleppey mudbank coastal sector, Kerala, India: inferences from the subsurface profiles of Ground... and productivity, physical and chemical aspects of the sea, annual drift etc. (Bristow et al., 1938; Varma and Kurup 1969; Gopinath and Qasim 1974; Jacob and Qasim (1974), Ramachandran and Mallik, 1985).Similar occurrences of mud banks in few other countries...

PhySIC_IST: cleaning source trees to infer more informative supertrees.

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Scornavacca, Celine; Berry, Vincent; Lefort, Vincent; Douzery, Emmanuel J P; Ranwez, Vincent

2008-10-04

Supertree methods combine phylogenies with overlapping sets of taxa into a larger one. Topological conflicts frequently arise among source trees for methodological or biological reasons, such as long branch attraction, lateral gene transfers, gene duplication/loss or deep gene coalescence. When topological conflicts occur among source trees, liberal methods infer supertrees containing the most frequent alternative, while veto methods infer supertrees not contradicting any source tree, i.e. discard all conflicting resolutions. When the source trees host a significant number of topological conflicts or have a small taxon overlap, supertree methods of both kinds can propose poorly resolved, hence uninformative, supertrees. To overcome this problem, we propose to infer non-plenary supertrees, i.e. supertrees that do not necessarily contain all the taxa present in the source trees, discarding those whose position greatly differs among source trees or for which insufficient information is provided. We detail a variant of the PhySIC veto method called PhySIC_IST that can infer non-plenary supertrees. PhySIC_IST aims at inferring supertrees that satisfy the same appealing theoretical properties as with PhySIC, while being as informative as possible under this constraint. The informativeness of a supertree is estimated using a variation of the CIC (Cladistic Information Content) criterion, that takes into account both the presence of multifurcations and the absence of some taxa. Additionally, we propose a statistical preprocessing step called STC (Source Trees Correction) to correct the source trees prior to the supertree inference. STC is a liberal step that removes the parts of each source tree that significantly conflict with other source trees. Combining STC with a veto method allows an explicit trade-off between veto and liberal approaches, tuned by a single parameter.Performing large-scale simulations, we observe that STC+PhySIC_IST infers much more informative

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

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Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

2000-01-01

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

Inference of RNA polymerase II transcription dynamics from chromatin immunoprecipitation time course data.

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Ciira wa Maina

2014-05-01

Full Text Available Gene transcription mediated by RNA polymerase II (pol-II is a key step in gene expression. The dynamics of pol-II moving along the transcribed region influence the rate and timing of gene expression. In this work, we present a probabilistic model of transcription dynamics which is fitted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity profile at the gene promoter. Model parameters are estimated using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides confidence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2. The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genome-wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. We find that rapidly induced genes are enriched for both estrogen receptor alpha (ERα and FOXA1 binding in their proximal promoter regions.

Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

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Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

2016-09-19

Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

A flood-based information flow analysis and network minimization method for gene regulatory networks.

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Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

2013-04-24

Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

Consequences of population topology for studying gene flow using link-based landscape genetic methods.

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van Strien, Maarten J

2017-07-01

Many landscape genetic studies aim to determine the effect of landscape on gene flow between populations. These studies frequently employ link-based methods that relate pairwise measures of historical gene flow to measures of the landscape and the geographical distance between populations. However, apart from landscape and distance, there is a third important factor that can influence historical gene flow, that is, population topology (i.e., the arrangement of populations throughout a landscape). As the population topology is determined in part by the landscape configuration, I argue that it should play a more prominent role in landscape genetics. Making use of existing literature and theoretical examples, I discuss how population topology can influence results in landscape genetic studies and how it can be taken into account to improve the accuracy of these results. In support of my arguments, I have performed a literature review of landscape genetic studies published during the first half of 2015 as well as several computer simulations of gene flow between populations. First, I argue why one should carefully consider which population pairs should be included in link-based analyses. Second, I discuss several ways in which the population topology can be incorporated in response and explanatory variables. Third, I outline why it is important to sample populations in such a way that a good representation of the population topology is obtained. Fourth, I discuss how statistical testing for link-based approaches could be influenced by the population topology. I conclude the article with six recommendations geared toward better incorporating population topology in link-based landscape genetic studies.

Gene flow and pathogen transmission among bobcats (Lynx rufus) in a fragmented urban landscape

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Lee, Justin S.; Ruell, Emily W.; Boydston, Erin E.; Lyren, Lisa M.; Alonso, Robert S.; Troyer, Jennifer L.; Crooks, Kevin R.; VandeWoude, Sue

2012-01-01

Urbanization can result in the fragmentation of once contiguous natural landscapes into a patchy habitat interspersed within a growing urban matrix. Animals living in fragmented landscapes often have reduced movement among habitat patches because of avoidance of intervening human development, which potentially leads to both reduced gene flow and pathogen transmission between patches. Mammalian carnivores with large home ranges, such as bobcats (Lynx rufus), may be particularly sensitive to habitat fragmentation. We performed genetic analyses on bobcats and their directly transmitted viral pathogen, feline immunodeficiency virus (FIV), to investigate the effects of urbanization on bobcat movement. We predicted that urban development, including major freeways, would limit bobcat movement and result in genetically structured host and pathogen populations. We analysed molecular markers from 106 bobcats and 19 FIV isolates from seropositive animals in urban southern California. Our findings indicate that reduced gene flow between two primary habitat patches has resulted in genetically distinct bobcat subpopulations separated by urban development including a major highway. However, the distribution of genetic diversity among FIV isolates determined through phylogenetic analyses indicates that pathogen genotypes are less spatially structured--exhibiting a more even distribution between habitat fragments. We conclude that the types of movement and contact sufficient for disease transmission occur with enough frequency to preclude structuring among the viral population, but that the bobcat population is structured owing to low levels of effective bobcat migration resulting in gene flow. We illustrate the utility in using multiple molecular markers that differentially detect movement and gene flow between subpopulations when assessing connectivity.

MAINTENANCE OF ECOLOGICALLY SIGNIFICANT GENETIC VARIATION IN THE TIGER SWALLOWTAIL BUTTERFLY THROUGH DIFFERENTIAL SELECTION AND GENE FLOW.

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Bossart, J L; Scriber, J M

1995-12-01

Differential selection in a heterogeneous environment is thought to promote the maintenance of ecologically significant genetic variation. Variation is maintained when selection is counterbalanced by the homogenizing effects of gene flow and random mating. In this study, we examine the relative importance of differential selection and gene flow in maintaining genetic variation in Papilio glaucus. Differential selection on traits contributing to successful use of host plants (oviposition preference and larval performance) was assessed by comparing the responses of southern Ohio, north central Georgia, and southern Florida populations of P. glaucus to three hosts: Liriodendron tulipifera, Magnolia virginiana, and Prunus serotina. Gene flow among populations was estimated using allozyme frequencies from nine polymorphic loci. Significant genetic differentiation was observed among populations for both oviposition preference and larval performance. This differentiation was interpreted to be the result of selection acting on Florida P. glaucus for enhanced use of Magnolia, the prevalent host in Florida. In contrast, no evidence of population differentiation was revealed by allozyme frequencies. F ST -values were very small and Nm, an estimate of the relative strengths of gene flow and genetic drift, was large, indicating that genetic exchange among P. glaucus populations is relatively unrestricted. The contrasting patterns of spatial differentiation for host-use traits and lack of differentiation for electrophoretically detectable variation implies that differential selection among populations will be counterbalanced by gene flow, thereby maintaining genetic variation for host-use traits. © 1995 The Society for the Study of Evolution.

Local evolution of pyrethroid resistance offsets gene flow among Aedes aegypti collections in Yucatan State, Mexico.

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Saavedra-Rodriguez, Karla; Beaty, Meaghan; Lozano-Fuentes, Saul; Denham, Steven; Garcia-Rejon, Julian; Reyes-Solis, Guadalupe; Machain-Williams, Carlos; Loroño-Pino, Maria Alba; Flores-Suarez, Adriana; Ponce-Garcia, Gustavo; Beaty, Barry; Eisen, Lars; Black, William C

2015-01-01

The mosquito Aedes aegypti is the major vector of the four serotypes of dengue virus (DENV1-4). Previous studies have shown that Ae. aegypti in Mexico have a high effective migration rate and that gene flow occurs among populations that are up to 150 km apart. Since 2000, pyrethroids have been widely used for suppression of Ae. aegypti in cities in Mexico. In Yucatan State in particular, pyrethroids have been applied in and around dengue case households creating an opportunity for local selection and evolution of resistance. Herein, we test for evidence of local adaptation by comparing patterns of variation among 27 Ae. aegypti collections at 13 single nucleotide polymorphisms (SNPs): two in the voltage-gated sodium channel gene para known to confer knockdown resistance, three in detoxification genes previously associated with pyrethroid resistance, and eight in putatively neutral loci. The SNPs in para varied greatly in frequency among collections, whereas SNPs at the remaining 11 loci showed little variation supporting previous evidence for extensive local gene flow. Among Ae. aegypti in Yucatan State, Mexico, local adaptation to pyrethroids appears to offset the homogenizing effects of gene flow. © The American Society of Tropical Medicine and Hygiene.

A Local Poisson Graphical Model for inferring networks from sequencing data.

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Allen, Genevera I; Liu, Zhandong

2013-09-01

Gaussian graphical models, a class of undirected graphs or Markov Networks, are often used to infer gene networks based on microarray expression data. Many scientists, however, have begun using high-throughput sequencing technologies such as RNA-sequencing or next generation sequencing to measure gene expression. As the resulting data consists of counts of sequencing reads for each gene, Gaussian graphical models are not optimal for this discrete data. In this paper, we propose a novel method for inferring gene networks from sequencing data: the Local Poisson Graphical Model. Our model assumes a Local Markov property where each variable conditional on all other variables is Poisson distributed. We develop a neighborhood selection algorithm to fit our model locally by performing a series of l1 penalized Poisson, or log-linear, regressions. This yields a fast parallel algorithm for estimating networks from next generation sequencing data. In simulations, we illustrate the effectiveness of our methods for recovering network structure from count data. A case study on breast cancer microRNAs (miRNAs), a novel application of graphical models, finds known regulators of breast cancer genes and discovers novel miRNA clusters and hubs that are targets for future research.

Constructing an integrated gene similarity network for the identification of disease genes.

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Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

2017-09-20

Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

Inferring biological functions of guanylyl cyclases with computational methods

KAUST Repository

Alquraishi, May Majed; Meier, Stuart Kurt

2013-01-01

A number of studies have shown that functionally related genes are often co-expressed and that computational based co-expression analysis can be used to accurately identify functional relationships between genes and by inference, their encoded proteins. Here we describe how a computational based co-expression analysis can be used to link the function of a specific gene of interest to a defined cellular response. Using a worked example we demonstrate how this methodology is used to link the function of the Arabidopsis Wall-Associated Kinase-Like 10 gene, which encodes a functional guanylyl cyclase, to host responses to pathogens. © Springer Science+Business Media New York 2013.

Inferring biological functions of guanylyl cyclases with computational methods

KAUST Repository

Alquraishi, May Majed

2013-09-03

A number of studies have shown that functionally related genes are often co-expressed and that computational based co-expression analysis can be used to accurately identify functional relationships between genes and by inference, their encoded proteins. Here we describe how a computational based co-expression analysis can be used to link the function of a specific gene of interest to a defined cellular response. Using a worked example we demonstrate how this methodology is used to link the function of the Arabidopsis Wall-Associated Kinase-Like 10 gene, which encodes a functional guanylyl cyclase, to host responses to pathogens. © Springer Science+Business Media New York 2013.

The scale of hydrothermal circulation of the Iheya-North field inferred from intensive heat flow measurements and ocean drilling

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Masaki, Y.; Kinoshita, M.; Yamamoto, H.; Nakajima, R.; Kumagai, H.; Takai, K.

2014-12-01

Iheya-North hydrothermal field situated in the middle Okinawa trough backarc basin is one of the largest ongoing Kuroko deposits in the world. Active chimneys as well as diffuse ventings (maximum fluid temperature 311 °C) have been located and studied in detail through various geological and geophysical surveys. To clarify the spatial scale of the hydrothermal circulation system, intensive heat flow measurements were carried out and ~100 heat flow data in and around the field from 2002 to 2014. In 2010, Integrated Ocean Drilling Program (IODP) Expedition 331 was carried out, and subbottom temperature data were obtained around the hydrothermal sites. During the JAMSTEC R/V Kaiyo cruise, KY14-01 in 2014, Iheya-North "Natsu" and "Aki" hydrothermal fields were newly found. The Iheya-Noth "Natsu" and "Aki" sites are located 1.2 km and 2.6 km south from the Iheya-North original site, respectively, and the maximum venting fluid temperature was 317 °C. We obtained one heat flow data at the "Aki" site. The value was 17 W/m2. Currently, the relationship between these hydrothermal sites are not well known. Three distinct zones are identified by heat flow values within 3 km from the active hydrothermal field. They are high-heat flow zone (>1 W/m2; HHZ), moderate-heat-flow zone (1-0.1 W/m2; MHZ); and low-heat-flow zone (<0.1 W/m2; LHZ). With increasing distance east of the HHZ, heat flow gradually decreases towards MHZ and LHZ. In the LHZ, temperature at 37m below the seafloor (mbsf) was 6 °C, that is consistent with the surface low heat flow suggesting the recharge of seawater. However, between 70 and 90 mbsf, the coarser sediments were cored, and temperature increased from 25 °C to 40°C. The temperature was 905°C at 151 mbsf, which was measured with thermoseal strips. The low thermal gradient in the upper 40 m suggests downward fluid flow. We infer that a hydrothermal circulation in the scale of ~1.5 km horizontal vs. ~a few hundred meters vertical.

The role of gene flow in shaping genetic structures of the subtropical conifer species Araucaria angustifolia.

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Stefenon, V M; Gailing, O; Finkeldey, R

2008-05-01

The morphological features of pollen and seed of Araucaria angustifolia have led to the proposal of limited gene dispersal for this species. We used nuclear microsatellite and AFLP markers to assess patterns of genetic variation in six natural populations at the intra- and inter-population level, and related our findings to gene dispersal in this species. Estimates of both fine-scale spatial genetic structure (SGS) and migration rate suggest relatively short-distance gene dispersal. However, gene dispersal differed among populations, and effects of more efficient dispersal within population were observed in at least one stand. In addition, even though some seed dispersal may be aggregated in this principally barochorous species, reasonable secondary seed dispersal, presumably facilitated by animals, and overlap of seed shadows within populations is suggested. Overall, no correlation was observed between levels of SGS and inbreeding, density or age structure, except that a higher level of SGS was revealed for the population with a higher number of juvenile individuals. A low estimate for the number of migrants per generation between two neighbouring populations implies limited gene flow. We expect that stepping-stone pollen flow may have contributed to low genetic differentiation among populations observed in a previous survey. Thus, strategies for maintenance of gene flow among remnant populations should be considered in order to avoid degrading effects of population fragmentation on the evolution of A. angustifolia.

Inferring clocks when lacking rocks: the variable rates of molecular evolution in bacteria

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Ochman Howard

2009-09-01

Full Text Available Abstract Background Because bacteria do not have a robust fossil record, attempts to infer the timing of events in their evolutionary history requires comparisons of molecular sequences. This use of molecular clocks is based on the assumptions that substitution rates for homologous genes or sites are fairly constant through time and across taxa. Violation of these conditions can lead to erroneous inferences and result in estimates that are off by orders of magnitude. In this study, we examine the consistency of substitution rates among a set of conserved genes in diverse bacterial lineages, and address the questions regarding the validity of molecular dating. Results By examining the evolution of 16S rRNA gene in obligate endosymbionts, which can be calibrated by the fossil record of their hosts, we found that the rates are consistent within a clade but varied widely across different bacterial lineages. Genome-wide estimates of nonsynonymous and synonymous substitutions suggest that these two measures are highly variable in their rates across bacterial taxa. Genetic drift plays a fundamental role in determining the accumulation of substitutions in 16S rRNA genes and at nonsynonymous sites. Moreover, divergence estimates based on a set of universally conserved protein-coding genes also exhibit low correspondence to those based on 16S rRNA genes. Conclusion Our results document a wide range of substitution rates across genes and bacterial taxa. This high level of variation cautions against the assumption of a universal molecular clock for inferring divergence times in bacteria. However, by applying relative-rate tests to homologous genes, it is possible to derive reliable local clocks that can be used to calibrate bacterial evolution. Reviewers This article was reviewed by Adam Eyre-Walker, Simonetta Gribaldo and Tal Pupko (nominated by Dan Graur.

Inference of expanded Lrp-like feast/famine transcription factor targets in a non-model organism using protein structure-based prediction.

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Ashworth, Justin; Plaisier, Christopher L; Lo, Fang Yin; Reiss, David J; Baliga, Nitin S

2014-01-01

Widespread microbial genome sequencing presents an opportunity to understand the gene regulatory networks of non-model organisms. This requires knowledge of the binding sites for transcription factors whose DNA-binding properties are unknown or difficult to infer. We adapted a protein structure-based method to predict the specificities and putative regulons of homologous transcription factors across diverse species. As a proof-of-concept we predicted the specificities and transcriptional target genes of divergent archaeal feast/famine regulatory proteins, several of which are encoded in the genome of Halobacterium salinarum. This was validated by comparison to experimentally determined specificities for transcription factors in distantly related extremophiles, chromatin immunoprecipitation experiments, and cis-regulatory sequence conservation across eighteen related species of halobacteria. Through this analysis we were able to infer that Halobacterium salinarum employs a divergent local trans-regulatory strategy to regulate genes (carA and carB) involved in arginine and pyrimidine metabolism, whereas Escherichia coli employs an operon. The prediction of gene regulatory binding sites using structure-based methods is useful for the inference of gene regulatory relationships in new species that are otherwise difficult to infer.

Barriers to gene flow in the marine environment: insights from two common intertidal limpet species of the Atlantic and Mediterranean.

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Alexandra Sá-Pinto

Full Text Available Knowledge of the scale of dispersal and the mechanisms governing gene flow in marine environments remains fragmentary despite being essential for understanding evolution of marine biota and to design management plans. We use the limpets Patella ulyssiponensis and Patella rustica as models for identifying factors affecting gene flow in marine organisms across the North-East Atlantic and the Mediterranean Sea. A set of allozyme loci and a fragment of the mitochondrial gene cytochrome C oxidase subunit I were screened for genetic variation through starch gel electrophoresis and DNA sequencing, respectively. An approach combining clustering algorithms with clinal analyses was used to test for the existence of barriers to gene flow and estimate their geographic location and abruptness. Sharp breaks in the genetic composition of individuals were observed in the transitions between the Atlantic and the Mediterranean and across southern Italian shores. An additional break within the Atlantic cluster separates samples from the Alboran Sea and Atlantic African shores from those of the Iberian Atlantic shores. The geographic congruence of the genetic breaks detected in these two limpet species strongly supports the existence of transpecific barriers to gene flow in the Mediterranean Sea and Northeastern Atlantic. This leads to testable hypotheses regarding factors restricting gene flow across the study area.

