A method to evaluate genome-wide methylation in archival formalin-fixed, paraffin-embedded ovarian epithelial cells.

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Qiling Li

Full Text Available The use of DNA from archival formalin and paraffin embedded (FFPE tissue for genetic and epigenetic analyses may be problematic, since the DNA is often degraded and only limited amounts may be available. Thus, it is currently not known whether genome-wide methylation can be reliably assessed in DNA from archival FFPE tissue.Ovarian tissues, which were obtained and formalin-fixed and paraffin-embedded in either 1999 or 2011, were sectioned and stained with hematoxylin-eosin (H&E.Epithelial cells were captured by laser micro dissection, and their DNA subjected to whole genomic bisulfite conversion, whole genomic polymerase chain reaction (PCR amplification, and purification. Sequencing and software analyses were performed to identify the extent of genomic methylation. We observed that 31.7% of sequence reads from the DNA in the 1999 archival FFPE tissue, and 70.6% of the reads from the 2011 sample, could be matched with the genome. Methylation rates of CpG on the Watson and Crick strands were 32.2% and 45.5%, respectively, in the 1999 sample, and 65.1% and 42.7% in the 2011 sample.We have developed an efficient method that allows DNA methylation to be assessed in archival FFPE tissue samples.

Bayesian methods for jointly estimating genomic breeding values of one continuous and one threshold trait.

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Chonglong Wang

Full Text Available Genomic selection has become a useful tool for animal and plant breeding. Currently, genomic evaluation is usually carried out using a single-trait model. However, a multi-trait model has the advantage of using information on the correlated traits, leading to more accurate genomic prediction. To date, joint genomic prediction for a continuous and a threshold trait using a multi-trait model is scarce and needs more attention. Based on the previously proposed methods BayesCπ for single continuous trait and BayesTCπ for single threshold trait, we developed a novel method based on a linear-threshold model, i.e., LT-BayesCπ, for joint genomic prediction of a continuous trait and a threshold trait. Computing procedures of LT-BayesCπ using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the advantages of LT-BayesCπ over BayesCπ and BayesTCπ with regard to the accuracy of genomic prediction on both traits. Factors affecting the performance of LT-BayesCπ were addressed. The results showed that, in all scenarios, the accuracy of genomic prediction obtained from LT-BayesCπ was significantly increased for the threshold trait compared to that from single trait prediction using BayesTCπ, while the accuracy for the continuous trait was comparable with that from single trait prediction using BayesCπ. The proposed LT-BayesCπ could be a method of choice for joint genomic prediction of one continuous and one threshold trait.

An assessment on epitope prediction methods for protozoa genomes

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Resende Daniela M

2012-11-01

Full Text Available Abstract Background Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve performance and a threshold dependent method that employs a confusion matrix; b integrating data from experimentally validated and in silico predicted epitopes; and c integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes. Results A database-driven epitope prediction method was developed with built-in functions that were capable of: a removing experimental data redundancy; b parsing algorithms predictions and storage experimental validated and predict data; and c evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value

Mapping the sensory perception of apple using descriptive sensory evaluation in a genome wide association study.

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Amyotte, Beatrice; Bowen, Amy J; Banks, Travis; Rajcan, Istvan; Somers, Daryl J

2017-01-01

Breeding apples is a long-term endeavour and it is imperative that new cultivars are selected to have outstanding consumer appeal. This study has taken the approach of merging sensory science with genome wide association analyses in order to map the human perception of apple flavour and texture onto the apple genome. The goal was to identify genomic associations that could be used in breeding apples for improved fruit quality. A collection of 85 apple cultivars was examined over two years through descriptive sensory evaluation by a trained sensory panel. The trained sensory panel scored randomized sliced samples of each apple cultivar for seventeen taste, flavour and texture attributes using controlled sensory evaluation practices. In addition, the apple collection was subjected to genotyping by sequencing for marker discovery. A genome wide association analysis suggested significant genomic associations for several sensory traits including juiciness, crispness, mealiness and fresh green apple flavour. The findings include previously unreported genomic regions that could be used in apple breeding and suggest that similar sensory association mapping methods could be applied in other plants.

Mapping the sensory perception of apple using descriptive sensory evaluation in a genome wide association study

Science.gov (United States)

Amyotte, Beatrice; Bowen, Amy J.; Banks, Travis; Rajcan, Istvan; Somers, Daryl J.

2017-01-01

Breeding apples is a long-term endeavour and it is imperative that new cultivars are selected to have outstanding consumer appeal. This study has taken the approach of merging sensory science with genome wide association analyses in order to map the human perception of apple flavour and texture onto the apple genome. The goal was to identify genomic associations that could be used in breeding apples for improved fruit quality. A collection of 85 apple cultivars was examined over two years through descriptive sensory evaluation by a trained sensory panel. The trained sensory panel scored randomized sliced samples of each apple cultivar for seventeen taste, flavour and texture attributes using controlled sensory evaluation practices. In addition, the apple collection was subjected to genotyping by sequencing for marker discovery. A genome wide association analysis suggested significant genomic associations for several sensory traits including juiciness, crispness, mealiness and fresh green apple flavour. The findings include previously unreported genomic regions that could be used in apple breeding and suggest that similar sensory association mapping methods could be applied in other plants. PMID:28231290

An empirical Bayes method for updating inferences in analysis of quantitative trait loci using information from related genome scans.

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Zhang, Kui; Wiener, Howard; Beasley, Mark; George, Varghese; Amos, Christopher I; Allison, David B

2006-08-01

Individual genome scans for quantitative trait loci (QTL) mapping often suffer from low statistical power and imprecise estimates of QTL location and effect. This lack of precision yields large confidence intervals for QTL location, which are problematic for subsequent fine mapping and positional cloning. In prioritizing areas for follow-up after an initial genome scan and in evaluating the credibility of apparent linkage signals, investigators typically examine the results of other genome scans of the same phenotype and informally update their beliefs about which linkage signals in their scan most merit confidence and follow-up via a subjective-intuitive integration approach. A method that acknowledges the wisdom of this general paradigm but formally borrows information from other scans to increase confidence in objectivity would be a benefit. We developed an empirical Bayes analytic method to integrate information from multiple genome scans. The linkage statistic obtained from a single genome scan study is updated by incorporating statistics from other genome scans as prior information. This technique does not require that all studies have an identical marker map or a common estimated QTL effect. The updated linkage statistic can then be used for the estimation of QTL location and effect. We evaluate the performance of our method by using extensive simulations based on actual marker spacing and allele frequencies from available data. Results indicate that the empirical Bayes method can account for between-study heterogeneity, estimate the QTL location and effect more precisely, and provide narrower confidence intervals than results from any single individual study. We also compared the empirical Bayes method with a method originally developed for meta-analysis (a closely related but distinct purpose). In the face of marked heterogeneity among studies, the empirical Bayes method outperforms the comparator.

An Evaluation Framework for Lossy Compression of Genome Sequencing Quality Values.

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Alberti, Claudio; Daniels, Noah; Hernaez, Mikel; Voges, Jan; Goldfeder, Rachel L; Hernandez-Lopez, Ana A; Mattavelli, Marco; Berger, Bonnie

2016-01-01

This paper provides the specification and an initial validation of an evaluation framework for the comparison of lossy compressors of genome sequencing quality values. The goal is to define reference data, test sets, tools and metrics that shall be used to evaluate the impact of lossy compression of quality values on human genome variant calling. The functionality of the framework is validated referring to two state-of-the-art genomic compressors. This work has been spurred by the current activity within the ISO/IEC SC29/WG11 technical committee (a.k.a. MPEG), which is investigating the possibility of starting a standardization activity for genomic information representation.

TSSer: an automated method to identify transcription start sites in prokaryotic genomes from differential RNA sequencing data.

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Jorjani, Hadi; Zavolan, Mihaela

2014-04-01

Accurate identification of transcription start sites (TSSs) is an essential step in the analysis of transcription regulatory networks. In higher eukaryotes, the capped analysis of gene expression technology enabled comprehensive annotation of TSSs in genomes such as those of mice and humans. In bacteria, an equivalent approach, termed differential RNA sequencing (dRNA-seq), has recently been proposed, but the application of this approach to a large number of genomes is hindered by the paucity of computational analysis methods. With few exceptions, when the method has been used, annotation of TSSs has been largely done manually. In this work, we present a computational method called 'TSSer' that enables the automatic inference of TSSs from dRNA-seq data. The method rests on a probabilistic framework for identifying both genomic positions that are preferentially enriched in the dRNA-seq data as well as preferentially captured relative to neighboring genomic regions. Evaluating our approach for TSS calling on several publicly available datasets, we find that TSSer achieves high consistency with the curated lists of annotated TSSs, but identifies many additional TSSs. Therefore, TSSer can accelerate genome-wide identification of TSSs in bacterial genomes and can aid in further characterization of bacterial transcription regulatory networks. TSSer is freely available under GPL license at http://www.clipz.unibas.ch/TSSer/index.php

Genome-wide comparison of ultraviolet and ethyl methanesulphonate mutagenesis methods for the brown alga Ectocarpus.

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Godfroy, Olivier; Peters, Akira F; Coelho, Susana M; Cock, J Mark

2015-12-01

Ectocarpus has emerged as a model organism for the brown algae and a broad range of genetic and genomic resources are being generated for this species. The aim of the work presented here was to evaluate two mutagenesis protocols based on ultraviolet irradiation and ethyl methanesulphonate treatment using genome resequencing to measure the number, type and distribution of mutations generated by the two methods. Ultraviolet irradiation generated a greater number of genetic lesions than ethyl methanesulphonate treatment, with more than 400 mutations being detected in the genome of the mutagenised individual. This study therefore confirms that the ultraviolet mutagenesis protocol is suitable for approaches that require a high density of mutations, such as saturation mutagenesis or Targeting Induced Local Lesions in Genomes (TILLING). Copyright © 2015 Elsevier B.V. All rights reserved.

An Evaluation Framework for Lossy Compression of Genome Sequencing Quality Values

OpenAIRE

Alberti, Claudio; Daniels, Noah; Hernaez, Mikel; Voges, Jan; Goldfeder, Rachel L.; Hernandez-Lopez, Ana A.; Mattavelli, Marco; Berger, Bonnie

2016-01-01

This paper provides the specification and an initial validation of an evaluation framework for the comparison of lossy compressors of genome sequencing quality values. The goal is to define reference data, test sets, tools and metrics that shall be used to evaluate the impact of lossy compression of quality values on human genome variant calling. The functionality of the framework is validated referring to two state-of-the-art genomic compressors. This work has been spurred by the current act...

GenoSets: visual analytic methods for comparative genomics.

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Aurora A Cain

Full Text Available Many important questions in biology are, fundamentally, comparative, and this extends to our analysis of a growing number of sequenced genomes. Existing genomic analysis tools are often organized around literal views of genomes as linear strings. Even when information is highly condensed, these views grow cumbersome as larger numbers of genomes are added. Data aggregation and summarization methods from the field of visual analytics can provide abstracted comparative views, suitable for sifting large multi-genome datasets to identify critical similarities and differences. We introduce a software system for visual analysis of comparative genomics data. The system automates the process of data integration, and provides the analysis platform to identify and explore features of interest within these large datasets. GenoSets borrows techniques from business intelligence and visual analytics to provide a rich interface of interactive visualizations supported by a multi-dimensional data warehouse. In GenoSets, visual analytic approaches are used to enable querying based on orthology, functional assignment, and taxonomic or user-defined groupings of genomes. GenoSets links this information together with coordinated, interactive visualizations for both detailed and high-level categorical analysis of summarized data. GenoSets has been designed to simplify the exploration of multiple genome datasets and to facilitate reasoning about genomic comparisons. Case examples are included showing the use of this system in the analysis of 12 Brucella genomes. GenoSets software and the case study dataset are freely available at http://genosets.uncc.edu. We demonstrate that the integration of genomic data using a coordinated multiple view approach can simplify the exploration of large comparative genomic data sets, and facilitate reasoning about comparisons and features of interest.

A systematic study of genome context methods: calibration, normalization and combination

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Dale Joseph M

2010-10-01

Full Text Available Abstract Background Genome context methods have been introduced in the last decade as automatic methods to predict functional relatedness between genes in a target genome using the patterns of existence and relative locations of the homologs of those genes in a set of reference genomes. Much work has been done in the application of these methods to different bioinformatics tasks, but few papers present a systematic study of the methods and their combination necessary for their optimal use. Results We present a thorough study of the four main families of genome context methods found in the literature: phylogenetic profile, gene fusion, gene cluster, and gene neighbor. We find that for most organisms the gene neighbor method outperforms the phylogenetic profile method by as much as 40% in sensitivity, being competitive with the gene cluster method at low sensitivities. Gene fusion is generally the worst performing of the four methods. A thorough exploration of the parameter space for each method is performed and results across different target organisms are presented. We propose the use of normalization procedures as those used on microarray data for the genome context scores. We show that substantial gains can be achieved from the use of a simple normalization technique. In particular, the sensitivity of the phylogenetic profile method is improved by around 25% after normalization, resulting, to our knowledge, on the best-performing phylogenetic profile system in the literature. Finally, we show results from combining the various genome context methods into a single score. When using a cross-validation procedure to train the combiners, with both original and normalized scores as input, a decision tree combiner results in gains of up to 20% with respect to the gene neighbor method. Overall, this represents a gain of around 15% over what can be considered the state of the art in this area: the four original genome context methods combined using a

Inverse PCR-based method for isolating novel SINEs from genome.

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Han, Yawei; Chen, Liping; Guan, Lihong; He, Shunping

2014-04-01

Short interspersed elements (SINEs) are moderately repetitive DNA sequences in eukaryotic genomes. Although eukaryotic genomes contain numerous SINEs copy, it is very difficult and laborious to isolate and identify them by the reported methods. In this study, the inverse PCR was successfully applied to isolate SINEs from Opsariichthys bidens genome in Eastern Asian Cyprinid. A group of SINEs derived from tRNA(Ala) molecular had been identified, which were named Opsar according to Opsariichthys. SINEs characteristics were exhibited in Opsar, which contained a tRNA(Ala)-derived region at the 5' end, a tRNA-unrelated region, and AT-rich region at the 3' end. The tRNA-derived region of Opsar shared 76 % sequence similarity with tRNA(Ala) gene. This result indicated that Opsar could derive from the inactive or pseudogene of tRNA(Ala). The reliability of method was tested by obtaining C-SINE, Ct-SINE, and M-SINEs from Ctenopharyngodon idellus, Megalobrama amblycephala, and Cyprinus carpio genomes. This method is simpler than the previously reported, which successfully omitted many steps, such as preparation of probes, construction of genomic libraries, and hybridization.

Kernel methods for large-scale genomic data analysis

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Xing, Eric P.; Schaid, Daniel J.

2015-01-01

Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today’s explosive data growth in genomics. They provide a practical and principled approach to learning how a large number of genetic variants are associated with complex phenotypes, to help reveal the complexity in the relationship between the genetic markers and the outcome of interest. In this review, we highlight the potential key role it will have in modern genomic data processing, especially with regard to integration with classical methods for gene prioritizing, prediction and data fusion. PMID:25053743

Defining and Evaluating a Core Genome Multilocus Sequence Typing Scheme for Genome-Wide Typing of Clostridium difficile.

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Bletz, Stefan; Janezic, Sandra; Harmsen, Dag; Rupnik, Maja; Mellmann, Alexander

2018-06-01

Clostridium difficile , recently renamed Clostridioides difficile , is the most common cause of antibiotic-associated nosocomial gastrointestinal infections worldwide. To differentiate endogenous infections and transmission events, highly discriminatory subtyping is necessary. Today, methods based on whole-genome sequencing data are increasingly used to subtype bacterial pathogens; however, frequently a standardized methodology and typing nomenclature are missing. Here we report a core genome multilocus sequence typing (cgMLST) approach developed for C. difficile Initially, we determined the breadth of the C. difficile population based on all available MLST sequence types with Bayesian inference (BAPS). The resulting BAPS partitions were used in combination with C. difficile clade information to select representative isolates that were subsequently used to define cgMLST target genes. Finally, we evaluated the novel cgMLST scheme with genomes from 3,025 isolates. BAPS grouping ( n = 6 groups) together with the clade information led to a total of 11 representative isolates that were included for cgMLST definition and resulted in 2,270 cgMLST genes that were present in all isolates. Overall, 2,184 to 2,268 cgMLST targets were detected in the genome sequences of 70 outbreak-associated and reference strains, and on average 99.3% cgMLST targets (1,116 to 2,270 targets) were present in 2,954 genomes downloaded from the NCBI database, underlining the representativeness of the cgMLST scheme. Moreover, reanalyzing different cluster scenarios with cgMLST were concordant to published single nucleotide variant analyses. In conclusion, the novel cgMLST is representative for the whole C. difficile population, is highly discriminatory in outbreak situations, and provides a unique nomenclature facilitating interlaboratory exchange. Copyright © 2018 American Society for Microbiology.

Development and evaluation of a genomics training program for community health workers in Texas.

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Chen, Lei-Shih; Zhao, Shixi; Stelzig, Donaji; Dhar, Shweta U; Eble, Tanya; Yeh, Yu-Chen; Kwok, Oi-Man

2018-01-04

PurposeGenomics services have the potential to reduce incidence and mortality of diseases by providing individualized, family health history (FHH)-based prevention strategies to clients. These services may benefit from the involvement of community health workers (CHWs) in the provision of FHH-based genomics education and services, as CHWs are frontline public health workers and lay health educators, who share similar ethnicities, languages, socioeconomic statuses, and life experiences with the communities they serve. We developed, implemented, and evaluated the FHH-based genomics training program for CHWs.MethodsThis theory- and evidence-based FHH-focused genomics curriculum was developed by an interdisciplinary team. Full-day workshops in English and Spanish were delivered to 145 Texas CHWs (91.6% were Hispanic/black). Preworkshop, postworkshop, and 3-month follow-up data were collected.ResultsCHWs significantly improved their attitudes, intention, self-efficacy, and knowledge regarding adopting FHH-based genomics into their practice after the workshops. At 3-month follow-up, these scores remained higher, and there was a significant increase in CHWs' genomics practices.ConclusionThis FHH-based genomics training successfully educated Texas CHWs, and the outcomes were promising. Dissemination of training to CHWs in and outside of Texas is needed to promote better access to and delivery of personalized genomics services for the lay and underserved communities.GENETICS in MEDICINE advance online publication, 4 January 2018; doi:10.1038/gim.2017.236.

Genome-wide engineering of an infectious clone of herpes simplex virus type 1 using synthetic genomics assembly methods.

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Oldfield, Lauren M; Grzesik, Peter; Voorhies, Alexander A; Alperovich, Nina; MacMath, Derek; Najera, Claudia D; Chandra, Diya Sabrina; Prasad, Sanjana; Noskov, Vladimir N; Montague, Michael G; Friedman, Robert M; Desai, Prashant J; Vashee, Sanjay

2017-10-17

Here, we present a transformational approach to genome engineering of herpes simplex virus type 1 (HSV-1), which has a large DNA genome, using synthetic genomics tools. We believe this method will enable more rapid and complex modifications of HSV-1 and other large DNA viruses than previous technologies, facilitating many useful applications. Yeast transformation-associated recombination was used to clone 11 fragments comprising the HSV-1 strain KOS 152 kb genome. Using overlapping sequences between the adjacent pieces, we assembled the fragments into a complete virus genome in yeast, transferred it into an Escherichia coli host, and reconstituted infectious virus following transfection into mammalian cells. The virus derived from this yeast-assembled genome, KOS YA , replicated with kinetics similar to wild-type virus. We demonstrated the utility of this modular assembly technology by making numerous modifications to a single gene, making changes to two genes at the same time and, finally, generating individual and combinatorial deletions to a set of five conserved genes that encode virion structural proteins. While the ability to perform genome-wide editing through assembly methods in large DNA virus genomes raises dual-use concerns, we believe the incremental risks are outweighed by potential benefits. These include enhanced functional studies, generation of oncolytic virus vectors, development of delivery platforms of genes for vaccines or therapy, as well as more rapid development of countermeasures against potential biothreats.

Predicting human height by Victorian and genomic methods.

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Aulchenko, Yurii S; Struchalin, Maksim V; Belonogova, Nadezhda M; Axenovich, Tatiana I; Weedon, Michael N; Hofman, Albert; Uitterlinden, Andre G; Kayser, Manfred; Oostra, Ben A; van Duijn, Cornelia M; Janssens, A Cecile J W; Borodin, Pavel M

2009-08-01

In the Victorian era, Sir Francis Galton showed that 'when dealing with the transmission of stature from parents to children, the average height of the two parents, ... is all we need care to know about them' (1886). One hundred and twenty-two years after Galton's work was published, 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people, as characterized by the area under the receiver-operating characteristic curve (AUC). In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. We have also explored how much variance a genomic profile should explain to reach certain AUC values. For highly heritable traits such as height, we conclude that in applications in which parental phenotypic information is available (eg, medicine), the Victorian Galton's method will long stay unsurpassed, in terms of both discriminative accuracy and costs. For less heritable traits, and in situations in which parental information is not available (eg, forensics), genomic methods may provide an alternative, given that the variants determining an essential proportion of the trait's variation can be identified.

Controversy and debate on clinical genomics sequencing-paper 1: genomics is not exceptional: rigorous evaluations are necessary for clinical applications of genomic sequencing.

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Wilson, Brenda J; Miller, Fiona Alice; Rousseau, François

2017-12-01

Next generation genomic sequencing (NGS) technologies-whole genome and whole exome sequencing-are now cheap enough to be within the grasp of many health care organizations. To many, NGS is symbolic of cutting edge health care, offering the promise of "precision" and "personalized" medicine. Historically, research and clinical application has been a two-way street in clinical genetics: research often driven directly by the desire to understand and try to solve immediate clinical problems affecting real, identifiable patients and families, accompanied by a low threshold of willingness to apply research-driven interventions without resort to formal empirical evaluations. However, NGS technologies are not simple substitutes for older technologies and need careful evaluation for use as screening, diagnostic, or prognostic tools. We have concerns across three areas. First, at the moment, analytic validity is unknown because technical platforms are not yet stable, laboratory quality assurance programs are in their infancy, and data interpretation capabilities are badly underdeveloped. Second, clinical validity of genomic findings for patient populations without pre-existing high genetic risk is doubtful, as most clinical experience with NGS technologies relates to patients with a high prior likelihood of a genetic etiology. Finally, we are concerned that proponents argue not only for clinically driven approaches to assessing a patient's genome, but also for seeking out variants associated with unrelated conditions or susceptibilities-so-called "secondary targets"-this is screening on a genomic scale. We argue that clinical uses of genomic sequencing should remain limited to specialist and research settings, that screening for secondary findings in clinical testing should be limited to the maximum extent possible, and that the benefits, harms, and economic implications of their routine use be systematically evaluated. All stakeholders have a responsibility to ensure that

A quantitative comparison of single-cell whole genome amplification methods.

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Charles F A de Bourcy

Full Text Available Single-cell sequencing is emerging as an important tool for studies of genomic heterogeneity. Whole genome amplification (WGA is a key step in single-cell sequencing workflows and a multitude of methods have been introduced. Here, we compare three state-of-the-art methods on both bulk and single-cell samples of E. coli DNA: Multiple Displacement Amplification (MDA, Multiple Annealing and Looping Based Amplification Cycles (MALBAC, and the PicoPLEX single-cell WGA kit (NEB-WGA. We considered the effects of reaction gain on coverage uniformity, error rates and the level of background contamination. We compared the suitability of the different WGA methods for the detection of copy-number variations, for the detection of single-nucleotide polymorphisms and for de-novo genome assembly. No single method performed best across all criteria and significant differences in characteristics were observed; the choice of which amplifier to use will depend strongly on the details of the type of question being asked in any given experiment.

Effect of genotyped cows in the reference population on the genomic evaluation of Holstein cattle.

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Uemoto, Y; Osawa, T; Saburi, J

2017-03-01

This study evaluated the dependence of reliability and prediction bias on the prediction method, the contribution of including animals (bulls or cows), and the genetic relatedness, when including genotyped cows in the progeny-tested bull reference population. We performed genomic evaluation using a Japanese Holstein population, and assessed the accuracy of genomic enhanced breeding value (GEBV) for three production traits and 13 linear conformation traits. A total of 4564 animals for production traits and 4172 animals for conformation traits were genotyped using Illumina BovineSNP50 array. Single- and multi-step methods were compared for predicting GEBV in genotyped bull-only and genotyped bull-cow reference populations. No large differences in realized reliability and regression coefficient were found between the two reference populations; however, a slight difference was found between the two methods for production traits. The accuracy of GEBV determined by single-step method increased slightly when genotyped cows were included in the bull reference population, but decreased slightly by multi-step method. A validation study was used to evaluate the accuracy of GEBV when 800 additional genotyped bulls (POPbull) or cows (POPcow) were included in the base reference population composed of 2000 genotyped bulls. The realized reliabilities of POPbull were higher than those of POPcow for all traits. For the gain of realized reliability over the base reference population, the average ratios of POPbull gain to POPcow gain for production traits and conformation traits were 2.6 and 7.2, respectively, and the ratios depended on heritabilities of the traits. For regression coefficient, no large differences were found between the results for POPbull and POPcow. Another validation study was performed to investigate the effect of genetic relatedness between cows and bulls in the reference and test populations. The effect of genetic relationship among bulls in the reference

A Web-Based Comparative Genomics Tutorial for Investigating Microbial Genomes

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Michael Strong

2009-12-01

Full Text Available As the number of completely sequenced microbial genomes continues to rise at an impressive rate, it is important to prepare students with the skills necessary to investigate microorganisms at the genomic level. As a part of the core curriculum for first-year graduate students in the biological sciences, we have implemented a web-based tutorial to introduce students to the fields of comparative and functional genomics. The tutorial focuses on recent computational methods for identifying functionally linked genes and proteins on a genome-wide scale and was used to introduce students to the Rosetta Stone, Phylogenetic Profile, conserved Gene Neighbor, and Operon computational methods. Students learned to use a number of publicly available web servers and databases to identify functionally linked genes in the Escherichia coli genome, with emphasis on genome organization and operon structure. The overall effectiveness of the tutorial was assessed based on student evaluations and homework assignments. The tutorial is available to other educators at http://www.doe-mbi.ucla.edu/~strong/m253.php.

Two efficient methods for isolation of high-quality genomic DNA from entomopathogenic fungi.

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Serna-Domínguez, María G; Andrade-Michel, Gilda Y; Arredondo-Bernal, Hugo C; Gallou, Adrien

2018-03-27

Conventional and commercial methods for isolation of nucleic acids are available for fungal samples including entomopathogenic fungi (EPF). However, there is not a unique optimal method for all organisms. The cell wall structure and the wide range of secondary metabolites of EPF can broadly interfere with the efficiency of the DNA extraction protocol. This study compares three commercial protocols: DNeasy® Plant Mini Kit (Qiagen), Wizard® Genomic DNA Purification Kit (Promega), and Axygen™ Multisource Genomic DNA Miniprep Kit (Axygen) and three conventional methods based on different buffers: SDS, CTAB/PVPP, and CTAB/β-mercaptoethanol versus three cell lysis procedures: liquid nitrogen homogenization and two bead-beating materials (i.e., tungsten-carbide and stainless-steel) for four representative species of EPF (i.e., Beauveria bassiana, Hirsutella citriformis, Isaria javanica, and Metarhizium anisopliae). Liquid nitrogen homogenization combined with DNeasy® Plant Mini Kit (i.e., QN) or SDS buffer (i.e., SN) significantly improved the yield with a good purity (~1.8) and high integrity (>20,000 bp) of genomic DNA in contrast with other methods, also, these results were better when compared with the two bead-beating materials. The purified DNA was evaluated by PCR-based techniques: amplification of translation elongation factor 1-α (TEF) and two highly sensitive molecular markers (i.e., ISSR and AFLP) with reliable and reproducible results. Despite a variation in yield, purity, and integrity of extracted DNA across the four species of EPF with the different DNA extraction methods, the SN and QN protocols maintained a high-quality of DNA which is required for downstream molecular applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Performance comparison of two efficient genomic selection methods (gsbay & MixP) applied in aquacultural organisms

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Su, Hailin; Li, Hengde; Wang, Shi; Wang, Yangfan; Bao, Zhenmin

2017-02-01

Genomic selection is more and more popular in animal and plant breeding industries all around the world, as it can be applied early in life without impacting selection candidates. The objective of this study was to bring the advantages of genomic selection to scallop breeding. Two different genomic selection tools MixP and gsbay were applied on genomic evaluation of simulated data and Zhikong scallop ( Chlamys farreri) field data. The data were compared with genomic best linear unbiased prediction (GBLUP) method which has been applied widely. Our results showed that both MixP and gsbay could accurately estimate single-nucleotide polymorphism (SNP) marker effects, and thereby could be applied for the analysis of genomic estimated breeding values (GEBV). In simulated data from different scenarios, the accuracy of GEBV acquired was ranged from 0.20 to 0.78 by MixP; it was ranged from 0.21 to 0.67 by gsbay; and it was ranged from 0.21 to 0.61 by GBLUP. Estimations made by MixP and gsbay were expected to be more reliable than those estimated by GBLUP. Predictions made by gsbay were more robust, while with MixP the computation is much faster, especially in dealing with large-scale data. These results suggested that both algorithms implemented by MixP and gsbay are feasible to carry out genomic selection in scallop breeding, and more genotype data will be necessary to produce genomic estimated breeding values with a higher accuracy for the industry.

Methods for Optimizing CRISPR-Cas9 Genome Editing Specificity

Science.gov (United States)

Tycko, Josh; Myer, Vic E.; Hsu, Patrick D.

2016-01-01

Summary Advances in the development of delivery, repair, and specificity strategies for the CRISPR-Cas9 genome engineering toolbox are helping researchers understand gene function with unprecedented precision and sensitivity. CRISPR-Cas9 also holds enormous therapeutic potential for the treatment of genetic disorders by directly correcting disease-causing mutations. Although the Cas9 protein has been shown to bind and cleave DNA at off-target sites, the field of Cas9 specificity is rapidly progressing with marked improvements in guide RNA selection, protein and guide engineering, novel enzymes, and off-target detection methods. We review important challenges and breakthroughs in the field as a comprehensive practical guide to interested users of genome editing technologies, highlighting key tools and strategies for optimizing specificity. The genome editing community should now strive to standardize such methods for measuring and reporting off-target activity, while keeping in mind that the goal for specificity should be continued improvement and vigilance. PMID:27494557

A genomic background based method for association analysis in related individuals.

Directory of Open Access Journals (Sweden)

Najaf Amin

Full Text Available BACKGROUND: Feasibility of genotyping of hundreds and thousands of single nucleotide polymorphisms (SNPs in thousands of study subjects have triggered the need for fast, powerful, and reliable methods for genome-wide association analysis. Here we consider a situation when study participants are genetically related (e.g. due to systematic sampling of families or because a study was performed in a genetically isolated population. Of the available methods that account for relatedness, the Measured Genotype (MG approach is considered the 'gold standard'. However, MG is not efficient with respect to time taken for the analysis of genome-wide data. In this context we proposed a fast two-step method called Genome-wide Association using Mixed Model and Regression (GRAMMAR for the analysis of pedigree-based quantitative traits. This method certainly overcomes the drawback of time limitation of the measured genotype (MG approach, but pays in power. One of the major drawbacks of both MG and GRAMMAR, is that they crucially depend on the availability of complete and correct pedigree data, which is rarely available. METHODOLOGY: In this study we first explore type 1 error and relative power of MG, GRAMMAR, and Genomic Control (GC approaches for genetic association analysis. Secondly, we propose an extension to GRAMMAR i.e. GRAMMAR-GC. Finally, we propose application of GRAMMAR-GC using the kinship matrix estimated through genomic marker data, instead of (possibly missing and/or incorrect genealogy. CONCLUSION: Through simulations we show that MG approach maintains high power across a range of heritabilities and possible pedigree structures, and always outperforms other contemporary methods. We also show that the power of our proposed GRAMMAR-GC approaches to that of the 'gold standard' MG for all models and pedigrees studied. We show that this method is both feasible and powerful and has correct type 1 error in the context of genome-wide association analysis

Genomics protocols [Methods in molecular biology, v. 175

National Research Council Canada - National Science Library

Starkey, Michael P; Elaswarapu, Ramnath

2001-01-01

... exploiting the potential of gene therapy. Highlights include methods for the analysis of differential gene expression, SNP detection, comparative genomic hybridization, and the functional analysis of genes, as well as the use of bio...

An overview of recent developments in genomics and associated statistical methods.

Science.gov (United States)

Bickel, Peter J; Brown, James B; Huang, Haiyan; Li, Qunhua

2009-11-13

The landscape of genomics has changed drastically in the last two decades. Increasingly inexpensive sequencing has shifted the primary focus from the acquisition of biological sequences to the study of biological function. Assays have been developed to study many intricacies of biological systems, and publicly available databases have given rise to integrative analyses that combine information from many sources to draw complex conclusions. Such research was the focus of the recent workshop at the Isaac Newton Institute, 'High dimensional statistics in biology'. Many computational methods from modern genomics and related disciplines were presented and discussed. Using, as much as possible, the material from these talks, we give an overview of modern genomics: from the essential assays that make data-generation possible, to the statistical methods that yield meaningful inference. We point to current analytical challenges, where novel methods, or novel applications of extant methods, are presently needed.

Use of simulated data sets to evaluate the fidelity of Metagenomicprocessing methods

Energy Technology Data Exchange (ETDEWEB)

Mavromatis, Konstantinos; Ivanova, Natalia; Barry, Kerri; Shapiro, Harris; Goltsman, Eugene; McHardy, Alice C.; Rigoutsos, Isidore; Salamov, Asaf; Korzeniewski, Frank; Land, Miriam; Lapidus, Alla; Grigoriev, Igor; Richardson, Paul; Hugenholtz, Philip; Kyrpides, Nikos C.

2006-12-01

Metagenomics is a rapidly emerging field of research for studying microbial communities. To evaluate methods presently used to process metagenomic sequences, we constructed three simulated data sets of varying complexity by combining sequencing reads randomly selected from 113 isolate genomes. These data sets were designed to model real metagenomes in terms of complexity and phylogenetic composition. We assembled sampled reads using three commonly used genome assemblers (Phrap, Arachne and JAZZ), and predicted genes using two popular gene finding pipelines (fgenesb and CRITICA/GLIMMER). The phylogenetic origins of the assembled contigs were predicted using one sequence similarity--based (blast hit distribution) and two sequence composition--based (PhyloPythia, oligonucleotide frequencies) binning methods. We explored the effects of the simulated community structure and method combinations on the fidelity of each processing step by comparison to the corresponding isolate genomes. The simulated data sets are available online to facilitate standardized benchmarking of tools for metagenomic analysis.

Calculation of evolutionary correlation between individual genes and full-length genome: a method useful for choosing phylogenetic markers for molecular epidemiology.

Directory of Open Access Journals (Sweden)

Shuai Wang

Full Text Available Individual genes or regions are still commonly used to estimate the phylogenetic relationships among viral isolates. The genomic regions that can faithfully provide assessments consistent with those predicted with full-length genome sequences would be preferable to serve as good candidates of the phylogenetic markers for molecular epidemiological studies of many viruses. Here we employed a statistical method to evaluate the evolutionary relationships between individual viral genes and full-length genomes without tree construction as a way to determine which gene can match the genome well in phylogenetic analyses. This method was performed by calculation of linear correlations between the genetic distance matrices of aligned individual gene sequences and aligned genome sequences. We applied this method to the phylogenetic analyses of porcine circovirus 2 (PCV2, measles virus (MV, hepatitis E virus (HEV and Japanese encephalitis virus (JEV. Phylogenetic trees were constructed for comparisons and the possible factors affecting the method accuracy were also discussed in the calculations. The results revealed that this method could produce results consistent with those of previous studies about the proper consensus sequences that could be successfully used as phylogenetic markers. And our results also suggested that these evolutionary correlations could provide useful information for identifying genes that could be used effectively to infer the genetic relationships.

Genomic prediction of traits related to canine hip dysplasia

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Enrique eSanchez-Molano

2015-03-01

Full Text Available Increased concern for the welfare of pedigree dogs has led to development of selection programs against inherited diseases. An example is canine hip dysplasia (CHD, which has a moderate heritability and a high prevalence in some large-sized breeds. To date, selection using phenotypes has led to only modest improvement, and alternative strategies such as genomic selection may prove more effective. The primary aims of this study were to compare the performance of pedigree- and genomic-based breeding against CHD in the UK Labrador retriever population and to evaluate the performance of different genomic selection methods. A sample of 1179 Labrador Retrievers evaluated for CHD according to the UK scoring method (hip score, HS was genotyped with the Illumina CanineHD BeadChip. Twelve functions of HS and its component traits were analyzed using different statistical methods (GBLUP, Bayes C and Single-Step methods, and results were compared with a pedigree-based approach (BLUP using cross-validation. Genomic methods resulted in similar or higher accuracies than pedigree-based methods with training sets of 944 individuals for all but the untransformed HS, suggesting that genomic selection is an effective strategy. GBLUP and Bayes C gave similar prediction accuracies for HS and related traits, indicating a polygenic architecture. This conclusion was also supported by the low accuracies obtained in additional GBLUP analyses performed using only the SNPs with highest test statistics, also indicating that marker-assisted selection would not be as effective as genomic selection. A Single-Step method that combines genomic and pedigree information also showed higher accuracy than GBLUP and Bayes C for the log-transformed HS, which is currently used for pedigree based evaluations in UK. In conclusion, genomic selection is a promising alternative to pedigree-based selection against CHD, requiring more phenotypes with genomic data to improve further the accuracy

Evaluation of three methods of DNA extraction from paraffin-embedded material for the amplification of genomic DNA by means of the PCR technique

Directory of Open Access Journals (Sweden)

MESQUITA Ricardo Alves

2001-01-01

Full Text Available There are several protocols reported in the literature for the extraction of genomic DNA from formalin-fixed paraffin-embedded samples. Genomic DNA is utilized in molecular analyses, including PCR. This study compares three different methods for the extraction of genomic DNA from formalin-fixed paraffin-embedded (inflammatory fibrous hyperplasia and non-formalin-fixed (normal oral mucosa samples: phenol with enzymatic digestion, and silica with and without enzymatic digestion. The amplification of DNA by means of the PCR technique was carried out with primers for the exon 7 of human keratin type 14. Amplicons were analyzed by means of electrophoresis in an 8% polyacrylamide gel with 5% glycerol, followed by silver-staining visualization. The phenol/enzymatic digestion and the silica/enzymatic digestion methods provided amplicons from both tissue samples. The method described is a potential aid in the establishment of the histopathologic diagnosis and in retrospective studies with archival paraffin-embedded samples.

QUAST: quality assessment tool for genome assemblies.

Science.gov (United States)

Gurevich, Alexey; Saveliev, Vladislav; Vyahhi, Nikolay; Tesler, Glenn

2013-04-15

Limitations of genome sequencing techniques have led to dozens of assembly algorithms, none of which is perfect. A number of methods for comparing assemblers have been developed, but none is yet a recognized benchmark. Further, most existing methods for comparing assemblies are only applicable to new assemblies of finished genomes; the problem of evaluating assemblies of previously unsequenced species has not been adequately considered. Here, we present QUAST-a quality assessment tool for evaluating and comparing genome assemblies. This tool improves on leading assembly comparison software with new ideas and quality metrics. QUAST can evaluate assemblies both with a reference genome, as well as without a reference. QUAST produces many reports, summary tables and plots to help scientists in their research and in their publications. In this study, we used QUAST to compare several genome assemblers on three datasets. QUAST tables and plots for all of them are available in the Supplementary Material, and interactive versions of these reports are on the QUAST website. http://bioinf.spbau.ru/quast . Supplementary data are available at Bioinformatics online.

TEGS-CN: A Statistical Method for Pathway Analysis of Genome-wide Copy Number Profile.

Science.gov (United States)

Huang, Yen-Tsung; Hsu, Thomas; Christiani, David C

2014-01-01

The effects of copy number alterations make up a significant part of the tumor genome profile, but pathway analyses of these alterations are still not well established. We proposed a novel method to analyze multiple copy numbers of genes within a pathway, termed Test for the Effect of a Gene Set with Copy Number data (TEGS-CN). TEGS-CN was adapted from TEGS, a method that we previously developed for gene expression data using a variance component score test. With additional development, we extend the method to analyze DNA copy number data, accounting for different sizes and thus various numbers of copy number probes in genes. The test statistic follows a mixture of X (2) distributions that can be obtained using permutation with scaled X (2) approximation. We conducted simulation studies to evaluate the size and the power of TEGS-CN and to compare its performance with TEGS. We analyzed a genome-wide copy number data from 264 patients of non-small-cell lung cancer. With the Molecular Signatures Database (MSigDB) pathway database, the genome-wide copy number data can be classified into 1814 biological pathways or gene sets. We investigated associations of the copy number profile of the 1814 gene sets with pack-years of cigarette smoking. Our analysis revealed five pathways with significant P values after Bonferroni adjustment (number data, and causal mechanisms of the five pathways require further study.

A multi-sample based method for identifying common CNVs in normal human genomic structure using high-resolution aCGH data.

Directory of Open Access Journals (Sweden)

Chihyun Park

Full Text Available BACKGROUND: It is difficult to identify copy number variations (CNV in normal human genomic data due to noise and non-linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples. Normal human genome analysis often requires a joint approach across multiple samples. However, the majority of existing methods can only identify CNVs from a single sample. METHODOLOGY AND PRINCIPAL FINDINGS: We developed a multi-sample-based genomic variations detector (MGVD that uses segmentation to identify common breakpoints across multiple samples and a k-means-based clustering strategy. Unlike previous methods, MGVD simultaneously considers multiple samples with different genomic intensities and identifies CNVs and CNV zones (CNVZs; CNVZ is a more precise measure of the location of a genomic variant than the CNV region (CNVR. CONCLUSIONS AND SIGNIFICANCE: We designed a specialized algorithm to detect common CNVs from extremely high-resolution multi-sample aCGH data. MGVD showed high sensitivity and a low false discovery rate for a simulated data set, and outperformed most current methods when real, high-resolution HapMap datasets were analyzed. MGVD also had the fastest runtime compared to the other algorithms evaluated when actual, high-resolution aCGH data were analyzed. The CNVZs identified by MGVD can be used in association studies for revealing relationships between phenotypes and genomic aberrations. Our algorithm was developed with standard C++ and is available in Linux and MS Windows format in the STL library. It is freely available at: http://embio.yonsei.ac.kr/~Park/mgvd.php.

Benchmarking Relatedness Inference Methods with Genome-Wide Data from Thousands of Relatives.

Science.gov (United States)

Ramstetter, Monica D; Dyer, Thomas D; Lehman, Donna M; Curran, Joanne E; Duggirala, Ravindranath; Blangero, John; Mezey, Jason G; Williams, Amy L

2017-09-01

Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these approaches in real data has been lacking. Here, we report an assessment of 12 state-of-the-art pairwise relatedness inference methods using a data set with 2485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (92-99%) when detecting first- and second-degree relationships, but their accuracy dwindles to 76% of relative pairs. Overall, the most accurate methods are Estimation of Recent Shared Ancestry (ERSA) and approaches that compute total IBD sharing using the output from GERMLINE and Refined IBD to infer relatedness. Combining information from the most accurate methods provides little accuracy improvement, indicating that novel approaches, such as new methods that leverage relatedness signals from multiple samples, are needed to achieve a sizeable jump in performance. Copyright © 2017 Ramstetter et al.

Use of simulated data sets to evaluate the fidelity of metagenomic processing methods

Energy Technology Data Exchange (ETDEWEB)

Mavromatis, K [U.S. Department of Energy, Joint Genome Institute; Ivanova, N [U.S. Department of Energy, Joint Genome Institute; Barry, Kerrie [U.S. Department of Energy, Joint Genome Institute; Shapiro, Harris [U.S. Department of Energy, Joint Genome Institute; Goltsman, Eugene [U.S. Department of Energy, Joint Genome Institute; McHardy, Alice C. [IBM T. J. Watson Research Center; Rigoutsos, Isidore [IBM T. J. Watson Research Center; Salamov, Asaf [U.S. Department of Energy, Joint Genome Institute; Korzeniewski, Frank [U.S. Department of Energy, Joint Genome Institute; Land, Miriam L [ORNL; Lapidus, Alla L. [U.S. Department of Energy, Joint Genome Institute; Grigoriev, Igor [U.S. Department of Energy, Joint Genome Institute; Hugenholtz, Philip [U.S. Department of Energy, Joint Genome Institute; Kyrpides, Nikos C [U.S. Department of Energy, Joint Genome Institute

2007-01-01

Metagenomics is a rapidly emerging field of research for studying microbial communities. To evaluate methods presently used to process metagenomic sequences, we constructed three simulated data sets of varying complexity by combining sequencing reads randomly selected from 113 isolate genomes. These data sets were designed to model real metagenomes in terms of complexity and phylogenetic composition. We assembled sampled reads using three commonly used genome assemblers (Phrap, Arachne and JAZZ), and predicted genes using two popular gene-finding pipelines (fgenesb and CRITICA/GLIMMER). The phylogenetic origins of the assembled contigs were predicted using one sequence similarity-based ( blast hit distribution) and two sequence composition-based (PhyloPythia, oligonucleotide frequencies) binning methods. We explored the effects of the simulated community structure and method combinations on the fidelity of each processing step by comparison to the corresponding isolate genomes. The simulated data sets are available online to facilitate standardized benchmarking of tools for metagenomic analysis.

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

Science.gov (United States)

Crossa, José; Pérez-Rodríguez, Paulino; Cuevas, Jaime; Montesinos-López, Osval; Jarquín, Diego; de Los Campos, Gustavo; Burgueño, Juan; González-Camacho, Juan M; Pérez-Elizalde, Sergio; Beyene, Yoseph; Dreisigacker, Susanne; Singh, Ravi; Zhang, Xuecai; Gowda, Manje; Roorkiwal, Manish; Rutkoski, Jessica; Varshney, Rajeev K

2017-11-01

Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reliability and applications of statistical methods based on oligonucleotide frequencies in bacterial and archaeal genomes

DEFF Research Database (Denmark)

Bohlin, J; Skjerve, E; Ussery, David

2008-01-01

with here are mainly used to examine similarities between archaeal and bacterial DNA from different genomes. These methods compare observed genomic frequencies of fixed-sized oligonucleotides with expected values, which can be determined by genomic nucleotide content, smaller oligonucleotide frequencies......, or be based on specific statistical distributions. Advantages with these statistical methods include measurements of phylogenetic relationship with relatively small pieces of DNA sampled from almost anywhere within genomes, detection of foreign/conserved DNA, and homology searches. Our aim was to explore...... the reliability and best suited applications for some popular methods, which include relative oligonucleotide frequencies (ROF), di- to hexanucleotide zero'th order Markov methods (ZOM) and 2.order Markov chain Method (MCM). Tests were performed on distant homology searches with large DNA sequences, detection...

Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding

Science.gov (United States)

de los Campos, Gustavo; Hickey, John M.; Pong-Wong, Ricardo; Daetwyler, Hans D.; Calus, Mario P. L.

2013-01-01

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with small-n regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade. PMID:22745228

Genomic comparisons of Brucella spp. and closely related bacteria using base compositional and proteome based methods

DEFF Research Database (Denmark)

Bohlin, Jon; Snipen, Lars; Cloeckaert, Axel

2010-01-01

BACKGROUND: Classification of bacteria within the genus Brucella has been difficult due in part to considerable genomic homogeneity between the different species and biovars, in spite of clear differences in phenotypes. Therefore, many different methods have been used to assess Brucella taxonomy....... In the current work, we examine 32 sequenced genomes from genus Brucella representing the six classical species, as well as more recently described species, using bioinformatical methods. Comparisons were made at the level of genomic DNA using oligonucleotide based methods (Markov chain based genomic signatures...... between the oligonucleotide based methods used. Whilst the Markov chain based genomic signatures grouped the different species in genus Brucella according to host preference, the codon and amino acid frequencies based methods reflected small differences between the Brucella species. Only minor differences...

Evaluating Phylogenetic Congruence in the Post-Genomic Era

Science.gov (United States)

Leigh, Jessica W.; Lapointe, François-Joseph; Lopez, Philippe; Bapteste, Eric

2011-01-01

Congruence is a broadly applied notion in evolutionary biology used to justify multigene phylogeny or phylogenomics, as well as in studies of coevolution, lateral gene transfer, and as evidence for common descent. Existing methods for identifying incongruence or heterogeneity using character data were designed for data sets that are both small and expected to be rarely incongruent. At the same time, methods that assess incongruence using comparison of trees test a null hypothesis of uncorrelated tree structures, which may be inappropriate for phylogenomic studies. As such, they are ill-suited for the growing number of available genome sequences, most of which are from prokaryotes and viruses, either for phylogenomic analysis or for studies of the evolutionary forces and events that have shaped these genomes. Specifically, many existing methods scale poorly with large numbers of genes, cannot accommodate high levels of incongruence, and do not adequately model patterns of missing taxa for different markers. We propose the development of novel incongruence assessment methods suitable for the analysis of the molecular evolution of the vast majority of life and support the investigation of homogeneity of evolutionary process in cases where markers do not share identical tree structures. PMID:21712432

A simple method for the parallel deep sequencing of full influenza A genomes

DEFF Research Database (Denmark)

Kampmann, Marie-Louise; Fordyce, Sarah Louise; Avila Arcos, Maria del Carmen

2011-01-01

Given the major threat of influenza A to human and animal health, and its ability to evolve rapidly through mutation and reassortment, tools that enable its timely characterization are necessary to help monitor its evolution and spread. For this purpose, deep sequencing can be a very valuable tool....... This study reports a comprehensive method that enables deep sequencing of the complete genomes of influenza A subtypes using the Illumina Genome Analyzer IIx (GAIIx). By using this method, the complete genomes of nine viruses were sequenced in parallel, representing the 2009 pandemic H1N1 virus, H5N1 virus...

Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network's Common Measures Working Group.

Science.gov (United States)

Orlando, Lori A; Sperber, Nina R; Voils, Corrine; Nichols, Marshall; Myers, Rachel A; Wu, R Ryanne; Rakhra-Burris, Tejinder; Levy, Kenneth D; Levy, Mia; Pollin, Toni I; Guan, Yue; Horowitz, Carol R; Ramos, Michelle; Kimmel, Stephen E; McDonough, Caitrin W; Madden, Ebony B; Damschroder, Laura J

2018-06-01

PurposeImplementation research provides a structure for evaluating the clinical integration of genomic medicine interventions. This paper describes the Implementing Genomics in Practice (IGNITE) Network's efforts to promote (i) a broader understanding of genomic medicine implementation research and (ii) the sharing of knowledge generated in the network.MethodsTo facilitate this goal, the IGNITE Network Common Measures Working Group (CMG) members adopted the Consolidated Framework for Implementation Research (CFIR) to guide its approach to identifying constructs and measures relevant to evaluating genomic medicine as a whole, standardizing data collection across projects, and combining data in a centralized resource for cross-network analyses.ResultsCMG identified 10 high-priority CFIR constructs as important for genomic medicine. Of those, eight did not have standardized measurement instruments. Therefore, we developed four survey tools to address this gap. In addition, we identified seven high-priority constructs related to patients, families, and communities that did not map to CFIR constructs. Both sets of constructs were combined to create a draft genomic medicine implementation model.ConclusionWe developed processes to identify constructs deemed valuable for genomic medicine implementation and codified them in a model. These resources are freely available to facilitate knowledge generation and sharing across the field.

A universal genomic coordinate translator for comparative genomics.

Science.gov (United States)

Zamani, Neda; Sundström, Görel; Meadows, Jennifer R S; Höppner, Marc P; Dainat, Jacques; Lantz, Henrik; Haas, Brian J; Grabherr, Manfred G

2014-06-30

Genomic duplications constitute major events in the evolution of species, allowing paralogous copies of genes to take on fine-tuned biological roles. Unambiguously identifying the orthology relationship between copies across multiple genomes can be resolved by synteny, i.e. the conserved order of genomic sequences. However, a comprehensive analysis of duplication events and their contributions to evolution would require all-to-all genome alignments, which increases at N2 with the number of available genomes, N. Here, we introduce Kraken, software that omits the all-to-all requirement by recursively traversing a graph of pairwise alignments and dynamically re-computing orthology. Kraken scales linearly with the number of targeted genomes, N, which allows for including large numbers of genomes in analyses. We first evaluated the method on the set of 12 Drosophila genomes, finding that orthologous correspondence computed indirectly through a graph of multiple synteny maps comes at minimal cost in terms of sensitivity, but reduces overall computational runtime by an order of magnitude. We then used the method on three well-annotated mammalian genomes, human, mouse, and rat, and show that up to 93% of protein coding transcripts have unambiguous pairwise orthologous relationships across the genomes. On a nucleotide level, 70 to 83% of exons match exactly at both splice junctions, and up to 97% on at least one junction. We last applied Kraken to an RNA-sequencing dataset from multiple vertebrates and diverse tissues, where we confirmed that brain-specific gene family members, i.e. one-to-many or many-to-many homologs, are more highly correlated across species than single-copy (i.e. one-to-one homologous) genes. Not limited to protein coding genes, Kraken also identifies thousands of newly identified transcribed loci, likely non-coding RNAs that are consistently transcribed in human, chimpanzee and gorilla, and maintain significant correlation of expression levels across

A universal, rapid, and inexpensive method for genomic DNA ...

Indian Academy of Sciences (India)

MOHAMMED BAQUR SAHIB A. AL-SHUHAIB

gels, containing 7% glycerol, and 1×TBE buffer. The gels were run under 200 .... Inc. Germany, GeneaidTM DNA Isolation Kit, Geneaid. Biotech., New Taipei City, .... C. L. and Arsenos G. 2015 Comparison of eleven methods for genomic DNA ...

Codon usage bias: causative factors, quantification methods and genome-wide patterns: with emphasis on insect genomes.

Science.gov (United States)

Behura, Susanta K; Severson, David W

2013-02-01

Codon usage bias refers to the phenomenon where specific codons are used more often than other synonymous codons during translation of genes, the extent of which varies within and among species. Molecular evolutionary investigations suggest that codon bias is manifested as a result of balance between mutational and translational selection of such genes and that this phenomenon is widespread across species and may contribute to genome evolution in a significant manner. With the advent of whole-genome sequencing of numerous species, both prokaryotes and eukaryotes, genome-wide patterns of codon bias are emerging in different organisms. Various factors such as expression level, GC content, recombination rates, RNA stability, codon position, gene length and others (including environmental stress and population size) can influence codon usage bias within and among species. Moreover, there has been a continuous quest towards developing new concepts and tools to measure the extent of codon usage bias of genes. In this review, we outline the fundamental concepts of evolution of the genetic code, discuss various factors that may influence biased usage of synonymous codons and then outline different principles and methods of measurement of codon usage bias. Finally, we discuss selected studies performed using whole-genome sequences of different insect species to show how codon bias patterns vary within and among genomes. We conclude with generalized remarks on specific emerging aspects of codon bias studies and highlight the recent explosion of genome-sequencing efforts on arthropods (such as twelve Drosophila species, species of ants, honeybee, Nasonia and Anopheles mosquitoes as well as the recent launch of a genome-sequencing project involving 5000 insects and other arthropods) that may help us to understand better the evolution of codon bias and its biological significance. © 2012 The Authors. Biological Reviews © 2012 Cambridge Philosophical Society.

Efficient Server-Aided Secure Two-Party Function Evaluation with Applications to Genomic Computation

Directory of Open Access Journals (Sweden)

Blanton Marina

2016-10-01

Full Text Available Computation based on genomic data is becoming increasingly popular today, be it for medical or other purposes. Non-medical uses of genomic data in a computation often take place in a server-mediated setting where the server offers the ability for joint genomic testing between the users. Undeniably, genomic data is highly sensitive, which in contrast to other biometry types, discloses a plethora of information not only about the data owner, but also about his or her relatives. Thus, there is an urgent need to protect genomic data. This is particularly true when the data is used in computation for what we call recreational non-health-related purposes. Towards this goal, in this work we put forward a framework for server-aided secure two-party computation with the security model motivated by genomic applications. One particular security setting that we treat in this work provides stronger security guarantees with respect to malicious users than the traditional malicious model. In particular, we incorporate certified inputs into secure computation based on garbled circuit evaluation to guarantee that a malicious user is unable to modify her inputs in order to learn unauthorized information about the other user’s data. Our solutions are general in the sense that they can be used to securely evaluate arbitrary functions and offer attractive performance compared to the state of the art. We apply the general constructions to three specific types of genomic tests: paternity, genetic compatibility, and ancestry testing and implement the constructions. The results show that all such private tests can be executed within a matter of seconds or less despite the large size of one’s genomic data.

The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets.

Science.gov (United States)

González-Recio, O; Jiménez-Montero, J A; Alenda, R

2013-01-01

In the next few years, with the advent of high-density single nucleotide polymorphism (SNP) arrays and genome sequencing, genomic evaluation methods will need to deal with a large number of genetic variants and an increasing sample size. The boosting algorithm is a machine-learning technique that may alleviate the drawbacks of dealing with such large data sets. This algorithm combines different predictors in a sequential manner with some shrinkage on them; each predictor is applied consecutively to the residuals from the committee formed by the previous ones to form a final prediction based on a subset of covariates. Here, a detailed description is provided and examples using a toy data set are included. A modification of the algorithm called "random boosting" was proposed to increase predictive ability and decrease computation time of genome-assisted evaluation in large data sets. Random boosting uses a random selection of markers to add a subsequent weak learner to the predictive model. These modifications were applied to a real data set composed of 1,797 bulls genotyped for 39,714 SNP. Deregressed proofs of 4 yield traits and 1 type trait from January 2009 routine evaluations were used as dependent variables. A 2-fold cross-validation scenario was implemented. Sires born before 2005 were used as a training sample (1,576 and 1,562 for production and type traits, respectively), whereas younger sires were used as a testing sample to evaluate predictive ability of the algorithm on yet-to-be-observed phenotypes. Comparison with the original algorithm was provided. The predictive ability of the algorithm was measured as Pearson correlations between observed and predicted responses. Further, estimated bias was computed as the average difference between observed and predicted phenotypes. The results showed that the modification of the original boosting algorithm could be run in 1% of the time used with the original algorithm and with negligible differences in accuracy

Accounting for discovery bias in genomic prediction

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Our objective was to evaluate an approach to mitigating discovery bias in genomic prediction. Accuracy may be improved by placing greater emphasis on regions of the genome expected to be more influential on a trait. Methods emphasizing regions result in a phenomenon known as “discovery bias” if info...

A simple and effective method for construction of Escherichia coli strains proficient for genome engineering.

Directory of Open Access Journals (Sweden)

Young Shin Ryu

Full Text Available Multiplex genome engineering is a standalone recombineering tool for large-scale programming and accelerated evolution of cells. However, this advanced genome engineering technique has been limited to use in selected bacterial strains. We developed a simple and effective strain-independent method for effective genome engineering in Escherichia coli. The method involves introducing a suicide plasmid carrying the λ Red recombination system into the mutS gene. The suicide plasmid can be excised from the chromosome via selection in the absence of antibiotics, thus allowing transient inactivation of the mismatch repair system during genome engineering. In addition, we developed another suicide plasmid that enables integration of large DNA fragments into the lacZ genomic locus. These features enable this system to be applied in the exploitation of the benefits of genome engineering in synthetic biology, as well as the metabolic engineering of different strains of E. coli.

An Assessment of Different Genomic Approaches for Inferring Phylogeny of Listeria monocytogenes

DEFF Research Database (Denmark)

Henri, Clementine; Leekitcharoenphon, Pimlapas; Carleton, Heather A.

2017-01-01

Background/objectives: Whole genome sequencing (WGS) has proven to be a powerful subtyping tool for foodborne pathogenic bacteria like L. monocytogenes. The interests of genome-scale analysis for national surveillance, outbreak detection or source tracking has been largely documented. The genomic......MLPPST) or pan genome (wgMLPPST). Currently, there are little comparisons studies of these different analytical approaches. Our objective was to assess and compare different genomic methods that can be implemented in order to cluster isolates of L monocytogenes.Methods: The clustering methods were evaluated...... on a collection of 207 L. monocytogenes genomes of food origin representative of the genetic diversity of the Anses collection. The trees were then compared using robust statistical analyses.Results: The backward comparability between conventional typing methods and genomic methods revealed a near...

Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

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Li, Yifeng; Shi, Wenqiang; Wasserman, Wyeth W

2018-05-31

In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

A simple and inexpensive method for genomic restriction mapping analysis

International Nuclear Information System (INIS)

Huang, C.H.; Lam, V.M.S.; Tam, J.W.O.

1988-01-01

The Southern blotting procedure for the transfer of DNA fragments from agarose gels to nitrocellulose membranes has revolutionized nucleic acid detection methods, and it forms the cornerstone of research in molecular biology. Basically, the method involves the denaturation of DNA fragments that have been separated on an agarose gel, the immobilization of the fragments by transfer to a nitrocellulose membrane, and the identification of the fragments of interest through hybridization to /sup 32/P-labeled probes and autoradiography. While the method is sensitive and applicable to both genomic and cloned DNA, it suffers from the disadvantages of being time consuming and expensive, and fragments of greater than 15 kb are difficult to transfer. Moreover, although theoretically the nitrocellulose membrane can be washed and hybridized repeatedly using different probes, in practice, the membrane becomes brittle and difficult to handle after a few cycles. A direct hybridization method for pure DNA clones was developed in 1975 but has not been widely exploited. The authors report here a modification of their procedure as applied to genomic DNA. The method is simple, rapid, and inexpensive, and it does not involve transfer to nitrocellulose membranes

Simultaneous gene finding in multiple genomes.

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König, Stefanie; Romoth, Lars W; Gerischer, Lizzy; Stanke, Mario

2016-11-15

As the tree of life is populated with sequenced genomes ever more densely, the new challenge is the accurate and consistent annotation of entire clades of genomes. We address this problem with a new approach to comparative gene finding that takes a multiple genome alignment of closely related species and simultaneously predicts the location and structure of protein-coding genes in all input genomes, thereby exploiting negative selection and sequence conservation. The model prefers potential gene structures in the different genomes that are in agreement with each other, or-if not-where the exon gains and losses are plausible given the species tree. We formulate the multi-species gene finding problem as a binary labeling problem on a graph. The resulting optimization problem is NP hard, but can be efficiently approximated using a subgradient-based dual decomposition approach. The proposed method was tested on whole-genome alignments of 12 vertebrate and 12 Drosophila species. The accuracy was evaluated for human, mouse and Drosophila melanogaster and compared to competing methods. Results suggest that our method is well-suited for annotation of (a large number of) genomes of closely related species within a clade, in particular, when RNA-Seq data are available for many of the genomes. The transfer of existing annotations from one genome to another via the genome alignment is more accurate than previous approaches that are based on protein-spliced alignments, when the genomes are at close to medium distances. The method is implemented in C ++ as part of Augustus and available open source at http://bioinf.uni-greifswald.de/augustus/ CONTACT: stefaniekoenig@ymail.com or mario.stanke@uni-greifswald.deSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Comprehensive evaluation of non-hybrid genome assembly tools for third-generation PacBio long-read sequence data.

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Jayakumar, Vasanthan; Sakakibara, Yasubumi

2017-11-03

Long reads obtained from third-generation sequencing platforms can help overcome the long-standing challenge of the de novo assembly of sequences for the genomic analysis of non-model eukaryotic organisms. Numerous long-read-aided de novo assemblies have been published recently, which exhibited superior quality of the assembled genomes in comparison with those achieved using earlier second-generation sequencing technologies. Evaluating assemblies is important in guiding the appropriate choice for specific research needs. In this study, we evaluated 10 long-read assemblers using a variety of metrics on Pacific Biosciences (PacBio) data sets from different taxonomic categories with considerable differences in genome size. The results allowed us to narrow down the list to a few assemblers that can be effectively applied to eukaryotic assembly projects. Moreover, we highlight how best to use limited genomic resources for effectively evaluating the genome assemblies of non-model organisms. © The Author 2017. Published by Oxford University Press.

Comparing Mycobacterium tuberculosis genomes using genome topology networks.

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Jiang, Jianping; Gu, Jianlei; Zhang, Liang; Zhang, Chenyi; Deng, Xiao; Dou, Tonghai; Zhao, Guoping; Zhou, Yan

2015-02-14

Over the last decade, emerging research methods, such as comparative genomic analysis and phylogenetic study, have yielded new insights into genotypes and phenotypes of closely related bacterial strains. Several findings have revealed that genomic structural variations (SVs), including gene gain/loss, gene duplication and genome rearrangement, can lead to different phenotypes among strains, and an investigation of genes affected by SVs may extend our knowledge of the relationships between SVs and phenotypes in microbes, especially in pathogenic bacteria. In this work, we introduce a 'Genome Topology Network' (GTN) method based on gene homology and gene locations to analyze genomic SVs and perform phylogenetic analysis. Furthermore, the concept of 'unfixed ortholog' has been proposed, whose members are affected by SVs in genome topology among close species. To improve the precision of 'unfixed ortholog' recognition, a strategy to detect annotation differences and complete gene annotation was applied. To assess the GTN method, a set of thirteen complete M. tuberculosis genomes was analyzed as a case study. GTNs with two different gene homology-assigning methods were built, the Clusters of Orthologous Groups (COG) method and the orthoMCL clustering method, and two phylogenetic trees were constructed accordingly, which may provide additional insights into whole genome-based phylogenetic analysis. We obtained 24 unfixable COG groups, of which most members were related to immunogenicity and drug resistance, such as PPE-repeat proteins (COG5651) and transcriptional regulator TetR gene family members (COG1309). The GTN method has been implemented in PERL and released on our website. The tool can be downloaded from http://homepage.fudan.edu.cn/zhouyan/gtn/ , and allows re-annotating the 'lost' genes among closely related genomes, analyzing genes affected by SVs, and performing phylogenetic analysis. With this tool, many immunogenic-related and drug resistance-related genes

Evaluation of approaches for estimating the accuracy of genomic prediction in plant breeding.

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Ould Estaghvirou, Sidi Boubacar; Ogutu, Joseph O; Schulz-Streeck, Torben; Knaak, Carsten; Ouzunova, Milena; Gordillo, Andres; Piepho, Hans-Peter

2013-12-06

In genomic prediction, an important measure of accuracy is the correlation between the predicted and the true breeding values. Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown. Instead, the correlation between the predicted breeding values and the observed phenotypic values, called predictive ability, is often computed. In order to indirectly estimate predictive accuracy, this latter correlation is usually divided by an estimate of the square root of heritability. In this study we use simulation to evaluate estimates of predictive accuracy for seven methods, four (1 to 4) of which use an estimate of heritability to divide predictive ability computed by cross-validation. Between them the seven methods cover balanced and unbalanced datasets as well as correlated and uncorrelated genotypes. We propose one new indirect method (4) and two direct methods (5 and 6) for estimating predictive accuracy and compare their performances and those of four other existing approaches (three indirect (1 to 3) and one direct (7)) with simulated true predictive accuracy as the benchmark and with each other. The size of the estimated genetic variance and hence heritability exerted the strongest influence on the variation in the estimated predictive accuracy. Increasing the number of genotypes considerably increases the time required to compute predictive accuracy by all the seven methods, most notably for the five methods that require cross-validation (Methods 1, 2, 3, 4 and 6). A new method that we propose (Method 5) and an existing method (Method 7) used in animal breeding programs were the fastest and gave the least biased, most precise and stable estimates of predictive accuracy. Of the methods that use cross-validation Methods 4 and 6 were often the best. The estimated genetic variance and the number of genotypes had the greatest influence on predictive accuracy. Methods 5 and 7 were the fastest and produced the least

Evaluating genomic tests from bench to bedside: a practical framework

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Lin Jennifer S

2012-10-01

Full Text Available Abstract The development of genomic tests is one of the most significant technological advances in medical testing in recent decades. As these tests become increasingly available, so does the need for a pragmatic framework to evaluate the evidence base and evidence gaps in order to facilitate informed decision-making. In this article we describe such a framework that can provide a common language and benchmarks for different stakeholders of genomic testing. Each stakeholder can use this framework to specify their respective thresholds for decision-making, depending on their perspective and particular needs. This framework is applicable across a broad range of test applications and can be helpful in the application and communication of a regulatory science for genomic testing. Our framework builds upon existing work and incorporates principles familiar to researchers involved in medical testing (both diagnostic and prognostic generally, as well as those involved in genomic testing. This framework is organized around six phases in the development of genomic tests beginning with marker identification and ending with population impact, and highlights the important knowledge gaps that need to be filled in establishing the clinical relevance of a test. Our framework focuses on the clinical appropriateness of the four main dimensions of test research questions (population/setting, intervention/index test, comparators/reference test, and outcomes rather than prescribing a hierarchy of study designs that should be used to address each phase.

Protecting and Evaluating Genomic Privacy in Medical Tests and Personalized Medicine

OpenAIRE

Ayday, Erman; Raisaro, Jean Louis; Rougemont, Jacques; Hubaux, Jean-Pierre

2013-01-01

In this paper, we propose privacy-enhancing technologies for medical tests and personalized medicine methods that use patients' genomic data. Focusing on genetic disease-susceptibility tests, we develop a new architecture (between the patient and the medical unit) and propose a "privacy-preserving disease susceptibility test" (PDS) by using homomorphic encryption and proxy re-encryption. Assuming the whole genome sequencing to be done by a certified institution, we propose to store patients' ...

Comprehensive evaluation of genome-wide 5-hydroxymethylcytosine profiling approaches in human DNA.

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Skvortsova, Ksenia; Zotenko, Elena; Luu, Phuc-Loi; Gould, Cathryn M; Nair, Shalima S; Clark, Susan J; Stirzaker, Clare

2017-01-01

The discovery that 5-methylcytosine (5mC) can be oxidized to 5-hydroxymethylcytosine (5hmC) by the ten-eleven translocation (TET) proteins has prompted wide interest in the potential role of 5hmC in reshaping the mammalian DNA methylation landscape. The gold-standard bisulphite conversion technologies to study DNA methylation do not distinguish between 5mC and 5hmC. However, new approaches to mapping 5hmC genome-wide have advanced rapidly, although it is unclear how the different methods compare in accurately calling 5hmC. In this study, we provide a comparative analysis on brain DNA using three 5hmC genome-wide approaches, namely whole-genome bisulphite/oxidative bisulphite sequencing (WG Bis/OxBis-seq), Infinium HumanMethylation450 BeadChip arrays coupled with oxidative bisulphite (HM450K Bis/OxBis) and antibody-based immunoprecipitation and sequencing of hydroxymethylated DNA (hMeDIP-seq). We also perform loci-specific TET-assisted bisulphite sequencing (TAB-seq) for validation of candidate regions. We show that whole-genome single-base resolution approaches are advantaged in providing precise 5hmC values but require high sequencing depth to accurately measure 5hmC, as this modification is commonly in low abundance in mammalian cells. HM450K arrays coupled with oxidative bisulphite provide a cost-effective representation of 5hmC distribution, at CpG sites with 5hmC levels >~10%. However, 5hmC analysis is restricted to the genomic location of the probes, which is an important consideration as 5hmC modification is commonly enriched at enhancer elements. Finally, we show that the widely used hMeDIP-seq method provides an efficient genome-wide profile of 5hmC and shows high correlation with WG Bis/OxBis-seq 5hmC distribution in brain DNA. However, in cell line DNA with low levels of 5hmC, hMeDIP-seq-enriched regions are not detected by WG Bis/OxBis or HM450K, either suggesting misinterpretation of 5hmC calls by hMeDIP or lack of sensitivity of the latter methods. We

Estimated allele substitution effects underlying genomic evaluation models depend on the scaling of allele counts

NARCIS (Netherlands)

Bouwman, Aniek C.; Hayes, Ben J.; Calus, Mario P.L.

2017-01-01

Background: Genomic evaluation is used to predict direct genomic values (DGV) for selection candidates in breeding programs, but also to estimate allele substitution effects (ASE) of single nucleotide polymorphisms (SNPs). Scaling of allele counts influences the estimated ASE, because scaling of

Methods to compute reliabilities for genomic predictions of feed intake

Science.gov (United States)

For new traits without historical reference data, cross-validation is often the preferred method to validate reliability (REL). Time truncation is less useful because few animals gain substantial REL after the truncation point. Accurate cross-validation requires separating genomic gain from pedigree...

Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations.

Science.gov (United States)

Schaid, Daniel J

2010-01-01

Measures of genomic similarity are the basis of many statistical analytic methods. We review the mathematical and statistical basis of similarity methods, particularly based on kernel methods. A kernel function converts information for a pair of subjects to a quantitative value representing either similarity (larger values meaning more similar) or distance (smaller values meaning more similar), with the requirement that it must create a positive semidefinite matrix when applied to all pairs of subjects. This review emphasizes the wide range of statistical methods and software that can be used when similarity is based on kernel methods, such as nonparametric regression, linear mixed models and generalized linear mixed models, hierarchical models, score statistics, and support vector machines. The mathematical rigor for these methods is summarized, as is the mathematical framework for making kernels. This review provides a framework to move from intuitive and heuristic approaches to define genomic similarities to more rigorous methods that can take advantage of powerful statistical modeling and existing software. A companion paper reviews novel approaches to creating kernels that might be useful for genomic analyses, providing insights with examples [1]. Copyright © 2010 S. Karger AG, Basel.

Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq)-A Method for High-Throughput Analysis of Differentially Methylated CCGG Sites in Plants with Large Genomes.

Science.gov (United States)

Chwialkowska, Karolina; Korotko, Urszula; Kosinska, Joanna; Szarejko, Iwona; Kwasniewski, Miroslaw

2017-01-01

Epigenetic mechanisms, including histone modifications and DNA methylation, mutually regulate chromatin structure, maintain genome integrity, and affect gene expression and transposon mobility. Variations in DNA methylation within plant populations, as well as methylation in response to internal and external factors, are of increasing interest, especially in the crop research field. Methylation Sensitive Amplification Polymorphism (MSAP) is one of the most commonly used methods for assessing DNA methylation changes in plants. This method involves gel-based visualization of PCR fragments from selectively amplified DNA that are cleaved using methylation-sensitive restriction enzymes. In this study, we developed and validated a new method based on the conventional MSAP approach called Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq). We improved the MSAP-based approach by replacing the conventional separation of amplicons on polyacrylamide gels with direct, high-throughput sequencing using Next Generation Sequencing (NGS) and automated data analysis. MSAP-Seq allows for global sequence-based identification of changes in DNA methylation. This technique was validated in Hordeum vulgare . However, MSAP-Seq can be straightforwardly implemented in different plant species, including crops with large, complex and highly repetitive genomes. The incorporation of high-throughput sequencing into MSAP-Seq enables parallel and direct analysis of DNA methylation in hundreds of thousands of sites across the genome. MSAP-Seq provides direct genomic localization of changes and enables quantitative evaluation. We have shown that the MSAP-Seq method specifically targets gene-containing regions and that a single analysis can cover three-quarters of all genes in large genomes. Moreover, MSAP-Seq's simplicity, cost effectiveness, and high-multiplexing capability make this method highly affordable. Therefore, MSAP-Seq can be used for DNA methylation analysis in crop

Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq—A Method for High-Throughput Analysis of Differentially Methylated CCGG Sites in Plants with Large Genomes

Directory of Open Access Journals (Sweden)

Karolina Chwialkowska

2017-11-01

Full Text Available Epigenetic mechanisms, including histone modifications and DNA methylation, mutually regulate chromatin structure, maintain genome integrity, and affect gene expression and transposon mobility. Variations in DNA methylation within plant populations, as well as methylation in response to internal and external factors, are of increasing interest, especially in the crop research field. Methylation Sensitive Amplification Polymorphism (MSAP is one of the most commonly used methods for assessing DNA methylation changes in plants. This method involves gel-based visualization of PCR fragments from selectively amplified DNA that are cleaved using methylation-sensitive restriction enzymes. In this study, we developed and validated a new method based on the conventional MSAP approach called Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq. We improved the MSAP-based approach by replacing the conventional separation of amplicons on polyacrylamide gels with direct, high-throughput sequencing using Next Generation Sequencing (NGS and automated data analysis. MSAP-Seq allows for global sequence-based identification of changes in DNA methylation. This technique was validated in Hordeum vulgare. However, MSAP-Seq can be straightforwardly implemented in different plant species, including crops with large, complex and highly repetitive genomes. The incorporation of high-throughput sequencing into MSAP-Seq enables parallel and direct analysis of DNA methylation in hundreds of thousands of sites across the genome. MSAP-Seq provides direct genomic localization of changes and enables quantitative evaluation. We have shown that the MSAP-Seq method specifically targets gene-containing regions and that a single analysis can cover three-quarters of all genes in large genomes. Moreover, MSAP-Seq's simplicity, cost effectiveness, and high-multiplexing capability make this method highly affordable. Therefore, MSAP-Seq can be used for DNA methylation

REGEN: Ancestral Genome Reconstruction for Bacteria.

Science.gov (United States)

Yang, Kuan; Heath, Lenwood S; Setubal, João C

2012-07-18

Ancestral genome reconstruction can be understood as a phylogenetic study with more details than a traditional phylogenetic tree reconstruction. We present a new computational system called REGEN for ancestral bacterial genome reconstruction at both the gene and replicon levels. REGEN reconstructs gene content, contiguous gene runs, and replicon structure for each ancestral genome. Along each branch of the phylogenetic tree, REGEN infers evolutionary events, including gene creation and deletion and replicon fission and fusion. The reconstruction can be performed by either a maximum parsimony or a maximum likelihood method. Gene content reconstruction is based on the concept of neighboring gene pairs. REGEN was designed to be used with any set of genomes that are sufficiently related, which will usually be the case for bacteria within the same taxonomic order. We evaluated REGEN using simulated genomes and genomes in the Rhizobiales order.

Methods to improve genomic prediction and GWAS using combined Holstein populations

DEFF Research Database (Denmark)

Li, Xiujin

The thesis focuses on methods to improve GWAS and genomic prediction using combined Holstein populations and investigations G by E interaction. The conclusions are: 1) Prediction reliabilities for Brazilian Holsteins can be increased by adding Nordic and Frensh genotyped bulls and a large G by E...... interaction exists between populations. 2) Combining data from Chinese and Danish Holstein populations increases the power of GWAS and detects new QTL regions for milk fatty acid traits. 3) The novel multi-trait Bayesian model efficiently estimates region-specific genomic variances, covariances...

Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

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Xiaochun Sun

Full Text Available Genomic selection (GS procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA and reproducing kernel Hilbert spaces (RKHS regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

Science.gov (United States)

Sun, Xiaochun; Ma, Ping; Mumm, Rita H

2012-01-01

Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

Evaluation of whole genome sequencing for outbreak detection of Salmonella enterica

DEFF Research Database (Denmark)

Leekitcharoenphon, Pimlapas; Nielsen, Eva M.; Kaas, Rolf Sommer

2014-01-01

Salmonella enterica is a common cause of minor and large food borne outbreaks. To achieve successful and nearly ‘real-time’ monitoring and identification of outbreaks, reliable sub-typing is essential. Whole genome sequencing (WGS) shows great promises for using as a routine epidemiological typing....... Enteritidis and 5 S. Derby were also sequenced and used for comparison. A number of different bioinformatics approaches were applied on the data; including pan-genome tree, k-mer tree, nucleotide difference tree and SNP tree. The outcome of each approach was evaluated in relation to the association...... of the isolates to specific outbreaks. The pan-genome tree clustered 65% of the S. Typhimurium isolates according to the pre-defined epidemiology, the k-mer tree 88%, the nucleotide difference tree 100% and the SNP tree 100% of the strains within S. Typhimurium. The resulting outcome of the four phylogenetic...

Evaluating genome-wide DNA methylation changes in mice by Methylation Specific Digital Karyotyping

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Maruoka Shuichiro

2008-12-01

Full Text Available Abstract Background The study of genome-wide DNA methylation changes has become more accessible with the development of various array-based technologies though when studying species other than human the choice of applications are limited and not always within reach. In this study, we adapted and tested the applicability of Methylation Specific Digital Karyotyping (MSDK, a non-array based method, for the prospective analysis of epigenetic changes after perinatal nutritional modifications in a mouse model of allergic airway disease. MSDK is a sequenced based method that allows a comprehensive and unbiased methylation profiling. The method generates 21 base pairs long sequence tags derived from specific locations in the genome. The resulting tag frequencies determine in a quantitative manner the methylation level of the corresponding loci. Results Genomic DNA from whole lung was isolated and subjected to MSDK analysis using the methylation-sensitive enzyme Not I as the mapping enzyme and Nla III as the fragmenting enzyme. In a pair wise comparison of the generated mouse MSDK libraries we identified 158 loci that are significantly differentially methylated (P-value = 0.05 after perinatal dietary changes in our mouse model. Quantitative methylation specific PCR and sequence analysis of bisulfate modified genomic DNA confirmed changes in methylation at specific loci. Differences in genomic MSDK tag counts for a selected set of genes, correlated well with changes in transcription levels as measured by real-time PCR. Furthermore serial analysis of gene expression profiling demonstrated a dramatic difference in expressed transcripts in mice exposed to perinatal nutritional changes. Conclusion The genome-wide methylation survey applied in this study allowed for an unbiased methylation profiling revealing subtle changes in DNA methylation in mice maternally exposed to dietary changes in methyl-donor content. The MSDK method is applicable for mouse models

r2VIM: A new variable selection method for random forests in genome-wide association studies.

Science.gov (United States)

Szymczak, Silke; Holzinger, Emily; Dasgupta, Abhijit; Malley, James D; Molloy, Anne M; Mills, James L; Brody, Lawrence C; Stambolian, Dwight; Bailey-Wilson, Joan E

2016-01-01

Machine learning methods and in particular random forests (RFs) are a promising alternative to standard single SNP analyses in genome-wide association studies (GWAS). RFs provide variable importance measures (VIMs) to rank SNPs according to their predictive power. However, in contrast to the established genome-wide significance threshold, no clear criteria exist to determine how many SNPs should be selected for downstream analyses. We propose a new variable selection approach, recurrent relative variable importance measure (r2VIM). Importance values are calculated relative to an observed minimal importance score for several runs of RF and only SNPs with large relative VIMs in all of the runs are selected as important. Evaluations on simulated GWAS data show that the new method controls the number of false-positives under the null hypothesis. Under a simple alternative hypothesis with several independent main effects it is only slightly less powerful than logistic regression. In an experimental GWAS data set, the same strong signal is identified while the approach selects none of the SNPs in an underpowered GWAS. The novel variable selection method r2VIM is a promising extension to standard RF for objectively selecting relevant SNPs in GWAS while controlling the number of false-positive results.

Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

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Ueki Masao

2012-05-01

Full Text Available Abstract Background Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. Results We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium data. Conclusions Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.

Multiplexed genome engineering and genotyping methods applications for synthetic biology and metabolic engineering.

Science.gov (United States)

Wang, Harris H; Church, George M

2011-01-01

Engineering at the scale of whole genomes requires fundamentally new molecular biology tools. Recent advances in recombineering using synthetic oligonucleotides enable the rapid generation of mutants at high efficiency and specificity and can be implemented at the genome scale. With these techniques, libraries of mutants can be generated, from which individuals with functionally useful phenotypes can be isolated. Furthermore, populations of cells can be evolved in situ by directed evolution using complex pools of oligonucleotides. Here, we discuss ways to utilize these multiplexed genome engineering methods, with special emphasis on experimental design and implementation. Copyright © 2011 Elsevier Inc. All rights reserved.

Genomic prediction using subsampling.

Science.gov (United States)

Xavier, Alencar; Xu, Shizhong; Muir, William; Rainey, Katy Martin

2017-03-24

Genome-wide assisted selection is a critical tool for the genetic improvement of plants and animals. Whole-genome regression models in Bayesian framework represent the main family of prediction methods. Fitting such models with a large number of observations involves a prohibitive computational burden. We propose the use of subsampling bootstrap Markov chain in genomic prediction. Such method consists of fitting whole-genome regression models by subsampling observations in each round of a Markov Chain Monte Carlo. We evaluated the effect of subsampling bootstrap on prediction and computational parameters. Across datasets, we observed an optimal subsampling proportion of observations around 50% with replacement, and around 33% without replacement. Subsampling provided a substantial decrease in computation time, reducing the time to fit the model by half. On average, losses on predictive properties imposed by subsampling were negligible, usually below 1%. For each dataset, an optimal subsampling point that improves prediction properties was observed, but the improvements were also negligible. Combining subsampling with Gibbs sampling is an interesting ensemble algorithm. The investigation indicates that the subsampling bootstrap Markov chain algorithm substantially reduces computational burden associated with model fitting, and it may slightly enhance prediction properties.

Genomic prediction when some animals are not genotyped

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Lund Mogens S

2010-01-01

Full Text Available Abstract Background The use of genomic selection in breeding programs may increase the rate of genetic improvement, reduce the generation time, and provide higher accuracy of estimated breeding values (EBVs. A number of different methods have been developed for genomic prediction of breeding values, but many of them assume that all animals have been genotyped. In practice, not all animals are genotyped, and the methods have to be adapted to this situation. Results In this paper we provide an extension of a linear mixed model method for genomic prediction to the situation with non-genotyped animals. The model specifies that a breeding value is the sum of a genomic and a polygenic genetic random effect, where genomic genetic random effects are correlated with a genomic relationship matrix constructed from markers and the polygenic genetic random effects are correlated with the usual relationship matrix. The extension of the model to non-genotyped animals is made by using the pedigree to derive an extension of the genomic relationship matrix to non-genotyped animals. As a result, in the extended model the estimated breeding values are obtained by blending the information used to compute traditional EBVs and the information used to compute purely genomic EBVs. Parameters in the model are estimated using average information REML and estimated breeding values are best linear unbiased predictions (BLUPs. The method is illustrated using a simulated data set. Conclusions The extension of the method to non-genotyped animals presented in this paper makes it possible to integrate all the genomic, pedigree and phenotype information into a one-step procedure for genomic prediction. Such a one-step procedure results in more accurate estimated breeding values and has the potential to become the standard tool for genomic prediction of breeding values in future practical evaluations in pig and cattle breeding.

REGEN: Ancestral Genome Reconstruction for Bacteria

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João C. Setubal

2012-07-01

Full Text Available Ancestral genome reconstruction can be understood as a phylogenetic study with more details than a traditional phylogenetic tree reconstruction. We present a new computational system called REGEN for ancestral bacterial genome reconstruction at both the gene and replicon levels. REGEN reconstructs gene content, contiguous gene runs, and replicon structure for each ancestral genome. Along each branch of the phylogenetic tree, REGEN infers evolutionary events, including gene creation and deletion and replicon fission and fusion. The reconstruction can be performed by either a maximum parsimony or a maximum likelihood method. Gene content reconstruction is based on the concept of neighboring gene pairs. REGEN was designed to be used with any set of genomes that are sufficiently related, which will usually be the case for bacteria within the same taxonomic order. We evaluated REGEN using simulated genomes and genomes in the Rhizobiales order.

A comparative evaluation of genome assembly reconciliation tools.

Science.gov (United States)

Alhakami, Hind; Mirebrahim, Hamid; Lonardi, Stefano

2017-05-18

The majority of eukaryotic genomes are unfinished due to the algorithmic challenges of assembling them. A variety of assembly and scaffolding tools are available, but it is not always obvious which tool or parameters to use for a specific genome size and complexity. It is, therefore, common practice to produce multiple assemblies using different assemblers and parameters, then select the best one for public release. A more compelling approach would allow one to merge multiple assemblies with the intent of producing a higher quality consensus assembly, which is the objective of assembly reconciliation. Several assembly reconciliation tools have been proposed in the literature, but their strengths and weaknesses have never been compared on a common dataset. We fill this need with this work, in which we report on an extensive comparative evaluation of several tools. Specifically, we evaluate contiguity, correctness, coverage, and the duplication ratio of the merged assembly compared to the individual assemblies provided as input. None of the tools we tested consistently improved the quality of the input GAGE and synthetic assemblies. Our experiments show an increase in contiguity in the consensus assembly when the original assemblies already have high quality. In terms of correctness, the quality of the results depends on the specific tool, as well as on the quality and the ranking of the input assemblies. In general, the number of misassemblies ranges from being comparable to the best of the input assembly to being comparable to the worst of the input assembly.

Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq)—A Method for High-Throughput Analysis of Differentially Methylated CCGG Sites in Plants with Large Genomes

Science.gov (United States)

Chwialkowska, Karolina; Korotko, Urszula; Kosinska, Joanna; Szarejko, Iwona; Kwasniewski, Miroslaw

2017-01-01

Epigenetic mechanisms, including histone modifications and DNA methylation, mutually regulate chromatin structure, maintain genome integrity, and affect gene expression and transposon mobility. Variations in DNA methylation within plant populations, as well as methylation in response to internal and external factors, are of increasing interest, especially in the crop research field. Methylation Sensitive Amplification Polymorphism (MSAP) is one of the most commonly used methods for assessing DNA methylation changes in plants. This method involves gel-based visualization of PCR fragments from selectively amplified DNA that are cleaved using methylation-sensitive restriction enzymes. In this study, we developed and validated a new method based on the conventional MSAP approach called Methylation Sensitive Amplification Polymorphism Sequencing (MSAP-Seq). We improved the MSAP-based approach by replacing the conventional separation of amplicons on polyacrylamide gels with direct, high-throughput sequencing using Next Generation Sequencing (NGS) and automated data analysis. MSAP-Seq allows for global sequence-based identification of changes in DNA methylation. This technique was validated in Hordeum vulgare. However, MSAP-Seq can be straightforwardly implemented in different plant species, including crops with large, complex and highly repetitive genomes. The incorporation of high-throughput sequencing into MSAP-Seq enables parallel and direct analysis of DNA methylation in hundreds of thousands of sites across the genome. MSAP-Seq provides direct genomic localization of changes and enables quantitative evaluation. We have shown that the MSAP-Seq method specifically targets gene-containing regions and that a single analysis can cover three-quarters of all genes in large genomes. Moreover, MSAP-Seq's simplicity, cost effectiveness, and high-multiplexing capability make this method highly affordable. Therefore, MSAP-Seq can be used for DNA methylation analysis in crop

Evaluation of nine popular de novo assemblers in microbial genome assembly.

Science.gov (United States)

Forouzan, Esmaeil; Maleki, Masoumeh Sadat Mousavi; Karkhane, Ali Asghar; Yakhchali, Bagher

2017-12-01

Next generation sequencing (NGS) technologies are revolutionizing biology, with Illumina being the most popular NGS platform. Short read assembly is a critical part of most genome studies using NGS. Hence, in this study, the performance of nine well-known assemblers was evaluated in the assembly of seven different microbial genomes. Effect of different read coverage and k-mer parameters on the quality of the assembly were also evaluated on both simulated and actual read datasets. Our results show that the performance of assemblers on real and simulated datasets could be significantly different, mainly because of coverage bias. According to outputs on actual read datasets, for all studied read coverages (of 7×, 25× and 100×), SPAdes and IDBA-UD clearly outperformed other assemblers based on NGA50 and accuracy metrics. Velvet is the most conservative assembler with the lowest NGA50 and error rate. Copyright © 2017. Published by Elsevier B.V.

Evaluation of genome-enabled selection for bacterial cold water disease resistance using progeny performance data in Rainbow Trout: Insights on genotyping methods and genomic prediction models

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Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation. We used genomic selection (GS) models to predict genomic br...

In Silico Genomic Fingerprints of the Bacillus anthracis Group Obtained by Virtual Hybridization

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Hueman Jaimes-Díaz

2015-02-01

Full Text Available In this study we evaluate the capacity of Virtual Hybridization to identify between highly related bacterial strains. Eight genomic fingerprints were obtained by virtual hybridization for the Bacillus anthracis genome set, and a set of 15,264 13-nucleotide short probes designed to produce genomic fingerprints unique for each organism. The data obtained from each genomic fingerprint were used to obtain hybridization patterns simulating a DNA microarray. Two virtual hybridization methods were used: the Direct and the Extended method to identify the number of potential hybridization sites and thus determine the minimum sensitivity value to discriminate between genomes with 99.9% similarity. Genomic fingerprints were compared using both methods and phylogenomic trees were constructed to verify that the minimum detection value is 0.000017. Results obtained from the genomic fingerprints suggest that the distribution in the trees is correct, as compared to other taxonomic methods. Specific virtual hybridization sites for each of the genomes studied were also identified.

Development of a fluorescence-activated cell sorting method coupled with whole genome amplification to analyze minority and trace Dehalococcoides genomes in microbial communities.

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Lee, Patrick K H; Men, Yujie; Wang, Shanquan; He, Jianzhong; Alvarez-Cohen, Lisa

2015-02-03

Dehalococcoides mccartyi are functionally important bacteria that catalyze the reductive dechlorination of chlorinated ethenes. However, these anaerobic bacteria are fastidious to isolate, making downstream genomic characterization challenging. In order to facilitate genomic analysis, a fluorescence-activated cell sorting (FACS) method was developed in this study to separate D. mccartyi cells from a microbial community, and the DNA of the isolated cells was processed by whole genome amplification (WGA) and hybridized onto a D. mccartyi microarray for comparative genomics against four sequenced strains. First, FACS was successfully applied to a D. mccartyi isolate as positive control, and then microarray results verified that WGA from 10(6) cells or ∼1 ng of genomic DNA yielded high-quality coverage detecting nearly all genes across the genome. As expected, some inter- and intrasample variability in WGA was observed, but these biases were minimized by performing multiple parallel amplifications. Subsequent application of the FACS and WGA protocols to two enrichment cultures containing ∼10% and ∼1% D. mccartyi cells successfully enabled genomic analysis. As proof of concept, this study demonstrates that coupling FACS with WGA and microarrays is a promising tool to expedite genomic characterization of target strains in environmental communities where the relative concentrations are low.

Calculation of 3D genome structures for comparison of chromosome conformation capture experiments with microscopy: An evaluation of single-cell Hi-C protocols.

Science.gov (United States)

Lando, David; Stevens, Tim J; Basu, Srinjan; Laue, Ernest D

2018-01-01

Single-cell chromosome conformation capture approaches are revealing the extent of cell-to-cell variability in the organization and packaging of genomes. These single-cell methods, unlike their multi-cell counterparts, allow straightforward computation of realistic chromosome conformations that may be compared and combined with other, independent, techniques to study 3D structure. Here we discuss how single-cell Hi-C and subsequent 3D genome structure determination allows comparison with data from microscopy. We then carry out a systematic evaluation of recently published single-cell Hi-C datasets to establish a computational approach for the evaluation of single-cell Hi-C protocols. We show that the calculation of genome structures provides a useful tool for assessing the quality of single-cell Hi-C data because it requires a self-consistent network of interactions, relating to the underlying 3D conformation, with few errors, as well as sufficient longer-range cis- and trans-chromosomal contacts.

Comprehensive evaluation of SNP identification with the Restriction Enzyme-based Reduced Representation Library (RRL method

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Du Ye

2012-02-01

Full Text Available Abstract Background Restriction Enzyme-based Reduced Representation Library (RRL method represents a relatively feasible and flexible strategy used for Single Nucleotide Polymorphism (SNP identification in different species. It has remarkable advantage of reducing the complexity of the genome by orders of magnitude. However, comprehensive evaluation for actual efficacy of SNP identification by this method is still unavailable. Results In order to evaluate the efficacy of Restriction Enzyme-based RRL method, we selected Tsp 45I enzyme which covers 266 Mb flanking region of the enzyme recognition site according to in silico simulation on human reference genome, then we sequenced YH RRL after Tsp 45I treatment and obtained reads of which 80.8% were mapped to target region with an 20-fold average coverage, about 96.8% of target region was covered by at least one read and 257 K SNPs were identified in the region using SOAPsnp software. Compared with whole genome resequencing data, we observed false discovery rate (FDR of 13.95% and false negative rate (FNR of 25.90%. The concordance rate of homozygote loci was over 99.8%, but that of heterozygote were only 92.56%. Repeat sequences and bases quality were proved to have a great effect on the accuracy of SNP calling, SNPs in recognition sites contributed evidently to the high FNR and the low concordance rate of heterozygote. Our results indicated that repeat masking and high stringent filter criteria could significantly decrease both FDR and FNR. Conclusions This study demonstrates that Restriction Enzyme-based RRL method was effective for SNP identification. The results highlight the important role of bias and the method-derived defects represented in this method and emphasize the special attentions noteworthy.

Analysis of IAV Replication and Co-infection Dynamics by a Versatile RNA Viral Genome Labeling Method

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Dan Dou

2017-07-01

Full Text Available Genome delivery to the proper cellular compartment for transcription and replication is a primary goal of viruses. However, methods for analyzing viral genome localization and differentiating genomes with high identity are lacking, making it difficult to investigate entry-related processes and co-examine heterogeneous RNA viral populations. Here, we present an RNA labeling approach for single-cell analysis of RNA viral replication and co-infection dynamics in situ, which uses the versatility of padlock probes. We applied this method to identify influenza A virus (IAV infections in cells and lung tissue with single-nucleotide specificity and to classify entry and replication stages by gene segment localization. Extending the classification strategy to co-infections of IAVs with single-nucleotide variations, we found that the dependence on intracellular trafficking places a time restriction on secondary co-infections necessary for genome reassortment. Altogether, these data demonstrate how RNA viral genome labeling can help dissect entry and co-infections.

Comparative Analysis of the Genomic DNA Isolation Methods on Inula sp. (Asteraceae

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Emre SEVİNDİK

2016-12-01

Full Text Available Simple, fast, low-cost and high throughput protocols are required for DNA isolation of plant species. In this study, phenol chloroform isoamyl alcohol and commercial (Sigma DNA isolation kit methods were applied on some Inula species that belong to Asteraceae family. Genomic DNA amounts, A260, A280, A260/A230 and purity degrees (A260/A280 that were obtained through both methods were measured through electrophoresis and spectrophotometer. Additionally, PCR amplification was realized by primer pairs specific to nrDNA ITS, cpDNA ndhF (972F-1603R and trnL-F regions. Results showed that maximum genomic DNA in nanograms obtained by phenol chloroform isoamyl alcohol method. The study also revealed that I. macrocephala had the maximum DNA and I. heterolepis had the minimum DNA amount. A260/A280 purity degrees showed that the highest and lowest purity in gDNAs obtained through phenol-choloform isoamyl alcohol method were in I.aucheriana and I. salicina, respectively. The highest and lowest purity degrees of gDNAs obtained through commercial kit was observed in I. fragilis and I. macrocephala samples, respectively. PCR amplification results showed that while band profiles of each three regions (ITS, trnL-F and ndhF did not yield positive results in PCR amplifications using phenol-choloform isoamyl alcohol method; PCR band profiles obtained through commercial kit yielded positive results. As a result, it is fair to say that the relation of genomic DNA with PCR was found to be more efficient although the maximum amount of genomic DNA was obtained through phenol chloroform isoamyl alcohol method.

Download - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive [Life Science Database Archive metadata

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Full Text Available List Contact us PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods ...t_db_link_en.zip (36.3 KB) - 6 Genome analysis methods pgdbj_dna_marker_linkage_map_genome_analysis_methods_... of This Database Site Policy | Contact Us Download - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive ...

Selection of Suitable DNA Extraction Methods for Genetically Modified Maize 3272, and Development and Evaluation of an Event-Specific Quantitative PCR Method for 3272.

Science.gov (United States)

Takabatake, Reona; Masubuchi, Tomoko; Futo, Satoshi; Minegishi, Yasutaka; Noguchi, Akio; Kondo, Kazunari; Teshima, Reiko; Kurashima, Takeyo; Mano, Junichi; Kitta, Kazumi

2016-01-01

A novel real-time PCR-based analytical method was developed for the event-specific quantification of a genetically modified (GM) maize, 3272. We first attempted to obtain genome DNA from this maize using a DNeasy Plant Maxi kit and a DNeasy Plant Mini kit, which have been widely utilized in our previous studies, but DNA extraction yields from 3272 were markedly lower than those from non-GM maize seeds. However, lowering of DNA extraction yields was not observed with GM quicker or Genomic-tip 20/G. We chose GM quicker for evaluation of the quantitative method. We prepared a standard plasmid for 3272 quantification. The conversion factor (Cf), which is required to calculate the amount of a genetically modified organism (GMO), was experimentally determined for two real-time PCR instruments, the Applied Biosystems 7900HT (the ABI 7900) and the Applied Biosystems 7500 (the ABI7500). The determined Cf values were 0.60 and 0.59 for the ABI 7900 and the ABI 7500, respectively. To evaluate the developed method, a blind test was conducted as part of an interlaboratory study. The trueness and precision were evaluated as the bias and reproducibility of the relative standard deviation (RSDr). The determined values were similar to those in our previous validation studies. The limit of quantitation for the method was estimated to be 0.5% or less, and we concluded that the developed method would be suitable and practical for detection and quantification of 3272.

Contributions to In Silico Genome Annotation

KAUST Repository

Kalkatawi, Manal M.

2017-11-30

Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation, while identifying functions of these regions is considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and timeconsuming. This study contributes to genome annotation by introducing several novel bioinformatics methods, some based on machine learning (ML) approaches. First, we present Dragon PolyA Spotter (DPS), a method for accurate identification of the polyadenylation signals (PAS) within human genomic DNA sequences. For this, we derived a novel feature-set able to characterize properties of the genomic region surrounding the PAS, enabling development of high accuracy optimized ML predictive models. DPS considerably outperformed the state-of-the-art results. The second contribution concerns developing generic models for structural annotation, i.e., the recognition of different genomic signals and regions (GSR) within eukaryotic DNA. We developed DeepGSR, a systematic framework that facilitates generating ML models to predict GSR with high accuracy. To the best of our knowledge, no available generic and automated method exists for such task that could facilitate the studies of newly sequenced organisms. The prediction module of DeepGSR uses deep learning algorithms to derive highly abstract features that depend mainly on proper data representation and hyperparameters calibration. DeepGSR, which was evaluated on recognition of PAS and translation initiation sites (TIS) in different organisms, yields a simpler and more precise representation of the problem under study, compared to some other hand-tailored models, while producing high accuracy prediction results. Finally

Genomic breeding value prediction:methods and procedures

NARCIS (Netherlands)

Calus, M.P.L.

2010-01-01

Animal breeding faces one of the most significant changes of the past decades – the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the

Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory

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Crisan, Anamaria; McKee, Geoffrey; Munzner, Tamara

2018-01-01

Background Microbial genome sequencing is now being routinely used in many clinical and public health laboratories. Understanding how to report complex genomic test results to stakeholders who may have varying familiarity with genomics—including clinicians, laboratorians, epidemiologists, and researchers—is critical to the successful and sustainable implementation of this new technology; however, there are no evidence-based guidelines for designing such a report in the pathogen genomics domain. Here, we describe an iterative, human-centered approach to creating a report template for communicating tuberculosis (TB) genomic test results. Methods We used Design Study Methodology—a human centered approach drawn from the information visualization domain—to redesign an existing clinical report. We used expert consults and an online questionnaire to discover various stakeholders’ needs around the types of data and tasks related to TB that they encounter in their daily workflow. We also evaluated their perceptions of and familiarity with genomic data, as well as its utility at various clinical decision points. These data shaped the design of multiple prototype reports that were compared against the existing report through a second online survey, with the resulting qualitative and quantitative data informing the final, redesigned, report. Results We recruited 78 participants, 65 of whom were clinicians, nurses, laboratorians, researchers, and epidemiologists involved in TB diagnosis, treatment, and/or surveillance. Our first survey indicated that participants were largely enthusiastic about genomic data, with the majority agreeing on its utility for certain TB diagnosis and treatment tasks and many reporting some confidence in their ability to interpret this type of data (between 58.8% and 94.1%, depending on the specific data type). When we compared our four prototype reports against the existing design, we found that for the majority (86.7%) of design

Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory

Directory of Open Access Journals (Sweden)

Anamaria Crisan

2018-01-01

Full Text Available Background Microbial genome sequencing is now being routinely used in many clinical and public health laboratories. Understanding how to report complex genomic test results to stakeholders who may have varying familiarity with genomics—including clinicians, laboratorians, epidemiologists, and researchers—is critical to the successful and sustainable implementation of this new technology; however, there are no evidence-based guidelines for designing such a report in the pathogen genomics domain. Here, we describe an iterative, human-centered approach to creating a report template for communicating tuberculosis (TB genomic test results. Methods We used Design Study Methodology—a human centered approach drawn from the information visualization domain—to redesign an existing clinical report. We used expert consults and an online questionnaire to discover various stakeholders’ needs around the types of data and tasks related to TB that they encounter in their daily workflow. We also evaluated their perceptions of and familiarity with genomic data, as well as its utility at various clinical decision points. These data shaped the design of multiple prototype reports that were compared against the existing report through a second online survey, with the resulting qualitative and quantitative data informing the final, redesigned, report. Results We recruited 78 participants, 65 of whom were clinicians, nurses, laboratorians, researchers, and epidemiologists involved in TB diagnosis, treatment, and/or surveillance. Our first survey indicated that participants were largely enthusiastic about genomic data, with the majority agreeing on its utility for certain TB diagnosis and treatment tasks and many reporting some confidence in their ability to interpret this type of data (between 58.8% and 94.1%, depending on the specific data type. When we compared our four prototype reports against the existing design, we found that for the majority (86.7% of

A comparison of multivariate genome-wide association methods

DEFF Research Database (Denmark)

Galesloot, Tessel E; Van Steen, Kristel; Kiemeney, Lambertus A L M

2014-01-01

Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six...... methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three...... for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between...

Comparison of methods used to identify superior individuals in genomic selection in plant breeding.

Science.gov (United States)

Bhering, L L; Junqueira, V S; Peixoto, L A; Cruz, C D; Laviola, B G

2015-09-10

The aim of this study was to evaluate different methods used in genomic selection, and to verify those that select a higher proportion of individuals with superior genotypes. Thus, F2 populations of different sizes were simulated (100, 200, 500, and 1000 individuals) with 10 replications each. These consisted of 10 linkage groups (LG) of 100 cM each, containing 100 equally spaced markers per linkage group, of which 200 controlled the characteristics, defined as the 20 initials of each LG. Genetic and phenotypic values were simulated assuming binomial distribution of effects for each LG, and the absence of dominance. For phenotypic values, heritabilities of 20, 50, and 80% were considered. To compare methodologies, the analysis processing time, coefficient of coincidence (selection of 5, 10, and 20% of superior individuals), and Spearman correlation between true genetic values, and the genomic values predicted by each methodology were determined. Considering the processing time, the three methodologies were statistically different, rrBLUP was the fastest, and Bayesian LASSO was the slowest. Spearman correlation revealed that the rrBLUP and GBLUP methodologies were equivalent, and Bayesian LASSO provided the lowest correlation values. Similar results were obtained in coincidence variables among the individuals selected, in which Bayesian LASSO differed statistically and presented a lower value than the other methodologies. Therefore, for the scenarios evaluated, rrBLUP is the best methodology for the selection of genetically superior individuals.

Using Partial Genomic Fosmid Libraries for Sequencing CompleteOrganellar Genomes

Energy Technology Data Exchange (ETDEWEB)

McNeal, Joel R.; Leebens-Mack, James H.; Arumuganathan, K.; Kuehl, Jennifer V.; Boore, Jeffrey L.; dePamphilis, Claude W.

2005-08-26

Organellar genome sequences provide numerous phylogenetic markers and yield insight into organellar function and molecular evolution. These genomes are much smaller in size than their nuclear counterparts; thus, their complete sequencing is much less expensive than total nuclear genome sequencing, making broader phylogenetic sampling feasible. However, for some organisms it is challenging to isolate plastid DNA for sequencing using standard methods. To overcome these difficulties, we constructed partial genomic libraries from total DNA preparations of two heterotrophic and two autotrophic angiosperm species using fosmid vectors. We then used macroarray screening to isolate clones containing large fragments of plastid DNA. A minimum tiling path of clones comprising the entire genome sequence of each plastid was selected, and these clones were shotgun-sequenced and assembled into complete genomes. Although this method worked well for both heterotrophic and autotrophic plants, nuclear genome size had a dramatic effect on the proportion of screened clones containing plastid DNA and, consequently, the overall number of clones that must be screened to ensure full plastid genome coverage. This technique makes it possible to determine complete plastid genome sequences for organisms that defy other available organellar genome sequencing methods, especially those for which limited amounts of tissue are available.

CompaGB: An open framework for genome browsers comparison

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Chiapello Hélène

2011-05-01

Full Text Available Abstract Background Tools to visualize and explore genomes hold a central place in genomics and the diversity of genome browsers has increased dramatically over the last few years. It often turns out to be a daunting task to compare and choose a well-adapted genome browser, as multidisciplinary knowledge is required to carry out this task and the number of tools, functionalities and features are overwhelming. Findings To assist in this task, we propose a community-based framework based on two cornerstones: (i the implementation of industry promoted software qualification method (QSOS adapted for genome browser evaluations, and (ii a web resource providing numerous facilities either for visualizing comparisons or performing new evaluations. We formulated 60 criteria specifically for genome browsers, and incorporated another 65 directly from QSOS's generic section. Those criteria aim to answer versatile needs, ranging from a biologist whose interest primarily lies into user-friendly and informative functionalities, a bioinformatician who wants to integrate the genome browser into a wider framework, or a computer scientist who might choose a software according to more technical features. We developed a dedicated web application to enrich the existing QSOS functionalities (weighting of criteria, user profile with features of interest to a community-based framework: easy management of evolving data, user comments... Conclusions The framework is available at http://genome.jouy.inra.fr/CompaGB. It is open to anyone who wishes to participate in the evaluations. It helps the scientific community to (1 choose a genome browser that would better fit their particular project, (2 visualize features comparatively with easily accessible formats, such as tables or radar plots and (3 perform their own evaluation against the defined criteria. To illustrate the CompaGB functionalities, we have evaluated seven genome browsers according to the implemented methodology

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Comparison on genomic predictions using GBLUP models and two single-step blending methods with different relationship matrices in the Nordic Holstein population

DEFF Research Database (Denmark)

Gao, Hongding; Christensen, Ole Fredslund; Madsen, Per

2012-01-01

Background A single-step blending approach allows genomic prediction using information of genotyped and non-genotyped animals simultaneously. However, the combined relationship matrix in a single-step method may need to be adjusted because marker-based and pedigree-based relationship matrices may...... not be on the same scale. The same may apply when a GBLUP model includes both genomic breeding values and residual polygenic effects. The objective of this study was to compare single-step blending methods and GBLUP methods with and without adjustment of the genomic relationship matrix for genomic prediction of 16......) a simple GBLUP method, 2) a GBLUP method with a polygenic effect, 3) an adjusted GBLUP method with a polygenic effect, 4) a single-step blending method, and 5) an adjusted single-step blending method. In the adjusted GBLUP and single-step methods, the genomic relationship matrix was adjusted...

Comparison of methods for genomic localization of gene trap sequences

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Ferrin Thomas E

2006-09-01

Full Text Available Abstract Background Gene knockouts in a model organism such as mouse provide a valuable resource for the study of basic biology and human disease. Determining which gene has been inactivated by an untargeted gene trapping event poses a challenging annotation problem because gene trap sequence tags, which represent sequence near the vector insertion site of a trapped gene, are typically short and often contain unresolved residues. To understand better the localization of these sequences on the mouse genome, we compared stand-alone versions of the alignment programs BLAT, SSAHA, and MegaBLAST. A set of 3,369 sequence tags was aligned to build 34 of the mouse genome using default parameters for each algorithm. Known genome coordinates for the cognate set of full-length genes (1,659 sequences were used to evaluate localization results. Results In general, all three programs performed well in terms of localizing sequences to a general region of the genome, with only relatively subtle errors identified for a small proportion of the sequence tags. However, large differences in performance were noted with regard to correctly identifying exon boundaries. BLAT correctly identified the vast majority of exon boundaries, while SSAHA and MegaBLAST missed the majority of exon boundaries. SSAHA consistently reported the fewest false positives and is the fastest algorithm. MegaBLAST was comparable to BLAT in speed, but was the most susceptible to localizing sequence tags incorrectly to pseudogenes. Conclusion The differences in performance for sequence tags and full-length reference sequences were surprisingly small. Characteristic variations in localization results for each program were noted that affect the localization of sequence at exon boundaries, in particular.

A novel bioinformatics method for efficient knowledge discovery by BLSOM from big genomic sequence data.

Science.gov (United States)

Bai, Yu; Iwasaki, Yuki; Kanaya, Shigehiko; Zhao, Yue; Ikemura, Toshimichi

2014-01-01

With remarkable increase of genomic sequence data of a wide range of species, novel tools are needed for comprehensive analyses of the big sequence data. Self-Organizing Map (SOM) is an effective tool for clustering and visualizing high-dimensional data such as oligonucleotide composition on one map. By modifying the conventional SOM, we have previously developed Batch-Learning SOM (BLSOM), which allows classification of sequence fragments according to species, solely depending on the oligonucleotide composition. In the present study, we introduce the oligonucleotide BLSOM used for characterization of vertebrate genome sequences. We first analyzed pentanucleotide compositions in 100 kb sequences derived from a wide range of vertebrate genomes and then the compositions in the human and mouse genomes in order to investigate an efficient method for detecting differences between the closely related genomes. BLSOM can recognize the species-specific key combination of oligonucleotide frequencies in each genome, which is called a "genome signature," and the specific regions specifically enriched in transcription-factor-binding sequences. Because the classification and visualization power is very high, BLSOM is an efficient powerful tool for extracting a wide range of information from massive amounts of genomic sequences (i.e., big sequence data).

Genomic Prediction from Whole Genome Sequence in Livestock: The 1000 Bull Genomes Project

DEFF Research Database (Denmark)

Hayes, Benjamin J; MacLeod, Iona M; Daetwyler, Hans D

Advantages of using whole genome sequence data to predict genomic estimated breeding values (GEBV) include better persistence of accuracy of GEBV across generations and more accurate GEBV across breeds. The 1000 Bull Genomes Project provides a database of whole genome sequenced key ancestor bulls....... In a dairy data set, predictions using BayesRC and imputed sequence data from 1000 Bull Genomes were 2% more accurate than with 800k data. We could demonstrate the method identified causal mutations in some cases. Further improvements will come from more accurate imputation of sequence variant genotypes...

EFSA BIOHAZ Panel (EFSA Panel on Biological Hazards) , 2013 . Scientific Opinion on the evaluation of molecular typing methods for major food-borne microbiological hazards and their use for attribution modelling, outbreak investigation and scanning surveillance: Part 1 (evaluation of methods

DEFF Research Database (Denmark)

Hald, Tine; Baggesen, Dorte Lau

An evaluation of molecular typing methods that can be applied to the food-borne pathogens Salmonella, Campylobacter, Shiga toxin-producing Escherichia coli and Listeria monocytogenes is presented. This evaluation is divided in two parts. Firstly, commonly used molecular typing methods are assessed...... against a set of predefined criteria relating to discriminatory capacity, reproducibility, repeatability and current or potential suitability for international harmonisation. Secondly, the methods are evaluated for their appropriateness for use in different public health-related applications...... and potential for use of molecular characterisation methods, including whole genome sequencing technologies, in microbial food safety. Recommendations are also made in order to encourage a holistic and structured approach to the use of molecular characterisation methods for food-borne pathogens; in particular...

Genomics protocols [Methods in molecular biology, v. 175

National Research Council Canada - National Science Library

Starkey, Michael P; Elaswarapu, Ramnath

2001-01-01

... to the larger community of researchers who have recognized the potential of genomics research and may themselves be beginning to explore the technologies involved. Some of the techniques described in Genomics Protocols are clearly not restricted to the genomics field; indeed, a prerequisite for many procedures in this discipline is that they require an extremely high throughput, beyond the scope of the average investigator. However, what we have endeavored here to achieve is both to compile a collection of...

Evaluation of potential novel variations and their interactions related to bipolar disorders: analysis of genome-wide association study data.

Science.gov (United States)

Acikel, Cengizhan; Aydin Son, Yesim; Celik, Cemil; Gul, Husamettin

2016-01-01

Multifactor dimensionality reduction (MDR) is a nonparametric approach that can be used to detect relevant interactions between single-nucleotide polymorphisms (SNPs). The aim of this study was to build the best genomic model based on SNP associations and to identify candidate polymorphisms that are the underlying molecular basis of the bipolar disorders. This study was performed on Whole-Genome Association Study of Bipolar Disorder (dbGaP [database of Genotypes and Phenotypes] study accession number: phs000017.v3.p1) data. After preprocessing of the genotyping data, three classification-based data mining methods (ie, random forest, naïve Bayes, and k-nearest neighbor) were performed. Additionally, as a nonparametric, model-free approach, the MDR method was used to evaluate the SNP profiles. The validity of these methods was evaluated using true classification rate, recall (sensitivity), precision (positive predictive value), and F-measure. Random forests, naïve Bayes, and k-nearest neighbors identified 16, 13, and ten candidate SNPs, respectively. Surprisingly, the top six SNPs were reported by all three methods. Random forests and k-nearest neighbors were more successful than naïve Bayes, with recall values >0.95. On the other hand, MDR generated a model with comparable predictive performance based on five SNPs. Although different SNP profiles were identified in MDR compared to the classification-based models, all models mapped SNPs to the DOCK10 gene. Three classification-based data mining approaches, random forests, naïve Bayes, and k-nearest neighbors, have prioritized similar SNP profiles as predictors of bipolar disorders, in contrast to MDR, which has found different SNPs through analysis of two-way and three-way interactions. The reduced number of associated SNPs discovered by MDR, without loss in the classification performance, would facilitate validation studies and decision support models, and would reduce the cost to develop predictive and

Platform comparison for evaluation of ALK protein immunohistochemical expression, genomic copy number and hotspot mutation status in neuroblastomas.

Directory of Open Access Journals (Sweden)

Benedict Yan

Full Text Available ALK is an established causative oncogenic driver in neuroblastoma, and is likely to emerge as a routine biomarker in neuroblastoma diagnostics. At present, the optimal strategy for clinical diagnostic evaluation of ALK protein, genomic and hotspot mutation status is not well-studied. We evaluated ALK immunohistochemical (IHC protein expression using three different antibodies (ALK1, 5A4 and D5F3 clones, ALK genomic status using single-color chromogenic in situ hybridization (CISH, and ALK hotspot mutation status using conventional Sanger sequencing and a next-generation sequencing platform (Ion Torrent Personal Genome Machine (IT-PGM, in archival formalin-fixed, paraffin-embedded neuroblastoma samples. We found a significant difference in IHC results using the three different antibodies, with the highest percentage of positive cases seen on D5F3 immunohistochemistry. Correlation with ALK genomic and hotspot mutational status revealed that the majority of D5F3 ALK-positive cases did not possess either ALK genomic amplification or hotspot mutations. Comparison of sequencing platforms showed a perfect correlation between conventional Sanger and IT-PGM sequencing. Our findings suggest that D5F3 immunohistochemistry, single-color CISH and IT-PGM sequencing are suitable assays for evaluation of ALK status in future neuroblastoma clinical trials.

Simplified extraction of good quality genomic DNA from a variety of ...

African Journals Online (AJOL)

Depending on the nature and complexity of plant material, proper method needs to be employed for extraction of genomic DNA, along with its performance evaluation by different molecular techniques. Here, we optimized and employed a simple genomic DNA isolation protocol suitable for a variety of plant materials ...

Genomic methods take the plunge

DEFF Research Database (Denmark)

Cammen, Kristina M.; Andrews, Kimberly R.; Carroll, Emma L.

2016-01-01

The dramatic increase in the application of genomic techniques to non-model organisms (NMOs) over the past decade has yielded numerous valuable contributions to evolutionary biology and ecology, many of which would not have been possible with traditional genetic markers. We review this recent...

License - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods ...t list, Marker list, QTL list, Plant DB link & Genome analysis methods © Satoshi ... Policy | Contact Us License - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive ...

Genome scale models of yeast: towards standardized evaluation and consistent omic integration

DEFF Research Database (Denmark)

Sanchez, Benjamin J.; Nielsen, Jens

2015-01-01

Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published and are curre......Genome scale models (GEMs) have enabled remarkable advances in systems biology, acting as functional databases of metabolism, and as scaffolds for the contextualization of high-throughput data. In the case of Saccharomyces cerevisiae (budding yeast), several GEMs have been published...... in which all levels of omics data (from gene expression to flux) have been integrated in yeast GEMs. Relevant conclusions and current challenges for both GEM evaluation and omic integration are highlighted....

Genomic growth curves of an outbred pig population

Directory of Open Access Journals (Sweden)

Fabyano Fonseca e Silva

2013-01-01

Full Text Available In the current post-genomic era, the genetic basis of pig growth can be understood by assessing SNP marker effects and genomic breeding values (GEBV based on estimates of these growth curve parameters as phenotypes. Although various statistical methods, such as random regression (RR-BLUP and Bayesian LASSO (BL, have been applied to genomic selection (GS, none of these has yet been used in a growth curve approach. In this work, we compared the accuracies of RR-BLUP and BL using empirical weight-age data from an outbred F2 (Brazilian Piau X commercial population. The phenotypes were determined by parameter estimates using a nonlinear logistic regression model and the halothane gene was considered as a marker for evaluating the assumptions of the GS methods in relation to the genetic variation explained by each locus. BL yielded more accurate values for all of the phenotypes evaluated and was used to estimate SNP effects and GEBV vectors. The latter allowed the construction of genomic growth curves, which showed substantial genetic discrimination among animals in the final growth phase. The SNP effect estimates allowed identification of the most relevant markers for each phenotype, the positions of which were coincident with reported QTL regions for growth traits.

Genomic Testing

Science.gov (United States)

... this database. Top of Page Evaluation of Genomic Applications in Practice and Prevention (EGAPP™) In 2004, the Centers for Disease Control and Prevention launched the EGAPP initiative to establish and test a ... and other applications of genomic technology that are in transition from ...

A simple method of genomic DNA extraction suitable for analysis of bulk fungal strains.

Science.gov (United States)

Zhang, Y J; Zhang, S; Liu, X Z; Wen, H A; Wang, M

2010-07-01

A simple and rapid method (designated thermolysis) for extracting genomic DNA from bulk fungal strains was described. In the thermolysis method, a few mycelia or yeast cells were first rinsed with pure water to remove potential PCR inhibitors and then incubated in a lysis buffer at 85 degrees C to break down cell walls and membranes. This method was used to extract genomic DNA from large numbers of fungal strains (more than 92 species, 35 genera of three phyla) isolated from different sections of natural Ophiocordyceps sinensis specimens. Regions of interest from high as well as single-copy number genes were successfully amplified from the extracted DNA samples. The DNA samples obtained by this method can be stored at -20 degrees C for over 1 year. The method was effective, easy and fast and allowed batch DNA extraction from multiple fungal isolates. Use of the thermolysis method will allow researchers to obtain DNA from fungi quickly for use in molecular assays. This method requires only minute quantities of starting material and is suitable for diverse fungal species.

Evaluation of phylogenetic reconstruction methods using bacterial whole genomes: a simulation based study [version 1; referees: 1 approved, 2 approved with reservations

Directory of Open Access Journals (Sweden)

John A. Lees

2018-03-01

Full Text Available Background: Phylogenetic reconstruction is a necessary first step in many analyses which use whole genome sequence data from bacterial populations. There are many available methods to infer phylogenies, and these have various advantages and disadvantages, but few unbiased comparisons of the range of approaches have been made. Methods: We simulated data from a defined “true tree” using a realistic evolutionary model. We built phylogenies from this data using a range of methods, and compared reconstructed trees to the true tree using two measures, noting the computational time needed for different phylogenetic reconstructions. We also used real data from Streptococcus pneumoniae alignments to compare individual core gene trees to a core genome tree. Results: We found that, as expected, maximum likelihood trees from good quality alignments were the most accurate, but also the most computationally intensive. Using less accurate phylogenetic reconstruction methods, we were able to obtain results of comparable accuracy; we found that approximate results can rapidly be obtained using genetic distance based methods. In real data we found that highly conserved core genes, such as those involved in translation, gave an inaccurate tree topology, whereas genes involved in recombination events gave inaccurate branch lengths. We also show a tree-of-trees, relating the results of different phylogenetic reconstructions to each other. Conclusions: We recommend three approaches, depending on requirements for accuracy and computational time. Quicker approaches that do not perform full maximum likelihood optimisation may be useful for many analyses requiring a phylogeny, as generating a high quality input alignment is likely to be the major limiting factor of accurate tree topology. We have publicly released our simulated data and code to enable further comparisons.

Genomic variation in Salmonella enterica core genes for epidemiological typing

DEFF Research Database (Denmark)

Leekitcharoenphon, Pimlapas; Lukjancenko, Oksana; Rundsten, Carsten Friis

2012-01-01

Background: Technological advances in high throughput genome sequencing are making whole genome sequencing (WGS) available as a routine tool for bacterial typing. Standardized procedures for identification of relevant genes and of variation are needed to enable comparison between studies and over...... genomes and evaluate their value as typing targets, comparing whole genome typing and traditional methods such as 16S and MLST. A consensus tree based on variation of core genes gives much better resolution than 16S and MLST; the pan-genome family tree is similar to the consensus tree, but with higher...... that there is a positive selection towards mutations leading to amino acid changes. Conclusions: Genomic variation within the core genome is useful for investigating molecular evolution and providing candidate genes for bacterial genome typing. Identification of genes with different degrees of variation is important...

The Glyphosate-Based Herbicide Roundup Does not Elevate Genome-Wide Mutagenesis of Escherichia coli.

Science.gov (United States)

Tincher, Clayton; Long, Hongan; Behringer, Megan; Walker, Noah; Lynch, Michael

2017-10-05

Mutations induced by pollutants may promote pathogen evolution, for example by accelerating mutations conferring antibiotic resistance. Generally, evaluating the genome-wide mutagenic effects of long-term sublethal pollutant exposure at single-nucleotide resolution is extremely difficult. To overcome this technical barrier, we use the mutation accumulation/whole-genome sequencing (MA/WGS) method as a mutagenicity test, to quantitatively evaluate genome-wide mutagenesis of Escherichia coli after long-term exposure to a wide gradient of the glyphosate-based herbicide (GBH) Roundup Concentrate Plus. The genome-wide mutation rate decreases as GBH concentration increases, suggesting that even long-term GBH exposure does not compromise the genome stability of bacteria. Copyright © 2017 Tincher et al.

Identification of genomic insertion and flanking sequence of G2-EPSPS and GAT transgenes in soybean using whole genome sequencing method

Directory of Open Access Journals (Sweden)

Bingfu Guo

2016-07-01

Full Text Available Molecular characterization of sequences flanking exogenous fragment insertions is essential for safety assessment and labeling of genetically modified organisms (GMO. In this study, the T-DNA insertion sites and flanking sequences were identified in two newly developed transgenic glyphosate-tolerant soybeans GE-J16 and ZH10-6 based on whole genome sequencing (WGS method. About 21 Gb sequence data (~21× coverage for each line was generated on Illumina HiSeq 2500 platform. The junction reads mapped to boundary of T-DNA and flanking sequences in these two events were identified by comparing all sequencing reads with soybean reference genome and sequence of transgenic vector. The putative insertion loci and flanking sequences were further confirmed by PCR amplification, Sanger sequencing, and co-segregation analysis. All these analyses supported that exogenous T-DNA fragments were integrated in positions of Chr19: 50543767-50543792 and Chr17: 7980527-7980541 in these two transgenic lines. Identification of the genomic insertion site of the G2-EPSPS and GAT transgenes will facilitate the use of their glyphosate-tolerant traits in soybean breeding program. These results also demonstrated that WGS is a cost-effective and rapid method of identifying sites of T-DNA insertions and flanking sequences in soybean.

Comparison of genomic-enhanced EPD systems using an external phenotypic database

Science.gov (United States)

The American Angus Association (AAA) is currently evaluating two methods to incorporate genomic information into their genetic evaluation program: 1) multi-trait incorporation of an externally produced molecular breeding value as an indicator trait (MT) and 2) single-step evaluation with an unweight...

A novel genome-information content-based statistic for genome-wide association analysis designed for next-generation sequencing data.

Science.gov (United States)

Luo, Li; Zhu, Yun; Xiong, Momiao

2012-06-01

The genome-wide association studies (GWAS) designed for next-generation sequencing data involve testing association of genomic variants, including common, low frequency, and rare variants. The current strategies for association studies are well developed for identifying association of common variants with the common diseases, but may be ill-suited when large amounts of allelic heterogeneity are present in sequence data. Recently, group tests that analyze their collective frequency differences between cases and controls shift the current variant-by-variant analysis paradigm for GWAS of common variants to the collective test of multiple variants in the association analysis of rare variants. However, group tests ignore differences in genetic effects among SNPs at different genomic locations. As an alternative to group tests, we developed a novel genome-information content-based statistics for testing association of the entire allele frequency spectrum of genomic variation with the diseases. To evaluate the performance of the proposed statistics, we use large-scale simulations based on whole genome low coverage pilot data in the 1000 Genomes Project to calculate the type 1 error rates and power of seven alternative statistics: a genome-information content-based statistic, the generalized T(2), collapsing method, multivariate and collapsing (CMC) method, individual χ(2) test, weighted-sum statistic, and variable threshold statistic. Finally, we apply the seven statistics to published resequencing dataset from ANGPTL3, ANGPTL4, ANGPTL5, and ANGPTL6 genes in the Dallas Heart Study. We report that the genome-information content-based statistic has significantly improved type 1 error rates and higher power than the other six statistics in both simulated and empirical datasets.

Ancient genomes

OpenAIRE

Hoelzel, A Rus

2005-01-01

Ever since its invention, the polymerase chain reaction has been the method of choice for work with ancient DNA. In an application of modern genomic methods to material from the Pleistocene, a recent study has instead undertaken to clone and sequence a portion of the ancient genome of the cave bear.

Evaluation and comparison of mammalian subcellular localization prediction methods

Directory of Open Access Journals (Sweden)

Fink J Lynn

2006-12-01

Full Text Available Abstract Background Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER, peroxisome, and lysosome. The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE

Detecting DNA double-stranded breaks in mammalian genomes by linear amplification-mediated high-throughput genome-wide translocation sequencing.

Science.gov (United States)

Hu, Jiazhi; Meyers, Robin M; Dong, Junchao; Panchakshari, Rohit A; Alt, Frederick W; Frock, Richard L

2016-05-01

Unbiased, high-throughput assays for detecting and quantifying DNA double-stranded breaks (DSBs) across the genome in mammalian cells will facilitate basic studies of the mechanisms that generate and repair endogenous DSBs. They will also enable more applied studies, such as those to evaluate the on- and off-target activities of engineered nucleases. Here we describe a linear amplification-mediated high-throughput genome-wide sequencing (LAM-HTGTS) method for the detection of genome-wide 'prey' DSBs via their translocation in cultured mammalian cells to a fixed 'bait' DSB. Bait-prey junctions are cloned directly from isolated genomic DNA using LAM-PCR and unidirectionally ligated to bridge adapters; subsequent PCR steps amplify the single-stranded DNA junction library in preparation for Illumina Miseq paired-end sequencing. A custom bioinformatics pipeline identifies prey sequences that contribute to junctions and maps them across the genome. LAM-HTGTS differs from related approaches because it detects a wide range of broken end structures with nucleotide-level resolution. Familiarity with nucleic acid methods and next-generation sequencing analysis is necessary for library generation and data interpretation. LAM-HTGTS assays are sensitive, reproducible, relatively inexpensive, scalable and straightforward to implement with a turnaround time of <1 week.

A sensitive, support-vector-machine method for the detection of horizontal gene transfers in viral, archaeal and bacterial genomes.

Science.gov (United States)

Tsirigos, Aristotelis; Rigoutsos, Isidore

2005-01-01

In earlier work, we introduced and discussed a generalized computational framework for identifying horizontal transfers. This framework relied on a gene's nucleotide composition, obviated the need for knowledge of codon boundaries and database searches, and was shown to perform very well across a wide range of archaeal and bacterial genomes when compared with previously published approaches, such as Codon Adaptation Index and C + G content. Nonetheless, two considerations remained outstanding: we wanted to further increase the sensitivity of detecting horizontal transfers and also to be able to apply the method to increasingly smaller genomes. In the discussion that follows, we present such a method, Wn-SVM, and show that it exhibits a very significant improvement in sensitivity compared with earlier approaches. Wn-SVM uses a one-class support-vector machine and can learn using rather small training sets. This property makes Wn-SVM particularly suitable for studying small-size genomes, similar to those of viruses, as well as the typically larger archaeal and bacterial genomes. We show experimentally that the new method results in a superior performance across a wide range of organisms and that it improves even upon our own earlier method by an average of 10% across all examined genomes. As a small-genome case study, we analyze the genome of the human cytomegalovirus and demonstrate that Wn-SVM correctly identifies regions that are known to be conserved and prototypical of all beta-herpesvirinae, regions that are known to have been acquired horizontally from the human host and, finally, regions that had not up to now been suspected to be horizontally transferred. Atypical region predictions for many eukaryotic viruses, including the alpha-, beta- and gamma-herpesvirinae, and 123 archaeal and bacterial genomes, have been made available online at http://cbcsrv.watson.ibm.com/HGT_SVM/.

Will genomic selection be a practical method for plant breeding?

Science.gov (United States)

Nakaya, Akihiro; Isobe, Sachiko N

2012-11-01

Genomic selection or genome-wide selection (GS) has been highlighted as a new approach for marker-assisted selection (MAS) in recent years. GS is a form of MAS that selects favourable individuals based on genomic estimated breeding values. Previous studies have suggested the utility of GS, especially for capturing small-effect quantitative trait loci, but GS has not become a popular methodology in the field of plant breeding, possibly because there is insufficient information available on GS for practical use. In this review, GS is discussed from a practical breeding viewpoint. Statistical approaches employed in GS are briefly described, before the recent progress in GS studies is surveyed. GS practices in plant breeding are then reviewed before future prospects are discussed. Statistical concepts used in GS are discussed with genetic models and variance decomposition, heritability, breeding value and linear model. Recent progress in GS studies is reviewed with a focus on empirical studies. For the practice of GS in plant breeding, several specific points are discussed including linkage disequilibrium, feature of populations and genotyped markers and breeding scheme. Currently, GS is not perfect, but it is a potent, attractive and valuable approach for plant breeding. This method will be integrated into many practical breeding programmes in the near future with further advances and the maturing of its theory.

4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments.

Science.gov (United States)

Raviram, Ramya; Rocha, Pedro P; Müller, Christian L; Miraldi, Emily R; Badri, Sana; Fu, Yi; Swanzey, Emily; Proudhon, Charlotte; Snetkova, Valentina; Bonneau, Richard; Skok, Jane A

2016-03-01

4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments.

Directory of Open Access Journals (Sweden)

Ramya Raviram

2016-03-01

Full Text Available 4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait" that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

Generation of Knock-in Mouse by Genome Editing.

Science.gov (United States)

Fujii, Wataru

2017-01-01

Knock-in mice are useful for evaluating endogenous gene expressions and functions in vivo. Instead of the conventional gene-targeting method using embryonic stem cells, an exogenous DNA sequence can be inserted into the target locus in the zygote using genome editing technology. In this chapter, I describe the generation of epitope-tagged mice using engineered endonuclease and single-stranded oligodeoxynucleotide through the mouse zygote as an example of how to generate a knock-in mouse by genome editing.

MIPS plant genome information resources.

Science.gov (United States)

Spannagl, Manuel; Haberer, Georg; Ernst, Rebecca; Schoof, Heiko; Mayer, Klaus F X

2007-01-01

The Munich Institute for Protein Sequences (MIPS) has been involved in maintaining plant genome databases since the Arabidopsis thaliana genome project. Genome databases and analysis resources have focused on individual genomes and aim to provide flexible and maintainable data sets for model plant genomes as a backbone against which experimental data, for example from high-throughput functional genomics, can be organized and evaluated. In addition, model genomes also form a scaffold for comparative genomics, and much can be learned from genome-wide evolutionary studies.

Rapid methods for the extraction and archiving of molecular grade fungal genomic DNA.

Science.gov (United States)

Borman, Andrew M; Palmer, Michael; Johnson, Elizabeth M

2013-01-01

The rapid and inexpensive extraction of fungal genomic DNA that is of sufficient quality for molecular approaches is central to the molecular identification, epidemiological analysis, taxonomy, and strain typing of pathogenic fungi. Although many commercially available and in-house extraction procedures do eliminate the majority of contaminants that commonly inhibit molecular approaches, the inherent difficulties in breaking fungal cell walls lead to protocols that are labor intensive and that routinely take several hours to complete. Here we describe several methods that we have developed in our laboratory that allow the extremely rapid and inexpensive preparation of fungal genomic DNA.

Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory.

Science.gov (United States)

Crisan, Anamaria; McKee, Geoffrey; Munzner, Tamara; Gardy, Jennifer L

2018-01-01

Microbial genome sequencing is now being routinely used in many clinical and public health laboratories. Understanding how to report complex genomic test results to stakeholders who may have varying familiarity with genomics-including clinicians, laboratorians, epidemiologists, and researchers-is critical to the successful and sustainable implementation of this new technology; however, there are no evidence-based guidelines for designing such a report in the pathogen genomics domain. Here, we describe an iterative, human-centered approach to creating a report template for communicating tuberculosis (TB) genomic test results. We used Design Study Methodology-a human centered approach drawn from the information visualization domain-to redesign an existing clinical report. We used expert consults and an online questionnaire to discover various stakeholders' needs around the types of data and tasks related to TB that they encounter in their daily workflow. We also evaluated their perceptions of and familiarity with genomic data, as well as its utility at various clinical decision points. These data shaped the design of multiple prototype reports that were compared against the existing report through a second online survey, with the resulting qualitative and quantitative data informing the final, redesigned, report. We recruited 78 participants, 65 of whom were clinicians, nurses, laboratorians, researchers, and epidemiologists involved in TB diagnosis, treatment, and/or surveillance. Our first survey indicated that participants were largely enthusiastic about genomic data, with the majority agreeing on its utility for certain TB diagnosis and treatment tasks and many reporting some confidence in their ability to interpret this type of data (between 58.8% and 94.1%, depending on the specific data type). When we compared our four prototype reports against the existing design, we found that for the majority (86.7%) of design comparisons, participants preferred the

Genomic prediction using subsampling

OpenAIRE

Xavier, Alencar; Xu, Shizhong; Muir, William; Rainey, Katy Martin

2017-01-01

Background Genome-wide assisted selection is a critical tool for the?genetic improvement of plants and animals. Whole-genome regression models in Bayesian framework represent the main family of prediction methods. Fitting such models with a large number of observations involves a prohibitive computational burden. We propose the use of subsampling bootstrap Markov chain in genomic prediction. Such method consists of fitting whole-genome regression models by subsampling observations in each rou...

Systematic differences in the response of genetic variation to pedigree and genome-based selection methods.

Science.gov (United States)

Heidaritabar, M; Vereijken, A; Muir, W M; Meuwissen, T; Cheng, H; Megens, H-J; Groenen, M A M; Bastiaansen, J W M

2014-12-01

Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60 K SNP chip with markers spaced throughout the entire chicken genome, we compared the impact of GS and traditional BLUP (best linear unbiased prediction) selection methods applied side-by-side in three different lines of egg-laying chickens. Differences were demonstrated between methods, both at the level and genomic distribution of allele frequency changes. In all three lines, the average allele frequency changes were larger with GS, 0.056 0.064 and 0.066, compared with BLUP, 0.044, 0.045 and 0.036 for lines B1, B2 and W1, respectively. With BLUP, 35 selected regions (empirical P selected regions were identified. Empirical thresholds for local allele frequency changes were determined from gene dropping, and differed considerably between GS (0.167-0.198) and BLUP (0.105-0.126). Between lines, the genomic regions with large changes in allele frequencies showed limited overlap. Our results show that GS applies selection pressure much more locally than BLUP, resulting in larger allele frequency changes. With these results, novel insights into the nature of selection on quantitative traits have been gained and important questions regarding the long-term impact of GS are raised. The rapid changes to a part of the genetic architecture, while another part may not be selected, at least in the short term, require careful consideration, especially when selection occurs before phenotypes are observed.