Progression inference for somatic mutations in cancer

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Leif E. Peterson

2017-04-01

Full Text Available Computational methods were employed to determine progression inference of genomic alterations in commonly occurring cancers. Using cross-sectional TCGA data, we computed evolutionary trajectories involving selectivity relationships among pairs of gene-specific genomic alterations such as somatic mutations, deletions, amplifications, downregulation, and upregulation among the top 20 driver genes associated with each cancer. Results indicate that the majority of hierarchies involved TP53, PIK3CA, ERBB2, APC, KRAS, EGFR, IDH1, VHL, etc. Research into the order and accumulation of genomic alterations among cancer driver genes will ever-increase as the costs of nextgen sequencing subside, and personalized/precision medicine incorporates whole-genome scans into the diagnosis and treatment of cancer. Keywords: Oncology, Cancer research, Genetics, Computational biology

Phylogeny of the Celastreae (Celastraceae) and the relationships of Catha edulis (qat) inferred from morphological characters and nuclear and plastid genes.

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Simmons, Mark P; Cappa, Jennifer J; Archer, Robert H; Ford, Andrew J; Eichstedt, Dedra; Clevinger, Curtis C

2008-08-01

The phylogeny of Celastraceae tribe Celastreae, which includes about 350 species of trees and shrubs in 15 genera, was inferred in a simultaneous analysis of morphological characters together with nuclear (ITS and 26S rDNA) and plastid (matK, trnL-F) genes. A strong correlation was found between the geography of the species sampled and their inferred relationships. Species of Maytenus and Gymnosporia from different regions were resolved as polyphyletic groups. Maytenus was resolved in three lineages (New World, African, and Austral-Pacific), while Gymnosporia was resolved in two lineages (New World and Old World). Putterlickia was resolved as nested within the Old World Gymnosporia. Catha edulis (qat, khat) was resolved as sister to the clade of Allocassine, Cassine, Lauridia, and Maurocenia. Gymnosporia cassinoides, which is reportedly chewed as a stimulant in the Canary Islands, was resolved as a derived member of Gymnosporia and is more closely related to Lydenburgia and Putterlickia than it is to Catha. Therefore, all eight of these genera are candidates for containing cathinone- and/or cathine-related alkaloids.

Genomic evidence of geographically widespread effect of gene flow from polar bears into brown bears.

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Cahill, James A; Stirling, Ian; Kistler, Logan; Salamzade, Rauf; Ersmark, Erik; Fulton, Tara L; Stiller, Mathias; Green, Richard E; Shapiro, Beth

2015-03-01

Polar bears are an arctic, marine adapted species that is closely related to brown bears. Genome analyses have shown that polar bears are distinct and genetically homogeneous in comparison to brown bears. However, these analyses have also revealed a remarkable episode of polar bear gene flow into the population of brown bears that colonized the Admiralty, Baranof and Chichagof islands (ABC islands) of Alaska. Here, we present an analysis of data from a large panel of polar bear and brown bear genomes that includes brown bears from the ABC islands, the Alaskan mainland and Europe. Our results provide clear evidence that gene flow between the two species had a geographically wide impact, with polar bear DNA found within the genomes of brown bears living both on the ABC islands and in the Alaskan mainland. Intriguingly, while brown bear genomes contain up to 8.8% polar bear ancestry, polar bear genomes appear to be devoid of brown bear ancestry, suggesting the presence of a barrier to gene flow in that direction. © 2014 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

Evolutionary relationships in Aspergillus section Fumigati inferred from partial beta-tubulin and hydrophobin sequences

DEFF Research Database (Denmark)

Geiser, D.M.; Frisvad, Jens Christian; Taylor, J.W.

1998-01-01

are heterothallic. Phylogenetic relationships were inferred among members of Aspergillus section Fumigati based on partial DNA sequences from the benA beta-tubulin and rodA hydrophobin genes. Aspergillus clavatus was chosen as an outgroup. The two gene regions provided nearly equal numbers of phylogenetically...... informative nucleotide characters. The rodA region possessed a considerably higher level of inferred amino acid variation than did the benA region. The results of a partition homogeneity test showed that the benA and rodA data sets were not in significant conflict, and the topologies of the most parsimonious...

Inferring biological tasks using Pareto analysis of high-dimensional data.

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Hart, Yuval; Sheftel, Hila; Hausser, Jean; Szekely, Pablo; Ben-Moshe, Noa Bossel; Korem, Yael; Tendler, Avichai; Mayo, Avraham E; Alon, Uri

2015-03-01

We present the Pareto task inference method (ParTI; http://www.weizmann.ac.il/mcb/UriAlon/download/ParTI) for inferring biological tasks from high-dimensional biological data. Data are described as a polytope, and features maximally enriched closest to the vertices (or archetypes) allow identification of the tasks the vertices represent. We demonstrate that human breast tumors and mouse tissues are well described by tetrahedrons in gene expression space, with specific tumor types and biological functions enriched at each of the vertices, suggesting four key tasks.

Inference of the Genetic Network Regulating Lateral Root Initiation in Arabidopsis thaliana

KAUST Repository

Muraro, D.

2013-01-01

Regulation of gene expression is crucial for organism growth, and it is one of the challenges in systems biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyze two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, and assess causality of their regulatory interactions by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation. © 2004-2012 IEEE.

Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model

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Meilijson, Isaac; Kupiec, Martin; Ruppin, Eytan

2011-01-01

We describe the first large scale analysis of gene translation that is based on a model that takes into account the physical and dynamical nature of this process. The Ribosomal Flow Model (RFM) predicts fundamental features of the translation process, including translation rates, protein abundance levels, ribosomal densities and the relation between all these variables, better than alternative (‘non-physical’) approaches. In addition, we show that the RFM can be used for accurate inference of various other quantities including genes' initiation rates and translation costs. These quantities could not be inferred by previous predictors. We find that increasing the number of available ribosomes (or equivalently the initiation rate) increases the genomic translation rate and the mean ribosome density only up to a certain point, beyond which both saturate. Strikingly, assuming that the translation system is tuned to work at the pre-saturation point maximizes the predictive power of the model with respect to experimental data. This result suggests that in all organisms that were analyzed (from bacteria to Human), the global initiation rate is optimized to attain the pre-saturation point. The fact that similar results were not observed for heterologous genes indicates that this feature is under selection. Remarkably, the gap between the performance of the RFM and alternative predictors is strikingly large in the case of heterologous genes, testifying to the model's promising biotechnological value in predicting the abundance of heterologous proteins before expressing them in the desired host. PMID:21909250

Gene flow rise with habitat fragmentation in the bog fritillary butterfly (Lepidoptera: Nymphalidae).

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Nève, Gabriel; Barascud, Bernard; Descimon, Henri; Baguette, Michel

2008-03-17

The main components of the spatial genetic structure of the populations are neighbourhood size and isolation by distance. These may be inferred from the allele frequencies across a series of populations within a region. Here, the spatial population structure of Proclossiana eunomia was investigated in two mountainous areas of southern Europe (Asturias, Spain and Pyrenees, France) and in two areas of intermediate elevation (Morvan, France and Ardennes, Belgium). A total of eight polymorphic loci were scored by allozyme electrophoresis, revealing a higher polymorphism in the populations of southern Europe than in those of central Europe. Isolation by distance effect was much stronger in the two mountain ranges (Pyrenees and Asturias) than in the two areas of lower elevation (Ardennes and Morvan). By contrast, the neighbourhood size estimates were smaller in the Ardennes and in the Morvan than in the two high mountain areas, indicating more common movements between neighbouring patches in the mountains than in plains. Short and long dispersal events are two phenomena with distinct consequences in the population genetics of natural populations. The differences in level of population differentiation within each the four regions may be explained by change in dispersal in lowland recently fragmented landscapes: on average, butterflies disperse to a shorter distance but the few ones which disperse long distance do so more efficiently. Habitat fragmentation has evolutionary consequences exceeding by far the selection of dispersal related traits: the balance between local specialisation and gene flow would be perturbed, which would modify the extent to which populations are adapted to heterogeneous environments.

Gene flow rise with habitat fragmentation in the bog fritillary butterfly (Lepidoptera: Nymphalidae

Directory of Open Access Journals (Sweden)

Descimon Henri

2008-03-01

Full Text Available Abstract Background The main components of the spatial genetic structure of the populations are neighbourhood size and isolation by distance. These may be inferred from the allele frequencies across a series of populations within a region. Here, the spatial population structure of Proclossiana eunomia was investigated in two mountainous areas of southern Europe (Asturias, Spain and Pyrenees, France and in two areas of intermediate elevation (Morvan, France and Ardennes, Belgium. Results A total of eight polymorphic loci were scored by allozyme electrophoresis, revealing a higher polymorphism in the populations of southern Europe than in those of central Europe. Isolation by distance effect was much stronger in the two mountain ranges (Pyrenees and Asturias than in the two areas of lower elevation (Ardennes and Morvan. By contrast, the neighbourhood size estimates were smaller in the Ardennes and in the Morvan than in the two high mountain areas, indicating more common movements between neighbouring patches in the mountains than in plains. Conclusion Short and long dispersal events are two phenomena with distinct consequences in the population genetics of natural populations. The differences in level of population differentiation within each the four regions may be explained by change in dispersal in lowland recently fragmented landscapes: on average, butterflies disperse to a shorter distance but the few ones which disperse long distance do so more efficiently. Habitat fragmentation has evolutionary consequences exceeding by far the selection of dispersal related traits: the balance between local specialisation and gene flow would be perturbed, which would modify the extent to which populations are adapted to heterogeneous environments.

Contrasting Patterns of Gene Flow for Amazonian Snakes That Actively Forage and Those That Wait in Ambush.

Science.gov (United States)

de Fraga, Rafael; Lima, Albertina P; Magnusson, William E; Ferrão, Miquéias; Stow, Adam J

2017-07-01

Knowledge of genetic structure, geographic distance and environmental heterogeneity can be used to identify environmental features and natural history traits that influence dispersal and gene flow. Foraging mode is a trait that might predict dispersal capacity in snakes, because actively foragers typically have greater movement rates than ambush predators. Here, we test the hypothesis that 2 actively foraging snakes have higher levels of gene flow than 2 ambush predators. We evaluated these 4 co-distributed species of snakes in the Brazilian Amazon. Snakes were sampled along an 880 km transect from the central to the southwest of the Amazon basin, which covered a mosaic of vegetation types and seasonal differences in climate. We analyzed thousands of single nucleotide polymorphisms to compare patterns of neutral gene flow based on isolation by geographic distance (IBD) and environmental resistance (IBR). We show that IBD and IBR were only evident in ambush predators, implying lower levels of dispersal than the active foragers. Therefore, gene flow was high enough in the active foragers analyzed here to prevent any build-up of spatial genotypic structure with respect to geographic distance and environmental heterogeneity. © The American Genetic Association 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Circumpolar Genetic Structure and Recent Gene Flow of Polar Bears: A Reanalysis.

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Malenfant, René M; Davis, Corey S; Cullingham, Catherine I; Coltman, David W

2016-01-01

Recently, an extensive study of 2,748 polar bears (Ursus maritimus) from across their circumpolar range was published in PLOS ONE, which used microsatellites and mitochondrial haplotypes to apparently show altered population structure and a dramatic change in directional gene flow towards the Canadian Archipelago-an area believed to be a future refugium for polar bears as their southernmost habitats decline under climate change. Although this study represents a major international collaborative effort and promised to be a baseline for future genetics work, methodological shortcomings and errors of interpretation undermine some of the study's main conclusions. Here, we present a reanalysis of this data in which we address some of these issues, including: (1) highly unbalanced sample sizes and large amounts of systematically missing data; (2) incorrect calculation of FST and of significance levels; (3) misleading estimates of recent gene flow resulting from non-convergence of the program BayesAss. In contrast to the original findings, in our reanalysis we find six genetic clusters of polar bears worldwide: the Hudson Bay Complex, the Western and Eastern Canadian Arctic Archipelago, the Western and Eastern Polar Basin, and-importantly-we reconfirm the presence of a unique and possibly endangered cluster of bears in Norwegian Bay near Canada's expected last sea-ice refugium. Although polar bears' abundance, distribution, and population structure will certainly be negatively affected by ongoing-and increasingly rapid-loss of Arctic sea ice, these genetic data provide no evidence of strong directional gene flow in response to recent climate change.

Circumpolar Genetic Structure and Recent Gene Flow of Polar Bears: A Reanalysis.

Directory of Open Access Journals (Sweden)

René M Malenfant

Full Text Available Recently, an extensive study of 2,748 polar bears (Ursus maritimus from across their circumpolar range was published in PLOS ONE, which used microsatellites and mitochondrial haplotypes to apparently show altered population structure and a dramatic change in directional gene flow towards the Canadian Archipelago-an area believed to be a future refugium for polar bears as their southernmost habitats decline under climate change. Although this study represents a major international collaborative effort and promised to be a baseline for future genetics work, methodological shortcomings and errors of interpretation undermine some of the study's main conclusions. Here, we present a reanalysis of this data in which we address some of these issues, including: (1 highly unbalanced sample sizes and large amounts of systematically missing data; (2 incorrect calculation of FST and of significance levels; (3 misleading estimates of recent gene flow resulting from non-convergence of the program BayesAss. In contrast to the original findings, in our reanalysis we find six genetic clusters of polar bears worldwide: the Hudson Bay Complex, the Western and Eastern Canadian Arctic Archipelago, the Western and Eastern Polar Basin, and-importantly-we reconfirm the presence of a unique and possibly endangered cluster of bears in Norwegian Bay near Canada's expected last sea-ice refugium. Although polar bears' abundance, distribution, and population structure will certainly be negatively affected by ongoing-and increasingly rapid-loss of Arctic sea ice, these genetic data provide no evidence of strong directional gene flow in response to recent climate change.

Passive larval transport explains recent gene flow in a Mediterranean gorgonian

Science.gov (United States)

Padrón, Mariana; Costantini, Federica; Baksay, Sandra; Bramanti, Lorenzo; Guizien, Katell

2018-06-01

Understanding the patterns of connectivity is required by the Strategic Plan for Biodiversity 2011-2020 and will be used to guide the extension of marine protection measures. Despite the increasing accuracy of ocean circulation modelling, the capacity to model the population connectivity of sessile benthic species with dispersal larval stages can be limited due to the potential effect of filters acting before or after dispersal, which modulates offspring release or settlement, respectively. We applied an interdisciplinary approach that combined demographic surveys, genetic methods (assignment tests and coalescent-based analyses) and larval transport simulations to test the relative importance of demographics and ocean currents in shaping the recent patterns of gene flow among populations of a Mediterranean gorgonian ( Eunicella singularis) in a fragmented rocky habitat (Gulf of Lion, NW Mediterranean Sea). We show that larval transport is a dominant driver of recent gene flow among the populations, and significant correlations were found between recent gene flow and larval transport during an average single dispersal event when the pelagic larval durations (PLDs) ranged from 7 to 14 d. Our results suggest that PLDs that efficiently connect populations distributed over a fragmented habitat are filtered by the habitat layout within the species competency period. Moreover, a PLD ranging from 7 to 14 d is sufficient to connect the fragmented rocky substrate of the Gulf of Lion. The rocky areas located in the centre of the Gulf of Lion, which are currently not protected, were identified as essential hubs for the distribution of migrants in the region. We encourage the use of a range of PLDs instead of a single value when estimating larval transport with biophysical models to identify potential connectivity patterns among a network of Marine Protected Areas or even solely a seascape.

Investigating Pollen and Gene Flow of WYMV-Resistant Transgenic Wheat N12-1 Using a Dwarf Male-Sterile Line as the Pollen Receptor.

Science.gov (United States)

Dong, Shanshan; Liu, Yan; Yu, Cigang; Zhang, Zhenhua; Chen, Ming; Wang, Changyong

2016-01-01

Pollen-mediated gene flow (PMGF) is the main mode of transgene flow in flowering plants. The study of pollen and gene flow of transgenic wheat can help to establish the corresponding strategy for preventing transgene escape and contamination between compatible genotypes in wheat. To investigate the pollen dispersal and gene flow frequency in various directions and distances around the pollen source and detect the association between frequency of transgene flow and pollen density from transgenic wheat, a concentric circle design was adopted to conduct a field experiment using transgenic wheat with resistance to wheat yellow mosaic virus (WYMV) as the pollen donor and dwarf male-sterile wheat as the pollen receptor. The results showed that the pollen and gene flow of transgenic wheat varied significantly among the different compass sectors. A higher pollen density and gene flow frequency was observed in the downwind SW and W sectors, with average frequencies of transgene flow of 26.37 and 23.69% respectively. The pollen and gene flow of transgenic wheat declined dramatically with increasing distance from its source. Most of the pollen grains concentrated within 5 m and only a few pollen grains were detected beyond 30 m. The percentage of transgene flow was the highest where adjacent to the pollen source, with an average of 48.24% for all eight compass directions at 0 m distance. Transgene flow was reduced to 50% and 95% between 1.61 to 3.15 m, and 10.71 to 20.93 m, respectively. Our results suggest that climate conditions, especially wind direction, may significantly affect pollen dispersal and gene flow of wheat. The isolation-by-distance model is one of the most effective methods for achieving stringent transgene confinement in wheat. The frequency of transgene flow is directly correlated with the relative density of GM pollen grains in air currents, and pollen competition may be a major factor influencing transgene flow.

Investigating Pollen and Gene Flow of WYMV-Resistant Transgenic Wheat N12-1 Using a Dwarf Male-Sterile Line as the Pollen Receptor.

Directory of Open Access Journals (Sweden)

Shanshan Dong

Full Text Available Pollen-mediated gene flow (PMGF is the main mode of transgene flow in flowering plants. The study of pollen and gene flow of transgenic wheat can help to establish the corresponding strategy for preventing transgene escape and contamination between compatible genotypes in wheat. To investigate the pollen dispersal and gene flow frequency in various directions and distances around the pollen source and detect the association between frequency of transgene flow and pollen density from transgenic wheat, a concentric circle design was adopted to conduct a field experiment using transgenic wheat with resistance to wheat yellow mosaic virus (WYMV as the pollen donor and dwarf male-sterile wheat as the pollen receptor. The results showed that the pollen and gene flow of transgenic wheat varied significantly among the different compass sectors. A higher pollen density and gene flow frequency was observed in the downwind SW and W sectors, with average frequencies of transgene flow of 26.37 and 23.69% respectively. The pollen and gene flow of transgenic wheat declined dramatically with increasing distance from its source. Most of the pollen grains concentrated within 5 m and only a few pollen grains were detected beyond 30 m. The percentage of transgene flow was the highest where adjacent to the pollen source, with an average of 48.24% for all eight compass directions at 0 m distance. Transgene flow was reduced to 50% and 95% between 1.61 to 3.15 m, and 10.71 to 20.93 m, respectively. Our results suggest that climate conditions, especially wind direction, may significantly affect pollen dispersal and gene flow of wheat. The isolation-by-distance model is one of the most effective methods for achieving stringent transgene confinement in wheat. The frequency of transgene flow is directly correlated with the relative density of GM pollen grains in air currents, and pollen competition may be a major factor influencing transgene flow.