A MITE-based genotyping method to reveal hundreds of DNA polymorphisms in an animal genome after a few generations of artificial selection

Directory of Open Access Journals (Sweden)

Tetreau Guillaume

2008-10-01

Full Text Available Abstract Background For most organisms, developing hundreds of genetic markers spanning the whole genome still requires excessive if not unrealistic efforts. In this context, there is an obvious need for methodologies allowing the low-cost, fast and high-throughput genotyping of virtually any species, such as the Diversity Arrays Technology (DArT. One of the crucial steps of the DArT technique is the genome complexity reduction, which allows obtaining a genomic representation characteristic of the studied DNA sample and necessary for subsequent genotyping. In this article, using the mosquito Aedes aegypti as a study model, we describe a new genome complexity reduction method taking advantage of the abundance of miniature inverted repeat transposable elements (MITEs in the genome of this species. Results Ae. aegypti genomic representations were produced following a two-step procedure: (1 restriction digestion of the genomic DNA and simultaneous ligation of a specific adaptor to compatible ends, and (2 amplification of restriction fragments containing a particular MITE element called Pony using two primers, one annealing to the adaptor sequence and one annealing to a conserved sequence motif of the Pony element. Using this protocol, we constructed a library comprising more than 6,000 DArT clones, of which at least 5.70% were highly reliable polymorphic markers for two closely related mosquito strains separated by only a few generations of artificial selection. Within this dataset, linkage disequilibrium was low, and marker redundancy was evaluated at 2.86% only. Most of the detected genetic variability was observed between the two studied mosquito strains, but individuals of the same strain could still be clearly distinguished. Conclusion The new complexity reduction method was particularly efficient to reveal genetic polymorphisms in Ae. egypti. Overall, our results testify of the flexibility of the DArT genotyping technique and open new

i-Genome: A database to summarize oligonucleotide data in genomes

Directory of Open Access Journals (Sweden)

Chang Yu-Chung

2004-10-01

Full Text Available Abstract Background Information on the occurrence of sequence features in genomes is crucial to comparative genomics, evolutionary analysis, the analyses of regulatory sequences and the quantitative evaluation of sequences. Computing the frequencies and the occurrences of a pattern in complete genomes is time-consuming. Results The proposed database provides information about sequence features generated by exhaustively computing the sequences of the complete genome. The repetitive elements in the eukaryotic genomes, such as LINEs, SINEs, Alu and LTR, are obtained from Repbase. The database supports various complete genomes including human, yeast, worm, and 128 microbial genomes. Conclusions This investigation presents and implements an efficiently computational approach to accumulate the occurrences of the oligonucleotides or patterns in complete genomes. A database is established to maintain the information of the sequence features, including the distributions of oligonucleotide, the gene distribution, the distribution of repetitive elements in genomes and the occurrences of the oligonucleotides. The database can provide more effective and efficient way to access the repetitive features in genomes.

Genomic Analysis of a Marine Bacterium: Bioinformatics for Comparison, Evaluation, and Interpretation of DNA Sequences

Directory of Open Access Journals (Sweden)

Bhagwan N. Rekadwad

2016-01-01

Full Text Available A total of five highly related strains of an unidentified marine bacterium were analyzed through their short genome sequences (AM260709–AM260713. Genome-to-Genome Distance (GGDC showed high similarity to Pseudoalteromonas haloplanktis (X67024. The generated unique Quick Response (QR codes indicated no identity to other microbial species or gene sequences. Chaos Game Representation (CGR showed the number of bases concentrated in the area. Guanine residues were highest in number followed by cytosine. Frequency of Chaos Game Representation (FCGR indicated that CC and GG blocks have higher frequency in the sequence from the evaluated marine bacterium strains. Maximum GC content for the marine bacterium strains ranged 53-54%. The use of QR codes, CGR, FCGR, and GC dataset helped in identifying and interpreting short genome sequences from specific isolates. A phylogenetic tree was constructed with the bootstrap test (1000 replicates using MEGA6 software. Principal Component Analysis (PCA was carried out using EMBL-EBI MUSCLE program. Thus, generated genomic data are of great assistance for hierarchical classification in Bacterial Systematics which combined with phenotypic features represents a basic procedure for a polyphasic approach on unambiguous bacterial isolate taxonomic classification.

Database Description - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods ... QTL list, Plant DB link & Genome analysis methods Alternative name - DOI 10.18908/lsdba.nbdc01194-01-000 Cr...ers and QTLs are curated manually from the published literature. The marker information includes marker sequences, genotyping methods... Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive ...

Evaluation of three automated genome annotations for Halorhabdus utahensis.

Directory of Open Access Journals (Sweden)

Peter Bakke

2009-07-01

Full Text Available Genome annotations are accumulating rapidly and depend heavily on automated annotation systems. Many genome centers offer annotation systems but no one has compared their output in a systematic way to determine accuracy and inherent errors. Errors in the annotations are routinely deposited in databases such as NCBI and used to validate subsequent annotation errors. We submitted the genome sequence of halophilic archaeon Halorhabdus utahensis to be analyzed by three genome annotation services. We have examined the output from each service in a variety of ways in order to compare the methodology and effectiveness of the annotations, as well as to explore the genes, pathways, and physiology of the previously unannotated genome. The annotation services differ considerably in gene calls, features, and ease of use. We had to manually identify the origin of replication and the species-specific consensus ribosome-binding site. Additionally, we conducted laboratory experiments to test H. utahensis growth and enzyme activity. Current annotation practices need to improve in order to more accurately reflect a genome's biological potential. We make specific recommendations that could improve the quality of microbial annotation projects.

An Assessment of Different Genomic Approaches for Inferring Phylogeny of Listeria monocytogenes

Directory of Open Access Journals (Sweden)

Clémentine Henri

2017-11-01

Full Text Available Background/objectives: Whole genome sequencing (WGS has proven to be a powerful subtyping tool for foodborne pathogenic bacteria like L. monocytogenes. The interests of genome-scale analysis for national surveillance, outbreak detection or source tracking has been largely documented. The genomic data however can be exploited with many different bioinformatics methods like single nucleotide polymorphism (SNP, core-genome multi locus sequence typing (cgMLST, whole-genome multi locus sequence typing (wgMLST or multi locus predicted protein sequence typing (MLPPST on either core-genome (cgMLPPST or pan-genome (wgMLPPST. Currently, there are little comparisons studies of these different analytical approaches. Our objective was to assess and compare different genomic methods that can be implemented in order to cluster isolates of L. monocytogenes.Methods: The clustering methods were evaluated on a collection of 207 L. monocytogenes genomes of food origin representative of the genetic diversity of the Anses collection. The trees were then compared using robust statistical analyses.Results: The backward comparability between conventional typing methods and genomic methods revealed a near-perfect concordance. The importance of selecting a proper reference when calling SNPs was highlighted, although distances between strains remained identical. The analysis also revealed that the topology of the phylogenetic trees between wgMLST and cgMLST were remarkably similar. The comparison between SNP and cgMLST or SNP and wgMLST approaches showed that the topologies of phylogenic trees were statistically similar with an almost equivalent clustering.Conclusion: Our study revealed high concordance between wgMLST, cgMLST, and SNP approaches which are all suitable for typing of L. monocytogenes. The comparable clustering is an important observation considering that the two approaches have been variously implemented among reference laboratories.

The large-scale blast score ratio (LS-BSR pipeline: a method to rapidly compare genetic content between bacterial genomes

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Jason W. Sahl

2014-04-01

Full Text Available Background. As whole genome sequence data from bacterial isolates becomes cheaper to generate, computational methods are needed to correlate sequence data with biological observations. Here we present the large-scale BLAST score ratio (LS-BSR pipeline, which rapidly compares the genetic content of hundreds to thousands of bacterial genomes, and returns a matrix that describes the relatedness of all coding sequences (CDSs in all genomes surveyed. This matrix can be easily parsed in order to identify genetic relationships between bacterial genomes. Although pipelines have been published that group peptides by sequence similarity, no other software performs the rapid, large-scale, full-genome comparative analyses carried out by LS-BSR.Results. To demonstrate the utility of the method, the LS-BSR pipeline was tested on 96 Escherichia coli and Shigella genomes; the pipeline ran in 163 min using 16 processors, which is a greater than 7-fold speedup compared to using a single processor. The BSR values for each CDS, which indicate a relative level of relatedness, were then mapped to each genome on an independent core genome single nucleotide polymorphism (SNP based phylogeny. Comparisons were then used to identify clade specific CDS markers and validate the LS-BSR pipeline based on molecular markers that delineate between classical E. coli pathogenic variant (pathovar designations. Scalability tests demonstrated that the LS-BSR pipeline can process 1,000 E. coli genomes in 27–57 h, depending upon the alignment method, using 16 processors.Conclusions. LS-BSR is an open-source, parallel implementation of the BSR algorithm, enabling rapid comparison of the genetic content of large numbers of genomes. The results of the pipeline can be used to identify specific markers between user-defined phylogenetic groups, and to identify the loss and/or acquisition of genetic information between bacterial isolates. Taxa-specific genetic markers can then be translated

A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

Science.gov (United States)

Moore, Jason H; Shestov, Maksim; Schmitt, Peter; Olson, Randal S

2018-01-01

A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient. This is because it is not possible to know what the ground truth is in real data. This must be accompanied by simulated data where that balance between signal and noise is known and can be directly evaluated. Unfortunately, there is a lack of methods and software for simulating data with the kind of complexity found in real biological and biomedical systems. We present here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating complex biological and biomedical data. Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods.

Genomic analyses of the Chlamydia trachomatis core genome show an association between chromosomal genome, plasmid type and disease

NARCIS (Netherlands)

Versteeg, Bart; Bruisten, Sylvia M.; Pannekoek, Yvonne; Jolley, Keith A.; Maiden, Martin C. J.; van der Ende, Arie; Harrison, Odile B.

2018-01-01

Background: Chlamydia trachomatis (Ct) plasmid has been shown to encode genes essential for infection. We evaluated the population structure of Ct using whole-genome sequence data (WGS). In particular, the relationship between the Ct genome, plasmid and disease was investigated. Results: WGS data

Comparative Genomics Reveals High Genomic Diversity in the Genus Photobacterium

OpenAIRE

Henrique Machado; Henrique Machado; Lone Gram

2017-01-01

Vibrionaceae is a large marine bacterial family, which can constitute up to 50% of the prokaryotic population in marine waters. Photobacterium is the second largest genus in the family and we used comparative genomics on 35 strains representing 16 of the 28 species described so far, to understand the genomic diversity present in the Photobacterium genus. Such understanding is important for ecophysiology studies of the genus. We used whole genome sequences to evaluate phylogenetic relationship...

Genome-wide characterization of centromeric satellites from multiple mammalian genomes.

Science.gov (United States)

Alkan, Can; Cardone, Maria Francesca; Catacchio, Claudia Rita; Antonacci, Francesca; O'Brien, Stephen J; Ryder, Oliver A; Purgato, Stefania; Zoli, Monica; Della Valle, Giuliano; Eichler, Evan E; Ventura, Mario

2011-01-01

Despite its importance in cell biology and evolution, the centromere has remained the final frontier in genome assembly and annotation due to its complex repeat structure. However, isolation and characterization of the centromeric repeats from newly sequenced species are necessary for a complete understanding of genome evolution and function. In recent years, various genomes have been sequenced, but the characterization of the corresponding centromeric DNA has lagged behind. Here, we present a computational method (RepeatNet) to systematically identify higher-order repeat structures from unassembled whole-genome shotgun sequence and test whether these sequence elements correspond to functional centromeric sequences. We analyzed genome datasets from six species of mammals representing the diversity of the mammalian lineage, namely, horse, dog, elephant, armadillo, opossum, and platypus. We define candidate monomer satellite repeats and demonstrate centromeric localization for five of the six genomes. Our analysis revealed the greatest diversity of centromeric sequences in horse and dog in contrast to elephant and armadillo, which showed high-centromeric sequence homogeneity. We could not isolate centromeric sequences within the platypus genome, suggesting that centromeres in platypus are not enriched in satellite DNA. Our method can be applied to the characterization of thousands of other vertebrate genomes anticipated for sequencing in the near future, providing an important tool for annotation of centromeres.

A New Method for the Evaluation of Vaccine Safety Based on Comprehensive Gene Expression Analysis

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Haruka Momose

2010-01-01

Full Text Available For the past 50 years, quality control and safety tests have been used to evaluate vaccine safety. However, conventional animal safety tests need to be improved in several aspects. For example, the number of test animals used needs to be reduced and the test period shortened. It is, therefore, necessary to develop a new vaccine evaluation system. In this review, we show that gene expression patterns are well correlated to biological responses in vaccinated rats. Our findings and methods using experimental biology and genome science provide an important means of assessment for vaccine toxicity.

Impact of QTL minor allele frequency on genomic evaluation using real genotype data and simulated phenotypes in Japanese Black cattle.

Science.gov (United States)

Uemoto, Yoshinobu; Sasaki, Shinji; Kojima, Takatoshi; Sugimoto, Yoshikazu; Watanabe, Toshio

2015-11-19

Genetic variance that is not captured by single nucleotide polymorphisms (SNPs) is due to imperfect linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTLs), and the extent of LD between SNPs and QTLs depends on different minor allele frequencies (MAF) between them. To evaluate the impact of MAF of QTLs on genomic evaluation, we performed a simulation study using real cattle genotype data. In total, 1368 Japanese Black cattle and 592,034 SNPs (Illumina BovineHD BeadChip) were used. We simulated phenotypes using real genotypes under different scenarios, varying the MAF categories, QTL heritability, number of QTLs, and distribution of QTL effect. After generating true breeding values and phenotypes, QTL heritability was estimated and the prediction accuracy of genomic estimated breeding value (GEBV) was assessed under different SNP densities, prediction models, and population size by a reference-test validation design. The extent of LD between SNPs and QTLs in this population was higher in the QTLs with high MAF than in those with low MAF. The effect of MAF of QTLs depended on the genetic architecture, evaluation strategy, and population size in genomic evaluation. In genetic architecture, genomic evaluation was affected by the MAF of QTLs combined with the QTL heritability and the distribution of QTL effect. The number of QTL was not affected on genomic evaluation if the number of QTL was more than 50. In the evaluation strategy, we showed that different SNP densities and prediction models affect the heritability estimation and genomic prediction and that this depends on the MAF of QTLs. In addition, accurate QTL heritability and GEBV were obtained using denser SNP information and the prediction model accounted for the SNPs with low and high MAFs. In population size, a large sample size is needed to increase the accuracy of GEBV. The MAF of QTL had an impact on heritability estimation and prediction accuracy. Most genetic variance can be captured

Systematic evaluation of bias in microbial community profiles induced by whole genome amplification

NARCIS (Netherlands)

Direito, S.O.L.; Zaura, E.; Little, M.; Ehrenfreund, P.; Röling, W.F.M.

2014-01-01

Whole genome amplification methods facilitate the detection and characterization of microbial communities in low biomass environments. We examined the extent to which the actual community structure is reliably revealed and factors contributing to bias. One widely used [multiple displacement

Systematic evaluation of bias in microbial community profiles induced by whole genome amplification.

NARCIS (Netherlands)

Direito, S.; Zaura, E.; Little, M.; Ehrenfreund, P.; Roling, W.F.M.

2014-01-01

Whole genome amplification methods facilitate the detection and characterization of microbial communities in low biomass environments. We examined the extent to which the actual community structure is reliably revealed and factors contributing to bias. One widely used [multiple displacement

A method for accurate detection of genomic microdeletions using real-time quantitative PCR

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Bassett Anne S

2005-12-01

Full Text Available Abstract Background Quantitative Polymerase Chain Reaction (qPCR is a well-established method for quantifying levels of gene expression, but has not been routinely applied to the detection of constitutional copy number alterations of human genomic DNA. Microdeletions or microduplications of the human genome are associated with a variety of genetic disorders. Although, clinical laboratories routinely use fluorescence in situ hybridization (FISH to identify such cryptic genomic alterations, there remains a significant number of individuals in which constitutional genomic imbalance is suspected, based on clinical parameters, but cannot be readily detected using current cytogenetic techniques. Results In this study, a novel application for real-time qPCR is presented that can be used to reproducibly detect chromosomal microdeletions and microduplications. This approach was applied to DNA from a series of patient samples and controls to validate genomic copy number alteration at cytoband 22q11. The study group comprised 12 patients with clinical symptoms of chromosome 22q11 deletion syndrome (22q11DS, 1 patient trisomic for 22q11 and 4 normal controls. 6 of the patients (group 1 had known hemizygous deletions, as detected by standard diagnostic FISH, whilst the remaining 6 patients (group 2 were classified as 22q11DS negative using the clinical FISH assay. Screening of the patients and controls with a set of 10 real time qPCR primers, spanning the 22q11.2-deleted region and flanking sequence, confirmed the FISH assay results for all patients with 100% concordance. Moreover, this qPCR enabled a refinement of the region of deletion at 22q11. Analysis of DNA from chromosome 22 trisomic sample demonstrated genomic duplication within 22q11. Conclusion In this paper we present a qPCR approach for the detection of chromosomal microdeletions and microduplications. The strategic use of in silico modelling for qPCR primer design to avoid regions of repetitive

An estimation of the prevalence of genomic disorders using chromosomal microarray data.

Science.gov (United States)

Gillentine, Madelyn A; Lupo, Philip J; Stankiewicz, Pawel; Schaaf, Christian P

2018-04-24

Multiple genomic disorders result from recurrent deletions or duplications between low copy repeat (LCR) clusters, mediated by nonallelic homologous recombination. These copy number variants (CNVs) often exhibit variable expressivity and/or incomplete penetrance. However, the population prevalence of many genomic disorders has not been estimated accurately. A subset of genomic disorders similarly characterized by CNVs between LCRs have been studied epidemiologically, including Williams-Beuren syndrome (7q11.23), Smith-Magenis syndrome (17p11.2), velocardiofacial syndrome (22q11.21), Prader-Willi/Angelman syndromes (15q11.2q12), 17q12 deletion syndrome, and Charcot-Marie-Tooth neuropathy type 1/hereditary neuropathy with liability to pressure palsy (PMP22, 17q11.2). We have generated a method to estimate prevalence of highly penetrant genomic disorders by (1) leveraging epidemiological data for genomic disorders with previously reported prevalence estimates, (2) obtaining chromosomal microarray data on genomic disorders from a large medical genetics clinic; and (3) utilizing these in a linear regression model to determine the prevalence of this syndromic copy number change among the general population. Using our algorithm, the prevalence for five clinically relevant recurrent genomic disorders: 1q21.1 microdeletion (1/6882 live births) and microduplication syndromes (1/6309), 15q13.3 microdeletion syndrome (1/5525), and 16p11.2 microdeletion (1/3021) and microduplication syndromes (1/4216), were determined. These findings will inform epidemiological strategies for evaluating those conditions, and our method may be useful to evaluate the prevalence of other highly penetrant genomic disorders.

Update History of This Database - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods ...B link & Genome analysis methods English archive site is opened. 2012/08/08 PGDBj... Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods is opened. About This...ate History of This Database - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive ...

Comparative Genomics Reveals High Genomic Diversity in the Genus Photobacterium

DEFF Research Database (Denmark)

Machado, Henrique; Gram, Lone

2017-01-01

was widespread and abundant in the genus, suggesting a role in genomic evolution. The high genetic variability and indications of genetic exchange make it difficult to elucidate genome evolutionary paths and raise the awareness of the roles of foreign DNA in the genomic evolution of environmental organisms.......Vibrionaceae is a large marine bacterial family, which can constitute up to 50% of the prokaryotic population in marine waters. Photobacterium is the second largest genus in the family and we used comparative genomics on 35 strains representing 16 of the 28 species described so far, to understand...... the genomic diversity present in the Photobacterium genus. Such understanding is important for ecophysiology studies of the genus. We used whole genome sequences to evaluate phylogenetic relationships using several analyses (16S rRNA, MLSA, fur, amino-acid usage, ANI), which allowed us to identify two...

Systematic differences in the response of genetic variation to pedigree and genome-based selection methods

NARCIS (Netherlands)

Heidaritabar, M.; Vereijken, A.; Muir, W.M.; Meuwissen, T.H.E.; Cheng, H.; Megens, H.J.W.C.; Groenen, M.; Bastiaansen, J.W.M.

2014-01-01

Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60¿K SNP chip with markers spaced throughout the

Implementing genomics and pharmacogenomics in the clinic: The National Human Genome Research Institute's genomic medicine portfolio.

Science.gov (United States)

Manolio, Teri A

2016-10-01

Increasing knowledge about the influence of genetic variation on human health and growing availability of reliable, cost-effective genetic testing have spurred the implementation of genomic medicine in the clinic. As defined by the National Human Genome Research Institute (NHGRI), genomic medicine uses an individual's genetic information in his or her clinical care, and has begun to be applied effectively in areas such as cancer genomics, pharmacogenomics, and rare and undiagnosed diseases. In 2011 NHGRI published its strategic vision for the future of genomic research, including an ambitious research agenda to facilitate and promote the implementation of genomic medicine. To realize this agenda, NHGRI is consulting and facilitating collaborations with the external research community through a series of "Genomic Medicine Meetings," under the guidance and leadership of the National Advisory Council on Human Genome Research. These meetings have identified and begun to address significant obstacles to implementation, such as lack of evidence of efficacy, limited availability of genomics expertise and testing, lack of standards, and difficulties in integrating genomic results into electronic medical records. The six research and dissemination initiatives comprising NHGRI's genomic research portfolio are designed to speed the evaluation and incorporation, where appropriate, of genomic technologies and findings into routine clinical care. Actual adoption of successful approaches in clinical care will depend upon the willingness, interest, and energy of professional societies, practitioners, patients, and payers to promote their responsible use and share their experiences in doing so. Published by Elsevier Ireland Ltd.

Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing

DEFF Research Database (Denmark)

Hou, Yong; Wu, Kui; Shi, Xulian

2015-01-01

methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly...... performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level....

Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures.

Science.gov (United States)

Bastiaansen, John W M; Coster, Albart; Calus, Mario P L; van Arendonk, Johan A M; Bovenhuis, Henk

2012-01-24

Genomic selection has become an important tool in the genetic improvement of animals and plants. The objective of this study was to investigate the impacts of breeding value estimation method, reference population structure, and trait genetic architecture, on long-term response to genomic selection without updating marker effects. Three methods were used to estimate genomic breeding values: a BLUP method with relationships estimated from genome-wide markers (GBLUP), a Bayesian method, and a partial least squares regression method (PLSR). A shallow (individuals from one generation) or deep reference population (individuals from five generations) was used with each method. The effects of the different selection approaches were compared under four different genetic architectures for the trait under selection. Selection was based on one of the three genomic breeding values, on pedigree BLUP breeding values, or performed at random. Selection continued for ten generations. Differences in long-term selection response were small. For a genetic architecture with a very small number of three to four quantitative trait loci (QTL), the Bayesian method achieved a response that was 0.05 to 0.1 genetic standard deviation higher than other methods in generation 10. For genetic architectures with approximately 30 to 300 QTL, PLSR (shallow reference) or GBLUP (deep reference) had an average advantage of 0.2 genetic standard deviation over the Bayesian method in generation 10. GBLUP resulted in 0.6% and 0.9% less inbreeding than PLSR and BM and on average a one third smaller reduction of genetic variance. Responses in early generations were greater with the shallow reference population while long-term response was not affected by reference population structure. The ranking of estimation methods was different with than without selection. Under selection, applying GBLUP led to lower inbreeding and a smaller reduction of genetic variance while a similar response to selection was

[Genome editing of industrial microorganism].

Science.gov (United States)

Zhu, Linjiang; Li, Qi

2015-03-01

Genome editing is defined as highly-effective and precise modification of cellular genome in a large scale. In recent years, such genome-editing methods have been rapidly developed in the field of industrial strain improvement. The quickly-updating methods thoroughly change the old mode of inefficient genetic modification, which is "one modification, one selection marker, and one target site". Highly-effective modification mode in genome editing have been developed including simultaneous modification of multiplex genes, highly-effective insertion, replacement, and deletion of target genes in the genome scale, cut-paste of a large DNA fragment. These new tools for microbial genome editing will certainly be applied widely, and increase the efficiency of industrial strain improvement, and promote the revolution of traditional fermentation industry and rapid development of novel industrial biotechnology like production of biofuel and biomaterial. The technological principle of these genome-editing methods and their applications were summarized in this review, which can benefit engineering and construction of industrial microorganism.

Non-additive Effects in Genomic Selection

Directory of Open Access Journals (Sweden)

Luis Varona

2018-03-01

Full Text Available In the last decade, genomic selection has become a standard in the genetic evaluation of livestock populations. However, most procedures for the implementation of genomic selection only consider the additive effects associated with SNP (Single Nucleotide Polymorphism markers used to calculate the prediction of the breeding values of candidates for selection. Nevertheless, the availability of estimates of non-additive effects is of interest because: (i they contribute to an increase in the accuracy of the prediction of breeding values and the genetic response; (ii they allow the definition of mate allocation procedures between candidates for selection; and (iii they can be used to enhance non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes. This study presents a review of methods for the incorporation of non-additive genetic effects into genomic selection procedures and their potential applications in the prediction of future performance, mate allocation, crossbreeding, and purebred selection. The work concludes with a brief outline of some ideas for future lines of that may help the standard inclusion of non-additive effects in genomic selection.

A comprehensive evaluation of rodent malaria parasite genomes and gene expression

KAUST Repository

Otto, Thomas D

2014-10-30

Background: Rodent malaria parasites (RMP) are used extensively as models of human malaria. Draft RMP genomes have been published for Plasmodium yoelii, P. berghei ANKA (PbA) and P. chabaudi AS (PcAS). Although availability of these genomes made a significant impact on recent malaria research, these genomes were highly fragmented and were annotated with little manual curation. The fragmented nature of the genomes has hampered genome wide analysis of Plasmodium gene regulation and function. Results: We have greatly improved the genome assemblies of PbA and PcAS, newly sequenced the virulent parasite P. yoelii YM genome, sequenced additional RMP isolates/lines and have characterized genotypic diversity within RMP species. We have produced RNA-seq data and utilized it to improve gene-model prediction and to provide quantitative, genome-wide, data on gene expression. Comparison of the RMP genomes with the genome of the human malaria parasite P. falciparum and RNA-seq mapping permitted gene annotation at base-pair resolution. Full-length chromosomal annotation permitted a comprehensive classification of all subtelomeric multigene families including the `Plasmodium interspersed repeat genes\\' (pir). Phylogenetic classification of the pir family, combined with pir expression patterns, indicates functional diversification within this family. Conclusions: Complete RMP genomes, RNA-seq and genotypic diversity data are excellent and important resources for gene-function and post-genomic analyses and to better interrogate Plasmodium biology. Genotypic diversity between P. chabaudi isolates makes this species an excellent parasite to study genotype-phenotype relationships. The improved classification of multigene families will enhance studies on the role of (variant) exported proteins in virulence and immune evasion/modulation.

A network-based method to evaluate quality of reproducibility of differential expression in cancer genomics studies.

Science.gov (United States)

Li, Robin; Lin, Xiao; Geng, Haijiang; Li, Zhihui; Li, Jiabing; Lu, Tao; Yan, Fangrong

2015-12-29

Personalized cancer treatments depend on the determination of a patient's genetic status according to known genetic profiles for which targeted treatments exist. Such genetic profiles must be scientifically validated before they is applied to general patient population. Reproducibility of findings that support such genetic profiles is a fundamental challenge in validation studies. The percentage of overlapping genes (POG) criterion and derivative methods produce unstable and misleading results. Furthermore, in a complex disease, comparisons between different tumor subtypes can produce high POG scores that do not capture the consistencies in the functions. We focused on the quality rather than the quantity of the overlapping genes. We defined the rank value of each gene according to importance or quality by PageRank on basis of a particular topological structure. Then, we used the p-value of the rank-sum of the overlapping genes (PRSOG) to evaluate the quality of reproducibility. Though the POG scores were low in different studies of the same disease, the PRSOG was statistically significant, which suggests that sets of differentially expressed genes might be highly reproducible. Evaluations of eight datasets from breast cancer, lung cancer and four other disorders indicate that quality-based PRSOG method performs better than a quantity-based method. Our analysis of the components of the sets of overlapping genes supports the utility of the PRSOG method.

Functional regression method for whole genome eQTL epistasis analysis with sequencing data.

Science.gov (United States)

Xu, Kelin; Jin, Li; Xiong, Momiao

2017-05-18

Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges. We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions. By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction

Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition.

Science.gov (United States)

Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen

2017-12-27

Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model. The BayesC model assumes a priori that markers have normally distributed effects with probability [Formula: see text] and no effect with probability (1 - [Formula: see text]). Marker effects and their PEV are estimated by using SVD and the posterior probability of the marker having a non-zero effect is calculated. These posterior probabilities are used to obtain marker-specific effect variances, which are subsequently used to approximate BayesC estimates of marker effects in a linear model. A computer simulation study was conducted to compare alternative genomic prediction methods, where a single reference generation was used to estimate marker effects, which were subsequently used for 10 generations of forward prediction, for which accuracies were evaluated. SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP

The genome BLASTatlas - a GeneWiz extension for visualization of whole-genome homology

DEFF Research Database (Denmark)

Hallin, Peter Fischer; Binnewies, Tim Terence; Ussery, David

2008-01-01

://www.cbs.dtu.dk/ws/BLASTatlas), where programming examples are available in Perl. By providing an interoperable method to carry out whole genome visualization of homology, this service offers bioinformaticians as well as biologists an easy-to-adopt workflow that can be directly called from the programming language of the user, hence......The development of fast and inexpensive methods for sequencing bacterial genomes has led to a wealth of data, often with many genomes being sequenced of the same species or closely related organisms. Thus, there is a need for visualization methods that will allow easy comparison of many sequenced...... genomes to a defined reference strain. The BLASTatlas is one such tool that is useful for mapping and visualizing whole genome homology of genes and proteins within a reference strain compared to other strains or species of one or more prokaryotic organisms. We provide examples of BLASTatlases, including...

Genome-wide prediction of discrete traits using bayesian regressions and machine learning

Directory of Open Access Journals (Sweden)

Forni Selma

2011-02-01

Full Text Available Abstract Background Genomic selection has gained much attention and the main goal is to increase the predictive accuracy and the genetic gain in livestock using dense marker information. Most methods dealing with the large p (number of covariates small n (number of observations problem have dealt only with continuous traits, but there are many important traits in livestock that are recorded in a discrete fashion (e.g. pregnancy outcome, disease resistance. It is necessary to evaluate alternatives to analyze discrete traits in a genome-wide prediction context. Methods This study shows two threshold versions of Bayesian regressions (Bayes A and Bayesian LASSO and two machine learning algorithms (boosting and random forest to analyze discrete traits in a genome-wide prediction context. These methods were evaluated using simulated and field data to predict yet-to-be observed records. Performances were compared based on the models' predictive ability. Results The simulation showed that machine learning had some advantages over Bayesian regressions when a small number of QTL regulated the trait under pure additivity. However, differences were small and disappeared with a large number of QTL. Bayesian threshold LASSO and boosting achieved the highest accuracies, whereas Random Forest presented the highest classification performance. Random Forest was the most consistent method in detecting resistant and susceptible animals, phi correlation was up to 81% greater than Bayesian regressions. Random Forest outperformed other methods in correctly classifying resistant and susceptible animals in the two pure swine lines evaluated. Boosting and Bayes A were more accurate with crossbred data. Conclusions The results of this study suggest that the best method for genome-wide prediction may depend on the genetic basis of the population analyzed. All methods were less accurate at correctly classifying intermediate animals than extreme animals. Among the different

Implementing genomics and pharmacogenomics in the clinic: The National Human Genome Research Institute’s genomic medicine portfolio

Science.gov (United States)

Manolio, Teri A.

2016-01-01

Increasing knowledge about the influence of genetic variation on human health and growing availability of reliable, cost-effective genetic testing have spurred the implementation of genomic medicine in the clinic. As defined by the National Human Genome Research Institute (NHGRI), genomic medicine uses an individual’s genetic information in his or her clinical care, and has begun to be applied effectively in areas such as cancer genomics, pharmacogenomics, and rare and undiagnosed diseases. In 2011 NHGRI published its strategic vision for the future of genomic research, including an ambitious research agenda to facilitate and promote the implementation of genomic medicine. To realize this agenda, NHGRI is consulting and facilitating collaborations with the external research community through a series of “Genomic Medicine Meetings,” under the guidance and leadership of the National Advisory Council on Human Genome Research. These meetings have identified and begun to address significant obstacles to implementation, such as lack of evidence of efficacy, limited availability of genomics expertise and testing, lack of standards, and diffficulties in integrating genomic results into electronic medical records. The six research and dissemination initiatives comprising NHGRI’s genomic research portfolio are designed to speed the evaluation and incorporation, where appropriate, of genomic technologies and findings into routine clinical care. Actual adoption of successful approaches in clinical care will depend upon the willingness, interest, and energy of professional societies, practitioners, patients, and payers to promote their responsible use and share their experiences in doing so. PMID:27612677

Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding.

Science.gov (United States)

Bjelland, D W; Weigel, K A; Vukasinovic, N; Nkrumah, J D

2013-07-01

The effects of increased pedigree inbreeding in dairy cattle populations have been well documented and result in a negative impact on profitability. Recent advances in genotyping technology have allowed researchers to move beyond pedigree analysis and study inbreeding at a molecular level. In this study, 5,853 animals were genotyped for 54,001 single nucleotide polymorphisms (SNP); 2,913 cows had phenotypic records including a single lactation for milk yield (from either lactation 1, 2, 3, or 4), reproductive performance, and linear type conformation. After removing SNP with poor call rates, low minor allele frequencies, and departure from Hardy-Weinberg equilibrium, 33,025 SNP remained for analyses. Three measures of genomic inbreeding were evaluated: percent homozygosity (FPH), inbreeding calculated from runs of homozygosity (FROH), and inbreeding derived from a genomic relationship matrix (FGRM). Average FPH was 60.5±1.1%, average FROH was 3.8±2.1%, and average FGRM was 20.8±2.3%, where animals with larger values for each of the genomic inbreeding indices were considered more inbred. Decreases in total milk yield to 205d postpartum of 53, 20, and 47kg per 1% increase in FPH, FROH, and FGRM, respectively, were observed. Increases in days open per 1% increase in FPH (1.76 d), FROH (1.72 d), and FGRM (1.06 d) were also noted, as well as increases in maternal calving difficulty (0.09, 0.03, and 0.04 on a 5-point scale for FPH, FROH, and FGRM, respectively). Several linear type traits, such as strength (-0.40, -0.11, and -0.19), rear legs rear view (-0.35, -0.16, and -0.14), front teat placement (0.35, 0.25, 0.18), and teat length (-0.24, -0.14, and -0.13) were also affected by increases in FPH, FROH, and FGRM, respectively. Overall, increases in each measure of genomic inbreeding in this study were associated with negative effects on production and reproductive ability in dairy cows. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc

Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy

Science.gov (United States)

Jia, Yi; Jannink, Jean-Luc

2012-01-01

Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCπ) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCπ to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored. PMID:23086217

Comparative Genomics Reveals High Genomic Diversity in the Genus Photobacterium.

Science.gov (United States)

Machado, Henrique; Gram, Lone

2017-01-01

Vibrionaceae is a large marine bacterial family, which can constitute up to 50% of the prokaryotic population in marine waters. Photobacterium is the second largest genus in the family and we used comparative genomics on 35 strains representing 16 of the 28 species described so far, to understand the genomic diversity present in the Photobacterium genus. Such understanding is important for ecophysiology studies of the genus. We used whole genome sequences to evaluate phylogenetic relationships using several analyses (16S rRNA, MLSA, fur , amino-acid usage, ANI), which allowed us to identify two misidentified strains. Genome analyses also revealed occurrence of higher and lower GC content clades, correlating with phylogenetic clusters. Pan- and core-genome analysis revealed the conservation of 25% of the genome throughout the genus, with a large and open pan-genome. The major source of genomic diversity could be traced to the smaller chromosome and plasmids. Several of the physiological traits studied in the genus did not correlate with phylogenetic data. Since horizontal gene transfer (HGT) is often suggested as a source of genetic diversity and a potential driver of genomic evolution in bacterial species, we looked into evidence of such in Photobacterium genomes. Genomic islands were the source of genomic differences between strains of the same species. Also, we found transposase genes and CRISPR arrays that suggest multiple encounters with foreign DNA. Presence of genomic exchange traits was widespread and abundant in the genus, suggesting a role in genomic evolution. The high genetic variability and indications of genetic exchange make it difficult to elucidate genome evolutionary paths and raise the awareness of the roles of foreign DNA in the genomic evolution of environmental organisms.

Evaluation and Validation of Assembling Corrected PacBio Long Reads for Microbial Genome Completion via Hybrid Approaches.

Science.gov (United States)

Lin, Hsin-Hung; Liao, Yu-Chieh

2015-01-01

Despite the ever-increasing output of next-generation sequencing data along with developing assemblers, dozens to hundreds of gaps still exist in de novo microbial assemblies due to uneven coverage and large genomic repeats. Third-generation single-molecule, real-time (SMRT) sequencing technology avoids amplification artifacts and generates kilobase-long reads with the potential to complete microbial genome assembly. However, due to the low accuracy (~85%) of third-generation sequences, a considerable amount of long reads (>50X) are required for self-correction and for subsequent de novo assembly. Recently-developed hybrid approaches, using next-generation sequencing data and as few as 5X long reads, have been proposed to improve the completeness of microbial assembly. In this study we have evaluated the contemporary hybrid approaches and demonstrated that assembling corrected long reads (by runCA) produced the best assembly compared to long-read scaffolding (e.g., AHA, Cerulean and SSPACE-LongRead) and gap-filling (SPAdes). For generating corrected long reads, we further examined long-read correction tools, such as ECTools, LSC, LoRDEC, PBcR pipeline and proovread. We have demonstrated that three microbial genomes including Escherichia coli K12 MG1655, Meiothermus ruber DSM1279 and Pdeobacter heparinus DSM2366 were successfully hybrid assembled by runCA into near-perfect assemblies using ECTools-corrected long reads. In addition, we developed a tool, Patch, which implements corrected long reads and pre-assembled contigs as inputs, to enhance microbial genome assemblies. With the additional 20X long reads, short reads of S. cerevisiae W303 were hybrid assembled into 115 contigs using the verified strategy, ECTools + runCA. Patch was subsequently applied to upgrade the assembly to a 35-contig draft genome. Our evaluation of the hybrid approaches shows that assembling the ECTools-corrected long reads via runCA generates near complete microbial genomes, suggesting

Extensive genome rearrangements and multiple horizontal gene transfers in a population of pyrococcus isolates from Vulcano Island, Italy.

Science.gov (United States)

White, James R; Escobar-Paramo, Patricia; Mongodin, Emmanuel F; Nelson, Karen E; DiRuggiero, Jocelyne

2008-10-01

The extent of chromosome rearrangements in Pyrococcus isolates from marine hydrothermal vents in Vulcano Island, Italy, was evaluated by high-throughput genomic methods. The results illustrate the dynamic nature of the genomes of the genus Pyrococcus and raise the possibility of a connection between rapidly changing environmental conditions and adaptive genomic properties.

Will genomic selection be a practical method for plant breeding?

OpenAIRE

Nakaya, Akihiro; Isobe, Sachiko N.

2012-01-01

Background Genomic selection or genome-wide selection (GS) has been highlighted as a new approach for marker-assisted selection (MAS) in recent years. GS is a form of MAS that selects favourable individuals based on genomic estimated breeding values. Previous studies have suggested the utility of GS, especially for capturing small-effect quantitative trait loci, but GS has not become a popular methodology in the field of plant breeding, possibly because there is insufficient information avail...

Comparison of whole genome amplification techniques for human single cell exome sequencing.

Science.gov (United States)

Borgström, Erik; Paterlini, Marta; Mold, Jeff E; Frisen, Jonas; Lundeberg, Joakim

2017-01-01

Whole genome amplification (WGA) is currently a prerequisite for single cell whole genome or exome sequencing. Depending on the method used the rate of artifact formation, allelic dropout and sequence coverage over the genome may differ significantly. The largest difference between the evaluated protocols was observed when analyzing the target coverage and read depth distribution. These differences also had impact on the downstream variant calling. Conclusively, the products from the AMPLI1 and MALBAC kits were shown to be most similar to the bulk samples and are therefore recommended for WGA of single cells. In this study four commercial kits for WGA (AMPLI1, MALBAC, Repli-G and PicoPlex) were used to amplify human single cells. The WGA products were exome sequenced together with non-amplified bulk samples from the same source. The resulting data was evaluated in terms of genomic coverage, allelic dropout and SNP calling.

Genomic signal processing methods for computation of alignment-free distances from DNA sequences.

Science.gov (United States)

Borrayo, Ernesto; Mendizabal-Ruiz, E Gerardo; Vélez-Pérez, Hugo; Romo-Vázquez, Rebeca; Mendizabal, Adriana P; Morales, J Alejandro

2014-01-01

Genomic signal processing (GSP) refers to the use of digital signal processing (DSP) tools for analyzing genomic data such as DNA sequences. A possible application of GSP that has not been fully explored is the computation of the distance between a pair of sequences. In this work we present GAFD, a novel GSP alignment-free distance computation method. We introduce a DNA sequence-to-signal mapping function based on the employment of doublet values, which increases the number of possible amplitude values for the generated signal. Additionally, we explore the use of three DSP distance metrics as descriptors for categorizing DNA signal fragments. Our results indicate the feasibility of employing GAFD for computing sequence distances and the use of descriptors for characterizing DNA fragments.

GI-POP: a combinational annotation and genomic island prediction pipeline for ongoing microbial genome projects.

Science.gov (United States)

Lee, Chi-Ching; Chen, Yi-Ping Phoebe; Yao, Tzu-Jung; Ma, Cheng-Yu; Lo, Wei-Cheng; Lyu, Ping-Chiang; Tang, Chuan Yi

2013-04-10

Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project. Copyright © 2012 Elsevier B.V. All rights reserved.

Nuclear data evaluation method and evaluation system

International Nuclear Information System (INIS)

Liu Tingjin

1995-01-01

The evaluation methods and Nuclear Data Evaluation System have been developed in China. A new version of the system has been established on Micro-VAX2 computer, which is supported by IAEA under the technology assistance program. The flow chart of Chinese Nuclear Data Evaluation System is shown out. For last ten years, the main efforts have been put on the double differential cross section, covariance data and evaluated data library validation. The developed evaluation method and Chinese Nuclear Data Evaluation System have been widely used at CNDC and in Chinese Nuclear Data Network for CENDL. (1 tab., 15 figs.)

Rapid detection of structural variation in a human genome using nanochannel-based genome mapping technology

DEFF Research Database (Denmark)

Cao, Hongzhi; Hastie, Alex R.; Cao, Dandan

2014-01-01

mutations; however, none of the current detection methods are comprehensive, and currently available methodologies are incapable of providing sufficient resolution and unambiguous information across complex regions in the human genome. To address these challenges, we applied a high-throughput, cost......-effective genome mapping technology to comprehensively discover genome-wide SVs and characterize complex regions of the YH genome using long single molecules (>150 kb) in a global fashion. RESULTS: Utilizing nanochannel-based genome mapping technology, we obtained 708 insertions/deletions and 17 inversions larger...... fosmid data. Of the remaining 270 SVs, 260 are insertions and 213 overlap known SVs in the Database of Genomic Variants. Overall, 609 out of 666 (90%) variants were supported by experimental orthogonal methods or historical evidence in public databases. At the same time, genome mapping also provides...

Evaluating Digital PCR for the Quantification of Human Genomic DNA: Accessible Amplifiable Targets.

Science.gov (United States)

Kline, Margaret C; Romsos, Erica L; Duewer, David L

2016-02-16

Polymerase chain reaction (PCR) multiplexed assays perform best when the input quantity of template DNA is controlled to within about a factor of √2. To help ensure that PCR assays yield consistent results over time and place, results from methods used to determine DNA quantity need to be metrologically traceable to a common reference. Many DNA quantitation systems can be accurately calibrated with solutions of DNA in aqueous buffer. Since they do not require external calibration, end-point limiting dilution technologies, collectively termed "digital PCR (dPCR)", have been proposed as suitable for value assigning such DNA calibrants. The performance characteristics of several commercially available dPCR systems have recently been documented using plasmid, viral, or fragmented genomic DNA; dPCR performance with more complex materials, such as human genomic DNA, has been less studied. With the goal of providing a human genomic reference material traceably certified for mass concentration, we are investigating the measurement characteristics of several dPCR systems. We here report results of measurements from multiple PCR assays, on four human genomic DNAs treated with four endonuclease restriction enzymes using both chamber and droplet dPCR platforms. We conclude that dPCR does not estimate the absolute number of PCR targets in a given volume but rather the number of accessible and amplifiable targets. While enzymatic restriction of human genomic DNA increases accessibility for some assays, in well-optimized PCR assays it can reduce the number of amplifiable targets and increase assay variability relative to uncut sample.

BIOETHICS METHODS IN THE ETHICAL, LEGAL, AND SOCIAL IMPLICATIONS OF THE HUMAN GENOME PROJECT LITERATURE

Science.gov (United States)

Walker, Rebecca; Morrissey, Clair

2013-01-01

While bioethics as a field has concerned itself with methodological issues since the early years, there has been no systematic examination of how ethics is incorporated into research on the Ethical, Legal and Social Implications (ELSI) of the Human Genome Project. Yet ELSI research may bear a particular burden of investigating and substantiating its methods given public funding, an explicitly cross-disciplinary approach, and the perceived significance of adequate responsiveness to advances in genomics. We undertook a qualitative content analysis of a sample of ELSI publications appearing between 2003-2008 with the aim of better understanding the methods, aims, and approaches to ethics that ELSI researchers employ. We found that the aims of ethics within ELSI are largely prescriptive and address multiple groups. We also found that the bioethics methods used in the ELSI literature are both diverse between publications and multiple within publications, but are usually not themselves discussed or employed as suggested by bioethics method proponents. Ethics in ELSI is also sometimes undistinguished from related inquiries (such as social, legal, or political investigations). PMID:23796275

Effects of sample treatments on genome recovery via single-cell genomics

Energy Technology Data Exchange (ETDEWEB)

Clingenpeel, Scott [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Schwientek, Patrick [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Hugenholtz, Philip [Univ. of Queensland, Brisbane (Australia); Woyke, Tanja [USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States)

2014-06-13

It is known that single-cell genomics is a powerful tool for accessing genetic information from uncultivated microorganisms. Methods of handling samples before single-cell genomic amplification may affect the quality of the genomes obtained. Using three bacterial strains we demonstrate that, compared to cryopreservation, lower-quality single-cell genomes are recovered when the sample is preserved in ethanol or if the sample undergoes fluorescence in situ hybridization, while sample preservation in paraformaldehyde renders it completely unsuitable for sequencing.