Extensive gene flow over Europe and possible speciation

Energy Technology Data Exchange (ETDEWEB)

VINCENOT, Dr. LUCIE [Centre d’Ecologie Fonctionnelle et Evolutive Montpellier, France; NARA, Dr. KAZUHIDE [Department of Natural Environmental Studies, The University of Tokyo, Japan; STHULTZ, CHRISTOPHER [Centre d’Ecologie Fonctionnelle et Evolutive Montpellier, France; Labbe, Jessy L [ORNL; DUBOIS, MARIE-PIERRE [Centre d’Ecologie Fonctionnelle et Evolutive Montpellier, France; TEDERSOO, LEHO [University of Tartu, Estonia; Martin, Francis [INRA, Nancy, France; SELOSSE, Dr. MARC-ANDRE [Centre d’Ecologie Fonctionnelle et Evolutive Montpellier, France

2012-01-01

Biogeographical patterns and large-scale genetic structure have been little studied in ectomycorrhizal (EM) fungi, despite the ecological and economic importance of EM symbioses. We coupled population genetics and phylogenetic approaches to understand spatial structure in fungal populations on a continental scale. Using nine microsatellite markers, we characterized gene flow among 16 populations of the widespread EM basidiomycete Laccaria amethystina over Europe (i.e. over 2900 km). We also widened our scope to two additional populations from Japan (104 km away) and compared them with European populations through microsatellite markers and multilocus phylogenies, using three nuclear genes (NAR, G6PD and ribosomal DNA) and two mitochondrial ribosomal genes. European L. amethystina populations displayed limited differentiation (average FST = 0.041) and very weak isolation by distance (IBD). This panmictic European pattern may result from effective aerial dispersal of spores, high genetic diversity in populations and mutualistic interactions with multiple hosts that all facilitate migration. The multilocus phylogeny based on nuclear genes confirmed that Japanese and European specimens were closely related but clustered on a geographical basis. By using microsatellite markers, we found that Japanese populations were strongly differentiated from the European populations (FST = 0.416), more than expected by extrapolating the European pattern of IBD. Population structure analyses clearly separated the populations into two clusters, i.e. European and Japanese clusters. We discuss the possibility of IBD in a continuous population (considering some evidence for a ring species over the Northern Hemisphere) vs. an allopatric speciation over Eurasia, making L. amethystina a promising model of intercontinental species for future studies.

Adaptive genetic markers discriminate migratory runs of Chinook salmon (Oncorhynchus tshawytscha) amid continued gene flow.

Science.gov (United States)

O'Malley, Kathleen G; Jacobson, Dave P; Kurth, Ryon; Dill, Allen J; Banks, Michael A

2013-12-01

Neutral genetic markers are routinely used to define distinct units within species that warrant discrete management. Human-induced changes to gene flow however may reduce the power of such an approach. We tested the efficiency of adaptive versus neutral genetic markers in differentiating temporally divergent migratory runs of Chinook salmon (Oncorhynchus tshawytscha) amid high gene flow owing to artificial propagation and habitat alteration. We compared seven putative migration timing genes to ten microsatellite loci in delineating three migratory groups of Chinook in the Feather River, CA: offspring of fall-run hatchery broodstock that returned as adults to freshwater in fall (fall run), spring-run offspring that returned in spring (spring run), and fall-run offspring that returned in spring (FRS). We found evidence for significant differentiation between the fall and federally listed threatened spring groups based on divergence at three circadian clock genes (OtsClock1b, OmyFbxw11, and Omy1009UW), but not neutral markers. We thus demonstrate the importance of genetic marker choice in resolving complex life history types. These findings directly impact conservation management strategies and add to previous evidence from Pacific and Atlantic salmon indicating that circadian clock genes influence migration timing.

Pollen-mediated gene flow and seed exchange in small-scale Zambian maize farming, implications for biosafety assessment.

Science.gov (United States)

Bøhn, Thomas; Aheto, Denis W; Mwangala, Felix S; Fischer, Klara; Bones, Inger Louise; Simoloka, Christopher; Mbeule, Ireen; Schmidt, Gunther; Breckling, Broder

2016-10-03

Gene flow in agricultural crops is important for risk assessment of genetically modified (GM) crops, particularly in countries with a large informal agricultural sector of subsistence cultivation. We present a pollen flow model for maize (Zea mays), a major staple crop in Africa. We use spatial properties of fields (size, position) in three small-scale maize farming communities in Zambia and estimate rates of cross-fertilisation between fields sown with different maize varieties (e.g. conventional and transgene). As an additional factor contributing to gene flow, we present data on seed saving and sharing among farmers that live in the same communities. Our results show that: i) maize fields were small and located in immediate vicinity of neighboring fields; ii) a majority of farmers saved and shared seed; iii) modeled rates of pollen-mediated gene flow showed extensive mixing of germplasm between fields and farms and iv) as a result, segregation of GM and non-GM varieties is not likely to be an option in these systems. We conclude that the overall genetic composition of maize, in this and similar agricultural contexts, will be strongly influenced both by self-organised ecological factors (pollen flow), and by socially mediated intervention (seed recycling and sharing).

OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks.

Science.gov (United States)

Lim, Néhémy; Senbabaoglu, Yasin; Michailidis, George; d'Alché-Buc, Florence

2013-06-01

Reverse engineering of gene regulatory networks remains a central challenge in computational systems biology, despite recent advances facilitated by benchmark in silico challenges that have aided in calibrating their performance. A number of approaches using either perturbation (knock-out) or wild-type time-series data have appeared in the literature addressing this problem, with the latter using linear temporal models. Nonlinear dynamical models are particularly appropriate for this inference task, given the generation mechanism of the time-series data. In this study, we introduce a novel nonlinear autoregressive model based on operator-valued kernels that simultaneously learns the model parameters, as well as the network structure. A flexible boosting algorithm (OKVAR-Boost) that shares features from L2-boosting and randomization-based algorithms is developed to perform the tasks of parameter learning and network inference for the proposed model. Specifically, at each boosting iteration, a regularized Operator-valued Kernel-based Vector AutoRegressive model (OKVAR) is trained on a random subnetwork. The final model consists of an ensemble of such models. The empirical estimation of the ensemble model's Jacobian matrix provides an estimation of the network structure. The performance of the proposed algorithm is first evaluated on a number of benchmark datasets from the DREAM3 challenge and then on real datasets related to the In vivo Reverse-Engineering and Modeling Assessment (IRMA) and T-cell networks. The high-quality results obtained strongly indicate that it outperforms existing approaches. The OKVAR-Boost Matlab code is available as the archive: http://amis-group.fr/sourcecode-okvar-boost/OKVARBoost-v1.0.zip. Supplementary data are available at Bioinformatics online.

Sexual selection and magic traits in speciation with gene flow

Directory of Open Access Journals (Sweden)

Maria R. SERVEDIO, Michael KOPP

2012-06-01

Full Text Available The extent to which sexual selection is involved in speciation with gene flow remains an open question and the subject of much research. Here, we propose that some insight can be gained from considering the concept of magic traits (i.e., traits involved in both reproductive isolation and ecological divergence. Both magic traits and other, “non-magic”, traits can contribute to speciation via a number of specific mechanisms. We argue that many of these mechanisms are likely to differ widely in the extent to which they involve sexual selection. Furthermore, in some cases where sexual selection is present, it may be prone to inhibit rather than drive speciation. Finally, there are a priori reasons to believe that certain categories of traits are much more effective than others in driving speciation. The combination of these points suggests a classification of traits that may shed light on the broader role of sexual selection in speciation with gene flow. In particular, we suggest that sexual selection can act as a driver of speciation in some scenarios, but may play a negligible role in potentially common categories of magic traits, and may be likely to inhibit speciation in common categories of non-magic traits [Current Zoology 58 (3: 507–513, 2012].

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

Science.gov (United States)

Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

1998-01-01

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

The Himalayas: barrier and conduit for gene flow.

Science.gov (United States)

Gayden, Tenzin; Perez, Annabel; Persad, Patrice J; Bukhari, Areej; Chennakrishnaiah, Shilpa; Simms, Tanya; Maloney, Trisha; Rodriguez, Kristina; Herrera, Rene J

2013-06-01

The Himalayan mountain range is strategically located at the crossroads of the major cultural centers in Asia, the Middle East and Europe. Although previous Y-chromosome studies indicate that the Himalayas served as a natural barrier for gene flow from the south to the Tibetan plateau, this region is believed to have played an important role as a corridor for human migrations between East and West Eurasia along the ancient Silk Road. To evaluate the effects of the Himalayan mountain range in shaping the maternal lineages of populations residing on either side of the cordillera, we analyzed mitochondrial DNA variation in 344 samples from three Nepalese collections (Newar, Kathmandu and Tamang) and a general population of Tibet. Our results revealed a predominantly East Asian-specific component in Tibet and Tamang, whereas Newar and Kathmandu are both characterized by a combination of East and South Central Asian lineages. Interestingly, Newar and Kathmandu harbor several deep-rooted Indian lineages, including M2, R5, and U2, whose coalescent times from this study (U2, >40 kya) and previous reports (M2 and R5, >50 kya) suggest that Nepal was inhabited during the initial peopling of South Central Asia. Comparisons with our previous Y-chromosome data indicate sex-biased migrations in Tamang and a founder effect and/or genetic drift in Tamang and Newar. Altogether, our results confirm that while the Himalayas acted as a geographic barrier for human movement from the Indian subcontinent to the Tibetan highland, it also served as a conduit for gene flow between Central and East Asia. Copyright © 2013 Wiley Periodicals, Inc.

Improved functional overview of protein complexes using inferred epistatic relationships

LENUS (Irish Health Repository)

Ryan, Colm

2011-05-23

Abstract Background Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization. Results We introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus. Conclusion Overall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.

Ploidy Levels among Species in the ‘Oxalis tuberosa Alliance’ as Inferred by Flow Cytometry

Science.gov (United States)

EMSHWILLER, EVE

2002-01-01

The ‘Oxalis tuberosa alliance’ is a group of Andean Oxalis species allied to the Andean tuber crop O. tuberosa Molina (Oxalidaceae), commonly known as ‘oca’. As part of a larger project studying the origins of polyploidy and domestication of cultivated oca, flow cytometry was used to survey DNA ploidy levels among Bolivian and Peruvian accessions of alliance members. In addition, this study provided a first assessment of C‐values in the alliance by estimating nuclear DNA contents of these accessions using chicken erythrocytes as internal standard. Ten Bolivian accessions of cultivated O. tuberosa were confirmed to be octoploid, with a mean nuclear DNA content of approx. 3·6 pg/2C. Two Peruvian wild Oxalis species, O. phaeotricha and O. picchensis, were inferred to be tetraploid (both with approx. 1·67 pg/2C), the latter being one of the putative progenitors of O. tuberosa identified by chloroplast‐expressed glutamine synthetase data in prior work. The remaining accessions (from 78 populations provisionally identified as 35 species) were DNA diploid, with nuclear DNA contents varying from 0·79 to 1·34 pg/2C. PMID:12102530

Amplified fragment length polymorphism fingerprints support limited gene flow among social spider populations

NARCIS (Netherlands)

Smith, Deborah; van Rijn, Sander; Henschel, Joh; Bilde, Trine; Lubin, Yael

We used DNA fingerprints to determine whether the population structure and colony composition of the cooperative social spider Stegodyphus dumicola are compatible with requirements of interdemic ('group') selection: differential proliferation of demes or groups and limited gene flow among groups. To

Inferring Allele Frequency Trajectories from Ancient DNA Indicates That Selection on a Chicken Gene Coincided with Changes in Medieval Husbandry Practices.

Science.gov (United States)

Loog, Liisa; Thomas, Mark G; Barnett, Ross; Allen, Richard; Sykes, Naomi; Paxinos, Ptolemaios D; Lebrasseur, Ophélie; Dobney, Keith; Peters, Joris; Manica, Andrea; Larson, Greger; Eriksson, Anders

2017-08-01

Ancient DNA provides an opportunity to infer the drivers of natural selection by linking allele frequency changes to temporal shifts in environment or cultural practices. However, analyses have often been hampered by uneven sampling and uncertainties in sample dating, as well as being confounded by demographic processes. Here, we present a Bayesian statistical framework for quantifying the timing and strength of selection using ancient DNA that explicitly addresses these challenges. We applied this method to time series data for two loci: TSHR and BCDO2, both hypothesised to have undergone strong and recent selection in domestic chickens. The derived variant in TSHR, associated with reduced aggression to conspecifics and faster onset of egg laying, shows strong selection beginning around 1,100 years ago, coincident with archaeological evidence for intensified chicken production and documented changes in egg and chicken consumption. To our knowledge, this is the first example of preindustrial domesticate trait selection in response to a historically attested cultural shift in food preference. For BCDO2, we find support for selection, but demonstrate that the recent rise in allele frequency could also have been driven by gene flow from imported Asian chickens during more recent breed formations. Our findings highlight that traits found ubiquitously in modern domestic species may not necessarily have originated during the early stages of domestication. In addition, our results demonstrate the importance of precise estimation of allele frequency trajectories through time for understanding the drivers of selection. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Chromosomal rearrangements and gene flow over time in an inter-specific hybrid zone of the Sorex araneus group.

Science.gov (United States)

Yannic, G; Basset, P; Hausser, J

2009-06-01

Most hybrid zones have existed for hundreds or thousands of years but have generally been observed for only a short time period. Studies extending over periods long enough to track evolutionary changes in the zones or assess the ultimate outcome of hybridization are scarce. Here, we describe the evolution over time of the level of genetic isolation between two karyotypically different species of shrews (Sorex araneus and Sorex antinorii) at a hybrid zone located in the Swiss Alps. We first evaluated hybrid zone movement by contrasting patterns of gene flow and changes in cline parameters (centre and width) using 24 microsatellite loci, between two periods separated by 10 years apart. Additionally, we tested the role of chromosomal rearrangements on gene flow by analysing microsatellite loci located on both rearranged and common chromosomes to both species. We did not detect any movement of the hybrid zone during the period analysed, suggesting that the zone is a typical tension zone. However, the gene flow was significantly lower among the rearranged than the common chromosomes for the second period, whereas the difference was only marginally significant for the first period. This further supports the role of chromosomal rearrangements on gene flow between these taxa.

Inferring species trees from gene trees in a radiation of California trapdoor spiders (Araneae, Antrodiaetidae, Aliatypus.

Directory of Open Access Journals (Sweden)

Jordan D Satler

Full Text Available The California Floristic Province is a biodiversity hotspot, reflecting a complex geologic history, strong selective gradients, and a heterogeneous landscape. These factors have led to high endemic diversity across many lifeforms within this region, including the richest diversity of mygalomorph spiders (tarantulas, trapdoor spiders, and kin in North America. The trapdoor spider genus Aliatypus encompasses twelve described species, eleven of which are endemic to California. Several Aliatypus species show disjunct distributional patterns in California (some are found on both sides of the vast Central Valley, and the genus as a whole occupies an impressive variety of habitats.We collected specimens from 89 populations representing all described species. DNA sequence data were collected from seven gene regions, including two newly developed for spider systematics. Bayesian inference (in individual gene tree and species tree approaches recovered a general "3 clade" structure for the genus (A. gulosus, californicus group, erebus group, with three other phylogenetically isolated species differing slightly in position across different phylogenetic analyses. Because of extremely high intraspecific divergences in mitochondrial COI sequences, the relatively slowly evolving 28S rRNA gene was found to be more useful than mitochondrial data for identification of morphologically indistinguishable immatures. For multiple species spanning the Central Valley, explicit hypothesis testing suggests a lack of monophyly for regional populations (e.g., western Coast Range populations. Phylogenetic evidence clearly shows that syntopy is restricted to distant phylogenetic relatives, consistent with ecological niche conservatism.This study provides fundamental insight into a radiation of trapdoor spiders found in the biodiversity hotspot of California. Species relationships are clarified and undescribed lineages are discovered, with more geographic sampling likely to

Simulating pattern-process relationships to validate landscape genetic models

Science.gov (United States)

A. J. Shirk; S. A. Cushman; E. L. Landguth

2012-01-01

Landscapes may resist gene flow and thereby give rise to a pattern of genetic isolation within a population. The mechanism by which a landscape resists gene flow can be inferred by evaluating the relationship between landscape models and an observed pattern of genetic isolation. This approach risks false inferences because researchers can never feasibly test all...

Revisiting the phylogeny of Zoanthidea (Cnidaria: Anthozoa): Staggered alignment of hypervariable sequences improves species tree inference.

Science.gov (United States)

Swain, Timothy D

2018-01-01

The recent rapid proliferation of novel taxon identification in the Zoanthidea has been accompanied by a parallel propagation of gene trees as a tool of species discovery, but not a corresponding increase in our understanding of phylogeny. This disparity is caused by the trade-off between the capabilities of automated DNA sequence alignment and data content of genes applied to phylogenetic inference in this group. Conserved genes or segments are easily aligned across the order, but produce poorly resolved trees; hypervariable genes or segments contain the evolutionary signal necessary for resolution and robust support, but sequence alignment is daunting. Staggered alignments are a form of phylogeny-informed sequence alignment composed of a mosaic of local and universal regions that allow phylogenetic inference to be applied to all nucleotides from both hypervariable and conserved gene segments. Comparisons between species tree phylogenies inferred from all data (staggered alignment) and hypervariable-excluded data (standard alignment) demonstrate improved confidence and greater topological agreement with other sources of data for the complete-data tree. This novel phylogeny is the most comprehensive to date (in terms of taxa and data) and can serve as an expandable tool for evolutionary hypothesis testing in the Zoanthidea. Spanish language abstract available in Text S1. Translation by L. O. Swain, DePaul University, Chicago, Illinois, 60604, USA. Copyright © 2017 Elsevier Inc. All rights reserved.

FARNA: knowledgebase of inferred functions of non-coding RNA transcripts

KAUST Repository

Alam, Tanvir

2016-10-12

Non-coding RNA (ncRNA) genes play a major role in control of heterogeneous cellular behavior. Yet, their functions are largely uncharacterized. Current available databases lack in-depth information of ncRNA functions across spectrum of various cells/tissues. Here, we present FARNA, a knowledgebase of inferred functions of 10,289 human ncRNA transcripts (2,734 microRNA and 7,555 long ncRNA) in 119 tissues and 177 primary cells of human. Since transcription factors (TFs) and TF co-factors (TcoFs) are crucial components of regulatory machinery for activation of gene transcription, cellular processes and diseases in which TFs and TcoFs are involved suggest functions of the transcripts they regulate. In FARNA, functions of a transcript are inferred from TFs and TcoFs whose genes co-express with the transcript controlled by these TFs and TcoFs in a considered cell/tissue. Transcripts were annotated using statistically enriched GO terms, pathways and diseases across cells/tissues based on guilt-by-association principle. Expression profiles across cells/tissues based on Cap Analysis of Gene Expression (CAGE) are provided. FARNA, having the most comprehensive function annotation of considered ncRNAs across widest spectrum of human cells/tissues, has a potential to greatly contribute to our understanding of ncRNA roles and their regulatory mechanisms in human. FARNA can be accessed at: http://cbrc.kaust.edu.sa/farna

FARNA: knowledgebase of inferred functions of non-coding RNA transcripts

KAUST Repository

Alam, Tanvir; Uludag, Mahmut; Essack, Magbubah; Salhi, Adil; Ashoor, Haitham; Hanks, John B.; Kapfer, Craig Eric; Mineta, Katsuhiko; Gojobori, Takashi; Bajic, Vladimir B.

2016-01-01

Non-coding RNA (ncRNA) genes play a major role in control of heterogeneous cellular behavior. Yet, their functions are largely uncharacterized. Current available databases lack in-depth information of ncRNA functions across spectrum of various cells/tissues. Here, we present FARNA, a knowledgebase of inferred functions of 10,289 human ncRNA transcripts (2,734 microRNA and 7,555 long ncRNA) in 119 tissues and 177 primary cells of human. Since transcription factors (TFs) and TF co-factors (TcoFs) are crucial components of regulatory machinery for activation of gene transcription, cellular processes and diseases in which TFs and TcoFs are involved suggest functions of the transcripts they regulate. In FARNA, functions of a transcript are inferred from TFs and TcoFs whose genes co-express with the transcript controlled by these TFs and TcoFs in a considered cell/tissue. Transcripts were annotated using statistically enriched GO terms, pathways and diseases across cells/tissues based on guilt-by-association principle. Expression profiles across cells/tissues based on Cap Analysis of Gene Expression (CAGE) are provided. FARNA, having the most comprehensive function annotation of considered ncRNAs across widest spectrum of human cells/tissues, has a potential to greatly contribute to our understanding of ncRNA roles and their regulatory mechanisms in human. FARNA can be accessed at: http://cbrc.kaust.edu.sa/farna

Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

Science.gov (United States)

Greenbaum, Gili; Templeton, Alan R; Bar-David, Shirli

2016-04-01

Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of

Quantitative inference of dynamic regulatory pathways via microarray data

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Chen Bor-Sen

2005-03-01

Full Text Available Abstract Background The cellular signaling pathway (network is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway are considered as the foundation of functional genomics. Thus, what genes and how much they influence each other through transcriptional binding or physical interactions are essential problems. Under the synchronous measures of gene expression via a microarray chip, an amount of dynamic information is embedded and remains to be discovered. Using a systematically dynamic modeling approach, we explore the causal relationship among genes in cellular signaling pathways from the system biology approach. Results In this study, a second-order dynamic model is developed to describe the regulatory mechanism of a target gene from the upstream causality point of view. From the expression profile and dynamic model of a target gene, we can estimate its upstream regulatory function. According to this upstream regulatory function, we would deduce the upstream regulatory genes with their regulatory abilities and activation delays, and then link up a regulatory pathway. Iteratively, these regulatory genes are considered as target genes to trace back their upstream regulatory genes. Then we could construct the regulatory pathway (or network to the genome wide. In short, we can infer the genetic regulatory pathways from gene-expression profiles quantitatively, which can confirm some doubted paths or seek some unknown paths in a regulatory pathway (network. Finally, the proposed approach is validated by randomly reshuffling the time order of microarray data. Conclusion We focus our algorithm on the inference of regulatory abilities of the identified causal genes, and how much delay before they regulate the downstream genes. With this information, a regulatory pathway would be built up using microarray data. In the present study, two signaling pathways, i.e. circadian regulatory

Fuzzy boundaries: color and gene flow patterns among parapatric lineages of the western shovel-nosed snake and taxonomic implication.

Science.gov (United States)

Wood, Dustin A; Fisher, Robert N; Vandergast, Amy G

2014-01-01

Accurate delineation of lineage diversity is increasingly important, as species distributions are becoming more reduced and threatened. During the last century, the subspecies category was often used to denote phenotypic variation within a species range and to provide a framework for understanding lineage differentiation, often considered incipient speciation. While this category has largely fallen into disuse, previously recognized subspecies often serve as important units for conservation policy and management when other information is lacking. In this study, we evaluated phenotypic subspecies hypotheses within shovel-nosed snakes on the basis of genetic data and considered how evolutionary processes such as gene flow influenced possible incongruence between phenotypic and genetic patterns. We used both traditional phylogenetic and Bayesian clustering analyses to infer range-wide genetic structure and spatially explicit analyses to detect possible boundary locations of lineage contact. Multilocus analyses supported three historically isolated groups with low to moderate levels of contemporary gene exchange. Genetic data did not support phenotypic subspecies as exclusive groups, and we detected patterns of discordance in areas where three subspecies are presumed to be in contact. Based on genetic and phenotypic evidence, we suggested that species-level diversity is underestimated in this group and we proposed that two species be recognized, Chionactis occipitalis and C. annulata. In addition, we recommend retention of two subspecific designations within C. annulata (C. a. annulata and C. a. klauberi) that reflect regional shifts in both genetic and phenotypic variation within the species. Our results highlight the difficultly in validating taxonomic boundaries within lineages that are evolving under a time-dependent, continuous process.

Inferred performance of surface hydraulic barriers from landfill operational data

International Nuclear Information System (INIS)

Gross, B.A.; Bonaparte, R.; Othman, M.A.

1997-01-01

There are few published data on the field performance of surface hydraulic barriers (SHBs) used in waste containment or remediation applications. In contrast, operational data for liner systems used beneath landfills are widely available. These data are frequently collected and reported as a facility permit condition. This paper uses leachate collection system (LCS) and leak detection system (LDS) liquid flow rate and chemical quality data collected from modem landfill double-liner systems to infer the likely hydraulic performance of SHBs. Operational data for over 200 waste management unit liner systems are currently being collected and evaluated by the authors as part of an ongoing research investigation for the United States Environmental Protection Agency (USEPA). The top liner of the double-liner system for the units is either a geomembrane (GMB) alone, geomembrane overlying a geosynthetic clay liner (GMB/GCL), or geomembrane overlying a compacted clay liner (GMB/CCL). In this paper, select data from the USEPA study are used to: (i) infer the likely efficiencies of SHBs incorporating GMBs and overlain by drainage layers; and (ii) evaluate the effectiveness of SHBs in reducing water infiltration into, and drainage from, the underlying waste (i.e., source control). SHB efficiencies are inferred from calculated landfill liner efficiencies and then used to estimate average water percolation rates through SHBs as a function of site average annual rainfall. The effectiveness of SHBs for source control is investigated by comparing LCS liquid flow rates for open and closed landfill cells. The LCS flow rates for closed cells are also compared to the estimated average water percolation rates through SHBs presented in the paper

The gene flow and mode of reproduction of Dothistroma septosporum in the Czech Republic

Czech Academy of Sciences Publication Activity Database

Tomšovský, M.; Tomešová, V.; Palovčíková, D.; Kostovčík, Martin; Rohrer, M.; Hanáček, P.; Jankovský, L.

2014-01-01

Roč. 62, č. 1 (2014), s. 59-68 ISSN 0032-0862 Institutional support: RVO:61388971 Keywords : Dothistroma * dothistroma needle blight * gene flow Subject RIV: EE - Microbiology, Virology Impact factor: 2.121, year: 2014

Scaffold filling, contig fusion and comparative gene order inference

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Rounsley Steve

2010-06-01

Full Text Available Abstract Background There has been a trend in increasing the phylogenetic scope of genome sequencing without finishing the sequence of the genome. Increasing numbers of genomes are being published in scaffold or contig form. Rearrangement algorithms, however, including gene order-based phylogenetic tools, require whole genome data on gene order or syntenic block order. How then can we use rearrangement algorithms to compare genomes available in scaffold form only? Can the comparative evidence predict the location of unsequenced genes? Results Our method involves optimally filling in genes missing from the scaffolds, while incorporating the augmented scaffolds directly into the rearrangement algorithms as if they were chromosomes. This is accomplished by an exact, polynomial-time algorithm. We then correct for the number of extra fusion/fission operations required to make scaffolds comparable to full assemblies. We model the relationship between the ratio of missing genes actually absent from the genome versus merely unsequenced ones, on one hand, and the increase of genomic distance after scaffold filling, on the other. We estimate the parameters of this model through simulations and by comparing the angiosperm genomes Ricinus communis and Vitis vinifera. Conclusions The algorithm solves the comparison of genomes with 18,300 genes, including 4500 missing from one genome, in less than a minute on a MacBook, putting virtually all genomes within range of the method.

Scaffold filling, contig fusion and comparative gene order inference.

Science.gov (United States)

Muñoz, Adriana; Zheng, Chunfang; Zhu, Qian; Albert, Victor A; Rounsley, Steve; Sankoff, David

2010-06-04

There has been a trend in increasing the phylogenetic scope of genome sequencing without finishing the sequence of the genome. Increasing numbers of genomes are being published in scaffold or contig form. Rearrangement algorithms, however, including gene order-based phylogenetic tools, require whole genome data on gene order or syntenic block order. How then can we use rearrangement algorithms to compare genomes available in scaffold form only? Can the comparative evidence predict the location of unsequenced genes? Our method involves optimally filling in genes missing from the scaffolds, while incorporating the augmented scaffolds directly into the rearrangement algorithms as if they were chromosomes. This is accomplished by an exact, polynomial-time algorithm. We then correct for the number of extra fusion/fission operations required to make scaffolds comparable to full assemblies. We model the relationship between the ratio of missing genes actually absent from the genome versus merely unsequenced ones, on one hand, and the increase of genomic distance after scaffold filling, on the other. We estimate the parameters of this model through simulations and by comparing the angiosperm genomes Ricinus communis and Vitis vinifera. The algorithm solves the comparison of genomes with 18,300 genes, including 4500 missing from one genome, in less than a minute on a MacBook, putting virtually all genomes within range of the method.

Inferring gene networks from discrete expression data

KAUST Repository

Zhang, L.; Mallick, B. K.

2013-01-01

graphical models applied to continuous data, which give a closedformmarginal likelihood. In this paper,we extend network modeling to discrete data, specifically data from serial analysis of gene expression, and RNA-sequencing experiments, both of which

Urban landscape genetics: canopy cover predicts gene flow between white-footed mouse (Peromyscus leucopus) populations in New York City.

Science.gov (United States)

Munshi-South, Jason

2012-03-01

In this study, I examine the influence of urban canopy cover on gene flow between 15 white-footed mouse (Peromyscus leucopus) populations in New York City parklands. Parks in the urban core are often highly fragmented, leading to rapid genetic differentiation of relatively nonvagile species. However, a diverse array of 'green' spaces may provide dispersal corridors through 'grey' urban infrastructure. I identify urban landscape features that promote genetic connectivity in an urban environment and compare the success of two different landscape connectivity approaches at explaining gene flow. Gene flow was associated with 'effective distances' between populations that were calculated based on per cent tree canopy cover using two different approaches: (i) isolation by effective distance (IED) that calculates the single best pathway to minimize passage through high-resistance (i.e. low canopy cover) areas, and (ii) isolation by resistance (IBR), an implementation of circuit theory that identifies all low-resistance paths through the landscape. IBR, but not IED, models were significantly associated with three measures of gene flow (Nm from F(ST) , BayesAss+ and Migrate-n) after factoring out the influence of isolation by distance using partial Mantel tests. Predicted corridors for gene flow between city parks were largely narrow, linear parklands or vegetated spaces that are not managed for wildlife, such as cemeteries and roadway medians. These results have implications for understanding the impacts of urbanization trends on native wildlife, as well as for urban reforestation efforts that aim to improve urban ecosystem processes. © 2012 Blackwell Publishing Ltd.

Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling.

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Junha Shin

Full Text Available Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life-Archaea, Bacteria, and Eukaryota-suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co

An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.

Science.gov (United States)

Liang, Ying; Liao, Bo; Zhu, Wen

2017-01-01

Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.

Genomic evidence for divergence with gene flow in host races of the larch budmoth

Czech Academy of Sciences Publication Activity Database

Emelianov, I.; Marec, František; Mallet, J.

2003-01-01

Roč. 271, - (2003), s. 97-105 ISSN 0962-8452 Institutional research plan: CEZ:AV0Z5007907 Keywords : speciation * gene flow * selection Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.544, year: 2003

Inference of the Genetic Network Regulating Lateral Root Initiation in Arabidopsis thaliana

KAUST Repository

Muraro, D.; Voss, U.; Wilson, M.; Bennett, M.; Byrne, H.; De Smet, I.; Hodgman, C.; King, J.

2013-01-01

thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based

Forecasting Water Level Fluctuations of Urmieh Lake Using Gene Expression Programming and Adaptive Neuro-Fuzzy Inference System

Directory of Open Access Journals (Sweden)

Sepideh Karimi

2012-06-01

Full Text Available Forecasting lake level at various prediction intervals is an essential issue in such industrial applications as navigation, water resource planning and catchment management. In the present study, two data driven techniques, namely Gene Expression Programming and Adaptive Neuro-Fuzzy Inference System, were applied for predicting daily lake levels for three prediction intervals. Daily water-level data from Urmieh Lake in Northwestern Iran were used to train, test and validate the used techniques. Three statistical indexes, coefficient of determination, root mean square error and variance accounted for were used to assess the performance of the used techniques. Technique inter-comparisons demonstrated that the GEP surpassed the ANFIS model at each of the prediction intervals. A traditional auto regressive moving average model was also applied to the same data sets; the obtained results were compared with those of the data driven approaches demonstrating superiority of the data driven models to ARMA.

The antibiotic resistome: gene flow in environments, animals and human beings.

Science.gov (United States)

Hu, Yongfei; Gao, George F; Zhu, Baoli

2017-06-01

The antibiotic resistance is natural in bacteria and predates the human use of antibiotics. Numerous antibiotic resistance genes (ARGs) have been discovered to confer resistance to a wide range of antibiotics. The ARGs in natural environments are highly integrated and tightly regulated in specific bacterial metabolic networks. However, the antibiotic selection pressure conferred by the use of antibiotics in both human medicine and agriculture practice leads to a significant increase of antibiotic resistance and a steady accumulation of ARGs in bacteria. In this review, we summarized, with an emphasis on an ecological point of view, the important research progress regarding the collective ARGs (antibiotic resistome) in bacterial communities of natural environments, human and animals, i.e., in the one health settings.We propose that the resistance gene flow in nature is "from the natural environments" and "to the natural environments"; human and animals, as intermediate recipients and disseminators, contribute greatly to such a resistance gene "circulation."

Models of gene gain and gene loss for probabilistic reconstruction of gene content in the last universal common ancestor of life.

Science.gov (United States)

Kannan, Lavanya; Li, Hua; Rubinstein, Boris; Mushegian, Arcady

2013-12-19

The problem of probabilistic inference of gene content in the last common ancestor of several extant species with completely sequenced genomes is: for each gene that is conserved in all or some of the genomes, assign the probability that its ancestral gene was present in the genome of their last common ancestor. We have developed a family of models of gene gain and gene loss in evolution, and applied the maximum-likelihood approach that uses phylogenetic tree of prokaryotes and the record of orthologous relationships between their genes to infer the gene content of LUCA, the Last Universal Common Ancestor of all currently living cellular organisms. The crucial parameter, the ratio of gene losses and gene gains, was estimated from the data and was higher in models that take account of the number of in-paralogs in genomes than in models that treat gene presences and absences as a binary trait. While the numbers of genes that are placed confidently into LUCA are similar in the ML methods and in previously published methods that use various parsimony-based approaches, the identities of genes themselves are different. Most of the models of either kind treat the genes found in many existing genomes in a similar way, assigning to them high probabilities of being ancestral ("high ancestrality"). The ML models are more likely than others to assign high ancestrality to the genes that are relatively rare in the present-day genomes.

Empirical phylogenies and species abundance distributions are consistent with pre-equilibrium dynamics of neutral community models with gene flow

KAUST Repository

Bonnet-Lebrun, Anne-Sophie

2017-03-17

Community characteristics reflect past ecological and evolutionary dynamics. Here, we investigate whether it is possible to obtain realistically shaped modelled communities - i.e., with phylogenetic trees and species abundance distributions shaped similarly to typical empirical bird and mammal communities - from neutral community models. To test the effect of gene flow, we contrasted two spatially explicit individual-based neutral models: one with protracted speciation, delayed by gene flow, and one with point mutation speciation, unaffected by gene flow. The former produced more realistic communities (shape of phylogenetic tree and species-abundance distribution), consistent with gene flow being a key process in macro-evolutionary dynamics. Earlier models struggled to capture the empirically observed branching tempo in phylogenetic trees, as measured by the gamma statistic. We show that the low gamma values typical of empirical trees can be obtained in models with protracted speciation, in pre-equilibrium communities developing from an initially abundant and widespread species. This was even more so in communities sampled incompletely, particularly if the unknown species are the youngest. Overall, our results demonstrate that the characteristics of empirical communities that we have studied can, to a large extent, be explained through a purely neutral model under pre-equilibrium conditions. This article is protected by copyright. All rights reserved.

Empirical phylogenies and species abundance distributions are consistent with pre-equilibrium dynamics of neutral community models with gene flow

KAUST Repository

Bonnet-Lebrun, Anne-Sophie; Manica, Andrea; Eriksson, Anders; Rodrigues, Ana S.L.

2017-01-01

Community characteristics reflect past ecological and evolutionary dynamics. Here, we investigate whether it is possible to obtain realistically shaped modelled communities - i.e., with phylogenetic trees and species abundance distributions shaped similarly to typical empirical bird and mammal communities - from neutral community models. To test the effect of gene flow, we contrasted two spatially explicit individual-based neutral models: one with protracted speciation, delayed by gene flow, and one with point mutation speciation, unaffected by gene flow. The former produced more realistic communities (shape of phylogenetic tree and species-abundance distribution), consistent with gene flow being a key process in macro-evolutionary dynamics. Earlier models struggled to capture the empirically observed branching tempo in phylogenetic trees, as measured by the gamma statistic. We show that the low gamma values typical of empirical trees can be obtained in models with protracted speciation, in pre-equilibrium communities developing from an initially abundant and widespread species. This was even more so in communities sampled incompletely, particularly if the unknown species are the youngest. Overall, our results demonstrate that the characteristics of empirical communities that we have studied can, to a large extent, be explained through a purely neutral model under pre-equilibrium conditions. This article is protected by copyright. All rights reserved.

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

Science.gov (United States)

Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

Identification of landscape features influencing gene flow: How useful are habitat selection models?

Science.gov (United States)

Gretchen H. Roffler; Michael K. Schwartz; Kristine Pilgrim; Sandra L. Talbot; George K. Sage; Layne G. Adams; Gordon Luikart

2016-01-01

Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is...