Significance of functional disease-causal/susceptible variants identified by whole-genome analyses for the understanding of human diseases.

Science.gov (United States)

Hitomi, Yuki; Tokunaga, Katsushi

2017-01-01

Human genome variation may cause differences in traits and disease risks. Disease-causal/susceptible genes and variants for both common and rare diseases can be detected by comprehensive whole-genome analyses, such as whole-genome sequencing (WGS), using next-generation sequencing (NGS) technology and genome-wide association studies (GWAS). Here, in addition to the application of an NGS as a whole-genome analysis method, we summarize approaches for the identification of functional disease-causal/susceptible variants from abundant genetic variants in the human genome and methods for evaluating their functional effects in human diseases, using an NGS and in silico and in vitro functional analyses. We also discuss the clinical applications of the functional disease causal/susceptible variants to personalized medicine.

Visualization of genome signatures of eukaryote genomes by batch-learning self-organizing map with a special emphasis on Drosophila genomes.

Science.gov (United States)

Abe, Takashi; Hamano, Yuta; Ikemura, Toshimichi

2014-01-01

A strategy of evolutionary studies that can compare vast numbers of genome sequences is becoming increasingly important with the remarkable progress of high-throughput DNA sequencing methods. We previously established a sequence alignment-free clustering method "BLSOM" for di-, tri-, and tetranucleotide compositions in genome sequences, which can characterize sequence characteristics (genome signatures) of a wide range of species. In the present study, we generated BLSOMs for tetra- and pentanucleotide compositions in approximately one million sequence fragments derived from 101 eukaryotes, for which almost complete genome sequences were available. BLSOM recognized phylotype-specific characteristics (e.g., key combinations of oligonucleotide frequencies) in the genome sequences, permitting phylotype-specific clustering of the sequences without any information regarding the species. In our detailed examination of 12 Drosophila species, the correlation between their phylogenetic classification and the classification on the BLSOMs was observed to visualize oligonucleotides diagnostic for species-specific clustering.

Facilitating comparative effectiveness research in cancer genomics: evaluating stakeholder perceptions of the engagement process.

Science.gov (United States)

Deverka, Patricia A; Lavallee, Danielle C; Desai, Priyanka J; Armstrong, Joanne; Gorman, Mark; Hole-Curry, Leah; O'Leary, James; Ruffner, B W; Watkins, John; Veenstra, David L; Baker, Laurence H; Unger, Joseph M; Ramsey, Scott D

2012-07-01

The Center for Comparative Effectiveness Research in Cancer Genomics completed a 2-year stakeholder-guided process for the prioritization of genomic tests for comparative effectiveness research studies. We sought to evaluate the effectiveness of engagement procedures in achieving project goals and to identify opportunities for future improvements. The evaluation included an online questionnaire, one-on-one telephone interviews and facilitated discussion. Responses to the online questionnaire were tabulated for descriptive purposes, while transcripts from key informant interviews were analyzed using a directed content analysis approach. A total of 11 out of 13 stakeholders completed both the online questionnaire and interview process, while nine participated in the facilitated discussion. Eighty-nine percent of questionnaire items received overall ratings of agree or strongly agree; 11% of responses were rated as neutral with the exception of a single rating of disagreement with an item regarding the clarity of how stakeholder input was incorporated into project decisions. Recommendations for future improvement included developing standard recruitment practices, role descriptions and processes for improved communication with clinical and comparative effectiveness research investigators. Evaluation of the stakeholder engagement process provided constructive feedback for future improvements and should be routinely conducted to ensure maximal effectiveness of stakeholder involvement.

QTL list - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods ...Policy | Contact Us QTL list - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive ...

Genome-reconstruction for eukaryotes from complex natural microbial communities.

Science.gov (United States)

West, Patrick T; Probst, Alexander J; Grigoriev, Igor V; Thomas, Brian C; Banfield, Jillian F

2018-04-01

Microbial eukaryotes are integral components of natural microbial communities, and their inclusion is critical for many ecosystem studies, yet the majority of published metagenome analyses ignore eukaryotes. In order to include eukaryotes in environmental studies, we propose a method to recover eukaryotic genomes from complex metagenomic samples. A key step for genome recovery is separation of eukaryotic and prokaryotic fragments. We developed a k -mer-based strategy, EukRep, for eukaryotic sequence identification and applied it to environmental samples to show that it enables genome recovery, genome completeness evaluation, and prediction of metabolic potential. We used this approach to test the effect of addition of organic carbon on a geyser-associated microbial community and detected a substantial change of the community metabolism, with selection against almost all candidate phyla bacteria and archaea and for eukaryotes. Near complete genomes were reconstructed for three fungi placed within the Eurotiomycetes and an arthropod. While carbon fixation and sulfur oxidation were important functions in the geyser community prior to carbon addition, the organic carbon-impacted community showed enrichment for secreted proteases, secreted lipases, cellulose targeting CAZymes, and methanol oxidation. We demonstrate the broader utility of EukRep by reconstructing and evaluating relatively high-quality fungal, protist, and rotifer genomes from complex environmental samples. This approach opens the way for cultivation-independent analyses of whole microbial communities. © 2018 West et al.; Published by Cold Spring Harbor Laboratory Press.

Marker list - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods ...Database Site Policy | Contact Us Marker list - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive ...

Should we use the single nucleotide polymorphism linked to in genomic evaluation of French trotter?

Science.gov (United States)

Brard, S; Ricard, A

2015-10-01

An A/C mutation responsible for the ability to pace in horses was recently discovered in the gene. It has also been proven that allele C has a negative effect on trotters' performances. However, in French trotters (FT), the frequency of allele A is only 77% due to an unexpected positive effect of allele C in late-career FT performances. Here we set out to ascertain whether the genotype at SNP (linked to ) should be used to compute EBV for FT. We used the genotypes of 630 horses, with 41,711 SNP retained. The pedigree comprised 5,699 horses. Qualification status (trotters need to complete a 2,000-m race within a limited time to begin their career) and earnings at different ages were precorrected for fixed effects and evaluated with a multitrait model. Estimated breeding values were computed with and without the genotype at SNP as a fixed effect in the model. The analyses were performed using pedigree only via BLUP and using the genotypes via genomic BLUP (GBLUP). The genotype at SNP was removed from the file of genotypes when already taken into account as a fixed effect. Alternatively, 3 groups of 100 candidates were used for validation. Validations were also performed on 50 random-clustered groups of 126 candidates and compared against the results of the 3 disjoint sets. For performances on which has a minor effect, the coefficients of correlation were not improved when the genotype at SNP was a fixed effect in the model (earnings at 3 and 4 yr). However, for traits proven strongly related to , the accuracy of evaluation was improved, increasing +0.17 for earnings at 2 yr, +0.04 for earnings at 5 yr and older, and +0.09 for qualification status (with the GBLUP method). For all traits, the bias was reduced when the SNP linked to was a fixed effect in the model. This work finds a clear rationale for using the genotype at for this multitrait evaluation. Genomic selection seemed to achieve better results than classic selection.

Extensive Genome Rearrangements and Multiple Horizontal Gene Transfers in a Population of Pyrococcus Isolates from Vulcano Island, Italy▿ †

Science.gov (United States)

White, James R.; Escobar-Paramo, Patricia; Mongodin, Emmanuel F.; Nelson, Karen E.; DiRuggiero, Jocelyne

2008-01-01

The extent of chromosome rearrangements in Pyrococcus isolates from marine hydrothermal vents in Vulcano Island, Italy, was evaluated by high-throughput genomic methods. The results illustrate the dynamic nature of the genomes of the genus Pyrococcus and raise the possibility of a connection between rapidly changing environmental conditions and adaptive genomic properties. PMID:18723649

DNA immunoprecipitation semiconductor sequencing (DIP-SC-seq) as a rapid method to generate genome wide epigenetic signatures

OpenAIRE

Thomson, John P.; Fawkes, Angie; Ottaviano, Raffaele; Hunter, Jennifer M.; Shukla, Ruchi; Mjoseng, Heidi K.; Clark, Richard; Coutts, Audrey; Murphy, Lee; Meehan, Richard R.

2015-01-01

Modification of DNA resulting in 5-methylcytosine (5 mC) or 5-hydroxymethylcytosine (5hmC) has been shown to influence the local chromatin environment and affect transcription. Although recent advances in next generation sequencing technology allow researchers to map epigenetic modifications across the genome, such experiments are often time-consuming and cost prohibitive. Here we present a rapid and cost effective method of generating genome wide DNA modification maps utilising commercially ...

Short communication: Genomic selection in a crossbred cattle population using data from the Dairy Genetics East Africa Project.

Science.gov (United States)

Brown, A; Ojango, J; Gibson, J; Coffey, M; Okeyo, M; Mrode, R

2016-09-01

Due to the absence of accurate pedigree information, it has not been possible to implement genetic evaluations for crossbred cattle in African small-holder systems. Genomic selection techniques that do not rely on pedigree information could, therefore, be a useful alternative. The objective of this study was to examine the feasibility of using genomic selection techniques in a crossbred cattle population using data from Kenya provided by the Dairy Genetics East Africa Project. Genomic estimated breeding values for milk yield were estimated using 2 prediction methods, GBLUP and BayesC, and accuracies were calculated as the correlation between yield deviations and genomic breeding values included in the estimation process, mimicking the situation for young bulls. The accuracy of evaluation ranged from 0.28 to 0.41, depending on the validation population and prediction method used. No significant differences were found in accuracy between the 2 prediction methods. The results suggest that there is potential for implementing genomic selection for young bulls in crossbred small-holder cattle populations, and targeted genotyping and phenotyping should be pursued to facilitate this. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Phytozome Comparative Plant Genomics Portal

Energy Technology Data Exchange (ETDEWEB)

Goodstein, David; Batra, Sajeev; Carlson, Joseph; Hayes, Richard; Phillips, Jeremy; Shu, Shengqiang; Schmutz, Jeremy; Rokhsar, Daniel

2014-09-09

The Dept. of Energy Joint Genome Institute is a genomics user facility supporting DOE mission science in the areas of Bioenergy, Carbon Cycling, and Biogeochemistry. The Plant Program at the JGI applies genomic, analytical, computational and informatics platforms and methods to: 1. Understand and accelerate the improvement (domestication) of bioenergy crops 2. Characterize and moderate plant response to climate change 3. Use comparative genomics to identify constrained elements and infer gene function 4. Build high quality genomic resource platforms of JGI Plant Flagship genomes for functional and experimental work 5. Expand functional genomic resources for Plant Flagship genomes

Evaluation methods for hospital facilities

DEFF Research Database (Denmark)

Fronczek-Munter, Aneta

2013-01-01

according to focus areas and proposes which evaluation methods to use in different building phases of healthcare facilities. Hospital evaluations with experts and users are also considered; their subjective view on space, function, technology, usability and aesthetics. Results & solutions: This paper...... presents the different methods for evaluating buildings in use in a new model, the Evaluation Focus Flower, and proposes which evaluation methods are suitable for various aims and building phases, i.e. which is giving best input for the initial briefing process of new hospital facilities with ambition...... of creating buildings with enhanced usability. Additionally various evaluation methods used in hospital cases in Denmark and Norway are presented. Involvement of users is proposed, not just in defining requirements but also in co-creation/design and evaluation of solutions. The theories and preliminary...

Automated ensemble assembly and validation of microbial genomes

Science.gov (United States)

2014-01-01

Background The continued democratization of DNA sequencing has sparked a new wave of development of genome assembly and assembly validation methods. As individual research labs, rather than centralized centers, begin to sequence the majority of new genomes, it is important to establish best practices for genome assembly. However, recent evaluations such as GAGE and the Assemblathon have concluded that there is no single best approach to genome assembly. Instead, it is preferable to generate multiple assemblies and validate them to determine which is most useful for the desired analysis; this is a labor-intensive process that is often impossible or unfeasible. Results To encourage best practices supported by the community, we present iMetAMOS, an automated ensemble assembly pipeline; iMetAMOS encapsulates the process of running, validating, and selecting a single assembly from multiple assemblies. iMetAMOS packages several leading open-source tools into a single binary that automates parameter selection and execution of multiple assemblers, scores the resulting assemblies based on multiple validation metrics, and annotates the assemblies for genes and contaminants. We demonstrate the utility of the ensemble process on 225 previously unassembled Mycobacterium tuberculosis genomes as well as a Rhodobacter sphaeroides benchmark dataset. On these real data, iMetAMOS reliably produces validated assemblies and identifies potential contamination without user intervention. In addition, intelligent parameter selection produces assemblies of R. sphaeroides comparable to or exceeding the quality of those from the GAGE-B evaluation, affecting the relative ranking of some assemblers. Conclusions Ensemble assembly with iMetAMOS provides users with multiple, validated assemblies for each genome. Although computationally limited to small or mid-sized genomes, this approach is the most effective and reproducible means for generating high-quality assemblies and enables users to

Ultrafast comparison of personal genomes

OpenAIRE

Mauldin, Denise; Hood, Leroy; Robinson, Max; Glusman, Gustavo

2017-01-01

We present an ultra-fast method for comparing personal genomes. We transform the standard genome representation (lists of variants relative to a reference) into 'genome fingerprints' that can be readily compared across sequencing technologies and reference versions. Because of their reduced size, computation on the genome fingerprints is fast and requires little memory. This enables scaling up a variety of important genome analyses, including quantifying relatedness, recognizing duplicative s...

Evaluation of the Cow Rumen Metagenome: Assembly by Single Copy Gene Analysis and Single Cell Genome Assemblies (Metagenomics Informatics Challenges Workshop: 10K Genomes at a Time)

Energy Technology Data Exchange (ETDEWEB)

Sczyrba, Alex

2011-10-13

DOE JGI's Alex Sczyrba on "Evaluation of the Cow Rumen Metagenome" and "Assembly by Single Copy Gene Analysis and Single Cell Genome Assemblies" at the Metagenomics Informatics Challenges Workshop held at the DOE JGI on October 12-13, 2011.

A unified and comprehensible view of parametric and kernel methods for genomic prediction with application to rice

Directory of Open Access Journals (Sweden)

Laval Jacquin

2016-08-01

Full Text Available One objective of this study was to provide readers with a clear and unified understanding ofparametric statistical and kernel methods, used for genomic prediction, and to compare some ofthese in the context of rice breeding for quantitative traits. Furthermore, another objective wasto provide a simple and user-friendly R package, named KRMM, which allows users to performRKHS regression with several kernels. After introducing the concept of regularized empiricalrisk minimization, the connections between well-known parametric and kernel methods suchas Ridge regression (i.e. genomic best linear unbiased predictor (GBLUP and reproducingkernel Hilbert space (RKHS regression were reviewed. Ridge regression was then reformulatedso as to show and emphasize the advantage of the kernel trick concept, exploited by kernelmethods in the context of epistatic genetic architectures, over parametric frameworks used byconventional methods. Some parametric and kernel methods; least absolute shrinkage andselection operator (LASSO, GBLUP, support vector machine regression (SVR and RKHSregression were thereupon compared for their genomic predictive ability in the context of ricebreeding using three real data sets. Among the compared methods, RKHS regression and SVRwere often the most accurate methods for prediction followed by GBLUP and LASSO. An Rfunction which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression,with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time hasbeen developed. Moreover, a modified version of this function, which allows users to tune kernelsfor RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding KRMM package and all scripts have been made publicly available.

Development of Mycoplasma synoviae (MS) core genome multilocus sequence typing (cgMLST) scheme.

Science.gov (United States)

Ghanem, Mostafa; El-Gazzar, Mohamed

2018-05-01

Mycoplasma synoviae (MS) is a poultry pathogen with reported increased prevalence and virulence in recent years. MS strain identification is essential for prevention, control efforts and epidemiological outbreak investigations. Multiple multilocus based sequence typing schemes have been developed for MS, yet the resolution of these schemes could be limited for outbreak investigation. The cost of whole genome sequencing became close to that of sequencing the seven MLST targets; however, there is no standardized method for typing MS strains based on whole genome sequences. In this paper, we propose a core genome multilocus sequence typing (cgMLST) scheme as a standardized and reproducible method for typing MS based whole genome sequences. A diverse set of 25 MS whole genome sequences were used to identify 302 core genome genes as cgMLST targets (35.5% of MS genome) and 44 whole genome sequences of MS isolates from six countries in four continents were used for typing applying this scheme. cgMLST based phylogenetic trees displayed a high degree of agreement with core genome SNP based analysis and available epidemiological information. cgMLST allowed evaluation of two conventional MLST schemes of MS. The high discriminatory power of cgMLST allowed differentiation between samples of the same conventional MLST type. cgMLST represents a standardized, accurate, highly discriminatory, and reproducible method for differentiation between MS isolates. Like conventional MLST, it provides stable and expandable nomenclature, allowing for comparing and sharing the typing results between different laboratories worldwide. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

The current state of funded NIH grants in implementation science in genomic medicine: a portfolio analysis.

Science.gov (United States)

Roberts, Megan C; Clyne, Mindy; Kennedy, Amy E; Chambers, David A; Khoury, Muin J

2017-10-26

PurposeImplementation science offers methods to evaluate the translation of genomic medicine research into practice. The extent to which the National Institutes of Health (NIH) human genomics grant portfolio includes implementation science is unknown. This brief report's objective is to describe recently funded implementation science studies in genomic medicine in the NIH grant portfolio, and identify remaining gaps.MethodsWe identified investigator-initiated NIH research grants on implementation science in genomic medicine (funding initiated 2012-2016). A codebook was adapted from the literature, three authors coded grants, and descriptive statistics were calculated for each code.ResultsForty-two grants fit the inclusion criteria (~1.75% of investigator-initiated genomics grants). The majority of included grants proposed qualitative and/or quantitative methods with cross-sectional study designs, and described clinical settings and primarily white, non-Hispanic study populations. Most grants were in oncology and examined genetic testing for risk assessment. Finally, grants lacked the use of implementation science frameworks, and most examined uptake of genomic medicine and/or assessed patient-centeredness.ConclusionWe identified large gaps in implementation science studies in genomic medicine in the funded NIH portfolio over the past 5 years. To move the genomics field forward, investigator-initiated research grants should employ rigorous implementation science methods within diverse settings and populations.Genetics in Medicine advance online publication, 26 October 2017; doi:10.1038/gim.2017.180.

A comparison of statistical methods for genomic selection in a mice population

Directory of Open Access Journals (Sweden)

Neves Haroldo HR

2012-11-01

Full Text Available Abstract Background The availability of high-density panels of SNP markers has opened new perspectives for marker-assisted selection strategies, such that genotypes for these markers are used to predict the genetic merit of selection candidates. Because the number of markers is often much larger than the number of phenotypes, marker effect estimation is not a trivial task. The objective of this research was to compare the predictive performance of ten different statistical methods employed in genomic selection, by analyzing data from a heterogeneous stock mice population. Results For the five traits analyzed (W6W: weight at six weeks, WGS: growth slope, BL: body length, %CD8+: percentage of CD8+ cells, CD4+/ CD8+: ratio between CD4+ and CD8+ cells, within-family predictions were more accurate than across-family predictions, although this superiority in accuracy varied markedly across traits. For within-family prediction, two kernel methods, Reproducing Kernel Hilbert Spaces Regression (RKHS and Support Vector Regression (SVR, were the most accurate for W6W, while a polygenic model also had comparable performance. A form of ridge regression assuming that all markers contribute to the additive variance (RR_GBLUP figured among the most accurate for WGS and BL, while two variable selection methods ( LASSO and Random Forest, RF had the greatest predictive abilities for %CD8+ and CD4+/ CD8+. RF, RKHS, SVR and RR_GBLUP outperformed the remainder methods in terms of bias and inflation of predictions. Conclusions Methods with large conceptual differences reached very similar predictive abilities and a clear re-ranking of methods was observed in function of the trait analyzed. Variable selection methods were more accurate than the remainder in the case of %CD8+ and CD4+/CD8+ and these traits are likely to be influenced by a smaller number of QTL than the remainder. Judged by their overall performance across traits and computational requirements, RR

Methods for evaluating imaging methods of limited reproducibility

International Nuclear Information System (INIS)

Krummenauer, F.

2005-01-01

Just like new drugs, new or modified imaging methods must be subjected to objective clinical tests, including tests on humans. In this, it must be ensured that the principle of Good Clinical Practice (GCP) are followed with regard to medical, administrative and methodical quality. Innovative methods fo clinical epidemiology and medical biometry should be applied from the planning stage to the final statistical evaluation. The author presents established and new methods for planning, evaluation and reporting of clinical tests of diagnostic methods, and especially imaging methods, in clinical medicine and illustrates these by means of current research projects in the various medical disciplines. The strategies presented are summarized in a recommendation based on the concept of phases I - IV of clinical drug testing in order to enable standardisation of the clinical evaluation of imaging methods. (orig.)

Evaluation of plasmid and genomic DNA calibrants used for the quantification of genetically modified organisms.

Science.gov (United States)

Caprioara-Buda, M; Meyer, W; Jeynov, B; Corbisier, P; Trapmann, S; Emons, H

2012-07-01

The reliable quantification of genetically modified organisms (GMOs) by real-time PCR requires, besides thoroughly validated quantitative detection methods, sustainable calibration systems. The latter establishes the anchor points for the measured value and the measurement unit, respectively. In this paper, the suitability of two types of DNA calibrants, i.e. plasmid DNA and genomic DNA extracted from plant leaves, for the certification of the GMO content in reference materials as copy number ratio between two targeted DNA sequences was investigated. The PCR efficiencies and coefficients of determination of the calibration curves as well as the measured copy number ratios for three powder certified reference materials (CRMs), namely ERM-BF415e (NK603 maize), ERM-BF425c (356043 soya), and ERM-BF427c (98140 maize), originally certified for their mass fraction of GMO, were compared for both types of calibrants. In all three systems investigated, the PCR efficiencies of plasmid DNA were slightly closer to the PCR efficiencies observed for the genomic DNA extracted from seed powders rather than those of the genomic DNA extracted from leaves. Although the mean DNA copy number ratios for each CRM overlapped within their uncertainties, the DNA copy number ratios were significantly different using the two types of calibrants. Based on these observations, both plasmid and leaf genomic DNA calibrants would be technically suitable as anchor points for the calibration of the real-time PCR methods applied in this study. However, the most suitable approach to establish a sustainable traceability chain is to fix a reference system based on plasmid DNA.

Systematic evaluation of bias in microbial community profiles induced by whole genome amplification.

Science.gov (United States)

Direito, Susana O L; Zaura, Egija; Little, Miranda; Ehrenfreund, Pascale; Röling, Wilfred F M

2014-03-01

Whole genome amplification methods facilitate the detection and characterization of microbial communities in low biomass environments. We examined the extent to which the actual community structure is reliably revealed and factors contributing to bias. One widely used [multiple displacement amplification (MDA)] and one new primer-free method [primase-based whole genome amplification (pWGA)] were compared using a polymerase chain reaction (PCR)-based method as control. Pyrosequencing of an environmental sample and principal component analysis revealed that MDA impacted community profiles more strongly than pWGA and indicated that this related to species GC content, although an influence of DNA integrity could not be excluded. Subsequently, biases by species GC content, DNA integrity and fragment size were separately analysed using defined mixtures of DNA from various species. We found significantly less amplification of species with the highest GC content for MDA-based templates and, to a lesser extent, for pWGA. DNA fragmentation also interfered severely: species with more fragmented DNA were less amplified with MDA and pWGA. pWGA was unable to amplify low molecular weight DNA (microbial communities in low-biomass environments and for currently planned astrobiological missions to Mars. © 2013 Society for Applied Microbiology and John Wiley & Sons Ltd.

Multiple-trait genetic evaluation using genomic matrix

African Journals Online (AJOL)

Jane

2011-07-06

Jul 6, 2011 ... relationships was estimated through computer simulation and was compared with the accuracy of ... programs, detect animals with superior genetic and select ... genomic matrices in the mixed model equations of BLUP.

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice.

Science.gov (United States)

Jacquin, Laval; Cao, Tuong-Vi; Ahmadi, Nourollah

2016-01-01

One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named KRMM, which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel "trick" concept, exploited by kernel methods in the context of epistatic genetic architectures, over parametric frameworks used by conventional methods. Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding KRMM package and all scripts have been made publicly available.

Genome Update: alignment of bacterial chromosomes

DEFF Research Database (Denmark)

Ussery, David; Jensen, Mette; Poulsen, Tine Rugh

2004-01-01

There are four new microbial genomes listed in this month's Genome Update, three belonging to Gram-positive bacteria and one belonging to an archaeon that lives at pH 0; all of these genomes are listed in Table 1⇓. The method of genome comparison this month is that of genome alignment and, as an ...

The research progress of genomic selection in livestock.

Science.gov (United States)

Li, Hong-wei; Wang, Rui-jun; Wang, Zhi-ying; Li, Xue-wu; Wang, Zhen-yu; Yanjun, Zhang; Rui, Su; Zhihong, Liu; Jinquan, Li

2017-05-20

With the development of gene chip and breeding technology, genomic selection in plants and animals has become research hotspots in recent years. Genomic selection has been extensively applied to all kinds of economic livestock, due to its high accuracy, short generation intervals and low breeding costs. In this review, we summarize genotyping technology and the methods for genomic breeding value estimation, the latter including the least square method, RR-BLUP, GBLUP, ssGBLUP, BayesA and BayesB. We also cover basic principles of genomic selection and compare their genetic marker ranges, genomic selection accuracy and operational speed. In addition, we list common indicators, methods and influencing factors that are related to genomic selection accuracy. Lastly, we discuss latest applications and the current problems of genomic selection at home and abroad. Importantly, we envision future status of genomic selection research, including multi-trait and multi-population genomic selection, as well as impact of whole genome sequencing and dominant effects on genomic selection. This review will provide some venues for other breeders to further understand genome selection.

Double-strand breaks in genome-sized DNA caused by mechanical stress under mixing: Quantitative evaluation through single-molecule observation

Science.gov (United States)

Kikuchi, Hayato; Nose, Keiji; Yoshikawa, Yuko; Yoshikawa, Kenichi

2018-06-01

It is becoming increasingly apparent that changes in the higher-order structure of genome-sized DNA molecules of more than several tens kbp play important roles in the self-control of genome activity in living cells. Unfortunately, it has been rather difficult to prepare genome-sized DNA molecules without damage or fragmentation. Here, we evaluated the degree of double-strand breaks (DSBs) caused by mechanical mixing by single-molecule observation with fluorescence microscopy. The results show that DNA breaks are most significant for the first second after the initiation of mechanical agitation. Based on such observation, we propose a novel mixing procedure to significantly decrease DSBs.

Reporting of Human Genome Epidemiology (HuGE association studies: An empirical assessment

Directory of Open Access Journals (Sweden)

Gwinn Marta

2008-05-01

Full Text Available Abstract Background Several thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies. Methods Articles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001–2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001–2003. Results During both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample. Conclusion We conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature.

An orphan gyrB in the Mycobacterium smegmatis genome ...

Indian Academy of Sciences (India)

Unknown

2002-12-13

Dec 13, 2002 ... ... respect to gene organization and regulation, biochemical characterization, ... marin class of drugs, resides in GyrB, providing essential energetics for the ... Materials and methods ... analysis of the M. smegmatis genome revealed presence of a gene .... outs of either one of the gyrB alleles to evaluate their.

Mitochondrial genome of the Komodo dragon: efficient sequencing method with reptile-oriented primers and novel gene rearrangements.

Science.gov (United States)

Kumazawa, Yoshinori; Endo, Hideki

2004-04-30

The mitochondrial genome of the Komodo dragon (Varanus komodoensis) was nearly completely sequenced, except for two highly repetitive noncoding regions. An efficient sequencing method for squamate mitochondrial genomes was established by combining the long polymerase chain reaction (PCR) technology and a set of reptile-oriented primers designed for nested PCR amplifications. It was found that the mitochondrial genome had novel gene arrangements in which genes from NADH dehydrogenase subunit 6 to proline tRNA were extensively shuffled with duplicate control regions. These control regions had 99% sequence similarity over 700 bp. Although snake mitochondrial genomes are also known to possess duplicate control regions with nearly identical sequences, the location of the second control region suggested independent occurrence of the duplication on lineages leading to snakes and the Komodo dragon. Another feature of the mitochondrial genome of the Komodo dragon was the considerable number of tandem repeats, including sequences with a strong secondary structure, as a possible site for the slipped-strand mispairing in replication. These observations are consistent with hypotheses that tandem duplications via the slipped-strand mispairing may induce mitochondrial gene rearrangements and may serve to maintain similar copies of the control region.

Mapping genomic features to functional traits through microbial whole genome sequences.

Science.gov (United States)

Zhang, Wei; Zeng, Erliang; Liu, Dan; Jones, Stuart E; Emrich, Scott

2014-01-01

Recently, the utility of trait-based approaches for microbial communities has been identified. Increasing availability of whole genome sequences provide the opportunity to explore the genetic foundations of a variety of functional traits. We proposed a machine learning framework to quantitatively link the genomic features with functional traits. Genes from bacteria genomes belonging to different functional traits were grouped to Cluster of Orthologs (COGs), and were used as features. Then, TF-IDF technique from the text mining domain was applied to transform the data to accommodate the abundance and importance of each COG. After TF-IDF processing, COGs were ranked using feature selection methods to identify their relevance to the functional trait of interest. Extensive experimental results demonstrated that functional trait related genes can be detected using our method. Further, the method has the potential to provide novel biological insights.

NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.

Science.gov (United States)

Zou, Meng; Liu, Zhaoqi; Zhang, Xiang-Sun; Wang, Yong

2015-10-15

In prognosis and survival studies, an important goal is to identify multi-biomarker panels with predictive power using molecular characteristics or clinical observations. Such analysis is often challenged by censored, small-sample-size, but high-dimensional genomic profiles or clinical data. Therefore, sophisticated models and algorithms are in pressing need. In this study, we propose a novel Area Under Curve (AUC) optimization method for multi-biomarker panel identification named Nearest Centroid Classifier for AUC optimization (NCC-AUC). Our method is motived by the connection between AUC score for classification accuracy evaluation and Harrell's concordance index in survival analysis. This connection allows us to convert the survival time regression problem to a binary classification problem. Then an optimization model is formulated to directly maximize AUC and meanwhile minimize the number of selected features to construct a predictor in the nearest centroid classifier framework. NCC-AUC shows its great performance by validating both in genomic data of breast cancer and clinical data of stage IB Non-Small-Cell Lung Cancer (NSCLC). For the genomic data, NCC-AUC outperforms Support Vector Machine (SVM) and Support Vector Machine-based Recursive Feature Elimination (SVM-RFE) in classification accuracy. It tends to select a multi-biomarker panel with low average redundancy and enriched biological meanings. Also NCC-AUC is more significant in separation of low and high risk cohorts than widely used Cox model (Cox proportional-hazards regression model) and L1-Cox model (L1 penalized in Cox model). These performance gains of NCC-AUC are quite robust across 5 subtypes of breast cancer. Further in an independent clinical data, NCC-AUC outperforms SVM and SVM-RFE in predictive accuracy and is consistently better than Cox model and L1-Cox model in grouping patients into high and low risk categories. In summary, NCC-AUC provides a rigorous optimization framework to

Genome-wide identification of the regulatory targets of a transcription factor using biochemical characterization and computational genomic analysis

Directory of Open Access Journals (Sweden)

Jolly Emmitt R

2005-11-01

Full Text Available Abstract Background A major challenge in computational genomics is the development of methodologies that allow accurate genome-wide prediction of the regulatory targets of a transcription factor. We present a method for target identification that combines experimental characterization of binding requirements with computational genomic analysis. Results Our method identified potential target genes of the transcription factor Ndt80, a key transcriptional regulator involved in yeast sporulation, using the combined information of binding affinity, positional distribution, and conservation of the binding sites across multiple species. We have also developed a mathematical approach to compute the false positive rate and the total number of targets in the genome based on the multiple selection criteria. Conclusion We have shown that combining biochemical characterization and computational genomic analysis leads to accurate identification of the genome-wide targets of a transcription factor. The method can be extended to other transcription factors and can complement other genomic approaches to transcriptional regulation.

Bacterial whole genome-based phylogeny: construction of a new benchmarking dataset and assessment of some existing methods.

Science.gov (United States)

Ahrenfeldt, Johanne; Skaarup, Carina; Hasman, Henrik; Pedersen, Anders Gorm; Aarestrup, Frank Møller; Lund, Ole

2017-01-05

Whole genome sequencing (WGS) is increasingly used in diagnostics and surveillance of infectious diseases. A major application for WGS is to use the data for identifying outbreak clusters, and there is therefore a need for methods that can accurately and efficiently infer phylogenies from sequencing reads. In the present study we describe a new dataset that we have created for the purpose of benchmarking such WGS-based methods for epidemiological data, and also present an analysis where we use the data to compare the performance of some current methods. Our aim was to create a benchmark data set that mimics sequencing data of the sort that might be collected during an outbreak of an infectious disease. This was achieved by letting an E. coli hypermutator strain grow in the lab for 8 consecutive days, each day splitting the culture in two while also collecting samples for sequencing. The result is a data set consisting of 101 whole genome sequences with known phylogenetic relationship. Among the sequenced samples 51 correspond to internal nodes in the phylogeny because they are ancestral, while the remaining 50 correspond to leaves. We also used the newly created data set to compare three different online available methods that infer phylogenies from whole-genome sequencing reads: NDtree, CSI Phylogeny and REALPHY. One complication when comparing the output of these methods with the known phylogeny is that phylogenetic methods typically build trees where all observed sequences are placed as leafs, even though some of them are in fact ancestral. We therefore devised a method for post processing the inferred trees by collapsing short branches (thus relocating some leafs to internal nodes), and also present two new measures of tree similarity that takes into account the identity of both internal and leaf nodes. Based on this analysis we find that, among the investigated methods, CSI Phylogeny had the best performance, correctly identifying 73% of all branches in the

Genomics protocols [Methods in molecular biology, v. 175

National Research Council Canada - National Science Library

Starkey, Michael P; Elaswarapu, Ramnath

2001-01-01

.... Drawing on emerging technologies in the fields of bioinformatics and proteomics, these protocols cover not only those traditionally recognized as genomics, but also early therapeutich approaches...

Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network.

Science.gov (United States)

Sperber, Nina R; Carpenter, Janet S; Cavallari, Larisa H; J Damschroder, Laura; Cooper-DeHoff, Rhonda M; Denny, Joshua C; Ginsburg, Geoffrey S; Guan, Yue; Horowitz, Carol R; Levy, Kenneth D; Levy, Mia A; Madden, Ebony B; Matheny, Michael E; Pollin, Toni I; Pratt, Victoria M; Rosenman, Marc; Voils, Corrine I; W Weitzel, Kristen; Wilke, Russell A; Ryanne Wu, R; Orlando, Lori A

2017-05-22

To realize potential public health benefits from genetic and genomic innovations, understanding how best to implement the innovations into clinical care is important. The objective of this study was to synthesize data on challenges identified by six diverse projects that are part of a National Human Genome Research Institute (NHGRI)-funded network focused on implementing genomics into practice and strategies to overcome these challenges. We used a multiple-case study approach with each project considered as a case and qualitative methods to elicit and describe themes related to implementation challenges and strategies. We describe challenges and strategies in an implementation framework and typology to enable consistent definitions and cross-case comparisons. Strategies were linked to challenges based on expert review and shared themes. Three challenges were identified by all six projects, and strategies to address these challenges varied across the projects. One common challenge was to increase the relative priority of integrating genomics within the health system electronic health record (EHR). Four projects used data warehousing techniques to accomplish the integration. The second common challenge was to strengthen clinicians' knowledge and beliefs about genomic medicine. To overcome this challenge, all projects developed educational materials and conducted meetings and outreach focused on genomic education for clinicians. The third challenge was engaging patients in the genomic medicine projects. Strategies to overcome this challenge included use of mass media to spread the word, actively involving patients in implementation (e.g., a patient advisory board), and preparing patients to be active participants in their healthcare decisions. This is the first collaborative evaluation focusing on the description of genomic medicine innovations implemented in multiple real-world clinical settings. Findings suggest that strategies to facilitate integration of genomic

Safeguards Evaluation Method for evaluating vulnerability to insider threats

International Nuclear Information System (INIS)

Al-Ayat, R.A.; Judd, B.R.; Renis, T.A.

1986-01-01

As protection of DOE facilities against outsiders increases to acceptable levels, attention is shifting toward achieving comparable protection against insiders. Since threats and protection measures for insiders are substantially different from those for outsiders, new perspectives and approaches are needed. One such approach is the Safeguards Evaluation Method. This method helps in assessing safeguards vulnerabilities to theft or diversion of special nuclear meterial (SNM) by insiders. The Safeguards Evaluation Method-Insider Threat is a simple model that can be used by safeguards and security planners to evaluate safeguards and proposed upgrades at their own facilities. The method is used to evaluate the effectiveness of safeguards in both timely detection (in time to prevent theft) and late detection (after-the-fact). The method considers the various types of potential insider adversaries working alone or in collusion with other insiders. The approach can be used for a wide variety of facilities with various quantities and forms of SNM. An Evaluation Workbook provides documentation of the baseline assessment; this simplifies subsequent on-site appraisals. Quantitative evaluation is facilitated by an accompanying computer program. The method significantly increases an evaluation team's on-site analytical capabilities, thereby producing a more thorough and accurate safeguards evaluation

GenomePeek—an online tool for prokaryotic genome and metagenome analysis

Directory of Open Access Journals (Sweden)

Katelyn McNair

2015-06-01

Full Text Available As more and more prokaryotic sequencing takes place, a method to quickly and accurately analyze this data is needed. Previous tools are mainly designed for metagenomic analysis and have limitations; such as long runtimes and significant false positive error rates. The online tool GenomePeek (edwards.sdsu.edu/GenomePeek was developed to analyze both single genome and metagenome sequencing files, quickly and with low error rates. GenomePeek uses a sequence assembly approach where reads to a set of conserved genes are extracted, assembled and then aligned against the highly specific reference database. GenomePeek was found to be faster than traditional approaches while still keeping error rates low, as well as offering unique data visualization options.

The ecoresponsive genome of Daphnia pulex

Energy Technology Data Exchange (ETDEWEB)

Colbourne, John K.; Pfrender, Michael E.; Gilbert, Donald; Thomas, W. Kelley; Tucker, Abraham; Oakley, Todd H.; Tokishita, Shinichi; Aerts, Andrea; Arnold, Georg J.; Basu, Malay Kumar; Bauer, Darren J.; Caceres, Carla E.; Carmel, Liran; Casola, Claudio; Choi, Jeong-Hyeon; Detter, John C.; Dong, Qunfeng; Dusheyko, Serge; Eads, Brian D.; Frohlich, Thomas; Geiler-Samerotte, Kerry A.; Gerlach, Daniel; Hatcher, Phil; Jogdeo, Sanjuro; Krijgsveld, Jeroen; Kriventseva, Evgenia V; Kültz, Dietmar; Laforsch, Christian; Lindquist, Erika; Lopez, Jacqueline; Manak, Robert; Muller, Jean; Pangilinan, Jasmyn; Patwardhan, Rupali P.; Pitluck, Samuel; Pritham, Ellen J.; Rechtsteiner, Andreas; Rho, Mina; Rogozin, Igor B.; Sakarya, Onur; Salamov, Asaf; Schaack, Sarah; Shapiro, Harris; Shiga, Yasuhiro; Skalitzky, Courtney; Smith, Zachary; Souvorov, Alexander; Sung, Way; Tang, Zuojian; Tsuchiya, Dai; Tu, Hank; Vos, Harmjan; Wang, Mei; Wolf, Yuri I.; Yamagata, Hideo; Yamada, Takuji; Ye, Yuzhen; Shaw, Joseph R.; Andrews, Justen; Crease, Teresa J.; Tang, Haixu; Lucas, Susan M.; Robertson, Hugh M.; Bork, Peer; Koonin, Eugene V.; Zdobnov, Evgeny M.; Grigoriev, Igor V.; Lynch, Michael; Boore, Jeffrey L.

2011-02-04

This document provides supporting material related to the sequencing of the ecoresponsive genome of Daphnia pulex. This material includes information on materials and methods and supporting text, as well as supplemental figures, tables, and references. The coverage of materials and methods addresses genome sequence, assembly, and mapping to chromosomes, gene inventory, attributes of a compact genome, the origin and preservation of Daphnia pulex genes, implications of Daphnia's genome structure, evolutionary diversification of duplicated genes, functional significance of expanded gene families, and ecoresponsive genes. Supporting text covers chromosome studies, gene homology among Daphnia genomes, micro-RNA and transposable elements and the 46 Daphnia pulex opsins. 36 figures, 50 tables, 183 references.

Plant DB link - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods ...e Site Policy | Contact Us Plant DB link - PGDBj Registered plant list, Marker list, QTL list, Plant DB link & Genome analysis methods | LSDB Archive ...

Organizational heterogeneity of vertebrate genomes.

Science.gov (United States)

Frenkel, Svetlana; Kirzhner, Valery; Korol, Abraham

2012-01-01

Genomes of higher eukaryotes are mosaics of segments with various structural, functional, and evolutionary properties. The availability of whole-genome sequences allows the investigation of their structure as "texts" using different statistical and computational methods. One such method, referred to as Compositional Spectra (CS) analysis, is based on scoring the occurrences of fixed-length oligonucleotides (k-mers) in the target DNA sequence. CS analysis allows generating species- or region-specific characteristics of the genome, regardless of their length and the presence of coding DNA. In this study, we consider the heterogeneity of vertebrate genomes as a joint effect of regional variation in sequence organization superimposed on the differences in nucleotide composition. We estimated compositional and organizational heterogeneity of genome and chromosome sequences separately and found that both heterogeneity types vary widely among genomes as well as among chromosomes in all investigated taxonomic groups. The high correspondence of heterogeneity scores obtained on three genome fractions, coding, repetitive, and the remaining part of the noncoding DNA (the genome dark matter--GDM) allows the assumption that CS-heterogeneity may have functional relevance to genome regulation. Of special interest for such interpretation is the fact that natural GDM sequences display the highest deviation from the corresponding reshuffled sequences.

Organizational heterogeneity of vertebrate genomes.

Directory of Open Access Journals (Sweden)

Svetlana Frenkel

Full Text Available Genomes of higher eukaryotes are mosaics of segments with various structural, functional, and evolutionary properties. The availability of whole-genome sequences allows the investigation of their structure as "texts" using different statistical and computational methods. One such method, referred to as Compositional Spectra (CS analysis, is based on scoring the occurrences of fixed-length oligonucleotides (k-mers in the target DNA sequence. CS analysis allows generating species- or region-specific characteristics of the genome, regardless of their length and the presence of coding DNA. In this study, we consider the heterogeneity of vertebrate genomes as a joint effect of regional variation in sequence organization superimposed on the differences in nucleotide composition. We estimated compositional and organizational heterogeneity of genome and chromosome sequences separately and found that both heterogeneity types vary widely among genomes as well as among chromosomes in all investigated taxonomic groups. The high correspondence of heterogeneity scores obtained on three genome fractions, coding, repetitive, and the remaining part of the noncoding DNA (the genome dark matter--GDM allows the assumption that CS-heterogeneity may have functional relevance to genome regulation. Of special interest for such interpretation is the fact that natural GDM sequences display the highest deviation from the corresponding reshuffled sequences.

Novel degenerate PCR method for whole genome amplification applied to Peru Margin (ODP Leg 201 subsurface samples

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Amanda eMartino

2012-01-01

Full Text Available A degenerate PCR-based method of whole-genome amplification, designed to work fluidly with 454 sequencing technology, was developed and tested for use on deep marine subsurface DNA samples. The method, which we have called Random Amplification Metagenomic PCR (RAMP, involves the use of specific primers from Roche 454 amplicon sequencing, modified by the addition of a degenerate region at the 3’ end. It utilizes a PCR reaction, which resulted in no amplification from blanks, even after 50 cycles of PCR. After efforts to optimize experimental conditions, the method was tested with DNA extracted from cultured E. coli cells, and genome coverage was estimated after sequencing on three different occasions. Coverage did not vary greatly with the different experimental conditions tested, and was around 62% with a sequencing effort equivalent to a theoretical genome coverage of 14.10X. The GC content of the sequenced amplification product was within 2% of the predicted values for this strain of E. coli. The method was also applied to DNA extracted from marine subsurface samples from ODP Leg 201 site 1229 (Peru Margin, and results of a taxonomic analysis revealed microbial communities dominated by Proteobacteria, Chloroflexi, Firmicutes, Euryarchaeota, and Crenarchaeota, among others. These results were similar to those obtained previously for those samples; however, variations in the proportions of taxa show that community analysis can be sensitive to both the amplification technique used and the method of assigning sequences to taxonomic groups. Overall, we find that RAMP represents a valid methodology for amplifying metagenomes from low biomass samples.

Distilled single-cell genome sequencing and de novo assembly for sparse microbial communities.

Science.gov (United States)

Taghavi, Zeinab; Movahedi, Narjes S; Draghici, Sorin; Chitsaz, Hamidreza

2013-10-01

Identification of every single genome present in a microbial sample is an important and challenging task with crucial applications. It is challenging because there are typically millions of cells in a microbial sample, the vast majority of which elude cultivation. The most accurate method to date is exhaustive single-cell sequencing using multiple displacement amplification, which is simply intractable for a large number of cells. However, there is hope for breaking this barrier, as the number of different cell types with distinct genome sequences is usually much smaller than the number of cells. Here, we present a novel divide and conquer method to sequence and de novo assemble all distinct genomes present in a microbial sample with a sequencing cost and computational complexity proportional to the number of genome types, rather than the number of cells. The method is implemented in a tool called Squeezambler. We evaluated Squeezambler on simulated data. The proposed divide and conquer method successfully reduces the cost of sequencing in comparison with the naïve exhaustive approach. Squeezambler and datasets are available at http://compbio.cs.wayne.edu/software/squeezambler/.

A simple and efficient total genomic DNA extraction method for individual zooplankton.