Geographical patterns of adaptation within a species' range : Interactions between drift and gene flow

NARCIS (Netherlands)

Alleaume-Benharira, M; Pen, IR; Ronce, O

We use individual-based stochastic simulations and analytical deterministic predictions to investigate the interaction between drift, natural selection and gene flow on the patterns of local adaptation across a fragmented species' range under clinally varying selection. Migration between populations

Intersectional gene flow between insular endemics of Ilex (Aquifoliaceae) on the Bonin Islands and the Ryukyu Islands.

Science.gov (United States)

Setoguchi, H; Watanabe, I

2000-06-01

Hybridization and introgression play important roles in plant evolution, and their occurrence on the oceanic islands provides good examples of plant speciation and diversification. Restriction fragment length polymorphisms (RFLPs) and trnL (UAA) 3'exon-trnF (GAA) intergenic spacer (IGS) sequences of chloroplast DNA (cpDNA), and the sequences of internal transcribed spacer (ITS) of nuclear ribosomal DNA were examined to investigate the occurrence of gene transfer in Ilex species on the Bonin Islands and the Ryukyu Islands in Japan. A gene phylogeny for the plastid genome is in agreement with the morphologically based taxonomy, whereas the nuclear genome phylogeny clusters putatively unrelated endemics both on the Bonin and the Ryukyu Islands. Intersectional hybridization and nuclear gene flow were independently observed in insular endemics of Ilex on both sets of islands without evidence of plastid introgression. Gene flow observed in these island systems can be explained by ecological features of insular endemics, i.e., limits of distribution range or sympatric distribution in a small land area.

Inference of Causal Relationships between Biomarkers and Outcomes in High Dimensions

Directory of Open Access Journals (Sweden)

Felix Agakov

2011-12-01

Full Text Available We describe a unified computational framework for learning causal dependencies between genotypes, biomarkers, and phenotypic outcomes from large-scale data. In contrast to previous studies, our framework allows for noisy measurements, hidden confounders, missing data, and pleiotropic effects of genotypes on outcomes. The method exploits the use of genotypes as “instrumental variables” to infer causal associations between phenotypic biomarkers and outcomes, without requiring the assumption that genotypic effects are mediated only through the observed biomarkers. The framework builds on sparse linear methods developed in statistics and machine learning and modified here for inferring structures of richer networks with latent variables. Where the biomarkers are gene transcripts, the method can be used for fine mapping of quantitative trait loci (QTLs detected in genetic linkage studies. To demonstrate our method, we examined effects of gene transcript levels in the liver on plasma HDL cholesterol levels in a sample of 260 mice from a heterogeneous stock.

Models of gene gain and gene loss for probabilistic reconstruction of gene content in the last universal common ancestor of life

OpenAIRE

Kannan, Lavanya; Li, Hua; Rubinstein, Boris; Mushegian, Arcady

2013-01-01

Background The problem of probabilistic inference of gene content in the last common ancestor of several extant species with completely sequenced genomes is: for each gene that is conserved in all or some of the genomes, assign the probability that its ancestral gene was present in the genome of their last common ancestor. Results We have developed a family of models of gene gain and gene loss in evolution, and applied the maximum-likelihood approach that uses phylogenetic tree of prokaryotes...

Admixture and gene flow from Russia in the recovering Northern European brown bear (Ursus arctos).

Science.gov (United States)

Kopatz, Alexander; Eiken, Hans Geir; Aspi, Jouni; Kojola, Ilpo; Tobiassen, Camilla; Tirronen, Konstantin F; Danilov, Pjotr I; Hagen, Snorre B

2014-01-01

Large carnivores were persecuted to near extinction during the last centuries, but have now recovered in some countries. It has been proposed earlier that the recovery of the Northern European brown bear is supported by migration from Russia. We tested this hypothesis by obtaining for the first time continuous sampling of the whole Finnish bear population, which is located centrally between the Russian and Scandinavian bear populations. The Finnish population is assumed to experience high gene flow from Russian Karelia. If so, no or a low degree of genetic differentiation between Finnish and Russian bears could be expected. We have genotyped bears extensively from all over Finland using 12 validated microsatellite markers and compared their genetic composition to bears from Russian Karelia, Sweden, and Norway. Our fine masked investigation identified two overlapping genetic clusters structured by isolation-by-distance in Finland (pairwise FST = 0.025). One cluster included Russian bears, and migration analyses showed a high number of migrants from Russia into Finland, providing evidence of eastern gene flow as an important driver during recovery. In comparison, both clusters excluded bears from Sweden and Norway, and we found no migrants from Finland in either country, indicating that eastern gene flow was probably not important for the population recovery in Scandinavia. Our analyses on different spatial scales suggest a continuous bear population in Finland and Russian Karelia, separated from Scandinavia.

High gene flow in epiphytic ferns despite habitat loss and fragmentation

OpenAIRE

Winkler, Manuela; Koch, Marcus; Hietz, Peter

2011-01-01

Tropical montane forests suffer from increasing fragmentation and replacement by other types of land-use such as coffee plantations. These processes are known to affect gene flow and genetic structure of plant populations. Epiphytes are particularly vulnerable because they depend on their supporting trees for their entire life-cycle. We compared population genetic structure and genetic diversity derived from AFLP markers of two epiphytic fern species differing in their ability to colonize sec...

Inference algorithms and learning theory for Bayesian sparse factor analysis

International Nuclear Information System (INIS)

Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John

2009-01-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Inference algorithms and learning theory for Bayesian sparse factor analysis

Energy Technology Data Exchange (ETDEWEB)

Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)

2009-12-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Fuzzy boundaries: color and gene flow patterns among parapatric lineages of the western shovel-nosed snake and taxonomic implication.

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Dustin A Wood

Full Text Available Accurate delineation of lineage diversity is increasingly important, as species distributions are becoming more reduced and threatened. During the last century, the subspecies category was often used to denote phenotypic variation within a species range and to provide a framework for understanding lineage differentiation, often considered incipient speciation. While this category has largely fallen into disuse, previously recognized subspecies often serve as important units for conservation policy and management when other information is lacking. In this study, we evaluated phenotypic subspecies hypotheses within shovel-nosed snakes on the basis of genetic data and considered how evolutionary processes such as gene flow influenced possible incongruence between phenotypic and genetic patterns. We used both traditional phylogenetic and Bayesian clustering analyses to infer range-wide genetic structure and spatially explicit analyses to detect possible boundary locations of lineage contact. Multilocus analyses supported three historically isolated groups with low to moderate levels of contemporary gene exchange. Genetic data did not support phenotypic subspecies as exclusive groups, and we detected patterns of discordance in areas where three subspecies are presumed to be in contact. Based on genetic and phenotypic evidence, we suggested that species-level diversity is underestimated in this group and we proposed that two species be recognized, Chionactis occipitalis and C. annulata. In addition, we recommend retention of two subspecific designations within C. annulata (C. a. annulata and C. a. klauberi that reflect regional shifts in both genetic and phenotypic variation within the species. Our results highlight the difficultly in validating taxonomic boundaries within lineages that are evolving under a time-dependent, continuous process.

Models of gene gain and gene loss for probabilistic reconstruction of gene content in the last universal common ancestor of life

Science.gov (United States)

2013-01-01

Background The problem of probabilistic inference of gene content in the last common ancestor of several extant species with completely sequenced genomes is: for each gene that is conserved in all or some of the genomes, assign the probability that its ancestral gene was present in the genome of their last common ancestor. Results We have developed a family of models of gene gain and gene loss in evolution, and applied the maximum-likelihood approach that uses phylogenetic tree of prokaryotes and the record of orthologous relationships between their genes to infer the gene content of LUCA, the Last Universal Common Ancestor of all currently living cellular organisms. The crucial parameter, the ratio of gene losses and gene gains, was estimated from the data and was higher in models that take account of the number of in-paralogs in genomes than in models that treat gene presences and absences as a binary trait. Conclusion While the numbers of genes that are placed confidently into LUCA are similar in the ML methods and in previously published methods that use various parsimony-based approaches, the identities of genes themselves are different. Most of the models of either kind treat the genes found in many existing genomes in a similar way, assigning to them high probabilities of being ancestral (“high ancestrality”). The ML models are more likely than others to assign high ancestrality to the genes that are relatively rare in the present-day genomes. Reviewers This article was reviewed by Martijn A Huynen, Toni Gabaldón and Fyodor Kondrashov. PMID:24354654

Genetic structure and gene flows within horses: a genealogical study at the french population scale.

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Pirault, Pauline; Danvy, Sophy; Verrier, Etienne; Leroy, Grégoire

2013-01-01

Since horse breeds constitute populations submitted to variable and multiple outcrossing events, we analyzed the genetic structure and gene flows considering horses raised in France. We used genealogical data, with a reference population of 547,620 horses born in France between 2002 and 2011, grouped according to 55 breed origins. On average, individuals had 6.3 equivalent generations known. Considering different population levels, fixation index decreased from an overall species FIT of 1.37%, to an average [Formula: see text] of -0.07% when considering the 55 origins, showing that most horse breeds constitute populations without genetic structure. We illustrate the complexity of gene flows existing among horse breeds, a few populations being closed to foreign influence, most, however, being submitted to various levels of introgression. In particular, Thoroughbred and Arab breeds are largely used as introgression sources, since those two populations explain together 26% of founder origins within the overall horse population. When compared with molecular data, breeds with a small level of coancestry also showed low genetic distance; the gene pool of the breeds was probably impacted by their reproducer exchanges.

Isolation with asymmetric gene flow during the nonsynchronous divergence of dry forest birds.

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Oswald, Jessica A; Overcast, Isaac; Mauck, William M; Andersen, Michael J; Smith, Brian Tilston

2017-03-01

Dry forest bird communities in South America are often fragmented by intervening mountains and rainforests, generating high local endemism. The historical assembly of dry forest communities likely results from dynamic processes linked to numerous population histories among codistributed species. Nevertheless, species may diversify in the same way through time if landscape and environmental features, or species ecologies, similarly structure populations. Here we tested whether six co-distributed taxon pairs that occur in the dry forests of the Tumbes and Marañón Valley of northwestern South America show concordant patterns and modes of diversification. We employed a genome reduction technique, double-digest restriction site-associated DNA sequencing, and obtained 4407-7186 genomewide SNPs. We estimated demographic history in each taxon pair and inferred that all pairs had the same best-fit demographic model: isolation with asymmetric gene flow from the Tumbes into the Marañón Valley, suggesting a common diversification mode. Overall, we also observed congruence in effective population size (N e ) patterns where ancestral N e were 2.9-11.0× larger than present-day Marañón Valley populations and 0.3-2.0× larger than Tumbesian populations. Present-day Marañón Valley N e was smaller than Tumbes. In contrast, we found simultaneous population isolation due to a single event to be unlikely as taxon pairs diverged over an extended period of time (0.1-2.9 Ma) with multiple nonoverlapping divergence periods. Our results show that even when populations of codistributed species asynchronously diverge, the mode of their differentiation can remain conserved over millions of years. Divergence by allopatric isolation due to barrier formation does not explain the mode of differentiation between these two bird assemblages; rather, migration of individuals occurred before and after geographic isolation. © 2017 John Wiley & Sons Ltd.

Using Semantic Association to Extend and Infer Literature-Oriented Relativity Between Terms.

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Cheng, Liang; Li, Jie; Hu, Yang; Jiang, Yue; Liu, Yongzhuang; Chu, Yanshuo; Wang, Zhenxing; Wang, Yadong

2015-01-01

Relative terms often appear together in the literature. Methods have been presented for weighting relativity of pairwise terms by their co-occurring literature and inferring new relationship. Terms in the literature are also in the directed acyclic graph of ontologies, such as Gene Ontology and Disease Ontology. Therefore, semantic association between terms may help for establishing relativities between terms in literature. However, current methods do not use these associations. In this paper, an adjusted R-scaled score (ARSS) based on information content (ARSSIC) method is introduced to infer new relationship between terms. First, set inclusion relationship between terms of ontology was exploited to extend relationships between these terms and literature. Next, the ARSS method was presented to measure relativity between terms across ontologies according to these extensional relationships. Then, the ARSSIC method using ratios of information shared of term's ancestors was designed to infer new relationship between terms across ontologies. The result of the experiment shows that ARSS identified more pairs of statistically significant terms based on corresponding gene sets than other methods. And the high average area under the receiver operating characteristic curve (0.9293) shows that ARSSIC achieved a high true positive rate and a low false positive rate. Data is available at http://mlg.hit.edu.cn/ARSSIC/.

Exploiting the Vulnerability of Flow Table Overflow in Software-Defined Network: Attack Model, Evaluation, and Defense

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Yadong Zhou

2018-01-01

Full Text Available As the most competitive solution for next-generation network, SDN and its dominant implementation OpenFlow are attracting more and more interests. But besides convenience and flexibility, SDN/OpenFlow also introduces new kinds of limitations and security issues. Of these limitations, the most obvious and maybe the most neglected one is the flow table capacity of SDN/OpenFlow switches. In this paper, we proposed a novel inference attack targeting at SDN/OpenFlow network, which is motivated by the limited flow table capacities of SDN/OpenFlow switches and the following measurable network performance decrease resulting from frequent interactions between data and control plane when the flow table is full. To the best of our knowledge, this is the first proposed inference attack model of this kind for SDN/OpenFlow. We implemented an inference attack framework according to our model and examined its efficiency and accuracy. The evaluation results demonstrate that our framework can infer the network parameters (flow table capacity and usage with an accuracy of 80% or higher. We also proposed two possible defense strategies for the discovered vulnerability, including routing aggregation algorithm and multilevel flow table architecture. These findings give us a deeper understanding of SDN/OpenFlow limitations and serve as guidelines to future improvements of SDN/OpenFlow.

Genetic structure, mating system, and long-distance gene flow in heart of palm (Euterpe edulis Mart.).

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Gaiotto, F A; Grattapaglia, D; Vencovsky, R

2003-01-01

We report a detailed analysis of the population genetic structure, mating system, and gene flow of heart of palm (Euterpe edulis Mart.-Arecaceae) in central Brazil. This palm is considered a keystone species because it supplies fruits for birds and rodents all year and is intensively harvested for culinary purposes. Two populations of this palm tree were examined, using 18 microsatellite loci. The species displays a predominantly outcrossed mating system (tm = 0.94), with a probability of full sibship greater than 70% within open-pollinated families. The following estimates of interpopulation genetic variation were calculated and found significant: FIT = 0.17, FIS = 0.12, FST = 0.06, and RST = 0.07. This low but significant level of interpopulation genetic variation indicates high levels of gene flow. Two adult trees were identified as likely seed parents (P > 99.9%) of juveniles located at a distance of 22 km. Gene flow over such distances has not been reported before for tropical tree species. The establishment and management of in situ genetic reserves or ex situ conservation and breeding populations for E. edulis should contemplate the collection of several hundreds open-pollinated maternal families from relatively few distant populations to maximize the genetic sampling of a larger number of pollen parents.

Structure, expression profile and phylogenetic inference of chalcone isomerase-like genes from the narrow-leafed lupin (Lupinus angustifolius L. genome

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Łucja ePrzysiecka

2015-04-01

Full Text Available Lupins, like other legumes, have a unique biosynthesis scheme of 5-deoxy-type flavonoids and isoflavonoids. A key enzyme in this pathway is chalcone isomerase (CHI, a member of CHI-fold protein family, encompassing subfamilies of CHI1, CHI2, CHI-like (CHIL, and fatty acid-binding (FAP proteins. Here, two Lupinus angustifolius (narrow-leafed lupin CHILs, LangCHIL1 and LangCHIL2, were identified and characterized using DNA fingerprinting, cytogenetic and linkage mapping, sequencing and expression profiling. Clones carrying CHIL sequences were assembled into two contigs. Full gene sequences were obtained from these contigs, and mapped in two L. angustifolius linkage groups by gene-specific markers. Bacterial artificial chromosome fluorescence in situ hybridization approach confirmed the localization of two LangCHIL genes in distinct chromosomes. The expression profiles of both LangCHIL isoforms were very similar. The highest level of transcription was in the roots of the third week of plant growth; thereafter, expression declined. The expression of both LangCHIL genes in leaves and stems was similar and low. Comparative mapping to reference legume genome sequences revealed strong syntenic links; however, LangCHIL2 contig had a much more conserved structure than LangCHIL1. LangCHIL2 is assumed to be an ancestor gene, whereas LangCHIL1 probably appeared as a result of duplication. As both copies are transcriptionally active, questions arise concerning their hypothetical functional divergence. Screening of the narrow-leafed lupin genome and transcriptome with CHI-fold protein sequences, followed by Bayesian inference of phylogeny and cross-genera synteny survey, identified representatives of all but one (CHI1 main subfamilies. They are as follows: two copies of CHI2, FAPa2 and CHIL, and single copies of FAPb and FAPa1. Duplicated genes are remnants of whole genome duplication which is assumed to have occurred after the divergence of Lupinus, Arachis

Highly Pathogenic H5N1 Avian Influenza Viruses Exhibit Few Barriers to Gene Flow in Vietnam

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Carrel, Margaret; Wan, Xiu-Feng; Nguyen, Tung; Emch, Michael

2013-01-01

Locating areas where genetic change is inhibited can illuminate underlying processes that drive evolution of pathogens. The persistence of highly pathogenic H5N1 avian influenza in Vietnam since 2003, and the continuous molecular evolution of Vietnamese avian influenza viruses, indicates that local environmental factors are supportive not only of incidence but also of viral adaptation. This article explores whether gene flow is constant across Vietnam, or whether there exist boundary areas where gene flow exhibits discontinuity. Using a dataset of 125 highly pathogenic H5N1 avian influenza viruses, principal components analysis and wombling analysis are used to indicate the location, magnitude, and statistical significance of genetic boundaries. Results show that a small number of geographically minor boundaries to gene flow in highly pathogenic H5N1 avian influenza viruses exist in Vietnam, but that overall there is little division in genetic exchange. This suggests that differences in genetic characteristics of viruses from one region to another are not the result of barriers to H5N1 viral exchange in Vietnam, and that H5N1 avian influenza is able to spread relatively unimpeded across the country. PMID:22350419

Limited contemporary gene flow and high self-replenishment drives peripheral isolation in an endemic coral reef fish.

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van der Meer, Martin H; Horne, John B; Gardner, Michael G; Hobbs, Jean-Paul A; Pratchett, Morgan; van Herwerden, Lynne

2013-06-01

Extensive ongoing degradation of coral reef habitats worldwide has lead to declines in abundance of coral reef fishes and local extinction of some species. Those most vulnerable are ecological specialists and endemic species. Determining connectivity between locations is vital to understanding recovery and long-term persistence of these species following local extinction. This study explored population connectivity in the ecologically-specialized endemic three-striped butterflyfish (Chaetodon tricinctus) using mt and msatDNA (nuclear microsatellites) to distinguish evolutionary versus contemporary gene flow, estimate self-replenishment and measure genetic diversity among locations at the remote Australian offshore coral reefs of Middleton Reef (MR), Elizabeth Reef (ER), Lord Howe Island (LHI), and Norfolk Island (NI). Mt and msatDNA suggested genetic differentiation of the most peripheral location (NI) from the remaining three locations (MR, ER, LHI). Despite high levels of mtDNA gene flow, there is limited msatDNA gene flow with evidence of high levels of self-replenishment (≥76%) at all four locations. Taken together, this suggests prolonged population recovery times following population declines. The peripheral population (NI) is most vulnerable to local extinction due to its relative isolation, extreme levels of self-replenishment (95%), and low contemporary abundance.

Entropic Inference

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Caticha, Ariel

2011-03-01

In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.

Tocantins river as an effective barrier to gene flow in Saguinus niger populations

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Marcelo Vallinoto

2006-01-01

Full Text Available The Saguinus represent the basal genus of the Callitrichinae subfamily. Traditionally this genus is divided into three groups: Hairy, Mottled and Bare-face, however, molecular data failed to validate these groups as monophyletic units, as well as raised some subspecies to the species status. This is the case of the former subspecies Saguinus midas midas and S. midas niger, which are now considered as different species. In the present study, we sequenced a portion of the D-loop mtDNA region in populations from the East bank of the Xingu and from both banks of the Tocantins river, in order to test the effectiveness of large rivers as barriers to the gene flow in Saguinus. According to our results, the populations from the East and West banks of the Tocantins river are more divergent than true species like S. mystax and S. imperator. The Tocantins river may be acting as a barrier to gene flow, and consequently these very divergent populations may represent distinct taxonomic entities (species?.

Adaptive population divergence and directional gene flow across steep elevational gradients in a climate-sensitive mammal.

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Waterhouse, Matthew D; P Erb, Liesl; Beever, Erik A; Russello, Michael A

2018-04-25

The ecological effects of climate change have been shown in most major taxonomic groups; however, the evolutionary consequences are less well-documented. Adaptation to new climatic conditions offers a potential long-term mechanism for species to maintain viability in rapidly changing environments, but mammalian examples remain scarce. The American pika (Ochotona princeps) has been impacted by recent climate-associated extirpations and range-wide reductions in population sizes, establishing it as a sentinel mammalian species for climate change. To investigate evidence for local adaptation and reconstruct patterns of genomic diversity and gene flow across rapidly changing environments, we used a space-for-time design and restriction site-associated DNA sequencing to genotype American pikas along two steep elevational gradients at 30,966 SNPs and employed independent outlier detection methods that scanned for genotype-environment associations. We identified 338 outlier SNPs detected by two separate analyses and/or replicated in both transects, several of which were annotated to genes involved in metabolic function and oxygen transport. Additionally, we found evidence of directional gene flow primarily downslope from high-elevation populations, along with reduced gene flow at outlier loci. If this trend continues, elevational range contractions in American pikas will likely be from local extirpation rather than upward movement of low-elevation individuals; this, in turn, could limit the potential for adaptation within this landscape. These findings are of particular relevance for future conservation and management of American pikas and other elevationally-restricted, thermally-sensitive species. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Flow Cytometry-Assisted Cloning of Specific Sequence Motifs from Complex 16S rRNA Gene Libraries

DEFF Research Database (Denmark)

Nielsen, Jeppe Lund; Schramm, Andreas; Bernhard, Anne E.

2004-01-01

for Systems Biology,3 Seattle, Washington, and Department of Ecological Microbiology, University of Bayreuth, Bayreuth, Germany2 A flow cytometry method was developed for rapid screening and recovery of cloned DNA containing common sequence motifs. This approach, termed fluorescence-activated cell sorting......  FLOW CYTOMETRY-ASSISTED CLONING OF SPECIFIC SEQUENCE MOTIFS FROM COMPLEX 16S RRNA GENE LIBRARIES Jeppe L. Nielsen,1 Andreas Schramm,1,2 Anne E. Bernhard,1 Gerrit J. van den Engh,3 and David A. Stahl1* Department of Civil and Environmental Engineering, University of Washington,1 and Institute......-assisted cloning, was used to recover sequences affiliated with a unique lineage within the Bacteroidetes not abundant in a clone library of environmental 16S rRNA genes.  ...

Mathematical inference and control of molecular networks from perturbation experiments

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Mohammed-Rasheed, Mohammed

One of the main challenges facing biologists and mathematicians in the post genomic era is to understand the behavior of molecular networks and harness this understanding into an educated intervention of the cell. The cell maintains its function via an elaborate network of interconnecting positive and negative feedback loops of genes, RNA and proteins that send different signals to a large number of pathways and molecules. These structures are referred to as genetic regulatory networks (GRNs) or molecular networks. GRNs can be viewed as dynamical systems with inherent properties and mechanisms, such as steady-state equilibriums and stability, that determine the behavior of the cell. The biological relevance of the mathematical concepts are important as they may predict the differentiation of a stem cell, the maintenance of a normal cell, the development of cancer and its aberrant behavior, and the design of drugs and response to therapy. Uncovering the underlying GRN structure from gene/protein expression data, e.g., microarrays or perturbation experiments, is called inference or reverse engineering of the molecular network. Because of the high cost and time consuming nature of biological experiments, the number of available measurements or experiments is very small compared to the number of molecules (genes, RNA and proteins). In addition, the observations are noisy, where the noise is due to the measurements imperfections as well as the inherent stochasticity of genetic expression levels. Intra-cellular activities and extra-cellular environmental attributes are also another source of variability. Thus, the inference of GRNs is, in general, an under-determined problem with a highly noisy set of observations. The ultimate goal of GRN inference and analysis is to be able to intervene within the network, in order to force it away from undesirable cellular states and into desirable ones. However, it remains a major challenge to design optimal intervention strategies

Inferring ontology graph structures using OWL reasoning

KAUST Repository

Rodriguez-Garcia, Miguel Angel

2018-01-05

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies\\' semantic content remains a challenge.We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies\\' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph .Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Inferring ontology graph structures using OWL reasoning.

Science.gov (United States)

Rodríguez-García, Miguel Ángel; Hoehndorf, Robert

2018-01-05

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Invasion of Ancestral Mammals into Dim-light Environments Inferred from Adaptive Evolution of the Phototransduction Genes.

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Wu, Yonghua; Wang, Haifeng; Hadly, Elizabeth A

2017-04-20

Nocturnality is a key evolutionary innovation of mammals that enables mammals to occupy relatively empty nocturnal niches. Invasion of ancestral mammals into nocturnality has long been inferred from the phylogenetic relationships of crown Mammalia, which is primarily nocturnal, and crown Reptilia, which is primarily diurnal, although molecular evidence for this is lacking. Here we used phylogenetic analyses of the vision genes involved in the phototransduction pathway to predict the diel activity patterns of ancestral mammals and reptiles. Our results demonstrated that the common ancestor of the extant Mammalia was dominated by positive selection for dim-light vision, supporting the predominate nocturnality of the ancestral mammals. Further analyses showed that the nocturnality of the ancestral mammals was probably derived from the predominate diurnality of the ancestral amniotes, which featured strong positive selection for bright-light vision. Like the ancestral amniotes, the common ancestor of the extant reptiles and various taxa in Squamata, one of the main competitors of the temporal niches of the ancestral mammals, were found to be predominate diurnality as well. Despite this relatively apparent temporal niche partitioning between ancestral mammals and the relevant reptiles, our results suggested partial overlap of their temporal niches during crepuscular periods.

Molecular inference of sources and spreading patterns of Plasmodium falciparum malaria parasites in internally displaced persons settlements in Myanmar-China border area.

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Lo, Eugenia; Zhou, Guofa; Oo, Winny; Lee, Ming-Chieh; Baum, Elisabeth; Felgner, Philip L; Yang, Zhaoqing; Cui, Liwang; Yan, Guiyun

2015-07-01

In Myanmar, civil unrest and establishment of internally displaced persons (IDP) settlement along the Myanmar-China border have impacted malaria transmission. The growing IDP populations raise deep concerns about health impact on local communities. Microsatellite markers were used to examine the source and spreading patterns of Plasmodium falciparum between IDP settlement and surrounding villages in Myanmar along the China border. Genotypic structure of P. falciparum was compared over the past three years from the same area and the demographic history was inferred to determine the source of recent infections. In addition, we examined if border migration is a factor of P. falciparum infections in China by determining gene flow patterns across borders. Compared to local community, the IDP samples showed a reduced and consistently lower genetic diversity over the past three years. A strong signature of genetic bottleneck was detected in the IDP samples. P. falciparum infections from the border regions in China were genetically similar to Myanmar and parasite gene flow was not constrained by geographical distance. Reduced genetic diversity of P. falciparum suggested intense malaria control within the IDP settlement. Human movement was a key factor to the spread of malaria both locally in Myanmar and across the international border. Copyright © 2015 Elsevier B.V. All rights reserved.

More than one kind of inference: re-examining what's learned in feature inference and classification.

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Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

Perceptual inference.

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Aggelopoulos, Nikolaos C

2015-08-01

Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience. Copyright © 2015 Elsevier Ltd. All rights reserved.

The impact of selection, gene flow and demographic history on heterogeneous genomic divergence: three-spine sticklebacks in divergent environments.

Science.gov (United States)

Ferchaud, Anne-Laure; Hansen, Michael M

2016-01-01

Heterogeneous genomic divergence between populations may reflect selection, but should also be seen in conjunction with gene flow and drift, particularly population bottlenecks. Marine and freshwater three-spine stickleback (Gasterosteus aculeatus) populations often exhibit different lateral armour plate morphs. Moreover, strikingly parallel genomic footprints across different marine-freshwater population pairs are interpreted as parallel evolution and gene reuse. Nevertheless, in some geographic regions like the North Sea and Baltic Sea, different patterns are observed. Freshwater populations in coastal regions are often dominated by marine morphs, suggesting that gene flow overwhelms selection, and genomic parallelism may also be less pronounced. We used RAD sequencing for analysing 28 888 SNPs in two marine and seven freshwater populations in Denmark, Europe. Freshwater populations represented a variety of environments: river populations accessible to gene flow from marine sticklebacks and large and small isolated lakes with and without fish predators. Sticklebacks in an accessible river environment showed minimal morphological and genomewide divergence from marine populations, supporting the hypothesis of gene flow overriding selection. Allele frequency spectra suggested bottlenecks in all freshwater populations, and particularly two small lake populations. However, genomic footprints ascribed to selection could nevertheless be identified. No genomic regions were consistent freshwater-marine outliers, and parallelism was much lower than in other comparable studies. Two genomic regions previously described to be under divergent selection in freshwater and marine populations were outliers between different freshwater populations. We ascribe these patterns to stronger environmental heterogeneity among freshwater populations in our study as compared to most other studies, although the demographic history involving bottlenecks should also be considered in the

High seed dispersal ability of Pinus canariensis in stands of contrasting density inferred from genotypic data

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Unai López de Heredia

2015-04-01

Full Text Available Aim of the study: Models that combine parentage analysis from molecular data with spatial information of seeds and seedlings provide a framework to describe and identify the factors involved in seed dispersal and recruitment of forest species. In the present study we used a spatially explicit method (the gene shadow model in order to assess primary and effective dispersal in Pinus canariensis. Area of study: Pinus canariensis is endemic to the Canary Islands (Spain. Sampling sites were a high density forest in southern slopes of Tenerife and a low density stand in South Gran Canaria. Materials and methods: We fitted models based on parentage analysis from seeds and seedlings collected in two sites with contrasting stand density, and then compared the resulting dispersal distributions. Main results: The results showed that: 1 P. canariensis has a remarkable dispersal ability compared to other pine species; 2 there is no discordance between primary and effective dispersals, suggesting limited secondary dispersal by animals and lack of Janzen-Connell effect; and 3 low stand densities enhance the extent of seed dispersal, which was higher in the low density stand. Research highlights: The efficient dispersal mechanism of P. canariensis by wind inferred by the gene shadow model is congruent with indirect measures of gene flow, and has utility in reconstructing past demographic events and in predicting future distribution ranges for the species.

Mantle Circulation Models with variational data assimilation: Inferring past mantle flow and structure from plate motion histories and seismic tomography

Science.gov (United States)

Bunge, H.; Hagelberg, C.; Travis, B.

2002-12-01

-Cretaceous mantle structure can be inferred accurately from our inverse approach assuming present-day mantle structure is well-known, even if an initial first guess assumption about the mid-Cretaceous mantle involved only a simple 1-D radial temperature profile. We suggest that geodynamic inverse modeling should make it possible to infer a number of flow parameters from observational constraints of the mantle.

SEMANTIC PATCH INFERENCE

DEFF Research Database (Denmark)

Andersen, Jesper

2009-01-01

Collateral evolution the problem of updating several library-using programs in response to API changes in the used library. In this dissertation we address the issue of understanding collateral evolutions by automatically inferring a high-level specification of the changes evident in a given set ...... specifications inferred by spdiff in Linux are shown. We find that the inferred specifications concisely capture the actual collateral evolution performed in the examples....

Adaptive traits are maintained on steep selective gradients despite gene flow and hybridization in the intertidal zone.

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Gerardo I Zardi

Full Text Available Gene flow among hybridizing species with incomplete reproductive barriers blurs species boundaries, while selection under heterogeneous local ecological conditions or along strong gradients may counteract this tendency. Congeneric, externally-fertilizing fucoid brown algae occur as distinct morphotypes along intertidal exposure gradients despite gene flow. Combining analyses of genetic and phenotypic traits, we investigate the potential for physiological resilience to emersion stressors to act as an isolating mechanism in the face of gene flow. Along vertical exposure gradients in the intertidal zone of Northern Portugal and Northwest France, the mid-low shore species Fucus vesiculosus, the upper shore species Fucus spiralis, and an intermediate distinctive morphotype of F. spiralis var. platycarpus were morphologically characterized. Two diagnostic microsatellite loci recovered 3 genetic clusters consistent with prior morphological assignment. Phylogenetic analysis based on single nucleotide polymorphisms in 14 protein coding regions unambiguously resolved 3 clades; sympatric F. vesiculosus, F. spiralis, and the allopatric (in southern Iberia population of F. spiralis var. platycarpus. In contrast, the sympatric F. spiralis var. platycarpus (from Northern Portugal was distributed across the 3 clades, strongly suggesting hybridization/introgression with both other entities. Common garden experiments showed that physiological resilience following exposure to desiccation/heat stress differed significantly between the 3 sympatric genetic taxa; consistent with their respective vertical distribution on steep environmental clines in exposure time. Phylogenetic analyses indicate that F. spiralis var. platycarpus is a distinct entity in allopatry, but that extensive gene flow occurs with both higher and lower shore species in sympatry. Experimental results suggest that strong selection on physiological traits across steep intertidal exposure gradients

Gene delivery by microfluidic flow-through electroporation based on constant DC and AC field.

Science.gov (United States)

Geng, Tao; Zhan, Yihong; Lu, Chang

2012-01-01

Electroporation is one of the most widely used physical methods to deliver exogenous nucleic acids into cells with high efficiency and low toxicity. Conventional electroporation systems typically require expensive pulse generators to provide short electrical pulses at high voltage. In this work, we demonstrate a flow-through electroporation method for continuous transfection of cells based on disposable chips, a syringe pump, and a low-cost power supply that provides a constant voltage. We successfully transfect cells using either DC or AC voltage with high flow rates (ranging from 40 µl/min to 20 ml/min) and high efficiency (up to 75%). We also enable the entire cell membrane to be uniformly permeabilized and dramatically improve gene delivery by inducing complex migrations of cells during the flow.

Probabilistic Inference of Biological Networks via Data Integration

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Mark F. Rogers

2015-01-01

Full Text Available There is significant interest in inferring the structure of subcellular networks of interaction. Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links. Many types of data are relevant to inferring functional links between genes, motivating the use of data integration. We use pairwise kernels to predict novel links, along with multiple kernel learning to integrate distinct sources of data into a decision function. We evaluate various pairwise kernels to establish which are most informative and compare individual kernel accuracies with accuracies for weighted combinations. By associating a probability measure with classifier predictions, we enable cautious classification, which can increase accuracy by restricting predictions to high-confidence instances, and data cleaning that can mitigate the influence of mislabeled training instances. Although one pairwise kernel (the tensor product pairwise kernel appears to work best, different kernels may contribute complimentary information about interactions: experiments in S. cerevisiae (yeast reveal that a weighted combination of pairwise kernels applied to different types of data yields the highest predictive accuracy. Combined with cautious classification and data cleaning, we can achieve predictive accuracies of up to 99.6%.

Genetic structure and gene flows within horses: a genealogical study at the french population scale.

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Pauline Pirault

Full Text Available Since horse breeds constitute populations submitted to variable and multiple outcrossing events, we analyzed the genetic structure and gene flows considering horses raised in France. We used genealogical data, with a reference population of 547,620 horses born in France between 2002 and 2011, grouped according to 55 breed origins. On average, individuals had 6.3 equivalent generations known. Considering different population levels, fixation index decreased from an overall species FIT of 1.37%, to an average [Formula: see text] of -0.07% when considering the 55 origins, showing that most horse breeds constitute populations without genetic structure. We illustrate the complexity of gene flows existing among horse breeds, a few populations being closed to foreign influence, most, however, being submitted to various levels of introgression. In particular, Thoroughbred and Arab breeds are largely used as introgression sources, since those two populations explain together 26% of founder origins within the overall horse population. When compared with molecular data, breeds with a small level of coancestry also showed low genetic distance; the gene pool of the breeds was probably impacted by their reproducer exchanges.

Revealing less derived nature of cartilaginous fish genomes with their evolutionary time scale inferred with nuclear genes.

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Adina J Renz

Full Text Available Cartilaginous fishes, divided into Holocephali (chimaeras and Elasmoblanchii (sharks, rays and skates, occupy a key phylogenetic position among extant vertebrates in reconstructing their evolutionary processes. Their accurate evolutionary time scale is indispensable for better understanding of the relationship between phenotypic and molecular evolution of cartilaginous fishes. However, our current knowledge on the time scale of cartilaginous fish evolution largely relies on estimates using mitochondrial DNA sequences. In this study, making the best use of the still partial, but large-scale sequencing data of cartilaginous fish species, we estimate the divergence times between the major cartilaginous fish lineages employing nuclear genes. By rigorous orthology assessment based on available genomic and transcriptomic sequence resources for cartilaginous fishes, we selected 20 protein-coding genes in the nuclear genome, spanning 2973 amino acid residues. Our analysis based on the Bayesian inference resulted in the mean divergence time of 421 Ma, the late Silurian, for the Holocephali-Elasmobranchii split, and 306 Ma, the late Carboniferous, for the split between sharks and rays/skates. By applying these results and other documented divergence times, we measured the relative evolutionary rate of the Hox A cluster sequences in the cartilaginous fish lineages, which resulted in a lower substitution rate with a factor of at least 2.4 in comparison to tetrapod lineages. The obtained time scale enables mapping phenotypic and molecular changes in a quantitative framework. It is of great interest to corroborate the less derived nature of cartilaginous fish at the molecular level as a genome-wide phenomenon.