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Fazhan, Hanafiah; Waiho, Khor; Shahreza, Md Sheriff

2016-01-01

Molecular approaches are widely applied in species identification and taxonomic studies of minute zooplankton. One of the most focused zooplankton nowadays is from Subclass Copepoda. Accurate species identification of all life stages of the generally small sized copepods through molecular analysis is important, especially in taxonomic and systematic assessment of harpacticoid copepod populations and to understand their dynamics within the marine community. However, total genomic DNA (TGDNA) extraction from individual harpacticoid copepods can be problematic due to their small size and epibenthic behavior. In this research, six TGDNA extraction methods done on individual harpacticoid copepods were compared. The first new simple, feasible, efficient and consistent TGDNA extraction method was designed and compared with the commercial kit and modified available TGDNA extraction methods. The newly described TGDNA extraction method, "Incubation in PCR buffer" method, yielded good and consistent results based on the high success rate of PCR amplification (82%) compared to other methods. Coupled with its relatively consistent and economical method the "Incubation in PCR buffer" method is highly recommended in the TGDNA extraction of other minute zooplankton species.

Evaluation of five methods for total DNA extraction from western corn rootworm beetles.

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Hong Chen

Full Text Available BACKGROUND: DNA extraction is a routine step in many insect molecular studies. A variety of methods have been used to isolate DNA molecules from insects, and many commercial kits are available. Extraction methods need to be evaluated for their efficiency, cost, and side effects such as DNA degradation during extraction. METHODOLOGY/PRINCIPAL FINDINGS: From individual western corn rootworm beetles, Diabrotica virgifera virgifera, DNA extractions by the SDS method, CTAB method, DNAzol reagent, Puregene solutions and DNeasy column were compared in terms of DNA quantity and quality, cost of materials, and time consumed. Although all five methods resulted in acceptable DNA concentrations and absorbance ratios, the SDS and CTAB methods resulted in higher DNA yield (ng DNA vs. mg tissue at much lower cost and less degradation as revealed on agarose gels. The DNeasy kit was most time-efficient but was the costliest among the methods tested. The effects of ethanol volume, temperature and incubation time on precipitation of DNA were also investigated. The DNA samples obtained by the five methods were tested in PCR for six microsatellites located in various positions of the beetle's genome, and all samples showed successful amplifications. CONCLUSION/SIGNIFICANCE: These evaluations provide a guide for choosing methods of DNA extraction from western corn rootworm beetles based on expected DNA yield and quality, extraction time, cost, and waste control. The extraction conditions for this mid-size insect were optimized. The DNA extracted by the five methods was suitable for further molecular applications such as PCR and sequencing by synthesis.

Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology

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Ping, Zheng; Siegal, Gene P.; Almeida, Jonas S.; Schnitt, Stuart J.; Shen, Dejun

2014-01-01

Background: Genetics and genomics have radically altered our understanding of breast cancer progression. However, the genomic basis of various histopathologic features of breast cancer is not yet well-defined. Materials and Methods: The Cancer Genome Atlas (TCGA) is an international database containing a large collection of human cancer genome sequencing data. cBioPortal is a web tool developed for mining these sequencing data. We performed mining of TCGA sequencing data in an attempt to characterize the genomic features correlated with breast cancer histopathology. We first assessed the quality of the TCGA data using a group of genes with known alterations in various cancers. Both genome-wide gene mutation and copy number changes as well as a group of genes with a high frequency of genetic changes were then correlated with various histopathologic features of invasive breast cancer. Results: Validation of TCGA data using a group of genes with known alterations in breast cancer suggests that the TCGA has accurately documented the genomic abnormalities of multiple malignancies. Further analysis of TCGA breast cancer sequencing data shows that accumulation of specific genomic defects is associated with higher tumor grade, larger tumor size and receptor negativity. Distinct groups of genomic changes were found to be associated with the different grades of invasive ductal carcinoma. The mutator role of the TP53 gene was validated by genomic sequencing data of invasive breast cancer and TP53 mutation was found to play a critical role in defining high tumor grade. Conclusions: Data mining of the TCGA genome sequencing data is an innovative and reliable method to help characterize the genomic abnormalities associated with histopathologic features of invasive breast cancer. PMID:24672738

Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology

Directory of Open Access Journals (Sweden)

Zheng Ping

2014-01-01

Full Text Available Background: Genetics and genomics have radically altered our understanding of breast cancer progression. However, the genomic basis of various histopathologic features of breast cancer is not yet well-defined. Materials and Methods: The Cancer Genome Atlas (TCGA is an international database containing a large collection of human cancer genome sequencing data. cBioPortal is a web tool developed for mining these sequencing data. We performed mining of TCGA sequencing data in an attempt to characterize the genomic features correlated with breast cancer histopathology. We first assessed the quality of the TCGA data using a group of genes with known alterations in various cancers. Both genome-wide gene mutation and copy number changes as well as a group of genes with a high frequency of genetic changes were then correlated with various histopathologic features of invasive breast cancer. Results: Validation of TCGA data using a group of genes with known alterations in breast cancer suggests that the TCGA has accurately documented the genomic abnormalities of multiple malignancies. Further analysis of TCGA breast cancer sequencing data shows that accumulation of specific genomic defects is associated with higher tumor grade, larger tumor size and receptor negativity. Distinct groups of genomic changes were found to be associated with the different grades of invasive ductal carcinoma. The mutator role of the TP53 gene was validated by genomic sequencing data of invasive breast cancer and TP53 mutation was found to play a critical role in defining high tumor grade. Conclusions: Data mining of the TCGA genome sequencing data is an innovative and reliable method to help characterize the genomic abnormalities associated with histopathologic features of invasive breast cancer.

Evaluation of multiple approaches to identify genome-wide polymorphisms in closely related genotypes of sweet cherry (Prunus avium L.

Directory of Open Access Journals (Sweden)

Seanna Hewitt

Full Text Available Identification of genetic polymorphisms and subsequent development of molecular markers is important for marker assisted breeding of superior cultivars of economically important species. Sweet cherry (Prunus avium L. is an economically important non-climacteric tree fruit crop in the Rosaceae family and has undergone a genetic bottleneck due to breeding, resulting in limited genetic diversity in the germplasm that is utilized for breeding new cultivars. Therefore, it is critical to recognize the best platforms for identifying genome-wide polymorphisms that can help identify, and consequently preserve, the diversity in a genetically constrained species. For the identification of polymorphisms in five closely related genotypes of sweet cherry, a gel-based approach (TRAP, reduced representation sequencing (TRAPseq, a 6k cherry SNParray, and whole genome sequencing (WGS approaches were evaluated in the identification of genome-wide polymorphisms in sweet cherry cultivars. All platforms facilitated detection of polymorphisms among the genotypes with variable efficiency. In assessing multiple SNP detection platforms, this study has demonstrated that a combination of appropriate approaches is necessary for efficient polymorphism identification, especially between closely related cultivars of a species. The information generated in this study provides a valuable resource for future genetic and genomic studies in sweet cherry, and the insights gained from the evaluation of multiple approaches can be utilized for other closely related species with limited genetic diversity in the breeding germplasm. Keywords: Polymorphisms, Prunus avium, Next-generation sequencing, Target region amplification polymorphism (TRAP, Genetic diversity, SNParray, Reduced representation sequencing, Whole genome sequencing (WGS

GAAP: Genome-organization-framework-Assisted Assembly Pipeline for prokaryotic genomes.

Science.gov (United States)

Yuan, Lina; Yu, Yang; Zhu, Yanmin; Li, Yulai; Li, Changqing; Li, Rujiao; Ma, Qin; Siu, Gilman Kit-Hang; Yu, Jun; Jiang, Taijiao; Xiao, Jingfa; Kang, Yu

2017-01-25

Next-generation sequencing (NGS) technologies have greatly promoted the genomic study of prokaryotes. However, highly fragmented assemblies due to short reads from NGS are still a limiting factor in gaining insights into the genome biology. Reference-assisted tools are promising in genome assembly, but tend to result in false assembly when the assigned reference has extensive rearrangements. Herein, we present GAAP, a genome assembly pipeline for scaffolding based on core-gene-defined Genome Organizational Framework (cGOF) described in our previous study. Instead of assigning references, we use the multiple-reference-derived cGOFs as indexes to assist in order and orientation of the scaffolds and build a skeleton structure, and then use read pairs to extend scaffolds, called local scaffolding, and distinguish between true and chimeric adjacencies in the scaffolds. In our performance tests using both empirical and simulated data of 15 genomes in six species with diverse genome size, complexity, and all three categories of cGOFs, GAAP outcompetes or achieves comparable results when compared to three other reference-assisted programs, AlignGraph, Ragout and MeDuSa. GAAP uses both cGOF and pair-end reads to create assemblies in genomic scale, and performs better than the currently available reference-assisted assembly tools as it recovers more assemblies and makes fewer false locations, especially for species with extensive rearranged genomes. Our method is a promising solution for reconstruction of genome sequence from short reads of NGS.

Large-scale genomic 2D visualization reveals extensive CG-AT skew correlation in bird genomes

Directory of Open Access Journals (Sweden)

Deng Xuemei

2007-11-01

Full Text Available Abstract Background Bird genomes have very different compositional structure compared with other warm-blooded animals. The variation in the base skew rules in the vertebrate genomes remains puzzling, but it must relate somehow to large-scale genome evolution. Current research is inclined to relate base skew with mutations and their fixation. Here we wish to explore base skew correlations in bird genomes, to develop methods for displaying and quantifying such correlations at different scales, and to discuss possible explanations for the peculiarities of the bird genomes in skew correlation. Results We have developed a method called Base Skew Double Triangle (BSDT for exhibiting the genome-scale change of AT/CG skew as a two-dimensional square picture, showing base skews at many scales simultaneously in a single image. By this method we found that most chicken chromosomes have high AT/CG skew correlation (symmetry in 2D picture, except for some microchromosomes. No other organisms studied (18 species show such high skew correlations. This visualized high correlation was validated by three kinds of quantitative calculations with overlapping and non-overlapping windows, all indicating that chicken and birds in general have a special genome structure. Similar features were also found in some of the mammal genomes, but clearly much weaker than in chickens. We presume that the skew correlation feature evolved near the time that birds separated from other vertebrate lineages. When we eliminated the repeat sequences from the genomes, the AT and CG skews correlation increased for some mammal genomes, but were still clearly lower than in chickens. Conclusion Our results suggest that BSDT is an expressive visualization method for AT and CG skew and enabled the discovery of the very high skew correlation in bird genomes; this peculiarity is worth further study. Computational analysis indicated that this correlation might be a compositional characteristic

Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato

Directory of Open Access Journals (Sweden)

Benjamin Stich

2018-03-01

Full Text Available Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP, BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a Phytophthora infestans infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY, and tuber yield (TY of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.

The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes.

Science.gov (United States)

Treangen, Todd J; Ondov, Brian D; Koren, Sergey; Phillippy, Adam M

2014-01-01

Whole-genome sequences are now available for many microbial species and clades, however existing whole-genome alignment methods are limited in their ability to perform sequence comparisons of multiple sequences simultaneously. Here we present the Harvest suite of core-genome alignment and visualization tools for the rapid and simultaneous analysis of thousands of intraspecific microbial strains. Harvest includes Parsnp, a fast core-genome multi-aligner, and Gingr, a dynamic visual platform. Together they provide interactive core-genome alignments, variant calls, recombination detection, and phylogenetic trees. Using simulated and real data we demonstrate that our approach exhibits unrivaled speed while maintaining the accuracy of existing methods. The Harvest suite is open-source and freely available from: http://github.com/marbl/harvest.

KGCAK: a K-mer based database for genome-wide phylogeny and complexity evaluation.

Science.gov (United States)

Wang, Dapeng; Xu, Jiayue; Yu, Jun

2015-09-16

The K-mer approach, treating genomic sequences as simple characters and counting the relative abundance of each string upon a fixed K, has been extensively applied to phylogeny inference for genome assembly, annotation, and comparison. To meet increasing demands for comparing large genome sequences and to promote the use of the K-mer approach, we develop a versatile database, KGCAK ( http://kgcak.big.ac.cn/KGCAK/ ), containing ~8,000 genomes that include genome sequences of diverse life forms (viruses, prokaryotes, protists, animals, and plants) and cellular organelles of eukaryotic lineages. It builds phylogeny based on genomic elements in an alignment-free fashion and provides in-depth data processing enabling users to compare the complexity of genome sequences based on K-mer distribution. We hope that KGCAK becomes a powerful tool for exploring relationship within and among groups of species in a tree of life based on genomic data.

Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.

Science.gov (United States)

Lourenco, D A L; Tsuruta, S; Fragomeni, B O; Masuda, Y; Aguilar, I; Legarra, A; Bertrand, J K; Amen, T S; Wang, L; Moser, D W; Misztal, I

2015-06-01

using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability.

Improving genetic evaluation of litter size and piglet mortality for both genotyped and nongenotyped individuals using a single-step method.

Science.gov (United States)

Guo, X; Christensen, O F; Ostersen, T; Wang, Y; Lund, M S; Su, G

2015-02-01

A single-step method allows genetic evaluation using information of phenotypes, pedigree, and markers from genotyped and nongenotyped individuals simultaneously. This paper compared genomic predictions obtained from a single-step BLUP (SSBLUP) method, a genomic BLUP (GBLUP) method, a selection index blending (SELIND) method, and a traditional pedigree-based method (BLUP) for total number of piglets born (TNB), litter size at d 5 after birth (LS5), and mortality rate before d 5 (Mort; including stillbirth) in Danish Landrace and Yorkshire pigs. Data sets of 778,095 litters from 309,362 Landrace sows and 472,001 litters from 190,760 Yorkshire sows were used for the analysis. There were 332,795 Landrace and 207,255 Yorkshire animals in the pedigree data, among which 3,445 Landrace pigs (1,366 boars and 2,079 sows) and 3,372 Yorkshire pigs (1,241 boars and 2,131 sows) were genotyped with the Illumina PorcineSNP60 BeadChip. The results showed that the 3 methods with marker information (SSBLUP, GBLUP, and SELIND) produced more accurate predictions for genotyped animals than the pedigree-based method. For genotyped animals, the average of reliabilities for all traits in both breeds using traditional BLUP was 0.091, which increased to 0.171 w+hen using GBLUP and to 0.179 when using SELIND and further increased to 0.209 when using SSBLUP. Furthermore, the average reliability of EBV for nongenotyped animals was increased from 0.091 for traditional BLUP to 0.105 for the SSBLUP. The results indicate that the SSBLUP is a good approach to practical genomic prediction of litter size and piglet mortality in Danish Landrace and Yorkshire populations.

Genome-scale Evaluation of the Biotechnological Potential of Red Sea Bacilli Strains

KAUST Repository

Othoum, Ghofran K.

2018-02-01

The increasing spectrum of multidrug-resistant bacteria has caused a major global public health concern, necessitating the discovery of novel antimicrobial agents. Additionally, recent advancements in the use of microbial cells for the scalable production of industrial enzymes has encouraged the screening of new environments for efficient microbial cell factories. The unique ecological niche of the Red Sea points to the promising metabolic and biosynthetic potential of its microbial system. Here, ten sequenced Bacilli strains, that are isolated from microbial mat and mangrove mud samples from the Red Sea, were evaluated for their use as platforms for protein production and biosynthesis of bioactive compounds. Two of the species (B.paralicheniformis Bac48 and B. litoralis Bac94) were found to secrete twice as much protein as Bacillus subtilis 168, and B. litoralis Bac94 had complete Tat and Sec protein secretion systems. Additionally, four Red Sea Species (B. paralicheniformis Bac48, Virgibacillus sp. Bac330, B. vallismortis Bac111, B. amyloliquefaciens Bac57) showed capabilities for genetic transformation and possessed competence genes. More specifically, the distinctive biosynthetic potential evident in the genomes of B. paralicheniformis Bac48 and B. paralicheniformis Bac84 was assessed and compared to nine available complete genomes of B. licheniformis and three genomes of B. paralicheniformis. A uniquely-structured trans-acyltransferase (trans-AT) polyketide synthase/nonribosomal peptide synthetase (PKS/NRPS) cluster in strains of this species was identified in the genome of B. paralicheniformis 48. In total, the two B. paralicheniformis Red Sea strains were found to be more enriched in modular clusters compared to B. licheniformis strains and B. paralicheniformis strains from other environments. These findings provided more insights into the potential of B. paralicheniformis 48 as a microbial cell factory and encouraged further focus on the strain�

Genomic rearrangement in radiation-induced murine myeloid leukemia

International Nuclear Information System (INIS)

Ishihara, Hiroshi

1994-01-01

After whole body irradiation of 3Gy X ray to C3H/He male mice, acute myeloid leukemia is induced at an incidence of 20 to 30% within 2 years. We have studied the mechanism of occurrence of this radiation-induced murine myeloid leukemia. Detection and isolation of genomic structural aberration which may be accumulated accompanied with leukemogenesis are helpful in analyzing the complicated molecular process from radiation damage to leukemogenesis. So, our research work was done in three phases. First, structures of previously characterized oncogenes and cytokine-related genes were analyzed, and abnormal structures of fms(protooncogene encoding M-CSF receptor gene)-related and myc-related genes were found in several leukemia cells. Additionally, genomic structural aberration of IL-3 gene was observed in some leukemia cells, so that construction of genomic libraries and cloning of the abnormal IL-3 genomic DNAs were performed to characterize the structure. Secondly, because the breakage of chromosome 2 that is frequently observed in myeloid leukemia locates in proximal position of IL-1 gene cluster in some cases, the copy number of IL-1 gene was determined and the gene was cloned. Lastly, the abnormal genome of leukemia cell was cloned by in-gel competence reassociation method. We discussed these findings and evaluated the analysis of the molecular process of leukemogenesis using these cloned genomic fragments. (author)

Discount method for programming language evaluation

DEFF Research Database (Denmark)

Kurtev, Svetomir; Christensen, Tommy Aagaard; Thomsen, Bent

2016-01-01

This paper presents work in progress on developing a Discount Method for Programming Language Evaluation inspired by the Discount Usability Evaluation method (Benyon 2010) and the Instant Data Analysis method (Kjeldskov et al. 2004). The method is intended to bridge the gap between small scale...... internal language design evaluation methods and large scale surveys and quantitative evaluation methods. The method is designed to be applicable even before a compiler or IDE is developed for a new language. To test the method, a usability evaluation experiment was carried out on the Quorum programming...... language (Stefik et al. 2016) using programmers with experience in C and C#. When comparing our results with previous studies of Quorum, most of the data was comparable though not strictly in agreement. However, the discrepancies were mainly related to the programmers pre-existing expectations...

Draft genome of the lined seahorse, Hippocampus erectus.

Science.gov (United States)

Lin, Qiang; Qiu, Ying; Gu, Ruobo; Xu, Meng; Li, Jia; Bian, Chao; Zhang, Huixian; Qin, Geng; Zhang, Yanhong; Luo, Wei; Chen, Jieming; You, Xinxin; Fan, Mingjun; Sun, Min; Xu, Pao; Venkatesh, Byrappa; Xu, Junming; Fu, Hongtuo; Shi, Qiong

2017-06-01

The lined seahorse, Hippocampus erectus , is an Atlantic species and mainly inhabits shallow sea beds or coral reefs. It has become very popular in China for its wide use in traditional Chinese medicine. In order to improve the aquaculture yield of this valuable fish species, we are trying to develop genomic resources for assistant selection in genetic breeding. Here, we provide whole genome sequencing, assembly, and gene annotation of the lined seahorse, which can enrich genome resource and further application for its molecular breeding. A total of 174.6 Gb (Gigabase) raw DNA sequences were generated by the Illumina Hiseq2500 platform. The final assembly of the lined seahorse genome is around 458 Mb, representing 94% of the estimated genome size (489 Mb by k-mer analysis). The contig N50 and scaffold N50 reached 14.57 kb and 1.97 Mb, respectively. Quality of the assembled genome was assessed by BUSCO with prediction of 85% of the known vertebrate genes and evaluated using the de novo assembled RNA-seq transcripts to prove a high mapping ratio (more than 99% transcripts could be mapped to the assembly). Using homology-based, de novo and transcriptome-based prediction methods, we predicted 20 788 protein-coding genes in the generated assembly, which is less than our previously reported gene number (23 458) of the tiger tail seahorse ( H. comes ). We report a draft genome of the lined seahorse. These generated genomic data are going to enrich genome resource of this economically important fish, and also provide insights into the genetic mechanisms of its iconic morphology and male pregnancy behavior. © The Authors 2017. Published by Oxford University Press.

Genomes to Proteomes

Energy Technology Data Exchange (ETDEWEB)

Panisko, Ellen A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Grigoriev, Igor [USDOE Joint Genome Inst., Walnut Creek, CA (United States); Daly, Don S. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Webb-Robertson, Bobbie-Jo [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Baker, Scott E. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2009-03-01

Biologists are awash with genomic sequence data. In large part, this is due to the rapid acceleration in the generation of DNA sequence that occurred as public and private research institutes raced to sequence the human genome. In parallel with the large human genome effort, mostly smaller genomes of other important model organisms were sequenced. Projects following on these initial efforts have made use of technological advances and the DNA sequencing infrastructure that was built for the human and other organism genome projects. As a result, the genome sequences of many organisms are available in high quality draft form. While in many ways this is good news, there are limitations to the biological insights that can be gleaned from DNA sequences alone; genome sequences offer only a bird's eye view of the biological processes endemic to an organism or community. Fortunately, the genome sequences now being produced at such a high rate can serve as the foundation for other global experimental platforms such as proteomics. Proteomic methods offer a snapshot of the proteins present at a point in time for a given biological sample. Current global proteomics methods combine enzymatic digestion, separations, mass spectrometry and database searching for peptide identification. One key aspect of proteomics is the prediction of peptide sequences from mass spectrometry data. Global proteomic analysis uses computational matching of experimental mass spectra with predicted spectra based on databases of gene models that are often generated computationally. Thus, the quality of gene models predicted from a genome sequence is crucial in the generation of high quality peptide identifications. Once peptides are identified they can be assigned to their parent protein. Proteins identified as expressed in a given experiment are most useful when compared to other expressed proteins in a larger biological context or biochemical pathway. In this chapter we will discuss the automatic

Constructs and methods for genome editing and genetic engineering of fungi and protists

Science.gov (United States)

Hittinger, Christopher Todd; Alexander, William Gerald

2018-01-30

Provided herein are constructs for genome editing or genetic engineering in fungi or protists, methods of using the constructs and media for use in selecting cells. The construct include a polynucleotide encoding a thymidine kinase operably connected to a promoter, suitably a constitutive promoter; a polynucleotide encoding an endonuclease operably connected to an inducible promoter; and a recognition site for the endonuclease. The constructs may also include selectable markers for use in selecting recombinations.

Whole Genome Sequencing and Multiplex qPCR Methods to Identify Campylobacter jejuni Encoding cst-II or cst-III Sialyltransferase

Directory of Open Access Journals (Sweden)

Jason M. Neal-McKinney

2018-03-01

Full Text Available Campylobacter jejuni causes more than 2 million cases of gastroenteritis annually in the United States, and is also linked to the autoimmune sequelae Guillan–Barre syndrome (GBS. GBS often results in flaccid paralysis, as the myelin sheaths of nerve cells are degraded by the adaptive immune response. Certain strains of C. jejuni modify their lipooligosaccharide (LOS with the addition of neuraminic acid, resulting in LOS moieties that are structurally similar to gangliosides present on nerve cells. This can trigger GBS in a susceptible host, as antibodies generated against C. jejuni can cross-react with gangliosides, leading to demyelination of nerves and a loss of signal transduction. The goal of this study was to develop a quantitative PCR (qPCR method and use whole genome sequencing data to detect the Campylobactersialyltransferase (cst genes responsible for the addition of neuraminic acid to LOS. The qPCR method was used to screen a library of 89 C. jejuni field samples collected by the Food and Drug Administration Pacific Northwest Lab (PNL as well as clinical isolates transferred to PNL. In silico analysis was used to screen 827 C. jejuni genomes in the FDA GenomeTrakr SRA database. The results indicate that a majority of C. jejuni strains could produce LOS with ganglioside mimicry, as 43.8% of PNL isolates and 46.9% of the GenomeTrakr isolates lacked the cst genes. The methods described in this study can be used by public health laboratories to rapidly determine whether a C. jejuni isolate has the potential to induce GBS. Based on these results, a majority of C. jejuni in the PNL collection and submitted to GenomeTrakr have the potential to produce LOS that mimics human gangliosides.

Use of Genomic Data in Risk Assessment Caes Study: II. Evaluation of the Dibutyl Phthalate Toxicogenomic Dataset

Science.gov (United States)

An evaluation of the toxicogenomic data set for dibutyl phthalate (DBP) and male reproductive developmental effects was performed as part of a larger case study to test an approach for incorporating genomic data in risk assessment. The DBP toxicogenomic data set is composed of ni...

Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.

Science.gov (United States)

Onogi, Akio; Watanabe, Maya; Mochizuki, Toshihiro; Hayashi, Takeshi; Nakagawa, Hiroshi; Hasegawa, Toshihiro; Iwata, Hiroyoshi

2016-04-01

It is suggested that accuracy in predicting plant phenotypes can be improved by integrating genomic prediction with crop modelling in a single hierarchical model. Accurate prediction of phenotypes is important for plant breeding and management. Although genomic prediction/selection aims to predict phenotypes on the basis of whole-genome marker information, it is often difficult to predict phenotypes of complex traits in diverse environments, because plant phenotypes are often influenced by genotype-environment interaction. A possible remedy is to integrate genomic prediction with crop/ecophysiological modelling, which enables us to predict plant phenotypes using environmental and management information. To this end, in the present study, we developed a novel method for integrating genomic prediction with phenological modelling of Asian rice (Oryza sativa, L.), allowing the heading date of untested genotypes in untested environments to be predicted. The method simultaneously infers the phenological model parameters and whole-genome marker effects on the parameters in a Bayesian framework. By cultivating backcross inbred lines of Koshihikari × Kasalath in nine environments, we evaluated the potential of the proposed method in comparison with conventional genomic prediction, phenological modelling, and two-step methods that applied genomic prediction to phenological model parameters inferred from Nelder-Mead or Markov chain Monte Carlo algorithms. In predicting heading dates of untested lines in untested environments, the proposed and two-step methods tended to provide more accurate predictions than the conventional genomic prediction methods, particularly in environments where phenotypes from environments similar to the target environment were unavailable for training genomic prediction. The proposed method showed greater accuracy in prediction than the two-step methods in all cross-validation schemes tested, suggesting the potential of the integrated approach in

Genome-wide DNA polymorphism analyses using VariScan

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Vilella Albert J

2006-09-01

Full Text Available Abstract Background DNA sequence polymorphisms analysis can provide valuable information on the evolutionary forces shaping nucleotide variation, and provides an insight into the functional significance of genomic regions. The recent ongoing genome projects will radically improve our capabilities to detect specific genomic regions shaped by natural selection. Current available methods and software, however, are unsatisfactory for such genome-wide analysis. Results We have developed methods for the analysis of DNA sequence polymorphisms at the genome-wide scale. These methods, which have been tested on a coalescent-simulated and actual data files from mouse and human, have been implemented in the VariScan software package version 2.0. Additionally, we have also incorporated a graphical-user interface. The main features of this software are: i exhaustive population-genetic analyses including those based on the coalescent theory; ii analysis adapted to the shallow data generated by the high-throughput genome projects; iii use of genome annotations to conduct a comprehensive analyses separately for different functional regions; iv identification of relevant genomic regions by the sliding-window and wavelet-multiresolution approaches; v visualization of the results integrated with current genome annotations in commonly available genome browsers. Conclusion VariScan is a powerful and flexible suite of software for the analysis of DNA polymorphisms. The current version implements new algorithms, methods, and capabilities, providing an important tool for an exhaustive exploratory analysis of genome-wide DNA polymorphism data.

Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus.

Science.gov (United States)

Müller, Bárbara S F; Neves, Leandro G; de Almeida Filho, Janeo E; Resende, Márcio F R; Muñoz, Patricio R; Dos Santos, Paulo E T; Filho, Estefano Paludzyszyn; Kirst, Matias; Grattapaglia, Dario

2017-07-11

The advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses. Predictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000-10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study. This study provides further experimental data supporting positive prospects of using genome-wide data to

The Drosophila genome nexus: a population genomic resource of 623 Drosophila melanogaster genomes, including 197 from a single ancestral range population.

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Lack, Justin B; Cardeno, Charis M; Crepeau, Marc W; Taylor, William; Corbett-Detig, Russell B; Stevens, Kristian A; Langley, Charles H; Pool, John E

2015-04-01

Hundreds of wild-derived Drosophila melanogaster genomes have been published, but rigorous comparisons across data sets are precluded by differences in alignment methodology. The most common approach to reference-based genome assembly is a single round of alignment followed by quality filtering and variant detection. We evaluated variations and extensions of this approach and settled on an assembly strategy that utilizes two alignment programs and incorporates both substitutions and short indels to construct an updated reference for a second round of mapping prior to final variant detection. Utilizing this approach, we reassembled published D. melanogaster population genomic data sets and added unpublished genomes from several sub-Saharan populations. Most notably, we present aligned data from phase 3 of the Drosophila Population Genomics Project (DPGP3), which provides 197 genomes from a single ancestral range population of D. melanogaster (from Zambia). The large sample size, high genetic diversity, and potentially simpler demographic history of the DPGP3 sample will make this a highly valuable resource for fundamental population genetic research. The complete set of assemblies described here, termed the Drosophila Genome Nexus, presently comprises 623 consistently aligned genomes and is publicly available in multiple formats with supporting documentation and bioinformatic tools. This resource will greatly facilitate population genomic analysis in this model species by reducing the methodological differences between data sets. Copyright © 2015 by the Genetics Society of America.

Genome BLAST distance phylogenies inferred from whole plastid and whole mitochondrion genome sequences

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Holland Barbara R

2006-07-01

Full Text Available Abstract Background Phylogenetic methods which do not rely on multiple sequence alignments are important tools in inferring trees directly from completely sequenced genomes. Here, we extend the recently described Genome BLAST Distance Phylogeny (GBDP strategy to compute phylogenetic trees from all completely sequenced plastid genomes currently available and from a selection of mitochondrial genomes representing the major eukaryotic lineages. BLASTN, TBLASTX, or combinations of both are used to locate high-scoring segment pairs (HSPs between two sequences from which pairwise similarities and distances are computed in different ways resulting in a total of 96 GBDP variants. The suitability of these distance formulae for phylogeny reconstruction is directly estimated by computing a recently described measure of "treelikeness", the so-called δ value, from the respective distance matrices. Additionally, we compare the trees inferred from these matrices using UPGMA, NJ, BIONJ, FastME, or STC, respectively, with the NCBI taxonomy tree of the taxa under study. Results Our results indicate that, at this taxonomic level, plastid genomes are much more valuable for inferring phylogenies than are mitochondrial genomes, and that distances based on breakpoints are of little use. Distances based on the proportion of "matched" HSP length to average genome length were best for tree estimation. Additionally we found that using TBLASTX instead of BLASTN and, particularly, combining TBLASTX and BLASTN leads to a small but significant increase in accuracy. Other factors do not significantly affect the phylogenetic outcome. The BIONJ algorithm results in phylogenies most in accordance with the current NCBI taxonomy, with NJ and FastME performing insignificantly worse, and STC performing as well if applied to high quality distance matrices. δ values are found to be a reliable predictor of phylogenetic accuracy. Conclusion Using the most treelike distance matrices, as

Evaluation of Genomic Instability in the Abnormal Prostate

National Research Council Canada - National Science Library

Haaland-Pullus, Christina; Griffith, Jeffrey K

2006-01-01

...: prognosis and diagnosis. Several tools are being used to investigate this effect, specifically the assessment of telomere length, allelic imbalance, and methylation status, all markers of genomic instability...

Evaluation of Genomic Instability in the Abnormal Prostate

National Research Council Canada - National Science Library

Haaland-Pullus, Christina; Griffth, Jeffrey K

2008-01-01

...: prognosis and diagnosis. Several tools are being used to investigate this effect, specifically the assessment of telomere length, allelic imbalance, and methylation status, all markers of genomic instability...

A simple, rapid and efficient method for the extraction of genomic ...

African Journals Online (AJOL)

The isolation of intact, high-molecular-mass genomic DNA is essential for many molecular biology applications including long range PCR, endonuclease restriction digestion, southern blot analysis, and genomic library construction. Many protocols are available for the extraction of DNA from plant material, but obtain it is ...

Correcting for cryptic relatedness by a regression-based genomic control method

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Yang Yaning

2009-12-01

Full Text Available Abstract Background Genomic control (GC method is a useful tool to correct for the cryptic relatedness in population-based association studies. It was originally proposed for correcting for the variance inflation of Cochran-Armitage's additive trend test by using information from unlinked null markers, and was later generalized to be applicable to other tests with the additional requirement that the null markers are matched with the candidate marker in allele frequencies. However, matching allele frequencies limits the number of available null markers and thus limits the applicability of the GC method. On the other hand, errors in genotype/allele frequencies may cause further bias and variance inflation and thereby aggravate the effect of GC correction. Results In this paper, we propose a regression-based GC method using null markers that are not necessarily matched in allele frequencies with the candidate marker. Variation of allele frequencies of the null markers is adjusted by a regression method. Conclusion The proposed method can be readily applied to the Cochran-Armitage's trend tests other than the additive trend test, the Pearson's chi-square test and other robust efficiency tests. Simulation results show that the proposed method is effective in controlling type I error in the presence of population substructure.

A Distance Measure for Genome Phylogenetic Analysis

Science.gov (United States)

Cao, Minh Duc; Allison, Lloyd; Dix, Trevor

Phylogenetic analyses of species based on single genes or parts of the genomes are often inconsistent because of factors such as variable rates of evolution and horizontal gene transfer. The availability of more and more sequenced genomes allows phylogeny construction from complete genomes that is less sensitive to such inconsistency. For such long sequences, construction methods like maximum parsimony and maximum likelihood are often not possible due to their intensive computational requirement. Another class of tree construction methods, namely distance-based methods, require a measure of distances between any two genomes. Some measures such as evolutionary edit distance of gene order and gene content are computational expensive or do not perform well when the gene content of the organisms are similar. This study presents an information theoretic measure of genetic distances between genomes based on the biological compression algorithm expert model. We demonstrate that our distance measure can be applied to reconstruct the consensus phylogenetic tree of a number of Plasmodium parasites from their genomes, the statistical bias of which would mislead conventional analysis methods. Our approach is also used to successfully construct a plausible evolutionary tree for the γ-Proteobacteria group whose genomes are known to contain many horizontally transferred genes.

Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods

Science.gov (United States)

Väremo, Leif; Nielsen, Jens; Nookaew, Intawat

2013-01-01

Gene set analysis (GSA) is used to elucidate genome-wide data, in particular transcriptome data. A multitude of methods have been proposed for this step of the analysis, and many of them have been compared and evaluated. Unfortunately, there is no consolidated opinion regarding what methods should be preferred, and the variety of available GSA software and implementations pose a difficulty for the end-user who wants to try out different methods. To address this, we have developed the R package Piano that collects a range of GSA methods into the same system, for the benefit of the end-user. Further on we refine the GSA workflow by using modifications of the gene-level statistics. This enables us to divide the resulting gene set P-values into three classes, describing different aspects of gene expression directionality at gene set level. We use our fully implemented workflow to investigate the impact of the individual components of GSA by using microarray and RNA-seq data. The results show that the evaluated methods are globally similar and the major separation correlates well with our defined directionality classes. As a consequence of this, we suggest to use a consensus scoring approach, based on multiple GSA runs. In combination with the directionality classes, this constitutes a more thorough basis for an enriched biological interpretation. PMID:23444143

Pragmatism, Evidence, and Mixed Methods Evaluation

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Hall, Jori N.

2013-01-01

Mixed methods evaluation has a long-standing history of enhancing the credibility of evaluation findings. However, using mixed methods in a utilitarian way implicitly emphasizes convenience over engaging with its philosophical underpinnings (Denscombe, 2008). Because of this, some mixed methods evaluators and social science researchers have been…

Controlling the signal: Practical privacy protection of genomic data sharing through Beacon services.

Science.gov (United States)

Wan, Zhiyu; Vorobeychik, Yevgeniy; Kantarcioglu, Murat; Malin, Bradley

2017-07-26

Genomic data is increasingly collected by a wide array of organizations. As such, there is a growing demand to make summary information about such collections available more widely. However, over the past decade, a series of investigations have shown that attacks, rooted in statistical inference methods, can be applied to discern the presence of a known individual's DNA sequence in the pool of subjects. Recently, it was shown that the Beacon Project of the Global Alliance for Genomics and Health, a web service for querying about the presence (or absence) of a specific allele, was vulnerable. The Integrating Data for Analysis, Anonymization, and Sharing (iDASH) Center modeled a track in their third Privacy Protection Challenge on how to mitigate the Beacon vulnerability. We developed the winning solution for this track. This paper describes our computational method to optimize the tradeoff between the utility and the privacy of the Beacon service. We generalize the genomic data sharing problem beyond that which was introduced in the iDASH Challenge to be more representative of real world scenarios to allow for a more comprehensive evaluation. We then conduct a sensitivity analysis of our method with respect to several state-of-the-art methods using a dataset of 400,000 positions in Chromosome 10 for 500 individuals from Phase 3 of the 1000 Genomes Project. All methods are evaluated for utility, privacy and efficiency. Our method achieves better performance than all state-of-the-art methods, irrespective of how key factors (e.g., the allele frequency in the population, the size of the pool and utility weights) change from the original parameters of the problem. We further illustrate that it is possible for our method to exhibit subpar performance under special cases of allele query sequences. However, we show our method can be extended to address this issue when the query sequence is fixed and known a priori to the data custodian, so that they may plan stage their

Approximation of reliability of direct genomic breeding values

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Two methods to efficiently approximate theoretical genomic reliabilities are presented. The first method is based on the direct inverse of the left hand side (LHS) of mixed model equations. It uses the genomic relationship matrix for a small subset of individuals with the highest genomic relationshi...

Genomes correction and assembling: present methods and tools

Science.gov (United States)

Wojcieszek, Michał; Pawełkowicz, Magdalena; Nowak, Robert; Przybecki, Zbigniew

2014-11-01

Recent rapid development of next generation sequencing (NGS) technologies provided significant impact into genomics field of study enabling implementation of many de novo sequencing projects of new species which was previously confined by technological costs. Along with advancement of NGS there was need for adjustment in assembly programs. New algorithms must cope with massive amounts of data computation in reasonable time limits and processing power and hardware is also an important factor. In this paper, we address the issue of assembly pipeline for de novo genome assembly provided by programs presently available for scientist both as commercial and as open - source software. The implementation of four different approaches - Greedy, Overlap - Layout - Consensus (OLC), De Bruijn and Integrated resulting in variation of performance is the main focus of our discussion with additional insight into issue of short and long reads correction.

Protecting genomic sequence anonymity with generalization lattices.

Science.gov (United States)

Malin, B A

2005-01-01

Current genomic privacy technologies assume the identity of genomic sequence data is protected if personal information, such as demographics, are obscured, removed, or encrypted. While demographic features can directly compromise an individual's identity, recent research demonstrates such protections are insufficient because sequence data itself is susceptible to re-identification. To counteract this problem, we introduce an algorithm for anonymizing a collection of person-specific DNA sequences. The technique is termed DNA lattice anonymization (DNALA), and is based upon the formal privacy protection schema of k -anonymity. Under this model, it is impossible to observe or learn features that distinguish one genetic sequence from k-1 other entries in a collection. To maximize information retained in protected sequences, we incorporate a concept generalization lattice to learn the distance between two residues in a single nucleotide region. The lattice provides the most similar generalized concept for two residues (e.g. adenine and guanine are both purines). The method is tested and evaluated with several publicly available human population datasets ranging in size from 30 to 400 sequences. Our findings imply the anonymization schema is feasible for the protection of sequences privacy. The DNALA method is the first computational disclosure control technique for general DNA sequences. Given the computational nature of the method, guarantees of anonymity can be formally proven. There is room for improvement and validation, though this research provides the groundwork from which future researchers can construct genomics anonymization schemas tailored to specific datasharing scenarios.

Optimizing a method for detection of hepatitis A virus in shellfish and study the effect of gamma radiation on the viral genome

International Nuclear Information System (INIS)

Amri, Islem

2008-01-01

Our work was aimed at detecting the hepatitis A virus (HAV) in bivalve mollusc collected from five shellfish harvesting areas and from a coastal region in Tunisia using RT-Nested-PCR and studying the effect of gamma radiation on HAV genome. Two methods used to recover HAV from mollusc flesh and two methods of extraction of virus RNA were compared in order to determine the most sensitive method. Glycine extraction and extraction of virus RNA using proteinase K were more convenient and then used in this study for detection of HAV in shellfish. The results of molecular analyses: RT-Nested-PCR using primers targeted at the P1 region revealed that 28 % of the samples were positive for HAV. Doses of gamma irradiation ranging between 5 to 30 kGy were used to study the effect of this radiation on HAV genome after the contamination of mollusc flesh with suspension of HAV (derived from stool specimens). HAV specific genomic band was observed for doses between 5 to 20 kGy. We didn't detect HAV genome with doses 25 and 30 kGy. (Author)

Radiation hybrid mapping as one of the main methods of the creation of high resolution maps of human and animal genomes

International Nuclear Information System (INIS)

Sulimova, G.E.; Kompanijtsev, A.A.; Mojsyak, E.V.; Rakhmanaliev, Eh.R.; Klimov, E.A.; Udina, I.G.; Zakharov, I.A.

2000-01-01

Radiation hybrid mapping (RH mapping) is considered as one of the main method of constructing physical maps of mammalian genomes. In introduction, theoretical prerequisites of developing of the RH mapping and statistical methods of data analysis are discussed. Comparative characteristics of universal commercial panels of the radiation hybrid somatic cells (RH panels) are shown. In experimental part of the work, RH mapping is used to localize nucleotide sequences adjacent to Not I sites of human chromosome 3 with the aim to integrate contig map of Nor I clones to comprehensive maps of human genome. Five nucleotide sequences adjacent to the sites of integration of papilloma virus in human genome and expressed in the cells of cervical cancer involved localized. It is demonstrated that the region 13q14.3-q21.1 was enriched with nucleotide sequences involved in the processes of carcinogenesis. RH mapping can be considered as one of the most perspective applications of modern radiation biology in the field of molecular genetics, that is, in constructing physical maps of mammalian genomes with high resolution level [ru

Challenges in Whole-Genome Annotation of Pyrosequenced Eukaryotic Genomes

Energy Technology Data Exchange (ETDEWEB)

Kuo, Alan; Grigoriev, Igor

2009-04-17

Pyrosequencing technologies such as 454/Roche and Solexa/Illumina vastly lower the cost of nucleotide sequencing compared to the traditional Sanger method, and thus promise to greatly expand the number of sequenced eukaryotic genomes. However, the new technologies also bring new challenges such as shorter reads and new kinds and higher rates of sequencing errors, which complicate genome assembly and gene prediction. At JGI we are deploying 454 technology for the sequencing and assembly of ever-larger eukaryotic genomes. Here we describe our first whole-genome annotation of a purely 454-sequenced fungal genome that is larger than a yeast (>30 Mbp). The pezizomycotine (filamentous ascomycote) Aspergillus carbonarius belongs to the Aspergillus section Nigri species complex, members of which are significant as platforms for bioenergy and bioindustrial technology, as members of soil microbial communities and players in the global carbon cycle, and as agricultural toxigens. Application of a modified version of the standard JGI Annotation Pipeline has so far predicted ~;;10k genes. ~;;12percent of these preliminary annotations suffer a potential frameshift error, which is somewhat higher than the ~;;9percent rate in the Sanger-sequenced and conventionally assembled and annotated genome of fellow Aspergillus section Nigri member A. niger. Also,>90percent of A. niger genes have potential homologs in the A. carbonarius preliminary annotation. Weconclude, and with further annotation and comparative analysis expect to confirm, that 454 sequencing strategies provide a promising substrate for annotation of modestly sized eukaryotic genomes. We will also present results of annotation of a number of other pyrosequenced fungal genomes of bioenergy interest.

Plantagora: modeling whole genome sequencing and assembly of plant genomes.

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Roger Barthelson

Full Text Available BACKGROUND: Genomics studies are being revolutionized by the next generation sequencing technologies, which have made whole genome sequencing much more accessible to the average researcher. Whole genome sequencing with the new technologies is a developing art that, despite the large volumes of data that can be produced, may still fail to provide a clear and thorough map of a genome. The Plantagora project was conceived to address specifically the gap between having the technical tools for genome sequencing and knowing precisely the best way to use them. METHODOLOGY/PRINCIPAL FINDINGS: For Plantagora, a platform was created for generating simulated reads from several different plant genomes of different sizes. The resulting read files mimicked either 454 or Illumina reads, with varying paired end spacing. Thousands of datasets of reads were created, most derived from our primary model genome, rice chromosome one. All reads were assembled with different software assemblers, including Newbler, Abyss, and SOAPdenovo, and the resulting assemblies were evaluated by an extensive battery of metrics chosen for these studies. The metrics included both statistics of the assembly sequences and fidelity-related measures derived by alignment of the assemblies to the original genome source for the reads. The results were presented in a website, which includes a data graphing tool, all created to help the user compare rapidly the feasibility and effectiveness of different sequencing and assembly strategies prior to testing an approach in the lab. Some of our own conclusions regarding the different strategies were also recorded on the website. CONCLUSIONS/SIGNIFICANCE: Plantagora provides a substantial body of information for comparing different approaches to sequencing a plant genome, and some conclusions regarding some of the specific approaches. Plantagora also provides a platform of metrics and tools for studying the process of sequencing and assembly

Novel Degenerate PCR Method for Whole-Genome Amplification Applied to Peru Margin (ODP Leg 201) Subsurface Samples

Science.gov (United States)

Martino, Amanda J.; Rhodes, Matthew E.; Biddle, Jennifer F.; Brandt, Leah D.; Tomsho, Lynn P.; House, Christopher H.

2011-01-01

A degenerate polymerase chain reaction (PCR)-based method of whole-genome amplification, designed to work fluidly with 454 sequencing technology, was developed and tested for use on deep marine subsurface DNA samples. While optimized here for use with Roche 454 technology, the general framework presented may be applicable to other next generation sequencing systems as well (e.g., Illumina, Ion Torrent). The method, which we have called random amplification metagenomic PCR (RAMP), involves the use of specific primers from Roche 454 amplicon sequencing, modified by the addition of a degenerate region at the 3′ end. It utilizes a PCR reaction, which resulted in no amplification from blanks, even after 50 cycles of PCR. After efforts to optimize experimental conditions, the method was tested with DNA extracted from cultured E. coli cells, and genome coverage was estimated after sequencing on three different occasions. Coverage did not vary greatly with the different experimental conditions tested, and was around 62% with a sequencing effort equivalent to a theoretical genome coverage of 14.10×. The GC content of the sequenced amplification product was within 2% of the predicted values for this strain of E. coli. The method was also applied to DNA extracted from marine subsurface samples from ODP Leg 201 site 1229 (Peru Margin), and results of a taxonomic analysis revealed microbial communities dominated by Proteobacteria, Chloroflexi, Firmicutes, Euryarchaeota, and Crenarchaeota, among others. These results were similar to those obtained previously for those samples; however, variations in the proportions of taxa identified illustrates well the generally accepted view that community analysis is sensitive to both the amplification technique used and the method of assigning sequences to taxonomic groups. Overall, we find that RAMP represents a valid methodology for amplifying metagenomes from low-biomass samples. PMID:22319519

An efficient genotyping method for genome-modified animals and human cells generated with CRISPR/Cas9 system.

Science.gov (United States)

Zhu, Xiaoxiao; Xu, Yajie; Yu, Shanshan; Lu, Lu; Ding, Mingqin; Cheng, Jing; Song, Guoxu; Gao, Xing; Yao, Liangming; Fan, Dongdong; Meng, Shu; Zhang, Xuewen; Hu, Shengdi; Tian, Yong

2014-09-19

The rapid generation of various species and strains of laboratory animals using CRISPR/Cas9 technology has dramatically accelerated the interrogation of gene function in vivo. So far, the dominant approach for genotyping of genome-modified animals has been the T7E1 endonuclease cleavage assay. Here, we present a polyacrylamide gel electrophoresis-based (PAGE) method to genotype mice harboring different types of indel mutations. We developed 6 strains of genome-modified mice using CRISPR/Cas9 system, and utilized this approach to genotype mice from F0 to F2 generation, which included single and multiplexed genome-modified mice. We also determined the maximal detection sensitivity for detecting mosaic DNA using PAGE-based assay as 0.5%. We further applied PAGE-based genotyping approach to detect CRISPR/Cas9-mediated on- and off-target effect in human 293T and induced pluripotent stem cells (iPSCs). Thus, PAGE-based genotyping approach meets the rapidly increasing demand for genotyping of the fast-growing number of genome-modified animals and human cell lines created using CRISPR/Cas9 system or other nuclease systems such as TALEN or ZFN.

Partial digestion with restriction enzymes of ultraviolet-irradiated human genomic DNA: a method for identifying restriction site polymorphisms

International Nuclear Information System (INIS)

Nobile, C.; Romeo, G.

1988-01-01

A method for partial digestion of total human DNA with restriction enzymes has been developed on the basis of a principle already utilized by P.A. Whittaker and E. Southern for the analysis of phage lambda recombinants. Total human DNA irradiated with uv light of 254 nm is partially digested by restriction enzymes that recognize sequences containing adjacent thymidines because of TT dimer formation. The products resulting from partial digestion of specific genomic regions are detected in Southern blots by genomic-unique DNA probes with high reproducibility. This procedure is rapid and simple to perform because the same conditions of uv irradiation are used for different enzymes and probes. It is shown that restriction site polymorphisms occurring in the genomic regions analyzed are recognized by the allelic partial digest patterns they determine

A Review of Study Designs and Statistical Methods for Genomic Epidemiology Studies using Next Generation Sequencing

Directory of Open Access Journals (Sweden)

Qian eWang

2015-04-01

Full Text Available Results from numerous linkage and association studies have greatly deepened scientists’ understanding of the genetic basis of many human diseases, yet some important questions remain unanswered. For example, although a large number of disease-associated loci have been identified from genome-wide association studies (GWAS in the past 10 years, it is challenging to interpret these results as most disease-associated markers have no clear functional roles in disease etiology, and all the identified genomic factors only explain a small portion of disease heritability. With the help of next-generation sequencing (NGS, diverse types of genomic and epigenetic variations can be detected with high accuracy. More importantly, instead of using linkage disequilibrium to detect association signals based on a set of pre-set probes, NGS allows researchers to directly study all the variants in each individual, therefore promises opportunities for identifying functional variants and a more comprehensive dissection of disease heritability. Although the current scale of NGS studies is still limited due to the high cost, the success of several recent studies suggests the great potential for applying NGS in genomic epidemiology, especially as the cost of sequencing continues to drop. In this review, we discuss several pioneer applications of NGS, summarize scientific discoveries for rare and complex diseases, and compare various study designs including targeted sequencing and whole-genome sequencing using population-based and family-based cohorts. Finally, we highlight recent advancements in statistical methods proposed for sequencing analysis, including group-based association tests, meta-analysis techniques, and annotation tools for variant prioritization.

Navigating the Interface Between Landscape Genetics and Landscape Genomics

Directory of Open Access Journals (Sweden)

Andrew Storfer

2018-03-01

Full Text Available As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species

Ranking of Prokaryotic Genomes Based on Maximization of Sortedness of Gene Lengths.

Science.gov (United States)

Bolshoy, A; Salih, B; Cohen, I; Tatarinova, T

How variations of gene lengths (some genes become longer than their predecessors, while other genes become shorter and the sizes of these factions are randomly different from organism to organism) depend on organismal evolution and adaptation is still an open question. We propose to rank the genomes according to lengths of their genes, and then find association between the genome rank and variousproperties, such as growth temperature, nucleotide composition, and pathogenicity. This approach reveals evolutionary driving factors. The main purpose of this study is to test effectiveness and robustness of several ranking methods. The selected method of evaluation is measuring of overall sortedness of the data. We have demonstrated that all considered methods give consistent results and Bubble Sort and Simulated Annealing achieve the highest sortedness. Also, Bubble Sort is considerably faster than the Simulated Annealing method.

Use of different marker pre-selection methods based on single SNP regression in the estimation of Genomic-EBVs

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Corrado Dimauro

2010-01-01

Full Text Available Two methods of SNPs pre-selection based on single marker regression for the estimation of genomic breeding values (G-EBVs were compared using simulated data provided by the XII QTL-MAS workshop: i Bonferroni correction of the significance threshold and ii Permutation test to obtain the reference distribution of the null hypothesis and identify significant markers at P<0.01 and P<0.001 significance thresholds. From the set of markers significant at P<0.001, random subsets of 50% and 25% markers were extracted, to evaluate the effect of further reducing the number of significant SNPs on G-EBV predictions. The Bonferroni correction method allowed the identification of 595 significant SNPs that gave the best G-EBV accuracies in prediction generations (82.80%. The permutation methods gave slightly lower G-EBV accuracies even if a larger number of SNPs resulted significant (2,053 and 1,352 for 0.01 and 0.001 significance thresholds, respectively. Interestingly, halving or dividing by four the number of SNPs significant at P<0.001 resulted in an only slightly decrease of G-EBV accuracies. The genetic structure of the simulated population with few QTL carrying large effects, might have favoured the Bonferroni method.

A simple method for encapsulating single cells in alginate microspheres allows for direct PCR and whole genome amplification.

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Saharnaz Bigdeli

Full Text Available Microdroplets are an effective platform for segregating individual cells and amplifying DNA. However, a key challenge is to recover the contents of individual droplets for downstream analysis. This paper offers a method for embedding cells in alginate microspheres and performing multiple serial operations on the isolated cells. Rhodobacter sphaeroides cells were diluted in alginate polymer and sprayed into microdroplets using a fingertip aerosol sprayer. The encapsulated cells were lysed and subjected either to conventional PCR, or whole genome amplification using either multiple displacement amplification (MDA or a two-step PCR protocol. Microscopic examination after PCR showed that the lumen of the occupied microspheres contained fluorescently stained DNA product, but multiple displacement amplification with phi29 produced only a small number of polymerase colonies. The 2-step WGA protocol was successful in generating fluorescent material, and quantitative PCR from DNA extracted from aliquots of microspheres suggested that the copy number inside the microspheres was amplified up to 3 orders of magnitude. Microspheres containing fluorescent material were sorted by a dilution series and screened with a fluorescent plate reader to identify single microspheres. The DNA was extracted from individual isolates, re-amplified with full-length sequencing adapters, and then a single isolate was sequenced using the Illumina MiSeq platform. After filtering the reads, the only sequences that collectively matched a genome in the NCBI nucleotide database belonged to R. sphaeroides. This demonstrated that sequencing-ready DNA could be generated from the contents of a single microsphere without culturing. However, the 2-step WGA strategy showed limitations in terms of low genome coverage and an uneven frequency distribution of reads across the genome. This paper offers a simple method for embedding cells in alginate microspheres and performing PCR on isolated

An integrative and applicable phylogenetic footprinting framework for cis-regulatory motifs identification in prokaryotic genomes.

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Liu, Bingqiang; Zhang, Hanyuan; Zhou, Chuan; Li, Guojun; Fennell, Anne; Wang, Guanghui; Kang, Yu; Liu, Qi; Ma, Qin

2016-08-09

Phylogenetic footprinting is an important computational technique for identifying cis-regulatory motifs in orthologous regulatory regions from multiple genomes, as motifs tend to evolve slower than their surrounding non-functional sequences. Its application, however, has several difficulties for optimizing the selection of orthologous data and reducing the false positives in motif prediction. Here we present an integrative phylogenetic footprinting framework for accurate motif predictions in prokaryotic genomes (MP(3)). The framework includes a new orthologous data preparation procedure, an additional promoter scoring and pruning method and an integration of six existing motif finding algorithms as basic motif search engines. Specifically, we collected orthologous genes from available prokaryotic genomes and built the orthologous regulatory regions based on sequence similarity of promoter regions. This procedure made full use of the large-scale genomic data and taxonomy information and filtered out the promoters with limited contribution to produce a high quality orthologous promoter set. The promoter scoring and pruning is implemented through motif voting by a set of complementary predicting tools that mine as many motif candidates as possible and simultaneously eliminate the effect of random noise. We have applied the framework to Escherichia coli k12 genome and evaluated the prediction performance through comparison with seven existing programs. This evaluation was systematically carried out at the nucleotide and binding site level, and the results showed that MP(3) consistently outperformed other popular motif finding tools. We have integrated MP(3) into our motif identification and analysis server DMINDA, allowing users to efficiently identify and analyze motifs in 2,072 completely sequenced prokaryotic genomes. The performance evaluation indicated that MP(3) is effective for predicting regulatory motifs in prokaryotic genomes. Its application may enhance

Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA

KAUST Repository

Magana-Mora, Arturo

2017-08-15

BackgroundPolyadenylation is a critical stage of RNA processing during the formation of mature mRNA, and is present in most of the known eukaryote protein-coding transcripts and many long non-coding RNAs. The correct identification of poly(A) signals (PAS) not only helps to elucidate the 3′-end genomic boundaries of a transcribed DNA region and gene regulatory mechanisms but also gives insight into the multiple transcript isoforms resulting from alternative PAS. Although progress has been made in the in-silico prediction of genomic signals, the recognition of PAS in DNA genomic sequences remains a challenge.ResultsIn this study, we analyzed human genomic DNA sequences for the 12 most common PAS variants. Our analysis has identified a set of features that helps in the recognition of true PAS, which may be involved in the regulation of the polyadenylation process. The proposed features, in combination with a recognition model, resulted in a novel method and tool, Omni-PolyA. Omni-PolyA combines several machine learning techniques such as different classifiers in a tree-like decision structure and genetic algorithms for deriving a robust classification model. We performed a comparison between results obtained by state-of-the-art methods, deep neural networks, and Omni-PolyA. Results show that Omni-PolyA significantly reduced the average classification error rate by 35.37% in the prediction of the 12 considered PAS variants relative to the state-of-the-art results.ConclusionsThe results of our study demonstrate that Omni-PolyA is currently the most accurate model for the prediction of PAS in human and can serve as a useful complement to other PAS recognition methods. Omni-PolyA is publicly available as an online tool accessible at www.cbrc.kaust.edu.sa/omnipolya/.

A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits

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Hayashi Takeshi

2013-01-01

Full Text Available Abstract Background Genomic selection is an effective tool for animal and plant breeding, allowing effective individual selection without phenotypic records through the prediction of genomic breeding value (GBV. To date, genomic selection has focused on a single trait. However, actual breeding often targets multiple correlated traits, and, therefore, joint analysis taking into consideration the correlation between traits, which might result in more accurate GBV prediction than analyzing each trait separately, is suitable for multi-trait genomic selection. This would require an extension of the prediction model for single-trait GBV to multi-trait case. As the computational burden of multi-trait analysis is even higher than that of single-trait analysis, an effective computational method for constructing a multi-trait prediction model is also needed. Results We described a Bayesian regression model incorporating variable selection for jointly predicting GBVs of multiple traits and devised both an MCMC iteration and variational approximation for Bayesian estimation of parameters in this multi-trait model. The proposed Bayesian procedures with MCMC iteration and variational approximation were referred to as MCBayes and varBayes, respectively. Using simulated datasets of SNP genotypes and phenotypes for three traits with high and low heritabilities, we compared the accuracy in predicting GBVs between multi-trait and single-trait analyses as well as between MCBayes and varBayes. The results showed that, compared to single-trait analysis, multi-trait analysis enabled much more accurate GBV prediction for low-heritability traits correlated with high-heritability traits, by utilizing the correlation structure between traits, while the prediction accuracy for uncorrelated low-heritability traits was comparable or less with multi-trait analysis in comparison with single-trait analysis depending on the setting for prior probability that a SNP has zero

Genomic selection in plant breeding.

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Newell, Mark A; Jannink, Jean-Luc

2014-01-01

Genomic selection (GS) is a method to predict the genetic value of selection candidates based on the genomic estimated breeding value (GEBV) predicted from high-density markers positioned throughout the genome. Unlike marker-assisted selection, the GEBV is based on all markers including both minor and major marker effects. Thus, the GEBV may capture more of the genetic variation for the particular trait under selection.

A note on mate allocation for dominance handling in genomic selection

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Toro Miguel A

2010-08-01

Full Text Available Abstract Estimation of non-additive genetic effects in animal breeding is important because it increases the accuracy of breeding value prediction and the value of mate allocation procedures. With the advent of genomic selection these ideas should be revisited. The objective of this study was to quantify the efficiency of including dominance effects and practising mating allocation under a whole-genome evaluation scenario. Four strategies of selection, carried out during five generations, were compared by simulation techniques. In the first scenario (MS, individuals were selected based on their own phenotypic information. In the second (GSA, they were selected based on the prediction generated by the Bayes A method of whole-genome evaluation under an additive model. In the third (GSD, the model was expanded to include dominance effects. These three scenarios used random mating to construct future generations, whereas in the fourth one (GSD + MA, matings were optimized by simulated annealing. The advantage of GSD over GSA ranges from 9 to 14% of the expected response and, in addition, using mate allocation (GSD + MA provides an additional response ranging from 6% to 22%. However, mate selection can improve the expected genetic response over random mating only in the first generation of selection. Furthermore, the efficiency of genomic selection is eroded after a few generations of selection, thus, a continued collection of phenotypic data and re-evaluation will be required.

Secure count query on encrypted genomic data.

Science.gov (United States)

Hasan, Mohammad Zahidul; Mahdi, Md Safiur Rahman; Sadat, Md Nazmus; Mohammed, Noman

2018-05-01

Human genomic information can yield more effective healthcare by guiding medical decisions. Therefore, genomics research is gaining popularity as it can identify potential correlations between a disease and a certain gene, which improves the safety and efficacy of drug treatment and can also develop more effective prevention strategies [1]. To reduce the sampling error and to increase the statistical accuracy of this type of research projects, data from different sources need to be brought together since a single organization does not necessarily possess required amount of data. In this case, data sharing among multiple organizations must satisfy strict policies (for instance, HIPAA and PIPEDA) that have been enforced to regulate privacy-sensitive data sharing. Storage and computation on the shared data can be outsourced to a third party cloud service provider, equipped with enormous storage and computation resources. However, outsourcing data to a third party is associated with a potential risk of privacy violation of the participants, whose genomic sequence or clinical profile is used in these studies. In this article, we propose a method for secure sharing and computation on genomic data in a semi-honest cloud server. In particular, there are two main contributions. Firstly, the proposed method can handle biomedical data containing both genotype and phenotype. Secondly, our proposed index tree scheme reduces the computational overhead significantly for executing secure count query operation. In our proposed method, the confidentiality of shared data is ensured through encryption, while making the entire computation process efficient and scalable for cutting-edge biomedical applications. We evaluated our proposed method in terms of efficiency on a database of Single-Nucleotide Polymorphism (SNP) sequences, and experimental results demonstrate that the execution time for a query of 50 SNPs in a database of 50,000 records is approximately 5 s, where each record

An alternative method for cDNA cloning from surrogate eukaryotic cells transfected with the corresponding genomic DNA.

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Hu, Lin-Yong; Cui, Chen-Chen; Song, Yu-Jie; Wang, Xiang-Guo; Jin, Ya-Ping; Wang, Ai-Hua; Zhang, Yong

2012-07-01

cDNA is widely used in gene function elucidation and/or transgenics research but often suitable tissues or cells from which to isolate mRNA for reverse transcription are unavailable. Here, an alternative method for cDNA cloning is described and tested by cloning the cDNA of human LALBA (human alpha-lactalbumin) from genomic DNA. First, genomic DNA containing all of the coding exons was cloned from human peripheral blood and inserted into a eukaryotic expression vector. Next, by delivering the plasmids into either 293T or fibroblast cells, surrogate cells were constructed. Finally, the total RNA was extracted from the surrogate cells and cDNA was obtained by RT-PCR. The human LALBA cDNA that was obtained was compared with the corresponding mRNA published in GenBank. The comparison showed that the two sequences were identical. The novel method for cDNA cloning from surrogate eukaryotic cells described here uses well-established techniques that are feasible and simple to use. We anticipate that this alternative method will have widespread applications.

Empirical evaluation methods in computer vision

CERN Document Server

Christensen, Henrik I

2002-01-01

This book provides comprehensive coverage of methods for the empirical evaluation of computer vision techniques. The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance. The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition and diffusion methods, image matching using correlation methods, and the performance of medical image processing algorithms. Sample Chapter(s). Foreword (228 KB). Chapter 1: Introduction (505 KB). Contents: Automate

Diversity and Genome Analysis of Australian and Global Oilseed Brassica napus L. Germplasm Using Transcriptomics and Whole Genome Re-sequencing

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M. Michelle Malmberg

2018-04-01

Full Text Available Intensive breeding of Brassica napus has resulted in relatively low diversity, such that B. napus would benefit from germplasm improvement schemes that sustain diversity. As such, samples representative of global germplasm pools need to be assessed for existing population structure, diversity and linkage disequilibrium (LD. Complexity reduction genotyping-by-sequencing (GBS methods, including GBS-transcriptomics (GBS-t, enable cost-effective screening of a large number of samples, while whole genome re-sequencing (WGR delivers the ability to generate large numbers of unbiased genomic single nucleotide polymorphisms (SNPs, and identify structural variants (SVs. Furthermore, the development of genomic tools based on whole genomes representative of global oilseed diversity and orientated by the reference genome has substantial industry relevance and will be highly beneficial for canola breeding. As recent studies have focused on European and Chinese varieties, a global diversity panel as well as a substantial number of Australian spring types were included in this study. Focusing on industry relevance, 633 varieties were initially genotyped using GBS-t to examine population structure using 61,037 SNPs. Subsequently, 149 samples representative of global diversity were selected for WGR and both data sets used for a side-by-side evaluation of diversity and LD. The WGR data was further used to develop genomic resources consisting of a list of 4,029,750 high-confidence SNPs annotated using SnpEff, and SVs in the form of 10,976 deletions and 2,556 insertions. These resources form the basis of a reliable and repeatable system allowing greater integration between canola genomics studies, with a strong focus on breeding germplasm and industry applicability.

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context.

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Martinez, Josue G; Carroll, Raymond J; Müller, Samuel; Sampson, Joshua N; Chatterjee, Nilanjan

2011-11-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.

Comparative scaffolding and gap filling of ancient bacterial genomes applied to two ancient Yersinia pestis genomes

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Doerr, Daniel; Chauve, Cedric

2017-01-01

Yersinia pestis is the causative agent of the bubonic plague, a disease responsible for several dramatic historical pandemics. Progress in ancient DNA (aDNA) sequencing rendered possible the sequencing of whole genomes of important human pathogens, including the ancient Y. pestis strains responsible for outbreaks of the bubonic plague in London in the 14th century and in Marseille in the 18th century, among others. However, aDNA sequencing data are still characterized by short reads and non-uniform coverage, so assembling ancient pathogen genomes remains challenging and often prevents a detailed study of genome rearrangements. It has recently been shown that comparative scaffolding approaches can improve the assembly of ancient Y. pestis genomes at a chromosome level. In the present work, we address the last step of genome assembly, the gap-filling stage. We describe an optimization-based method AGapEs (ancestral gap estimation) to fill in inter-contig gaps using a combination of a template obtained from related extant genomes and aDNA reads. We show how this approach can be used to refine comparative scaffolding by selecting contig adjacencies supported by a mix of unassembled aDNA reads and comparative signal. We applied our method to two Y. pestis data sets from the London and Marseilles outbreaks, for which we obtained highly improved genome assemblies for both genomes, comprised of, respectively, five and six scaffolds with 95 % of the assemblies supported by ancient reads. We analysed the genome evolution between both ancient genomes in terms of genome rearrangements, and observed a high level of synteny conservation between these strains. PMID:29114402

Evaluation of Three Automated Genome Annotations for Halorhabdus utahensis

DEFF Research Database (Denmark)

Bakke, Peter; Carney, Nick; DeLoache, Will

2009-01-01

in databases such as NCBI and used to validate subsequent annotation errors. We submitted the genome sequence of halophilic archaeon Halorhabdus utahensis to be analyzed by three genome annotation services. We have examined the output from each service in a variety of ways in order to compare the methodology...

Methods for open innovation on a genome-design platform associating scientific, commercial, and educational communities in synthetic biology.

Science.gov (United States)

Toyoda, Tetsuro

2011-01-01

Synthetic biology requires both engineering efficiency and compliance with safety guidelines and ethics. Focusing on the rational construction of biological systems based on engineering principles, synthetic biology depends on a genome-design platform to explore the combinations of multiple biological components or BIO bricks for quickly producing innovative devices. This chapter explains the differences among various platform models and details a methodology for promoting open innovation within the scope of the statutory exemption of patent laws. The detailed platform adopts a centralized evaluation model (CEM), computer-aided design (CAD) bricks, and a freemium model. It is also important for the platform to support the legal aspects of copyrights as well as patent and safety guidelines because intellectual work including DNA sequences designed rationally by human intelligence is basically copyrightable. An informational platform with high traceability, transparency, auditability, and security is required for copyright proof, safety compliance, and incentive management for open innovation in synthetic biology. GenoCon, which we have organized and explained here, is a competition-styled, open-innovation method involving worldwide participants from scientific, commercial, and educational communities that aims to improve the designs of genomic sequences that confer a desired function on an organism. Using only a Web browser, a participating contributor proposes a design expressed with CAD bricks that generate a relevant DNA sequence, which is then experimentally and intensively evaluated by the GenoCon organizers. The CAD bricks that comprise programs and databases as a Semantic Web are developed, executed, shared, reused, and well stocked on the secure Semantic Web platform called the Scientists' Networking System or SciNetS/SciNeS, based on which a CEM research center for synthetic biology and open innovation should be established. Copyright © 2011 Elsevier Inc

Genome-wide prediction methods in highly diverse and heterozygous species: proof-of-concept through simulation in grapevine.

Directory of Open Access Journals (Sweden)

Agota Fodor

Full Text Available Nowadays, genome-wide association studies (GWAS and genomic selection (GS methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9 using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.

Unified method to integrate and blend several, potentially related, sources of information for genetic evaluation.

Science.gov (United States)

Vandenplas, Jérémie; Colinet, Frederic G; Gengler, Nicolas

2014-09-30

information and, therefore, more reliable estimated breeding values were obtained. The proposed unified method integrated and blended several sources of information well into a genetic evaluation by avoiding double-counting of contributions due to relationships and due to records. The unified method can also be extended to other types of situations such as single-step genomic or multi-trait evaluations, combining information across different traits.

Whole-genome regression and prediction methods applied to plant and animal breeding

NARCIS (Netherlands)

Los Campos, De G.; Hickey, J.M.; Pong-Wong, R.; Daetwyler, H.D.; Calus, M.P.L.

2013-01-01

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding, and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of

Evaluation of dna extraction methods of the Salmonella sp. bacterium in artificially infected chickens eggs

Directory of Open Access Journals (Sweden)

Ana Cristina dos Reis Ferreira

2015-06-01

Full Text Available ABSTRACT. Ferreira A.C.dosR. & dos Santos B.M. [Evaluation of dna extraction methods of the Salmonella sp. bacterium in artificially infected chickens eggs.] Avaliação de três métodos de extração de DNA de Salmonella sp. em ovos de galinhas contaminados artificialmente. Revista Brasileira de Medicina Veterinária, 37(2:115-119, 2015. Departamento de Veterinária, Universidade Federal de Viçosa, Campus Universitário, Av. Peter Henry Rolfs, s/n, Viçosa, MG 36571-000, Brasil. E-mail: bmsantos@ufv.br The present study evaluated the efficiency of different protocols for the genomic DNA extraction of Salmonella bacteria in chicken eggs free of specific pathogens – SPF. Seventy-five eggs were used and divided into five groups with fifteen eggs each. Three of the five groups of eggs were inoculated with enteric Salmonella cultures. One of the five groups was inoculated with Escherichia coli bacterium culture. And another group of eggs was the negative control that received saline solution 0.85% infertile. The eggs were incubated on a temperature that varied from 20 to 25°C during 24, 48 and 72 hours. Five yolks of each group were collected every 24 hours. These yolks were homogenized and centrifuged during 10 minutes. The supernatant was rejected. After the discard, PBS ph 7.2 was added and centrifuged again. The sediment obtained of each group was used for the extraction of bacterial genomic DNA. Silica particles and a commercial kit were utilized as the extraction methods. The extracted DNA was kept on a temperature of 20°C until the evaluation through PCR. The primers utilized were related with the invA gene and they were the following: 5’ GTA AAA TTA TCG CCA CGT TCG GGC AA 3’ and 5’ TCA TCG CAC CGT CAA AGG AAC C 3’. The amplification products were visualized in transilluminator with ultraviolet light. The obtained results through the bacterial DNA extractions demonstrated that the extraction method utilizing silica particles was

Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples.

Science.gov (United States)

Elloumi, Fathi; Hu, Zhiyuan; Li, Yan; Parker, Joel S; Gulley, Margaret L; Amos, Keith D; Troester, Melissa A

2011-06-30

Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

Murasaki: a fast, parallelizable algorithm to find anchors from multiple genomes.

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Kris Popendorf

Full Text Available BACKGROUND: With the number of available genome sequences increasing rapidly, the magnitude of sequence data required for multiple-genome analyses is a challenging problem. When large-scale rearrangements break the collinearity of gene orders among genomes, genome comparison algorithms must first identify sets of short well-conserved sequences present in each genome, termed anchors. Previously, anchor identification among multiple genomes has been achieved using pairwise alignment tools like BLASTZ through progressive alignment tools like TBA, but the computational requirements for sequence comparisons of multiple genomes quickly becomes a limiting factor as the number and scale of genomes grows. METHODOLOGY/PRINCIPAL FINDINGS: Our algorithm, named Murasaki, makes it possible to identify anchors within multiple large sequences on the scale of several hundred megabases in few minutes using a single CPU. Two advanced features of Murasaki are (1 adaptive hash function generation, which enables efficient use of arbitrary mismatch patterns (spaced seeds and therefore the comparison of multiple mammalian genomes in a practical amount of computation time, and (2 parallelizable execution that decreases the required wall-clock and CPU times. Murasaki can perform a sensitive anchoring of eight mammalian genomes (human, chimp, rhesus, orangutan, mouse, rat, dog, and cow in 21 hours CPU time (42 minutes wall time. This is the first single-pass in-core anchoring of multiple mammalian genomes. We evaluated Murasaki by comparing it with the genome alignment programs BLASTZ and TBA. We show that Murasaki can anchor multiple genomes in near linear time, compared to the quadratic time requirements of BLASTZ and TBA, while improving overall accuracy. CONCLUSIONS/SIGNIFICANCE: Murasaki provides an open source platform to take advantage of long patterns, cluster computing, and novel hash algorithms to produce accurate anchors across multiple genomes with

A new method for detecting signal regions in ordered sequences of real numbers, and application to viral genomic data.

Science.gov (United States)

Gog, Julia R; Lever, Andrew M L; Skittrall, Jordan P

2018-01-01

We present a fast, robust and parsimonious approach to detecting signals in an ordered sequence of numbers. Our motivation is in seeking a suitable method to take a sequence of scores corresponding to properties of positions in virus genomes, and find outlying regions of low scores. Suitable statistical methods without using complex models or making many assumptions are surprisingly lacking. We resolve this by developing a method that detects regions of low score within sequences of real numbers. The method makes no assumptions a priori about the length of such a region; it gives the explicit location of the region and scores it statistically. It does not use detailed mechanistic models so the method is fast and will be useful in a wide range of applications. We present our approach in detail, and test it on simulated sequences. We show that it is robust to a wide range of signal morphologies, and that it is able to capture multiple signals in the same sequence. Finally we apply it to viral genomic data to identify regions of evolutionary conservation within influenza and rotavirus.

Haplotype assembly in polyploid genomes and identical by descent shared tracts.

Science.gov (United States)

Aguiar, Derek; Istrail, Sorin

2013-07-01

Genome-wide haplotype reconstruction from sequence data, or haplotype assembly, is at the center of major challenges in molecular biology and life sciences. For complex eukaryotic organisms like humans, the genome is vast and the population samples are growing so rapidly that algorithms processing high-throughput sequencing data must scale favorably in terms of both accuracy and computational efficiency. Furthermore, current models and methodologies for haplotype assembly (i) do not consider individuals sharing haplotypes jointly, which reduces the size and accuracy of assembled haplotypes, and (ii) are unable to model genomes having more than two sets of homologous chromosomes (polyploidy). Polyploid organisms are increasingly becoming the target of many research groups interested in the genomics of disease, phylogenetics, botany and evolution but there is an absence of theory and methods for polyploid haplotype reconstruction. In this work, we present a number of results, extensions and generalizations of compass graphs and our HapCompass framework. We prove the theoretical complexity of two haplotype assembly optimizations, thereby motivating the use of heuristics. Furthermore, we present graph theory-based algorithms for the problem of haplotype assembly using our previously developed HapCompass framework for (i) novel implementations of haplotype assembly optimizations (minimum error correction), (ii) assembly of a pair of individuals sharing a haplotype tract identical by descent and (iii) assembly of polyploid genomes. We evaluate our methods on 1000 Genomes Project, Pacific Biosciences and simulated sequence data. HapCompass is available for download at http://www.brown.edu/Research/Istrail_Lab/. Supplementary data are available at Bioinformatics online.

Efficient Breeding by Genomic Mating.

Science.gov (United States)

Akdemir, Deniz; Sánchez, Julio I

2016-01-01

Selection in breeding programs can be done by using phenotypes (phenotypic selection), pedigree relationship (breeding value selection) or molecular markers (marker assisted selection or genomic selection). All these methods are based on truncation selection, focusing on the best performance of parents before mating. In this article we proposed an approach to breeding, named genomic mating, which focuses on mating instead of truncation selection. Genomic mating uses information in a similar fashion to genomic selection but includes information on complementation of parents to be mated. Following the efficiency frontier surface, genomic mating uses concepts of estimated breeding values, risk (usefulness) and coefficient of ancestry to optimize mating between parents. We used a genetic algorithm to find solutions to this optimization problem and the results from our simulations comparing genomic selection, phenotypic selection and the mating approach indicate that current approach for breeding complex traits is more favorable than phenotypic and genomic selection. Genomic mating is similar to genomic selection in terms of estimating marker effects, but in genomic mating the genetic information and the estimated marker effects are used to decide which genotypes should be crossed to obtain the next breeding population.

Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae : Implications for the microbial "pan-genome"

NARCIS (Netherlands)

Tettelin, H; Masignani, [No Value; Cieslewicz, MJ; Donati, C; Medini, D; Ward, NL; Angiuoli, SV; Crabtree, J; Jones, AL; Durkin, AS; DeBoy, RT; Davidsen, TM; Mora, M; Scarselli, M; Ros, IMY; Peterson, JD; Hauser, CR; Sundaram, JP; Nelson, WC; Madupu, R; Brinkac, LM; Dodson, RJ; Rosovitz, MJ; Sullivan, SA; Daugherty, SC; Haft, DH; Selengut, J; Gwinn, ML; Zhou, LW; Zafar, N; Khouri, H; Radune, D; Dimitrov, G; Watkins, K; O'Connor, KJB; Smith, S; Utterback, TR; White, O; Rubens, CE; Grandi, G; Madoff, LC; Kasper, DL; Telford, JL; Wessels, MR; Rappuoli, R; Fraser, CM

2005-01-01

The development of efficient and inexpensive genome sequencing methods has revolutionized the study of human bacterial pathogens and improved vaccine design. Unfortunately, the sequence of a single genome does not reflect how genetic variability drives pathogenesis within a bacterial species and

Genomic prediction of reproduction traits for Merino sheep.

Science.gov (United States)

Bolormaa, S; Brown, D J; Swan, A A; van der Werf, J H J; Hayes, B J; Daetwyler, H D

2017-06-01

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy

[Advances in microbial genome reduction and modification].

Science.gov (United States)

Wang, Jianli; Wang, Xiaoyuan

2013-08-01

Microbial genome reduction and modification are important strategies for constructing cellular chassis used for synthetic biology. This article summarized the essential genes and the methods to identify them in microorganisms, compared various strategies for microbial genome reduction, and analyzed the characteristics of some microorganisms with the minimized genome. This review shows the important role of genome reduction in constructing cellular chassis.

A haplotype regression approach for genetic evaluation using sequences from the 1000 bull genomes Project

International Nuclear Information System (INIS)

Lakhssassi, K.; González-Recio, O.

2017-01-01

Haplotypes from sequencing data may improve the prediction accuracy in genomic evaluations as haplotypes are in stronger linkage disequilibrium with quantitative trait loci than markers from SNP chips. This study focuses first, on the creation of haplotypes in a population sample of 450 Holstein animals, with full-sequence data from the 1000 bull genomes project; and second, on incorporating them into the whole genome prediction model. In total, 38,319,258 SNPs (and indels) from Next Generation Sequencing were included in the analysis. After filtering variants with minor allele frequency (MAF< 0.025) 13,912,326 SNPs were available for the haplotypes extraction with findhap.f90. The number of SNPs in the haploblocks was on average 924 SNP (166,552 bp). Unique haplotypes were around 97% in all chromosomes and were ignored leaving 153,428 haplotypes. Estimated haplotypes had a large contribution to the total variance of genomic estimated breeding values for kilogram of protein, Global Type Index, Somatic Cell Score and Days Open (between 32 and 99.9%). Haploblocks containing haplotypes with large effects were selected by filtering for each trait, haplotypes whose effect was larger/lower than the mean plus/minus 3 times the standard deviation (SD) and 1 SD above the mean of the haplotypes effect distribution. Results showed that filtering by 3 SD would not be enough to capture a large proportion of genetic variance, whereas filtering by 1 SD could be useful but model convergence should be considered. Additionally, sequence haplotypes were able to capture additional genetic variance to the polygenic effect for traits undergoing lower selection intensity like fertility and health traits.

A haplotype regression approach for genetic evaluation using sequences from the 1000 bull genomes Project

Energy Technology Data Exchange (ETDEWEB)

Lakhssassi, K.; González-Recio, O.

2017-07-01

Haplotypes from sequencing data may improve the prediction accuracy in genomic evaluations as haplotypes are in stronger linkage disequilibrium with quantitative trait loci than markers from SNP chips. This study focuses first, on the creation of haplotypes in a population sample of 450 Holstein animals, with full-sequence data from the 1000 bull genomes project; and second, on incorporating them into the whole genome prediction model. In total, 38,319,258 SNPs (and indels) from Next Generation Sequencing were included in the analysis. After filtering variants with minor allele frequency (MAF< 0.025) 13,912,326 SNPs were available for the haplotypes extraction with findhap.f90. The number of SNPs in the haploblocks was on average 924 SNP (166,552 bp). Unique haplotypes were around 97% in all chromosomes and were ignored leaving 153,428 haplotypes. Estimated haplotypes had a large contribution to the total variance of genomic estimated breeding values for kilogram of protein, Global Type Index, Somatic Cell Score and Days Open (between 32 and 99.9%). Haploblocks containing haplotypes with large effects were selected by filtering for each trait, haplotypes whose effect was larger/lower than the mean plus/minus 3 times the standard deviation (SD) and 1 SD above the mean of the haplotypes effect distribution. Results showed that filtering by 3 SD would not be enough to capture a large proportion of genetic variance, whereas filtering by 1 SD could be useful but model convergence should be considered. Additionally, sequence haplotypes were able to capture additional genetic variance to the polygenic effect for traits undergoing lower selection intensity like fertility and health traits.

Consumer behavior changing: methods of evaluation

Directory of Open Access Journals (Sweden)

Elīna Gaile-Sarkane

2013-11-01

Full Text Available The article is devoted to methods of analyses of consumer buying behavior as well as to evaluation of most important factors what influences consumer behavior. This research aims at investigations about the changes in consumer behavior caused by globalization and development of information technologies; it helps to understand the specific factors what should be taken into account in evaluation of consumer behavior. The authors employ well-established quantitative and qualitative methods of research: grouping, analysis, synthesis, expert method, statistic method, etc. Research findings disclosed that there is possibility to introduce new methods for evaluation of changing consumer behavior.

Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle

Directory of Open Access Journals (Sweden)

Northcutt Sally L

2010-04-01

Full Text Available Abstract Background Molecular estimates of breeding value are expected to increase selection response due to improvements in the accuracy of selection and a reduction in generation interval, particularly for traits that are difficult or expensive to record or are measured late in life. Several statistical methods for incorporating molecular data into breeding value estimation have been proposed, however, most studies have utilized simulated data in which the generated linkage disequilibrium may not represent the targeted livestock population. A genomic relationship matrix was developed for 698 Angus steers and 1,707 Angus sires using 41,028 single nucleotide polymorphisms and breeding values were estimated using feed efficiency phenotypes (average daily feed intake, residual feed intake, and average daily gain recorded on the steers. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population. Results Results were compared to estimates produced from pedigree-based mixed model analysis of 862 Angus steers with 34,864 identified paternal relatives but no female ancestors. Estimates of additive genetic variance and breeding value accuracies were similar for AFI and RFI using the numerator and genomic relationship matrices despite fewer animals in the genomic analysis. Bootstrap analyses indicated that 2,500-10,000 markers are required for robust estimation of genomic relationship matrices in cattle. Conclusions This research shows that breeding values and their accuracies may be estimated for commercially important sires for traits recorded in experimental populations without the need for pedigree data to establish identity by descent between members of the commercial and experimental populations when at least 2,500 SNPs are available for the generation of a genomic relationship matrix.

A Probabilistic Genome-Wide Gene Reading Frame Sequence Model

DEFF Research Database (Denmark)

Have, Christian Theil; Mørk, Søren

We introduce a new type of probabilistic sequence model, that model the sequential composition of reading frames of genes in a genome. Our approach extends gene finders with a model of the sequential composition of genes at the genome-level -- effectively producing a sequential genome annotation...... as output. The model can be used to obtain the most probable genome annotation based on a combination of i: a gene finder score of each gene candidate and ii: the sequence of the reading frames of gene candidates through a genome. The model --- as well as a higher order variant --- is developed and tested...... and are evaluated by the effect on prediction performance. Since bacterial gene finding to a large extent is a solved problem it forms an ideal proving ground for evaluating the explicit modeling of larger scale gene sequence composition of genomes. We conclude that the sequential composition of gene reading frames...

De novo assembly of a haplotype-resolved human genome.

Science.gov (United States)

Cao, Hongzhi; Wu, Honglong; Luo, Ruibang; Huang, Shujia; Sun, Yuhui; Tong, Xin; Xie, Yinlong; Liu, Binghang; Yang, Hailong; Zheng, Hancheng; Li, Jian; Li, Bo; Wang, Yu; Yang, Fang; Sun, Peng; Liu, Siyang; Gao, Peng; Huang, Haodong; Sun, Jing; Chen, Dan; He, Guangzhu; Huang, Weihua; Huang, Zheng; Li, Yue; Tellier, Laurent C A M; Liu, Xiao; Feng, Qiang; Xu, Xun; Zhang, Xiuqing; Bolund, Lars; Krogh, Anders; Kristiansen, Karsten; Drmanac, Radoje; Drmanac, Snezana; Nielsen, Rasmus; Li, Songgang; Wang, Jian; Yang, Huanming; Li, Yingrui; Wong, Gane Ka-Shu; Wang, Jun

2015-06-01

The human genome is diploid, and knowledge of the variants on each chromosome is important for the interpretation of genomic information. Here we report the assembly of a haplotype-resolved diploid genome without using a reference genome. Our pipeline relies on fosmid pooling together with whole-genome shotgun strategies, based solely on next-generation sequencing and hierarchical assembly methods. We applied our sequencing method to the genome of an Asian individual and generated a 5.15-Gb assembled genome with a haplotype N50 of 484 kb. Our analysis identified previously undetected indels and 7.49 Mb of novel coding sequences that could not be aligned to the human reference genome, which include at least six predicted genes. This haplotype-resolved genome represents the most complete de novo human genome assembly to date. Application of our approach to identify individual haplotype differences should aid in translating genotypes to phenotypes for the development of personalized medicine.

Challenging a bioinformatic tool's ability to detect microbial contaminants using in silico whole genome sequencing data.

Science.gov (United States)

Olson, Nathan D; Zook, Justin M; Morrow, Jayne B; Lin, Nancy J

2017-01-01

High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus, with Staphylococcus , Escherichia , and Shigella having the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in the in-silico datasets at the equivalent of 1 in 1,000 cells, though F. tularensis was not detected in any of the simulated contaminant mixtures and Y. pestis was only detected at the equivalent of one in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in efforts to ensure that contaminants are characterized in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods.

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.

Large-Scale Genomic Analysis of Codon Usage in Dengue Virus and Evaluation of Its Phylogenetic Dependence

Science.gov (United States)

Lara-Ramírez, Edgar E.; Salazar, Ma Isabel; López-López, María de Jesús; Salas-Benito, Juan Santiago; Sánchez-Varela, Alejandro

2014-01-01

The increasing number of dengue virus (DENV) genome sequences available allows identifying the contributing factors to DENV evolution. In the present study, the codon usage in serotypes 1–4 (DENV1–4) has been explored for 3047 sequenced genomes using different statistics methods. The correlation analysis of total GC content (GC) with GC content at the three nucleotide positions of codons (GC1, GC2, and GC3) as well as the effective number of codons (ENC, ENCp) versus GC3 plots revealed mutational bias and purifying selection pressures as the major forces influencing the codon usage, but with distinct pressure on specific nucleotide position in the codon. The correspondence analysis (CA) and clustering analysis on relative synonymous codon usage (RSCU) within each serotype showed similar clustering patterns to the phylogenetic analysis of nucleotide sequences for DENV1–4. These clustering patterns are strongly related to the virus geographic origin. The phylogenetic dependence analysis also suggests that stabilizing selection acts on the codon usage bias. Our analysis of a large scale reveals new feature on DENV genomic evolution. PMID:25136631

Digestion-ligation-only Hi-C is an efficient and cost-effective method for chromosome conformation capture.

Science.gov (United States)

Lin, Da; Hong, Ping; Zhang, Siheng; Xu, Weize; Jamal, Muhammad; Yan, Keji; Lei, Yingying; Li, Liang; Ruan, Yijun; Fu, Zhen F; Li, Guoliang; Cao, Gang

2018-05-01

Chromosome conformation capture (3C) technologies can be used to investigate 3D genomic structures. However, high background noise, high costs, and a lack of straightforward noise evaluation in current methods impede the advancement of 3D genomic research. Here we developed a simple digestion-ligation-only Hi-C (DLO Hi-C) technology to explore the 3D landscape of the genome. This method requires only two rounds of digestion and ligation, without the need for biotin labeling and pulldown. Non-ligated DNA was efficiently removed in a cost-effective step by purifying specific linker-ligated DNA fragments. Notably, random ligation could be quickly evaluated in an early quality-control step before sequencing. Moreover, an in situ version of DLO Hi-C using a four-cutter restriction enzyme has been developed. We applied DLO Hi-C to delineate the genomic architecture of THP-1 and K562 cells and uncovered chromosomal translocations. This technology may facilitate investigation of genomic organization, gene regulation, and (meta)genome assembly.

V-GAP: Viral genome assembly pipeline

KAUST Repository

Nakamura, Yoji

2015-10-22

Next-generation sequencing technologies have allowed the rapid determination of the complete genomes of many organisms. Although shotgun sequences from large genome organisms are still difficult to reconstruct perfect contigs each of which represents a full chromosome, those from small genomes have been assembled successfully into a very small number of contigs. In this study, we show that shotgun reads from phage genomes can be reconstructed into a single contig by controlling the number of read sequences used in de novo assembly. We have developed a pipeline to assemble small viral genomes with good reliability using a resampling method from shotgun data. This pipeline, named V-GAP (Viral Genome Assembly Pipeline), will contribute to the rapid genome typing of viruses, which are highly divergent, and thus will meet the increasing need for viral genome comparisons in metagenomic studies.

V-GAP: Viral genome assembly pipeline

KAUST Repository

Nakamura, Yoji; Yasuike, Motoshige; Nishiki, Issei; Iwasaki, Yuki; Fujiwara, Atushi; Kawato, Yasuhiko; Nakai, Toshihiro; Nagai, Satoshi; Kobayashi, Takanori; Gojobori, Takashi; Ototake, Mitsuru

2015-01-01

Next-generation sequencing technologies have allowed the rapid determination of the complete genomes of many organisms. Although shotgun sequences from large genome organisms are still difficult to reconstruct perfect contigs each of which represents a full chromosome, those from small genomes have been assembled successfully into a very small number of contigs. In this study, we show that shotgun reads from phage genomes can be reconstructed into a single contig by controlling the number of read sequences used in de novo assembly. We have developed a pipeline to assemble small viral genomes with good reliability using a resampling method from shotgun data. This pipeline, named V-GAP (Viral Genome Assembly Pipeline), will contribute to the rapid genome typing of viruses, which are highly divergent, and thus will meet the increasing need for viral genome comparisons in metagenomic studies.