Multimodel inference and adaptive management

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Rehme, S.E.; Powell, L.A.; Allen, Craig R.

2011-01-01

Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.

A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region

Science.gov (United States)

He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun

2014-02-01

Data driven models are very useful for river flow forecasting when the underlying physical relationships are not fully understand, but it is not clear whether these data driven models still have a good performance in the small river basin of semiarid mountain regions where have complicated topography. In this study, the potential of three different data driven methods, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for forecasting river flow in the semiarid mountain region, northwestern China. The models analyzed different combinations of antecedent river flow values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANN, ANFIS and SVM models in training and validation sets are compared with the observed data. The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting. To get more accurate evaluation of the results of ANN, ANFIS and SVM models, the four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and mean absolute relative error (MARE), were employed to evaluate the performances of various models developed. The results indicate that the performance obtained by ANN, ANFIS and SVM in terms of different evaluation criteria during the training and validation period does not vary substantially; the performance of the ANN, ANFIS and SVM models in river flow forecasting was satisfactory. A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets. The results also suggest that ANN, ANFIS and SVM method can be successfully applied to establish river flow with complicated topography forecasting models in the semiarid mountain regions.

Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

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Christopher E Hart

2006-12-01

Full Text Available A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes are explicitly disfavored in one network module (G2, relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of

Lineage diversification of fringe-toed lizards (Phrynosomatidae: Uma notata complex) in the Colorado Desert: Delimiting species in the presence of gene flow

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Gottscho, Andrew D.; Wood, Dustin A.; Vandergast, Amy; Lemos Espinal, Julio A.; Gatesy, John; Reeder, Tod

2017-01-01

Multi-locus nuclear DNA data were used to delimit species of fringe-toed lizards of theUma notata complex, which are specialized for living in wind-blown sand habitats in the deserts of southwestern North America, and to infer whether Quaternary glacial cycles or Tertiary geological events were important in shaping the historical biogeography of this group. We analyzed ten nuclear loci collected using Sanger sequencing and genome-wide sequence and single-nucleotide polymorphism (SNP) data collected using restriction-associated DNA (RAD) sequencing. A combination of species discovery methods (concatenated phylogenies, parametric and non-parametric clustering algorithms) and species validation approaches (coalescent-based species tree/isolation-with-migration models) were used to delimit species, infer phylogenetic relationships, and to estimate effective population sizes, migration rates, and speciation times. Uma notata, U. inornata, U. cowlesi, and an undescribed species from Mohawk Dunes, Arizona (U. sp.) were supported as distinct in the concatenated analyses and by clustering algorithms, and all operational taxonomic units were decisively supported as distinct species by ranking hierarchical nested speciation models with Bayes factors based on coalescent-based species tree methods. However, significant unidirectional gene flow (2NM >1) from U. cowlesi and U. notata into U. rufopunctata was detected under the isolation-with-migration model. Therefore, we conservatively delimit four species-level lineages within this complex (U. inornata, U. notata, U. cowlesi, and U. sp.), treating U. rufopunctata as a hybrid population (U. notata x cowlesi). Both concatenated and coalescent-based estimates of speciation times support the hypotheses that speciation within the complex occurred during the late Pleistocene, and that the geological evolution of the Colorado River delta during this period was an important process shaping the observed phylogeographic patterns.

Prokaryotic Phylogenies Inferred from Whole-Genome Sequence and Annotation Data

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Wei Du

2013-01-01

Full Text Available Phylogenetic trees are used to represent the evolutionary relationship among various groups of species. In this paper, a novel method for inferring prokaryotic phylogenies using multiple genomic information is proposed. The method is called CGCPhy and based on the distance matrix of orthologous gene clusters between whole-genome pairs. CGCPhy comprises four main steps. First, orthologous genes are determined by sequence similarity, genomic function, and genomic structure information. Second, genes involving potential HGT events are eliminated, since such genes are considered to be the highly conserved genes across different species and the genes located on fragments with abnormal genome barcode. Third, we calculate the distance of the orthologous gene clusters between each genome pair in terms of the number of orthologous genes in conserved clusters. Finally, the neighbor-joining method is employed to construct phylogenetic trees across different species. CGCPhy has been examined on different datasets from 617 complete single-chromosome prokaryotic genomes and achieved applicative accuracies on different species sets in agreement with Bergey's taxonomy in quartet topologies. Simulation results show that CGCPhy achieves high average accuracy and has a low standard deviation on different datasets, so it has an applicative potential for phylogenetic analysis.

Measurement and inference of profile soil-water dynamics at different hillslope positions in a semiarid agricultural watershed

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Green, Timothy R.; Erskine, Robert H.

2011-12-01

Dynamics of profile soil water vary with terrain, soil, and plant characteristics. The objectives addressed here are to quantify dynamic soil water content over a range of slope positions, infer soil profile water fluxes, and identify locations most likely influenced by multidimensional flow. The instrumented 56 ha watershed lies mostly within a dryland (rainfed) wheat field in semiarid eastern Colorado. Dielectric capacitance sensors were used to infer hourly soil water content for approximately 8 years (minus missing data) at 18 hillslope positions and four or more depths. Based on previous research and a new algorithm, sensor measurements (resonant frequency) were rescaled to estimate soil permittivity, then corrected for temperature effects on bulk electrical conductivity before inferring soil water content. Using a mass-conservation method, we analyzed multitemporal changes in soil water content at each sensor to infer the dynamics of water flux at different depths and landscape positions. At summit positions vertical processes appear to control profile soil water dynamics. At downslope positions infrequent overland flow and unsaturated subsurface lateral flow appear to influence soil water dynamics. Crop water use accounts for much of the variability in soil water between transects that are either cropped or fallow in alternating years, while soil hydraulic properties and near-surface hydrology affect soil water variability across landscape positions within each management zone. The observed spatiotemporal patterns exhibit the joint effects of short-term hydrology and long-term soil development. Quantitative methods of analyzing soil water patterns in space and time improve our understanding of dominant soil hydrological processes and provide alternative measures of model performance.

Intergenic DNA sequences from the human X chromosome reveal high rates of global gene flow

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Wall Jeffrey D

2008-11-01

Full Text Available Abstract Background Despite intensive efforts devoted to collecting human polymorphism data, little is known about the role of gene flow in the ancestry of human populations. This is partly because most analyses have applied one of two simple models of population structure, the island model or the splitting model, which make unrealistic biological assumptions. Results Here, we analyze 98-kb of DNA sequence from 20 independently evolving intergenic regions on the X chromosome in a sample of 90 humans from six globally diverse populations. We employ an isolation-with-migration (IM model, which assumes that populations split and subsequently exchange migrants, to independently estimate effective population sizes and migration rates. While the maximum effective size of modern humans is estimated at ~10,000, individual populations vary substantially in size, with African populations tending to be larger (2,300–9,000 than non-African populations (300–3,300. We estimate mean rates of bidirectional gene flow at 4.8 × 10-4/generation. Bidirectional migration rates are ~5-fold higher among non-African populations (1.5 × 10-3 than among African populations (2.7 × 10-4. Interestingly, because effective sizes and migration rates are inversely related in African and non-African populations, population migration rates are similar within Africa and Eurasia (e.g., global mean Nm = 2.4. Conclusion We conclude that gene flow has played an important role in structuring global human populations and that migration rates should be incorporated as critical parameters in models of human demography.

Discovering time-lagged rules from microarray data using gene profile classifiers

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Ponzoni Ignacio

2011-04-01

Full Text Available Abstract Background Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes. Results This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2, which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations. Conclusions A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.

Occurrence of alfalfa (Medicago sativa L.) populations along roadsides in southern Manitoba, Canada and their potential role in intraspecific gene flow.

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Bagavathiannan, Muthukumar V; Gulden, Robert H; Van Acker, Rene C

2011-04-01

Alfalfa is a highly outcrossing perennial species that can be noticed in roadsides as feral populations. There remains little information available on the extent of feral alfalfa populations in western Canadian prairies and their role in gene flow. The main objectives of this study were (a) to document the occurrence of feral alfalfa populations, and (b) to estimate the levels of outcrossing facilitated by feral populations. A roadside survey confirmed widespread occurrence of feral alfalfa populations, particularly in alfalfa growing regions. The feral populations were dynamic and their frequency ranged from 0.2 to 1.7 populations km(-1). In many cases, the nearest feral alfalfa population from alfalfa production field was located within a distance sufficient for outcrossing in alfalfa. The gene flow study confirmed that genes can move back and forth between feral and cultivated alfalfa populations. In this study, the estimated outcrossing levels were 62% (seed fields to feral), 78% (feral to seed fields), 82% (hay fields to feral) and 85% (feral to feral). Overall, the results show that feral alfalfa plants are prevalent in alfalfa producing regions in western Canada and they can serve as bridges for gene flow at landscape level. Management of feral populations should be considered, if gene flow is a concern. Emphasis on preventing seed spill/escapes and intentional roadside planting of alfalfa cultivars will be particularly helpful. Further, realistic and pragmatic threshold levels should be established for markets sensitive to the presence of GE traits.

Inferring kangaroo phylogeny from incongruent nuclear and mitochondrial genes.

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Matthew J Phillips

Full Text Available The marsupial genus Macropus includes three subgenera, the familiar large grazing kangaroos and wallaroos of M. (Macropus and M. (Osphranter, as well as the smaller mixed grazing/browsing wallabies of M. (Notamacropus. A recent study of five concatenated nuclear genes recommended subsuming the predominantly browsing Wallabia bicolor (swamp wallaby into Macropus. To further examine this proposal we sequenced partial mitochondrial genomes for kangaroos and wallabies. These sequences strongly favour the morphological placement of W. bicolor as sister to Macropus, although place M. irma (black-gloved wallaby within M. (Osphranter rather than as expected, with M. (Notamacropus. Species tree estimation from separately analysed mitochondrial and nuclear genes favours retaining Macropus and Wallabia as separate genera. A simulation study finds that incomplete lineage sorting among nuclear genes is a plausible explanation for incongruence with the mitochondrial placement of W. bicolor, while mitochondrial introgression from a wallaroo into M. irma is the deepest such event identified in marsupials. Similar such coalescent simulations for interpreting gene tree conflicts will increase in both relevance and statistical power as species-level phylogenetics enters the genomic age. Ecological considerations in turn, hint at a role for selection in accelerating the fixation of introgressed or incompletely sorted loci. More generally the inclusion of the mitochondrial sequences substantially enhanced phylogenetic resolution. However, we caution that the evolutionary dynamics that enhance mitochondria as speciation indicators in the presence of incomplete lineage sorting may also render them especially susceptible to introgression.

Testing the effect of the Himalayan mountains as a physical barrier to gene flow in Hippophae tibetana Schlect. (Elaeagnaceae.

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La Qiong

Full Text Available Hippophae tibetana is a small, dioecious wind-pollinated shrub endemic to the Tibetan-Qinghai Plateau. It is one of the shrubs that occur at very high elevations (5250 m a.s.l.. The Himalayan mountains provides a significant geographical barrier to the Qinghai-Tibetan Plateau, dividing the Himalayan area into two regions with Nepal to the south and Tibet to the north. There is no information on how the Himalayan mountains influence gene flow and population differentiation of alpine plants. In this study, we analyzed eight nuclear microsatellite markers and cpDNA trnT-trnF regions to test the role of the Himalayan mountains as a barrier to gene flow between populations of H. tibetana. We also examined the fine-scale genetic structure within a population of H. tibetana on the north slope of Mount (Mt. Everest. For microsatellite analyses, a total of 241 individuals were sampled from seven populations in our study area (4 from Nepal, 3 from Tibet, including 121 individuals that were spatially mapped within a 100 m × 100 m plot. To test for seed flow, the cpDNA trnT-trnF regions of 100 individuals from 6 populations (4 from Nepal, 2 from Tibet were also sequenced. Significant genetic differentiation was detected between the two regions by both microsatellite and cpDNA data analyses. These two datasets agree about southern and northern population differentiation, indicating that the Himalayan mountains represent a barrier to H. tibetana limiting gene flow between these two areas. At a fine scale, spatial autocorrelation analysis suggests significant genetic structure within a distance of less than 45 m, which may be attributed mainly to vegetative reproduction and habitat fragmentation, as well as limited gene flow.

Xp21 contiguous gene syndromes: Deletion quantitation with bivariate flow karyotyping allows mapping of patient breakpoints

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McCabe, E.R.B.; Towbin, J.A. (Baylor College of Medicine, Houston, TX (United States)); Engh, G. van den; Trask, B.J. (Lawrence Livermore National Lab., CA (United States))

1992-12-01

Bivariate flow karyotyping was used to estimate the deletion sizes for a series of patients with Xp21 contiguous gene syndromes. The deletion estimates were used to develop an approximate scale for the genomic map in Xp21. The bivariate flow karyotype results were compared with clinical and molecular genetic information on the extent of the patients' deletions, and these various types of data were consistent. The resulting map spans >15 Mb, from the telomeric interval between DXS41 (99-6) and DXS68 (1-4) to a position centromeric to the ornithine transcarbamylase locus. The deletion sizing was considered to be accurate to [plus minus]1 Mb. The map provides information on the relative localization of genes and markers within this region. For example, the map suggests that the adrenal hypoplasia congenita and glycerol kinase genes are physically close to each other, are within 1-2 Mb of the telomeric end of the Duchenne muscular dystrophy (DMD) gene, and are nearer to the DMD locus than to the more distal marker DXS28 (C7). Information of this type is useful in developing genomic strategies for positional cloning in Xp21. These investigations demonstrate that the DNA from patients with Xp21 contiguous gene syndromes can be valuable reagents, not only for ordering loci and markers but also for providing an approximate scale to the map of the Xp21 region surrounding DMD. 44 refs., 3 figs.

Assessment of genetic diversity, population structure, and gene flow of tigers (Panthera tigris tigris) across Nepal's Terai Arc Landscape.

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Thapa, Kanchan; Manandhar, Sulochana; Bista, Manisha; Shakya, Jivan; Sah, Govind; Dhakal, Maheshwar; Sharma, Netra; Llewellyn, Bronwyn; Wultsch, Claudia; Waits, Lisette P; Kelly, Marcella J; Hero, Jean-Marc; Hughes, Jane; Karmacharya, Dibesh

2018-01-01

With fewer than 200 tigers (Panthera tigris tigris) left in Nepal, that are generally confined to five protected areas across the Terai Arc Landscape, genetic studies are needed to provide crucial information on diversity and connectivity for devising an effective country-wide tiger conservation strategy. As part of the Nepal Tiger Genome Project, we studied landscape change, genetic variation, population structure, and gene flow of tigers across the Terai Arc Landscape by conducting Nepal's first comprehensive and systematic scat-based, non-invasive genetic survey. Of the 770 scat samples collected opportunistically from five protected areas and six presumed corridors, 412 were tiger (57%). Out of ten microsatellite loci, we retain eight markers that were used in identifying 78 individual tigers. We used this dataset to examine population structure, genetic variation, contemporary gene flow, and potential population bottlenecks of tigers in Nepal. We detected three genetic clusters consistent with three demographic sub-populations and found moderate levels of genetic variation (He = 0.61, AR = 3.51) and genetic differentiation (FST = 0.14) across the landscape. We detected 3-7 migrants, confirming the potential for dispersal-mediated gene flow across the landscape. We found evidence of a bottleneck signature likely caused by large-scale land-use change documented in the last two centuries in the Terai forest. Securing tiger habitat including functional forest corridors is essential to enhance gene flow across the landscape and ensure long-term tiger survival. This requires cooperation among multiple stakeholders and careful conservation planning to prevent detrimental effects of anthropogenic activities on tigers.

InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk.

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Cheng, Liang; Jiang, Yue; Ju, Hong; Sun, Jie; Peng, Jiajie; Zhou, Meng; Hu, Yang

2018-01-19

Since the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and Human Phenotype Ontology (HPO). Because of the advantage of identifying novel relationships between terms, calculating similarity between ontology terms is one of the major tasks in this research area. Though similarities between terms within each ontology have been studied with in silico methods, term similarities across different ontologies were not investigated as deeply. The latest method took advantage of gene functional interaction network (GFIN) to explore such inter-ontology similarities of terms. However, it only used gene interactions and failed to make full use of the connectivity among gene nodes of the network. In addition, all existent methods are particularly designed for GO and their performances on the extended ontology community remain unknown. We proposed a method InfAcrOnt to infer similarities between terms across ontologies utilizing the entire GFIN. InfAcrOnt builds a term-gene-gene network which comprised ontology annotations and GFIN, and acquires similarities between terms across ontologies through modeling the information flow within the network by random walk. In our benchmark experiments on sub-ontologies of GO, InfAcrOnt achieves a high average area under the receiver operating characteristic curve (AUC) (0.9322 and 0.9309) and low standard deviations (1.8746e-6 and 3.0977e-6) in both human and yeast benchmark datasets exhibiting superior performance. Meanwhile, comparisons of InfAcrOnt results and prior knowledge on pair-wise DO-HPO terms and pair-wise DO-GO terms show high correlations. The experiment results show that InfAcrOnt significantly improves the performance of inferring similarities between terms across ontologies in benchmark set.

Optimal inference with suboptimal models: Addiction and active Bayesian inference

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Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl

2015-01-01

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321

Loss of the flagellum happened only once in the fungal lineage: phylogenetic structure of Kingdom Fungi inferred from RNA polymerase II subunit genes

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Hodson Matthew C

2006-09-01

Full Text Available Abstract Background At present, there is not a widely accepted consensus view regarding the phylogenetic structure of kingdom Fungi although two major phyla, Ascomycota and Basidiomycota, are clearly delineated. Regarding the lower fungi, Zygomycota and Chytridiomycota, a variety of proposals have been advanced. Microsporidia may or may not be fungi; the Glomales (vesicular-arbuscular mycorrhizal fungi may or may not constitute a fifth fungal phylum, and the loss of the flagellum may have occurred either once or multiple times during fungal evolution. All of these issues are capable of being resolved by a molecular phylogenetic analysis which achieves strong statistical support for major branches. To date, no fungal phylogeny based upon molecular characters has satisfied this criterion. Results Using the translated amino acid sequences of the RPB1 and RPB2 genes, we have inferred a fungal phylogeny that consists largely of well-supported monophyletic phyla. Our major results, each with significant statistical support, are: (1 Microsporidia are sister to kingdom Fungi and are not members of Zygomycota; that is, Microsporidia and fungi originated from a common ancestor. (2 Chytridiomycota, the only fungal phylum having a developmental stage with a flagellum, is paraphyletic and is the basal lineage. (3 Zygomycota is monophyletic based upon sampling of Trichomycetes, Zygomycetes, and Glomales. (4 Zygomycota, Basidiomycota, and Ascomycota form a monophyletic group separate from Chytridiomycota. (5 Basidiomycota and Ascomycota are monophyletic sister groups. Conclusion In general, this paper highlights the evolutionary position and significance of the lower fungi (Zygomycota and Chytridiomycota. Our results suggest that loss of the flagellum happened only once during early stages of fungal evolution; consequently, the majority of fungi, unlike plants and animals, are nonflagellated. The phylogeny we infer from gene sequences is the first one that is

Loss of the flagellum happened only once in the fungal lineage: phylogenetic structure of kingdom Fungi inferred from RNA polymerase II subunit genes.