A human-machine interface evaluation method: A difficulty evaluation method in information searching (DEMIS)

International Nuclear Information System (INIS)

Ha, Jun Su; Seong, Poong Hyun

2009-01-01

A human-machine interface (HMI) evaluation method, which is named 'difficulty evaluation method in information searching (DEMIS)', is proposed and demonstrated with an experimental study. The DEMIS is based on a human performance model and two measures of attentional-resource effectiveness in monitoring and detection tasks in nuclear power plants (NPPs). Operator competence and HMI design are modeled to be most significant factors to human performance. One of the two effectiveness measures is fixation-to-importance ratio (FIR) which represents attentional resource (eye fixations) spent on an information source compared to importance of the information source. The other measure is selective attention effectiveness (SAE) which incorporates FIRs for all information sources. The underlying principle of the measures is that the information source should be selectively attended to according to its informational importance. In this study, poor performance in information searching tasks is modeled to be coupled with difficulties caused by poor mental models of operators or/and poor HMI design. Human performance in information searching tasks is evaluated by analyzing the FIR and the SAE. Operator mental models are evaluated by a questionnaire-based method. Then difficulties caused by a poor HMI design are evaluated by a focused interview based on the FIR evaluation and then root causes leading to poor performance are identified in a systematic way.

Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration.

Science.gov (United States)

Thorvaldsdóttir, Helga; Robinson, James T; Mesirov, Jill P

2013-03-01

Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today's sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.

Technical note: Equivalent genomic models with a residual polygenic effect.

Science.gov (United States)

Liu, Z; Goddard, M E; Hayes, B J; Reinhardt, F; Reents, R

2016-03-01

Routine genomic evaluations in animal breeding are usually based on either a BLUP with genomic relationship matrix (GBLUP) or single nucleotide polymorphism (SNP) BLUP model. For a multi-step genomic evaluation, these 2 alternative genomic models were proven to give equivalent predictions for genomic reference animals. The model equivalence was verified also for young genotyped animals without phenotypes. Due to incomplete linkage disequilibrium of SNP markers to genes or causal mutations responsible for genetic inheritance of quantitative traits, SNP markers cannot explain all the genetic variance. A residual polygenic effect is normally fitted in the genomic model to account for the incomplete linkage disequilibrium. In this study, we start by showing the proof that the multi-step GBLUP and SNP BLUP models are equivalent for the reference animals, when they have a residual polygenic effect included. Second, the equivalence of both multi-step genomic models with a residual polygenic effect was also verified for young genotyped animals without phenotypes. Additionally, we derived formulas to convert genomic estimated breeding values of the GBLUP model to its components, direct genomic values and residual polygenic effect. Third, we made a proof that the equivalence of these 2 genomic models with a residual polygenic effect holds also for single-step genomic evaluation. Both the single-step GBLUP and SNP BLUP models lead to equal prediction for genotyped animals with phenotypes (e.g., reference animals), as well as for (young) genotyped animals without phenotypes. Finally, these 2 single-step genomic models with a residual polygenic effect were proven to be equivalent for estimation of SNP effects, too. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Evaluation of SNP Data from the Malus Infinium Array Identifies Challenges for Genetic Analysis of Complex Genomes of Polyploid Origin.

Directory of Open Access Journals (Sweden)

Michela Troggio

Full Text Available High throughput arrays for the simultaneous genotyping of thousands of single-nucleotide polymorphisms (SNPs have made the rapid genetic characterisation of plant genomes and the development of saturated linkage maps a realistic prospect for many plant species of agronomic importance. However, the correct calling of SNP genotypes in divergent polyploid genomes using array technology can be problematic due to paralogy, and to divergence in probe sequences causing changes in probe binding efficiencies. An Illumina Infinium II whole-genome genotyping array was recently developed for the cultivated apple and used to develop a molecular linkage map for an apple rootstock progeny (M432, but a large proportion of segregating SNPs were not mapped in the progeny, due to unexpected genotype clustering patterns. To investigate the causes of this unexpected clustering we performed BLAST analysis of all probe sequences against the 'Golden Delicious' genome sequence and discovered evidence for paralogous annealing sites and probe sequence divergence for a high proportion of probes contained on the array. Following visual re-evaluation of the genotyping data generated for 8,788 SNPs for the M432 progeny using the array, we manually re-scored genotypes at 818 loci and mapped a further 797 markers to the M432 linkage map. The newly mapped markers included the majority of those that could not be mapped previously, as well as loci that were previously scored as monomorphic, but which segregated due to divergence leading to heterozygosity in probe annealing sites. An evaluation of the 8,788 probes in a diverse collection of Malus germplasm showed that more than half the probes returned genotype clustering patterns that were difficult or impossible to interpret reliably, highlighting implications for the use of the array in genome-wide association studies.

Cytogenetic method of determining effect of threshold values of anthropogenic factors on the plant and animal genome

International Nuclear Information System (INIS)

Arkhipchuk, V.V.; Romanenko, V.D.; Arkhipchuk, M.V.; Kipnis, L.S.

1993-01-01

The use of nucleolar characteristics to access the action of physical and chemical factors on living objects is a promising trend in the creation of new and highly sensitive biological tests. The advantages of this process are that the effect of the threshold values of the anthropogenic factors is recorded as a change in functional activity of the cell genome and not as the restructuring of the karyotype. The aim of this research was to test a cytogenetic method of determining the modifying action of various factors on the plant and animal genome, based on analysis of quantitative characteristics of the nucleoli and to extend its use to different groups of organisms

Divergence of RNA polymerase ? subunits in angiosperm plastid genomes is mediated by genomic rearrangement

OpenAIRE

Blazier, J. Chris; Ruhlman, Tracey A.; Weng, Mao-Lun; Rehman, Sumaiyah K.; Sabir, Jamal S. M.; Jansen, Robert K.

2016-01-01

Genes for the plastid-encoded RNA polymerase (PEP) persist in the plastid genomes of all photosynthetic angiosperms. However, three unrelated lineages (Annonaceae, Passifloraceae and Geraniaceae) have been identified with unusually divergent open reading frames (ORFs) in the conserved region of rpoA, the gene encoding the PEP ? subunit. We used sequence-based approaches to evaluate whether these genes retain function. Both gene sequences and complete plastid genome sequences were assembled an...

PSAT: A web tool to compare genomic neighborhoods of multiple prokaryotic genomes

Directory of Open Access Journals (Sweden)

Wasnick Michael

2008-03-01

Full Text Available Abstract Background The conservation of gene order among prokaryotic genomes can provide valuable insight into gene function, protein interactions, or events by which genomes have evolved. Although some tools are available for visualizing and comparing the order of genes between genomes of study, few support an efficient and organized analysis between large numbers of genomes. The Prokaryotic Sequence homology Analysis Tool (PSAT is a web tool for comparing gene neighborhoods among multiple prokaryotic genomes. Results PSAT utilizes a database that is preloaded with gene annotation, BLAST hit results, and gene-clustering scores designed to help identify regions of conserved gene order. Researchers use the PSAT web interface to find a gene of interest in a reference genome and efficiently retrieve the sequence homologs found in other bacterial genomes. The tool generates a graphic of the genomic neighborhood surrounding the selected gene and the corresponding regions for its homologs in each comparison genome. Homologs in each region are color coded to assist users with analyzing gene order among various genomes. In contrast to common comparative analysis methods that filter sequence homolog data based on alignment score cutoffs, PSAT leverages gene context information for homologs, including those with weak alignment scores, enabling a more sensitive analysis. Features for constraining or ordering results are designed to help researchers browse results from large numbers of comparison genomes in an organized manner. PSAT has been demonstrated to be useful for helping to identify gene orthologs and potential functional gene clusters, and detecting genome modifications that may result in loss of function. Conclusion PSAT allows researchers to investigate the order of genes within local genomic neighborhoods of multiple genomes. A PSAT web server for public use is available for performing analyses on a growing set of reference genomes through any

Color image definition evaluation method based on deep learning method

Science.gov (United States)

Liu, Di; Li, YingChun

2018-01-01

In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

Genome-wide analytical approaches for reverse metabolic engineering of industrially relevant phenotypes in yeast

Science.gov (United States)

Oud, Bart; Maris, Antonius J A; Daran, Jean-Marc; Pronk, Jack T

2012-01-01

Successful reverse engineering of mutants that have been obtained by nontargeted strain improvement has long presented a major challenge in yeast biotechnology. This paper reviews the use of genome-wide approaches for analysis of Saccharomyces cerevisiae strains originating from evolutionary engineering or random mutagenesis. On the basis of an evaluation of the strengths and weaknesses of different methods, we conclude that for the initial identification of relevant genetic changes, whole genome sequencing is superior to other analytical techniques, such as transcriptome, metabolome, proteome, or array-based genome analysis. Key advantages of this technique over gene expression analysis include the independency of genome sequences on experimental context and the possibility to directly and precisely reproduce the identified changes in naive strains. The predictive value of genome-wide analysis of strains with industrially relevant characteristics can be further improved by classical genetics or simultaneous analysis of strains derived from parallel, independent strain improvement lineages. PMID:22152095

Genome-wide analytical approaches for reverse metabolic engineering of industrially relevant phenotypes in yeast.

Science.gov (United States)

Oud, Bart; van Maris, Antonius J A; Daran, Jean-Marc; Pronk, Jack T

2012-03-01

Successful reverse engineering of mutants that have been obtained by nontargeted strain improvement has long presented a major challenge in yeast biotechnology. This paper reviews the use of genome-wide approaches for analysis of Saccharomyces cerevisiae strains originating from evolutionary engineering or random mutagenesis. On the basis of an evaluation of the strengths and weaknesses of different methods, we conclude that for the initial identification of relevant genetic changes, whole genome sequencing is superior to other analytical techniques, such as transcriptome, metabolome, proteome, or array-based genome analysis. Key advantages of this technique over gene expression analysis include the independency of genome sequences on experimental context and the possibility to directly and precisely reproduce the identified changes in naive strains. The predictive value of genome-wide analysis of strains with industrially relevant characteristics can be further improved by classical genetics or simultaneous analysis of strains derived from parallel, independent strain improvement lineages. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Gene design, cloning and protein-expression methods for high-value targets at the Seattle Structural Genomics Center for Infectious Disease

International Nuclear Information System (INIS)

Raymond, Amy; Haffner, Taryn; Ng, Nathan; Lorimer, Don; Staker, Bart; Stewart, Lance

2011-01-01

An overview of one salvage strategy for high-value SSGCID targets is given. Any structural genomics endeavor, particularly ambitious ones such as the NIAID-funded Seattle Structural Genomics Center for Infectious Disease (SSGCID) and Center for Structural Genomics of Infectious Disease (CSGID), face technical challenges at all points of the production pipeline. One salvage strategy employed by SSGCID is combined gene engineering and structure-guided construct design to overcome challenges at the levels of protein expression and protein crystallization. Multiple constructs of each target are cloned in parallel using Polymerase Incomplete Primer Extension cloning and small-scale expressions of these are rapidly analyzed by capillary electrophoresis. Using the methods reported here, which have proven particularly useful for high-value targets, otherwise intractable targets can be resolved

PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions.

Science.gov (United States)

Bendl, Jaroslav; Musil, Miloš; Štourač, Jan; Zendulka, Jaroslav; Damborský, Jiří; Brezovský, Jan

2016-05-01

An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools' predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To

Glycogenomics as a mass spectrometry-guided genome-mining method for microbial glycosylated molecules.

Science.gov (United States)

Kersten, Roland D; Ziemert, Nadine; Gonzalez, David J; Duggan, Brendan M; Nizet, Victor; Dorrestein, Pieter C; Moore, Bradley S

2013-11-19

Glycosyl groups are an essential mediator of molecular interactions in cells and on cellular surfaces. There are very few methods that directly relate sugar-containing molecules to their biosynthetic machineries. Here, we introduce glycogenomics as an experiment-guided genome-mining approach for fast characterization of glycosylated natural products (GNPs) and their biosynthetic pathways from genome-sequenced microbes by targeting glycosyl groups in microbial metabolomes. Microbial GNPs consist of aglycone and glycosyl structure groups in which the sugar unit(s) are often critical for the GNP's bioactivity, e.g., by promoting binding to a target biomolecule. GNPs are a structurally diverse class of molecules with important pharmaceutical and agrochemical applications. Herein, O- and N-glycosyl groups are characterized in their sugar monomers by tandem mass spectrometry (MS) and matched to corresponding glycosylation genes in secondary metabolic pathways by a MS-glycogenetic code. The associated aglycone biosynthetic genes of the GNP genotype then classify the natural product to further guide structure elucidation. We highlight the glycogenomic strategy by the characterization of several bioactive glycosylated molecules and their gene clusters, including the anticancer agent cinerubin B from Streptomyces sp. SPB74 and an antibiotic, arenimycin B, from Salinispora arenicola CNB-527.

Anonymizing patient genomic data for public sharing association studies.

Science.gov (United States)

Fernandez-Lozano, Carlos; Lopez-Campos, Guillermo; Seoane, Jose A; Lopez-Alonso, Victoria; Dorado, Julian; Martín-Sanchez, Fernando; Pazos, Alejandro

2013-01-01

The development of personalized medicine is tightly linked with the correct exploitation of molecular data, especially those associated with the genome sequence along with these use of genomic data there is an increasing demand to share these data for research purposes. Transition of clinical data to research is based in the anonymization of these data so the patient cannot be identified, the use of genomic data poses a great challenge because its nature of identifying data. In this work we have analyzed current methods for genome anonymization and propose a one way encryption method that may enable the process of genomic data sharing accessing only to certain regions of genomes for research purposes.

Genomic markers for decision making: what is preventing us from using markers?

Science.gov (United States)

Coyle, Vicky M; Johnston, Patrick G

2010-02-01

The advent of novel genomic technologies that enable the evaluation of genomic alterations on a genome-wide scale has significantly altered the field of genomic marker research in solid tumors. Researchers have moved away from the traditional model of identifying a particular genomic alteration and evaluating the association between this finding and a clinical outcome measure to a new approach involving the identification and measurement of multiple genomic markers simultaneously within clinical studies. This in turn has presented additional challenges in considering the use of genomic markers in oncology, such as clinical study design, reproducibility and interpretation and reporting of results. This Review will explore these challenges, focusing on microarray-based gene-expression profiling, and highlights some common failings in study design that have impacted on the use of putative genomic markers in the clinic. Despite these rapid technological advances there is still a paucity of genomic markers in routine clinical use at present. A rational and focused approach to the evaluation and validation of genomic markers is needed, whereby analytically validated markers are investigated in clinical studies that are adequately powered and have pre-defined patient populations and study endpoints. Furthermore, novel adaptive clinical trial designs, incorporating putative genomic markers into prospective clinical trials, will enable the evaluation of these markers in a rigorous and timely fashion. Such approaches have the potential to facilitate the implementation of such markers into routine clinical practice and consequently enable the rational and tailored use of cancer therapies for individual patients.

Genomic dark matter: the reliability of short read mapping illustrated by the genome mappability score.

Science.gov (United States)

Lee, Hayan; Schatz, Michael C

2012-08-15

Genome resequencing and short read mapping are two of the primary tools of genomics and are used for many important applications. The current state-of-the-art in mapping uses the quality values and mapping quality scores to evaluate the reliability of the mapping. These attributes, however, are assigned to individual reads and do not directly measure the problematic repeats across the genome. Here, we present the Genome Mappability Score (GMS) as a novel measure of the complexity of resequencing a genome. The GMS is a weighted probability that any read could be unambiguously mapped to a given position and thus measures the overall composition of the genome itself. We have developed the Genome Mappability Analyzer to compute the GMS of every position in a genome. It leverages the parallelism of cloud computing to analyze large genomes, and enabled us to identify the 5-14% of the human, mouse, fly and yeast genomes that are difficult to analyze with short reads. We examined the accuracy of the widely used BWA/SAMtools polymorphism discovery pipeline in the context of the GMS, and found discovery errors are dominated by false negatives, especially in regions with poor GMS. These errors are fundamental to the mapping process and cannot be overcome by increasing coverage. As such, the GMS should be considered in every resequencing project to pinpoint the 'dark matter' of the genome, including of known clinically relevant variations in these regions. The source code and profiles of several model organisms are available at http://gma-bio.sourceforge.net

An efficient approach to BAC based assembly of complex genomes.

Science.gov (United States)

Visendi, Paul; Berkman, Paul J; Hayashi, Satomi; Golicz, Agnieszka A; Bayer, Philipp E; Ruperao, Pradeep; Hurgobin, Bhavna; Montenegro, Juan; Chan, Chon-Kit Kenneth; Staňková, Helena; Batley, Jacqueline; Šimková, Hana; Doležel, Jaroslav; Edwards, David

2016-01-01

There has been an exponential growth in the number of genome sequencing projects since the introduction of next generation DNA sequencing technologies. Genome projects have increasingly involved assembly of whole genome data which produces inferior assemblies compared to traditional Sanger sequencing of genomic fragments cloned into bacterial artificial chromosomes (BACs). While whole genome shotgun sequencing using next generation sequencing (NGS) is relatively fast and inexpensive, this method is extremely challenging for highly complex genomes, where polyploidy or high repeat content confounds accurate assembly, or where a highly accurate 'gold' reference is required. Several attempts have been made to improve genome sequencing approaches by incorporating NGS methods, to variable success. We present the application of a novel BAC sequencing approach which combines indexed pools of BACs, Illumina paired read sequencing, a sequence assembler specifically designed for complex BAC assembly, and a custom bioinformatics pipeline. We demonstrate this method by sequencing and assembling BAC cloned fragments from bread wheat and sugarcane genomes. We demonstrate that our assembly approach is accurate, robust, cost effective and scalable, with applications for complete genome sequencing in large and complex genomes.

Genomic footprinting in mammalian cells with ultraviolet light

International Nuclear Information System (INIS)

Becker, M.M.; Wang, Z.; Grossmann, G.; Becherer, K.A.

1989-01-01

A simple and accurate genomic primer extension method has been developed to detect ultraviolet footprinting patterns of regulatory protein-DNA interactions in mammalian genomic DNA. The technique can also detect footprinting or sequencing patterns introduced into genomic DNA by other methods. Purified genomic DNA, containing either damaged bases or strand breaks introduced by footprinting or sequencing reactions, is first cut with a convenient restriction enzyme to reduce its molecular weight. A highly radioactive single-stranded DNA primer that is complementary to a region of genomic DNA whose sequence or footprint one wishes to examine is then mixed with 50 micrograms of restriction enzyme-cut genomic DNA. The primer is approximately 100 bases long and contains 85 radioactive phosphates, each of specific activity 3000 Ci/mmol (1 Ci = 37 GBq). A simple and fast method for preparing such primers is described. Following brief heat denaturation at 100 degrees C, the solution of genomic DNA and primer is cooled to 74 degrees C and a second solution containing Taq polymerase (Thermus aquaticus DNA polymerase) and the four deoxynucleotide triphosphates is added to initiate primer extension of genomic DNA. Taq polymerase extends genomic hybridized primer until its polymerization reaction is terminated either by a damaged base or strand break in genomic DNA or by the addition of dideoxynucleotide triphosphates in the polymerization reaction. The concurrent primer hybridization-extension reaction is terminated after 5 hr and unhybridized primer is digested away by mung bean nuclease. Primer-extended genomic DNA is then denatured and electrophoresed on a polyacrylamide sequencing gel, and radioactive primer extension products are revealed by autoradiography

Systematic evaluation of the impact of ChIP-seq read designs on genome coverage, peak identification, and allele-specific binding detection.

Science.gov (United States)

Zhang, Qi; Zeng, Xin; Younkin, Sam; Kawli, Trupti; Snyder, Michael P; Keleş, Sündüz

2016-02-24

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments revolutionized genome-wide profiling of transcription factors and histone modifications. Although maturing sequencing technologies allow these experiments to be carried out with short (36-50 bps), long (75-100 bps), single-end, or paired-end reads, the impact of these read parameters on the downstream data analysis are not well understood. In this paper, we evaluate the effects of different read parameters on genome sequence alignment, coverage of different classes of genomic features, peak identification, and allele-specific binding detection. We generated 101 bps paired-end ChIP-seq data for many transcription factors from human GM12878 and MCF7 cell lines. Systematic evaluations using in silico variations of these data as well as fully simulated data, revealed complex interplay between the sequencing parameters and analysis tools, and indicated clear advantages of paired-end designs in several aspects such as alignment accuracy, peak resolution, and most notably, allele-specific binding detection. Our work elucidates the effect of design on the downstream analysis and provides insights to investigators in deciding sequencing parameters in ChIP-seq experiments. We present the first systematic evaluation of the impact of ChIP-seq designs on allele-specific binding detection and highlights the power of pair-end designs in such studies.

Comparison of Model Reliabilities from Single-Step and Bivariate Blending Methods

DEFF Research Database (Denmark)

Taskinen, Matti; Mäntysaari, Esa; Lidauer, Martin

2013-01-01

Model based reliabilities in genetic evaluation are compared between three methods: animal model BLUP, single-step BLUP, and bivariate blending after genomic BLUP. The original bivariate blending is revised in this work to better account animal models. The study data is extracted from...... be calculated. Model reliabilities by the single-step and the bivariate blending methods were higher than by animal model due to genomic information. Compared to the single-step method, the bivariate blending method reliability estimates were, in general, lower. Computationally bivariate blending method was......, on the other hand, lighter than the single-step method....

The genomic applications in practice and prevention network.

Science.gov (United States)

Khoury, Muin J; Feero, W Gregory; Reyes, Michele; Citrin, Toby; Freedman, Andrew; Leonard, Debra; Burke, Wylie; Coates, Ralph; Croyle, Robert T; Edwards, Karen; Kardia, Sharon; McBride, Colleen; Manolio, Teri; Randhawa, Gurvaneet; Rasooly, Rebekah; St Pierre, Jeannette; Terry, Sharon

2009-07-01

The authors describe the rationale and initial development of a new collaborative initiative, the Genomic Applications in Practice and Prevention Network. The network convened by the Centers for Disease Control and Prevention and the National Institutes of Health includes multiple stakeholders from academia, government, health care, public health, industry and consumers. The premise of Genomic Applications in Practice and Prevention Network is that there is an unaddressed chasm between gene discoveries and demonstration of their clinical validity and utility. This chasm is due to the lack of readily accessible information about the utility of most genomic applications and the lack of necessary knowledge by consumers and providers to implement what is known. The mission of Genomic Applications in Practice and Prevention Network is to accelerate and streamline the effective integration of validated genomic knowledge into the practice of medicine and public health, by empowering and sponsoring research, evaluating research findings, and disseminating high quality information on candidate genomic applications in practice and prevention. Genomic Applications in Practice and Prevention Network will develop a process that links ongoing collection of information on candidate genomic applications to four crucial domains: (1) knowledge synthesis and dissemination for new and existing technologies, and the identification of knowledge gaps, (2) a robust evidence-based recommendation development process, (3) translation research to evaluate validity, utility and impact in the real world and how to disseminate and implement recommended genomic applications, and (4) programs to enhance practice, education, and surveillance.

On the Epistemological Crisis in Genomics

Science.gov (United States)

Dougherty, Edward R

2008-01-01

There is an epistemological crisis in genomics. At issue is what constitutes scientific knowledge in genomic science, or systems biology in general. Does this crisis require a new perspective on knowledge heretofore absent from science or is it merely a matter of interpreting new scientific developments in an existing epistemological framework? This paper discusses the manner in which the experimental method, as developed and understood over recent centuries, leads naturally to a scientific epistemology grounded in an experimental-mathematical duality. It places genomics into this epistemological framework and examines the current situation in genomics. Meaning and the constitution of scientific knowledge are key concerns for genomics, and the nature of the epistemological crisis in genomics depends on how these are understood. PMID:19440447

Post-genomics nanotechnology is gaining momentum: nanoproteomics and applications in life sciences.

Science.gov (United States)

Kobeissy, Firas H; Gulbakan, Basri; Alawieh, Ali; Karam, Pierre; Zhang, Zhiqun; Guingab-Cagmat, Joy D; Mondello, Stefania; Tan, Weihong; Anagli, John; Wang, Kevin

2014-02-01

The post-genomics era has brought about new Omics biotechnologies, such as proteomics and metabolomics, as well as their novel applications to personal genomics and the quantified self. These advances are now also catalyzing other and newer post-genomics innovations, leading to convergences between Omics and nanotechnology. In this work, we systematically contextualize and exemplify an emerging strand of post-genomics life sciences, namely, nanoproteomics and its applications in health and integrative biological systems. Nanotechnology has been utilized as a complementary component to revolutionize proteomics through different kinds of nanotechnology applications, including nanoporous structures, functionalized nanoparticles, quantum dots, and polymeric nanostructures. Those applications, though still in their infancy, have led to several highly sensitive diagnostics and new methods of drug delivery and targeted therapy for clinical use. The present article differs from previous analyses of nanoproteomics in that it offers an in-depth and comparative evaluation of the attendant biotechnology portfolio and their applications as seen through the lens of post-genomics life sciences and biomedicine. These include: (1) immunosensors for inflammatory, pathogenic, and autoimmune markers for infectious and autoimmune diseases, (2) amplified immunoassays for detection of cancer biomarkers, and (3) methods for targeted therapy and automatically adjusted drug delivery such as in experimental stroke and brain injury studies. As nanoproteomics becomes available both to the clinician at the bedside and the citizens who are increasingly interested in access to novel post-genomics diagnostics through initiatives such as the quantified self, we anticipate further breakthroughs in personalized and targeted medicine.

Genome analysis methods: Sorghum bicolor [PGDBj Registered plant list, Marker list, QTL list, Plant DB link and Genome analysis methods[Archive

Lifescience Database Archive (English)

Full Text Available Sorghum bicolor Finished 2n=20 760 Mb 2009 Sanger (Clone-based) 10,717,203 reads 7...30 Mb 8.5x Arachne2 v.20060705 3,304 12,873 BLAST, GenomeScan 34,496 (Sbi1.4) JGI; http://www.phytozome.net/sorghum Sbi1 Sbi1.4 10.1038/nature07723 19189423 ...

An analysis of normalization methods for Drosophila RNAi genomic screens and development of a robust validation scheme

Science.gov (United States)

Wiles, Amy M.; Ravi, Dashnamoorthy; Bhavani, Selvaraj; Bishop, Alexander J.R.

2010-01-01

Genome-wide RNAi screening is a powerful, yet relatively immature technology that allows investigation into the role of individual genes in a process of choice. Most RNAi screens identify a large number of genes with a continuous gradient in the assessed phenotype. Screeners must then decide whether to examine just those genes with the most robust phenotype or to examine the full gradient of genes that cause an effect and how to identify the candidate genes to be validated. We have used RNAi in Drosophila cells to examine viability in a 384-well plate format and compare two screens, untreated control and treatment. We compare multiple normalization methods, which take advantage of different features within the data, including quantile normalization, background subtraction, scaling, cellHTS2 1, and interquartile range measurement. Considering the false-positive potential that arises from RNAi technology, a robust validation method was designed for the purpose of gene selection for future investigations. In a retrospective analysis, we describe the use of validation data to evaluate each normalization method. While no normalization method worked ideally, we found that a combination of two methods, background subtraction followed by quantile normalization and cellHTS2, at different thresholds, captures the most dependable and diverse candidate genes. Thresholds are suggested depending on whether a few candidate genes are desired or a more extensive systems level analysis is sought. In summary, our normalization approaches and experimental design to perform validation experiments are likely to apply to those high-throughput screening systems attempting to identify genes for systems level analysis. PMID:18753689

Using physicochemical and compositional characteristics of DNA sequence for prediction of genomic signals

KAUST Repository

Mulamba, Pierre Abraham

2014-12-01

The challenge in finding genes in eukaryotic organisms using computational methods is an ongoing problem in the biology. Based on various genomic signals found in eukaryotic genomes, this problem can be divided into many different sub­‐problems such as identification of transcription start sites, translation initiation sites, splice sites, poly (A) signals, etc. Each sub-­problem deals with a particular type of genomic signals and various computational methods are used to solve each sub-­problem. Aggregating information from all these individual sub-­problems can lead to a complete annotation of a gene and its component signals. The fundamental principle of most of these computational methods is the mapping principle – building an input-­output model for the prediction of a particular genomic signal based on a set of known input signals and their corresponding output signal. The type of input signals used to build the model is an essential element in most of these computational methods. The common factor of most of these methods is that they are mainly based on the statistical analysis of the basic nucleotide sequence string composition. 4 Our study is based on a novel approach to predict genomic signals in which uniquely generated structural profiles that combine compressed physicochemical properties with topological and compositional properties of DNA sequences are used to develop machine learning predictive models. The compression of the physicochemical properties is made using principal component analysis transformation. Our ideas are evaluated through prediction models of canonical splice sites using support vector machine models. We demonstrate across several species that the proposed methodology has resulted in the most accurate splice site predictors that are publicly available or described. We believe that the approach in this study is quite general and has various applications in other biological modeling problems.

MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects.

Science.gov (United States)

Holt, Carson; Yandell, Mark

2011-12-22

Second-generation sequencing technologies are precipitating major shifts with regards to what kinds of genomes are being sequenced and how they are annotated. While the first generation of genome projects focused on well-studied model organisms, many of today's projects involve exotic organisms whose genomes are largely terra incognita. This complicates their annotation, because unlike first-generation projects, there are no pre-existing 'gold-standard' gene-models with which to train gene-finders. Improvements in genome assembly and the wide availability of mRNA-seq data are also creating opportunities to update and re-annotate previously published genome annotations. Today's genome projects are thus in need of new genome annotation tools that can meet the challenges and opportunities presented by second-generation sequencing technologies. We present MAKER2, a genome annotation and data management tool designed for second-generation genome projects. MAKER2 is a multi-threaded, parallelized application that can process second-generation datasets of virtually any size. We show that MAKER2 can produce accurate annotations for novel genomes where training-data are limited, of low quality or even non-existent. MAKER2 also provides an easy means to use mRNA-seq data to improve annotation quality; and it can use these data to update legacy annotations, significantly improving their quality. We also show that MAKER2 can evaluate the quality of genome annotations, and identify and prioritize problematic annotations for manual review. MAKER2 is the first annotation engine specifically designed for second-generation genome projects. MAKER2 scales to datasets of any size, requires little in the way of training data, and can use mRNA-seq data to improve annotation quality. It can also update and manage legacy genome annotation datasets.

Comparison of analyses of the XVth QTLMAS common dataset III: Genomic Estimations of Breeding Values

Directory of Open Access Journals (Sweden)

Demeure Olivier

2012-05-01

Full Text Available Abstract Background The QTLMAS XVth dataset consisted of pedigree, marker genotypes and quantitative trait performances of animals with a sib family structure. Pedigree and genotypes concerned 3,000 progenies among those 2,000 were phenotyped. The trait was regulated by 8 QTLs which displayed additive, imprinting or epistatic effects. The 1,000 unphenotyped progenies were considered as candidates to selection and their Genomic Estimated Breeding Values (GEBV were evaluated by participants of the XVth QTLMAS workshop. This paper aims at comparing the GEBV estimation results obtained by seven participants to the workshop. Methods From the known QTL genotypes of each candidate, two "true" genomic values (TV were estimated by organizers: the genotypic value of the candidate (TGV and the expectation of its progeny genotypic values (TBV. GEBV were computed by the participants following different statistical methods: random linear models (including BLUP and Ridge Regression, selection variable techniques (LASSO, Elastic Net and Bayesian methods. Accuracy was evaluated by the correlation between TV (TGV or TBV and GEBV presented by participants. Rank correlation of the best 10% of individuals and error in predictions were also evaluated. Bias was tested by regression of TV on GEBV. Results Large differences between methods were found for all criteria and type of genetic values (TGV, TBV. In general, the criteria ranked consistently methods belonging to the same family. Conclusions Bayesian methods - A

Detecting non-orthology in the COGs database and other approaches grouping orthologs using genome-specific best hits.

Science.gov (United States)

Dessimoz, Christophe; Boeckmann, Brigitte; Roth, Alexander C J; Gonnet, Gaston H

2006-01-01

Correct orthology assignment is a critical prerequisite of numerous comparative genomics procedures, such as function prediction, construction of phylogenetic species trees and genome rearrangement analysis. We present an algorithm for the detection of non-orthologs that arise by mistake in current orthology classification methods based on genome-specific best hits, such as the COGs database. The algorithm works with pairwise distance estimates, rather than computationally expensive and error-prone tree-building methods. The accuracy of the algorithm is evaluated through verification of the distribution of predicted cases, case-by-case phylogenetic analysis and comparisons with predictions from other projects using independent methods. Our results show that a very significant fraction of the COG groups include non-orthologs: using conservative parameters, the algorithm detects non-orthology in a third of all COG groups. Consequently, sequence analysis sensitive to correct orthology assignments will greatly benefit from these findings.

A Ranking Approach to Genomic Selection.

Science.gov (United States)

Blondel, Mathieu; Onogi, Akio; Iwata, Hiroyoshi; Ueda, Naonori

2015-01-01

Genomic selection (GS) is a recent selective breeding method which uses predictive models based on whole-genome molecular markers. Until now, existing studies formulated GS as the problem of modeling an individual's breeding value for a particular trait of interest, i.e., as a regression problem. To assess predictive accuracy of the model, the Pearson correlation between observed and predicted trait values was used. In this paper, we propose to formulate GS as the problem of ranking individuals according to their breeding value. Our proposed framework allows us to employ machine learning methods for ranking which had previously not been considered in the GS literature. To assess ranking accuracy of a model, we introduce a new measure originating from the information retrieval literature called normalized discounted cumulative gain (NDCG). NDCG rewards more strongly models which assign a high rank to individuals with high breeding value. Therefore, NDCG reflects a prerequisite objective in selective breeding: accurate selection of individuals with high breeding value. We conducted a comparison of 10 existing regression methods and 3 new ranking methods on 6 datasets, consisting of 4 plant species and 25 traits. Our experimental results suggest that tree-based ensemble methods including McRank, Random Forests and Gradient Boosting Regression Trees achieve excellent ranking accuracy. RKHS regression and RankSVM also achieve good accuracy when used with an RBF kernel. Traditional regression methods such as Bayesian lasso, wBSR and BayesC were found less suitable for ranking. Pearson correlation was found to correlate poorly with NDCG. Our study suggests two important messages. First, ranking methods are a promising research direction in GS. Second, NDCG can be a useful evaluation measure for GS.

Identification of optimum sequencing depth especially for de novo genome assembly of small genomes using next generation sequencing data.

Science.gov (United States)

Desai, Aarti; Marwah, Veer Singh; Yadav, Akshay; Jha, Vineet; Dhaygude, Kishor; Bangar, Ujwala; Kulkarni, Vivek; Jere, Abhay

2013-01-01

Next Generation Sequencing (NGS) is a disruptive technology that has found widespread acceptance in the life sciences research community. The high throughput and low cost of sequencing has encouraged researchers to undertake ambitious genomic projects, especially in de novo genome sequencing. Currently, NGS systems generate sequence data as short reads and de novo genome assembly using these short reads is computationally very intensive. Due to lower cost of sequencing and higher throughput, NGS systems now provide the ability to sequence genomes at high depth. However, currently no report is available highlighting the impact of high sequence depth on genome assembly using real data sets and multiple assembly algorithms. Recently, some studies have evaluated the impact of sequence coverage, error rate and average read length on genome assembly using multiple assembly algorithms, however, these evaluations were performed using simulated datasets. One limitation of using simulated datasets is that variables such as error rates, read length and coverage which are known to impact genome assembly are carefully controlled. Hence, this study was undertaken to identify the minimum depth of sequencing required for de novo assembly for different sized genomes using graph based assembly algorithms and real datasets. Illumina reads for E.coli (4.6 MB) S.kudriavzevii (11.18 MB) and C.elegans (100 MB) were assembled using SOAPdenovo, Velvet, ABySS, Meraculous and IDBA-UD. Our analysis shows that 50X is the optimum read depth for assembling these genomes using all assemblers except Meraculous which requires 100X read depth. Moreover, our analysis shows that de novo assembly from 50X read data requires only 6-40 GB RAM depending on the genome size and assembly algorithm used. We believe that this information can be extremely valuable for researchers in designing experiments and multiplexing which will enable optimum utilization of sequencing as well as analysis resources.

"System evaluates system": method for evaluating the efficiency of IS

Directory of Open Access Journals (Sweden)

Dita Blazkova

2016-10-01

Full Text Available In paper I deal with the possible solution of evaluating the efficiency of information systems in companies. The large number of existing methods used to address the efficiency of information systems is dependent on the subjective responses of the user that may distort output evaluation. Therefore, I propose a method that eliminates the subjective opinion of a user as the primary data source. Applications, which I suggests as part of the method, collects relevant data. In this paper I describe the application in detail. This is a follow-on program on any system that runs parallel with it. The program automatically collects data for evaluation. Data include mainly time data, positions the mouse cursor, printScreens, i-grams of previous, etc. I propose a method of evaluation of the data, which identifies the degree of the friendliness of the information system to the user. Thus, the output of the method is the conclusion whether users, who work with the information system, can handle effectively work with it.

Classes evaluation: Methods and tools

Directory of Open Access Journals (Sweden)

Grabiński Tadeusz

2013-01-01

Full Text Available This study presents a method, tools, course and results of foreign language classes evaluation conducted in the summer semester 2012/2013 in the Andrzej Frycz - Modrzewski Krakow University. Because a new evaluation procedure has been implemented at the University, the former method - based on paper forms filled in by the students - was abandoned. On the surveyanyplace.com website, a free account has been registered and the form of the evaluation questionnaire has been inserted. This coverage presents results of a taxometric analysis aimed at checking the degree of mutual correspondence (correlation between certain criteria and instancing a graphic presentation of the evaluation results in a multidimensional perspective. In order to classify the grading criteria, the Ward's agglomerative method, along with Euclidean metric as a measure of criteria similarity, have been used. Calculations have been made with the use of Statistica package. Results of the questionnaire show that foreign language teaching at the Andrzej Frycz Modrzewski Krakow University is conducted professionally and on a high factual level.

Genomic treasure troves: complete genome sequencing of herbarium and insect museum specimens.

Science.gov (United States)

Staats, Martijn; Erkens, Roy H J; van de Vossenberg, Bart; Wieringa, Jan J; Kraaijeveld, Ken; Stielow, Benjamin; Geml, József; Richardson, James E; Bakker, Freek T

2013-01-01

Unlocking the vast genomic diversity stored in natural history collections would create unprecedented opportunities for genome-scale evolutionary, phylogenetic, domestication and population genomic studies. Many researchers have been discouraged from using historical specimens in molecular studies because of both generally limited success of DNA extraction and the challenges associated with PCR-amplifying highly degraded DNA. In today's next-generation sequencing (NGS) world, opportunities and prospects for historical DNA have changed dramatically, as most NGS methods are actually designed for taking short fragmented DNA molecules as templates. Here we show that using a standard multiplex and paired-end Illumina sequencing approach, genome-scale sequence data can be generated reliably from dry-preserved plant, fungal and insect specimens collected up to 115 years ago, and with minimal destructive sampling. Using a reference-based assembly approach, we were able to produce the entire nuclear genome of a 43-year-old Arabidopsis thaliana (Brassicaceae) herbarium specimen with high and uniform sequence coverage. Nuclear genome sequences of three fungal specimens of 22-82 years of age (Agaricus bisporus, Laccaria bicolor, Pleurotus ostreatus) were generated with 81.4-97.9% exome coverage. Complete organellar genome sequences were assembled for all specimens. Using de novo assembly we retrieved between 16.2-71.0% of coding sequence regions, and hence remain somewhat cautious about prospects for de novo genome assembly from historical specimens. Non-target sequence contaminations were observed in 2 of our insect museum specimens. We anticipate that future museum genomics projects will perhaps not generate entire genome sequences in all cases (our specimens contained relatively small and low-complexity genomes), but at least generating vital comparative genomic data for testing (phylo)genetic, demographic and genetic hypotheses, that become increasingly more horizontal

Hapsembler: An Assembler for Highly Polymorphic Genomes

Science.gov (United States)

Donmez, Nilgun; Brudno, Michael

As whole genome sequencing has become a routine biological experiment, algorithms for assembly of whole genome shotgun data has become a topic of extensive research, with a plethora of off-the-shelf methods that can reconstruct the genomes of many organisms. Simultaneously, several recently sequenced genomes exhibit very high polymorphism rates. For these organisms genome assembly remains a challenge as most assemblers are unable to handle highly divergent haplotypes in a single individual. In this paper we describe Hapsembler, an assembler for highly polymorphic genomes, which makes use of paired reads. Our experiments show that Hapsembler produces accurate and contiguous assemblies of highly polymorphic genomes, while performing on par with the leading tools on haploid genomes. Hapsembler is available for download at http://compbio.cs.toronto.edu/hapsembler.

Consequences of normalizing transcriptomic and genomic libraries of plant genomes using a duplex-specific nuclease and tetramethylammonium chloride.

Science.gov (United States)

Matvienko, Marta; Kozik, Alexander; Froenicke, Lutz; Lavelle, Dean; Martineau, Belinda; Perroud, Bertrand; Michelmore, Richard

2013-01-01

Several applications of high throughput genome and transcriptome sequencing would benefit from a reduction of the high-copy-number sequences in the libraries being sequenced and analyzed, particularly when applied to species with large genomes. We adapted and analyzed the consequences of a method that utilizes a thermostable duplex-specific nuclease for reducing the high-copy components in transcriptomic and genomic libraries prior to sequencing. This reduces the time, cost, and computational effort of obtaining informative transcriptomic and genomic sequence data for both fully sequenced and non-sequenced genomes. It also reduces contamination from organellar DNA in preparations of nuclear DNA. Hybridization in the presence of 3 M tetramethylammonium chloride (TMAC), which equalizes the rates of hybridization of GC and AT nucleotide pairs, reduced the bias against sequences with high GC content. Consequences of this method on the reduction of high-copy and enrichment of low-copy sequences are reported for Arabidopsis and lettuce.

Consequences of normalizing transcriptomic and genomic libraries of plant genomes using a duplex-specific nuclease and tetramethylammonium chloride.

Directory of Open Access Journals (Sweden)

Marta Matvienko

Full Text Available Several applications of high throughput genome and transcriptome sequencing would benefit from a reduction of the high-copy-number sequences in the libraries being sequenced and analyzed, particularly when applied to species with large genomes. We adapted and analyzed the consequences of a method that utilizes a thermostable duplex-specific nuclease for reducing the high-copy components in transcriptomic and genomic libraries prior to sequencing. This reduces the time, cost, and computational effort of obtaining informative transcriptomic and genomic sequence data for both fully sequenced and non-sequenced genomes. It also reduces contamination from organellar DNA in preparations of nuclear DNA. Hybridization in the presence of 3 M tetramethylammonium chloride (TMAC, which equalizes the rates of hybridization of GC and AT nucleotide pairs, reduced the bias against sequences with high GC content. Consequences of this method on the reduction of high-copy and enrichment of low-copy sequences are reported for Arabidopsis and lettuce.

Multiplexed precision genome editing with trackable genomic barcodes in yeast.

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Roy, Kevin R; Smith, Justin D; Vonesch, Sibylle C; Lin, Gen; Tu, Chelsea Szu; Lederer, Alex R; Chu, Angela; Suresh, Sundari; Nguyen, Michelle; Horecka, Joe; Tripathi, Ashutosh; Burnett, Wallace T; Morgan, Maddison A; Schulz, Julia; Orsley, Kevin M; Wei, Wu; Aiyar, Raeka S; Davis, Ronald W; Bankaitis, Vytas A; Haber, James E; Salit, Marc L; St Onge, Robert P; Steinmetz, Lars M

2018-07-01

Our understanding of how genotype controls phenotype is limited by the scale at which we can precisely alter the genome and assess the phenotypic consequences of each perturbation. Here we describe a CRISPR-Cas9-based method for multiplexed accurate genome editing with short, trackable, integrated cellular barcodes (MAGESTIC) in Saccharomyces cerevisiae. MAGESTIC uses array-synthesized guide-donor oligos for plasmid-based high-throughput editing and features genomic barcode integration to prevent plasmid barcode loss and to enable robust phenotyping. We demonstrate that editing efficiency can be increased more than fivefold by recruiting donor DNA to the site of breaks using the LexA-Fkh1p fusion protein. We performed saturation editing of the essential gene SEC14 and identified amino acids critical for chemical inhibition of lipid signaling. We also constructed thousands of natural genetic variants, characterized guide mismatch tolerance at the genome scale, and ascertained that cryptic Pol III termination elements substantially reduce guide efficacy. MAGESTIC will be broadly useful to uncover the genetic basis of phenotypes in yeast.

Large-Scale Genomic Analysis of Codon Usage in Dengue Virus and Evaluation of Its Phylogenetic Dependence

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Edgar E. Lara-Ramírez

2014-01-01

Full Text Available The increasing number of dengue virus (DENV genome sequences available allows identifying the contributing factors to DENV evolution. In the present study, the codon usage in serotypes 1–4 (DENV1–4 has been explored for 3047 sequenced genomes using different statistics methods. The correlation analysis of total GC content (GC with GC content at the three nucleotide positions of codons (GC1, GC2, and GC3 as well as the effective number of codons (ENC, ENCp versus GC3 plots revealed mutational bias and purifying selection pressures as the major forces influencing the codon usage, but with distinct pressure on specific nucleotide position in the codon. The correspondence analysis (CA and clustering analysis on relative synonymous codon usage (RSCU within each serotype showed similar clustering patterns to the phylogenetic analysis of nucleotide sequences for DENV1–4. These clustering patterns are strongly related to the virus geographic origin. The phylogenetic dependence analysis also suggests that stabilizing selection acts on the codon usage bias. Our analysis of a large scale reveals new feature on DENV genomic evolution.

Uses of antimicrobial genes from microbial genome

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Sorek, Rotem; Rubin, Edward M.

2013-08-20

We describe a method for mining microbial genomes to discover antimicrobial genes and proteins having broad spectrum of activity. Also described are antimicrobial genes and their expression products from various microbial genomes that were found using this method. The products of such genes can be used as antimicrobial agents or as tools for molecular biology.

Genome analysis methods: Arabidopsis lyrata [PGDBj Registered plant list, Marker list, QTL list, Plant DB link and Genome analysis methods[Archive

Lifescience Database Archive (English)

Full Text Available (http://genome.imim.es/software/geneid/) applying dicot and A. thaliana specific matrices 32,670 (v1.0) JGI; http://www.phytozome.net/alyrata v1.0 v1.0 10.1038/ng.807 21478890 ... ...8.3x Arachne 1,309 ... Fgenesh package of ab initio and homology-based gene predictors, EuGene12, and GeneID13

Genome analysis methods: Lotus japonicus [PGDBj Registered plant list, Marker list, QTL list, Plant DB link and Genome analysis methods[Archive

Lifescience Database Archive (English)

Full Text Available Lotus japonicus