Science.gov (United States)

Liu, Yajuan J; Hodson, Matthew C; Hall, Benjamin D

2006-09-29

At present, there is not a widely accepted consensus view regarding the phylogenetic structure of kingdom Fungi although two major phyla, Ascomycota and Basidiomycota, are clearly delineated. Regarding the lower fungi, Zygomycota and Chytridiomycota, a variety of proposals have been advanced. Microsporidia may or may not be fungi; the Glomales (vesicular-arbuscular mycorrhizal fungi) may or may not constitute a fifth fungal phylum, and the loss of the flagellum may have occurred either once or multiple times during fungal evolution. All of these issues are capable of being resolved by a molecular phylogenetic analysis which achieves strong statistical support for major branches. To date, no fungal phylogeny based upon molecular characters has satisfied this criterion. Using the translated amino acid sequences of the RPB1 and RPB2 genes, we have inferred a fungal phylogeny that consists largely of well-supported monophyletic phyla. Our major results, each with significant statistical support, are: (1) Microsporidia are sister to kingdom Fungi and are not members of Zygomycota; that is, Microsporidia and fungi originated from a common ancestor. (2) Chytridiomycota, the only fungal phylum having a developmental stage with a flagellum, is paraphyletic and is the basal lineage. (3) Zygomycota is monophyletic based upon sampling of Trichomycetes, Zygomycetes, and Glomales. (4) Zygomycota, Basidiomycota, and Ascomycota form a monophyletic group separate from Chytridiomycota. (5) Basidiomycota and Ascomycota are monophyletic sister groups. In general, this paper highlights the evolutionary position and significance of the lower fungi (Zygomycota and Chytridiomycota). Our results suggest that loss of the flagellum happened only once during early stages of fungal evolution; consequently, the majority of fungi, unlike plants and animals, are nonflagellated. The phylogeny we infer from gene sequences is the first one that is congruent with the widely accepted morphology

Bayesian inference in mass flow simulations - from back calculation to prediction

Science.gov (United States)

Kofler, Andreas; Fischer, Jan-Thomas; Hellweger, Valentin; Huber, Andreas; Mergili, Martin; Pudasaini, Shiva; Fellin, Wolfgang; Oberguggenberger, Michael

2017-04-01

Mass flow simulations are an integral part of hazard assessment. Determining the hazard potential requires a multidisciplinary approach, including different scientific fields such as geomorphology, meteorology, physics, civil engineering and mathematics. An important task in snow avalanche simulation is to predict process intensities (runout, flow velocity and depth, ...). The application of probabilistic methods allows one to develop a comprehensive simulation concept, ranging from back to forward calculation and finally to prediction of mass flow events. In this context optimized parameter sets for the used simulation model or intensities of the modeled mass flow process (e.g. runout distances) are represented by probability distributions. Existing deterministic flow models, in particular with respect to snow avalanche dynamics, contain several parameters (e.g. friction). Some of these parameters are more conceptual than physical and their direct measurement in the field is hardly possible. Hence, parameters have to be optimized by matching simulation results to field observations. This inverse problem can be solved by a Bayesian approach (Markov chain Monte Carlo). The optimization process yields parameter distributions, that can be utilized for probabilistic reconstruction and prediction of avalanche events. Arising challenges include the limited amount of observations, correlations appearing in model parameters or observed avalanche characteristics (e.g. velocity and runout) and the accurate handling of ensemble simulations, always taking into account the related uncertainties. Here we present an operational Bayesian simulation framework with r.avaflow, the open source GIS simulation model for granular avalanches and debris flows.

Large scale statistical inference of signaling pathways from RNAi and microarray data

Directory of Open Access Journals (Sweden)

Poustka Annemarie

2007-10-01

Full Text Available Abstract Background The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. Results In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. Conclusion Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-α pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package nem, which can be obtained from the Bioconductor homepage.

Dispersal and gene flow in the rare, parasitic Large Blue butterfly Maculinea arion

DEFF Research Database (Denmark)

Ugelvig, Line Vej; Andersen, Anne; Boomsma, Jacobus Jan

2012-01-01

decline throughout Europe and extinction in Britain followed by reintroduction of a seed population from the Swedish island of Öland. We find that populations are highly structured genetically, but that gene flow occurs over distances 15 times longer than the maximum distance recorded from mark...

Inference rule and problem solving

Energy Technology Data Exchange (ETDEWEB)

Goto, S

1982-04-01

Intelligent information processing signifies an opportunity of having man's intellectual activity executed on the computer, in which inference, in place of ordinary calculation, is used as the basic operational mechanism for such an information processing. Many inference rules are derived from syllogisms in formal logic. The problem of programming this inference function is referred to as a problem solving. Although logically inference and problem-solving are in close relation, the calculation ability of current computers is on a low level for inferring. For clarifying the relation between inference and computers, nonmonotonic logic has been considered. The paper deals with the above topics. 16 references.

Generating Models of Infinite-State Communication Protocols Using Regular Inference with Abstraction

Science.gov (United States)

Aarts, Fides; Jonsson, Bengt; Uijen, Johan

In order to facilitate model-based verification and validation, effort is underway to develop techniques for generating models of communication system components from observations of their external behavior. Most previous such work has employed regular inference techniques which generate modest-size finite-state models. They typically suppress parameters of messages, although these have a significant impact on control flow in many communication protocols. We present a framework, which adapts regular inference to include data parameters in messages and states for generating components with large or infinite message alphabets. A main idea is to adapt the framework of predicate abstraction, successfully used in formal verification. Since we are in a black-box setting, the abstraction must be supplied externally, using information about how the component manages data parameters. We have implemented our techniques by connecting the LearnLib tool for regular inference with the protocol simulator ns-2, and generated a model of the SIP component as implemented in ns-2.

Knowledge and inference

CERN Document Server

Nagao, Makoto

1990-01-01

Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of """"knowledge"""" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intellig

Use of Gene Expression Programming in regionalization of flow duration curve

Science.gov (United States)

Hashmi, Muhammad Z.; Shamseldin, Asaad Y.

2014-06-01

In this paper, a recently introduced artificial intelligence technique known as Gene Expression Programming (GEP) has been employed to perform symbolic regression for developing a parametric scheme of flow duration curve (FDC) regionalization, to relate selected FDC characteristics to catchment characteristics. Stream flow records of selected catchments located in the Auckland Region of New Zealand were used. FDCs of the selected catchments were normalised by dividing the ordinates by their median value. Input for the symbolic regression analysis using GEP was (a) selected characteristics of normalised FDCs; and (b) 26 catchment characteristics related to climate, morphology, soil properties and land cover properties obtained using the observed data and GIS analysis. Our study showed that application of this artificial intelligence technique expedites the selection of a set of the most relevant independent variables out of a large set, because these are automatically selected through the GEP process. Values of the FDC characteristics obtained from the developed relationships have high correlations with the observed values.

Extensive dispersal of Roanoke logperch (Percina rex) inferred from genetic marker data

Science.gov (United States)

Roberts, James H.; Angermeier, Paul; Hallerman, Eric M.

2016-01-01

The dispersal ecology of most stream fishes is poorly characterised, complicating conservation efforts for these species. We used microsatellite DNA marker data to characterise dispersal patterns and effective population size (Ne) for a population of Roanoke logperchPercina rex, an endangered darter (Percidae). Juveniles and candidate parents were sampled for 2 years at sites throughout the Roanoke River watershed. Dispersal was inferred via genetic assignment tests (ATs), pedigree reconstruction (PR) and estimation of lifetime dispersal distance under a genetic isolation-by-distance model. Estimates of Ne varied from 105 to 1218 individuals, depending on the estimation method. Based on PR, polygamy was frequent in parents of both sexes, with individuals spawning with an average of 2.4 mates. The sample contained 61 half-sibling pairs, but only one parent–offspring pair and no full-sib pairs, which limited our ability to discriminate natal dispersal of juveniles from breeding dispersal of their parents between spawning events. Nonetheless, all methods indicated extensive dispersal. The AT indicated unrestricted dispersal among sites ≤15 km apart, while siblings inferred by the PR were captured an average of 14 km and up to 55 km apart. Model-based estimates of median lifetime dispersal distance (6–24 km, depending on assumptions) bracketed AT and PR estimates, indicating that widely dispersed individuals do, on average, contribute to gene flow. Extensive dispersal of P. rex suggests that darters and other small benthic stream fishes may be unexpectedly mobile. Monitoring and management activities for such populations should encompass entire watersheds to fully capture population dynamics.

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

Directory of Open Access Journals (Sweden)

de los Reyes Benildo G

2008-04-01

Full Text Available Abstract Background Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM, facilitates the inference. Identifying NMs defined by specific transcription factors (TF establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO information. Results The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1 Gene set enrichment

Geometric statistical inference

International Nuclear Information System (INIS)

Periwal, Vipul

1999-01-01

A reparametrization-covariant formulation of the inverse problem of probability is explicitly solved for finite sample sizes. The inferred distribution is explicitly continuous for finite sample size. A geometric solution of the statistical inference problem in higher dimensions is outlined

Phylogenetic position of Loricifera inferred from nearly complete 18S and 28S rRNA gene sequences.

Science.gov (United States)

Yamasaki, Hiroshi; Fujimoto, Shinta; Miyazaki, Katsumi

2015-01-01

Loricifera is an enigmatic metazoan phylum; its morphology appeared to place it with Priapulida and Kinorhyncha in the group Scalidophora which, along with Nematoida (Nematoda and Nematomorpha), comprised the group Cycloneuralia. Scarce molecular data have suggested an alternative phylogenetic hypothesis, that the phylum Loricifera is a sister taxon to Nematomorpha, although the actual phylogenetic position of the phylum remains unclear. Ecdysozoan phylogeny was reconstructed through maximum-likelihood (ML) and Bayesian inference (BI) analyses of nuclear 18S and 28S rRNA gene sequences from 60 species representing all eight ecdysozoan phyla, and including a newly collected loriciferan species. Ecdysozoa comprised two clades with high support values in both the ML and BI trees. One consisted of Priapulida and Kinorhyncha, and the other of Loricifera, Nematoida, and Panarthropoda (Tardigrada, Onychophora, and Arthropoda). The relationships between Loricifera, Nematoida, and Panarthropoda were not well resolved. Loricifera appears to be closely related to Nematoida and Panarthropoda, rather than grouping with Priapulida and Kinorhyncha, as had been suggested by previous studies. Thus, both Scalidophora and Cycloneuralia are a polyphyletic or paraphyletic groups. In addition, Loricifera and Nematomorpha did not emerge as sister groups.

Bayesian Inference of Forces Causing Cytoplasmic Streaming in Caenorhabditis elegans Embryos and Mouse Oocytes.

Science.gov (United States)

Niwayama, Ritsuya; Nagao, Hiromichi; Kitajima, Tomoya S; Hufnagel, Lars; Shinohara, Kyosuke; Higuchi, Tomoyuki; Ishikawa, Takuji; Kimura, Akatsuki

2016-01-01

Cellular structures are hydrodynamically interconnected, such that force generation in one location can move distal structures. One example of this phenomenon is cytoplasmic streaming, whereby active forces at the cell cortex induce streaming of the entire cytoplasm. However, it is not known how the spatial distribution and magnitude of these forces move distant objects within the cell. To address this issue, we developed a computational method that used cytoplasm hydrodynamics to infer the spatial distribution of shear stress at the cell cortex induced by active force generators from experimentally obtained flow field of cytoplasmic streaming. By applying this method, we determined the shear-stress distribution that quantitatively reproduces in vivo flow fields in Caenorhabditis elegans embryos and mouse oocytes during meiosis II. Shear stress in mouse oocytes were predicted to localize to a narrower cortical region than that with a high cortical flow velocity and corresponded with the localization of the cortical actin cap. The predicted patterns of pressure gradient in both species were consistent with species-specific cytoplasmic streaming functions. The shear-stress distribution inferred by our method can contribute to the characterization of active force generation driving biological streaming.

Alignment-free genome tree inference by learning group-specific distance metrics.

Science.gov (United States)

Patil, Kaustubh R; McHardy, Alice C

2013-01-01

Understanding the evolutionary relationships between organisms is vital for their in-depth study. Gene-based methods are often used to infer such relationships, which are not without drawbacks. One can now attempt to use genome-scale information, because of the ever increasing number of genomes available. This opportunity also presents a challenge in terms of computational efficiency. Two fundamentally different methods are often employed for sequence comparisons, namely alignment-based and alignment-free methods. Alignment-free methods rely on the genome signature concept and provide a computationally efficient way that is also applicable to nonhomologous sequences. The genome signature contains evolutionary signal as it is more similar for closely related organisms than for distantly related ones. We used genome-scale sequence information to infer taxonomic distances between organisms without additional information such as gene annotations. We propose a method to improve genome tree inference by learning specific distance metrics over the genome signature for groups of organisms with similar phylogenetic, genomic, or ecological properties. Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process. By applying this method to more than a thousand prokaryotic genomes, we showed that, indeed, better distance metrics could be learned for most of the 18 groups of organisms tested here. Once a group-specific metric is available, it can be used to estimate the taxonomic distances for other sequenced organisms from the group. This study also presents a large scale comparison between 10 methods--9 alignment-free and 1 alignment-based.

PlantTribes: a gene and gene family resource for comparative genomics in plants

OpenAIRE

Wall, P. Kerr; Leebens-Mack, Jim; Müller, Kai F.; Field, Dawn; Altman, Naomi S.; dePamphilis, Claude W.

2007-01-01

The PlantTribes database (http://fgp.huck.psu.edu/tribe.html) is a plant gene family database based on the inferred proteomes of five sequenced plant species: Arabidopsis thaliana, Carica papaya, Medicago truncatula, Oryza sativa and Populus trichocarpa. We used the graph-based clustering algorithm MCL [Van Dongen (Technical Report INS-R0010 2000) and Enright et al. (Nucleic Acids Res. 2002; 30: 1575–1584)] to classify all of these species’ protein-coding genes into putative gene families, ca...

Convergent evolution of gene networks by single-gene duplications in higher eukaryotes

OpenAIRE

Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

2004-01-01

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix–loop–helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks e...

Genetic population structure of the desert shrub species lycium ruthenicum inferred from chloroplast dna

International Nuclear Information System (INIS)

Chen, H.; Yonezawa, T.

2014-01-01

Lycium ruthenicum (Solananeae), a spiny shrub mostly distributed in the desert regions of north and northwest China, has been shown to exhibit high tolerance to the extreme environment. In this study, the phylogeography and evolutionary history of L. ruthenicum were examined, on the basis of 80 individuals from eight populations. Using the sequence variations of two spacer regions of chloroplast DNA (trnH-psbA and rps16-trnK) , the absence of a geographic component in the chloroplast DNA genetic structure was identified (GST = 0.351, NST = 0.304, NST< GST), which was consisted with the result of SAMOVA, suggesting weak phylogeographic structure of this species. Phylogenetic and network analyses showed that a total of 10 haplotypes identified in the present study clustered into two clades, in which clade I harbored the ancestral haplotypes that inferred two independent glacial refugia in the middle of Qaidam Basin and the western Inner Mongolia. The existence of regional evolutionary differences was supported by GENETREE, which revealed that one of the population in Qaidam Basin and the two populations in Tarim Basin had experienced rapid expansion, and the other populations retained relatively stable population size during the Pleistocene . Given the results of long-term gene flow and pairwise differences, strong gene flow was insufficient to reduce the genetic differentiation among populations or within populations, probably due to the genetic composition containing a common haplotype and the high number of private haplotypes fixed for most of the population. The divergence times of different lineages were consistent with the rapid uplift phases of the Qinghai-Tibetan Plateau and the initiation and expansion of deserts in northern China, suggesting that the origin and evolution of L. ruthenicum were strongly influenced by Quaternary environment changes. (author)

Improving the extraction of complex regulatory events from scientific text by using ontology-based inference.

Science.gov (United States)

Kim, Jung-Jae; Rebholz-Schuhmann, Dietrich

2011-10-06

The extraction of complex events from biomedical text is a challenging task and requires in-depth semantic analysis. Previous approaches associate lexical and syntactic resources with ontologies for the semantic analysis, but fall short in testing the benefits from the use of domain knowledge. We developed a system that deduces implicit events from explicitly expressed events by using inference rules that encode domain knowledge. We evaluated the system with the inference module on three tasks: First, when tested against a corpus with manually annotated events, the inference module of our system contributes 53.2% of correct extractions, but does not cause any incorrect results. Second, the system overall reproduces 33.1% of the transcription regulatory events contained in RegulonDB (up to 85.0% precision) and the inference module is required for 93.8% of the reproduced events. Third, we applied the system with minimum adaptations to the identification of cell activity regulation events, confirming that the inference improves the performance of the system also on this task. Our research shows that the inference based on domain knowledge plays a significant role in extracting complex events from text. This approach has great potential in recognizing the complex concepts of such biomedical ontologies as Gene Ontology in the literature.

Improving the extraction of complex regulatory events from scientific text by using ontology-based inference

Directory of Open Access Journals (Sweden)

Kim Jung-jae

2011-10-01

Full Text Available Abstract Background The extraction of complex events from biomedical text is a challenging task and requires in-depth semantic analysis. Previous approaches associate lexical and syntactic resources with ontologies for the semantic analysis, but fall short in testing the benefits from the use of domain knowledge. Results We developed a system that deduces implicit events from explicitly expressed events by using inference rules that encode domain knowledge. We evaluated the system with the inference module on three tasks: First, when tested against a corpus with manually annotated events, the inference module of our system contributes 53.2% of correct extractions, but does not cause any incorrect results. Second, the system overall reproduces 33.1% of the transcription regulatory events contained in RegulonDB (up to 85.0% precision and the inference module is required for 93.8% of the reproduced events. Third, we applied the system with minimum adaptations to the identification of cell activity regulation events, confirming that the inference improves the performance of the system also on this task. Conclusions Our research shows that the inference based on domain knowledge plays a significant role in extracting complex events from text. This approach has great potential in recognizing the complex concepts of such biomedical ontologies as Gene Ontology in the literature.

Goal inferences about robot behavior : goal inferences and human response behaviors

NARCIS (Netherlands)

Broers, H.A.T.; Ham, J.R.C.; Broeders, R.; De Silva, P.; Okada, M.

2014-01-01

This explorative research focused on the goal inferences human observers draw based on a robot's behavior, and the extent to which those inferences predict people's behavior in response to that robot. Results show that different robot behaviors cause different response behavior from people.

The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks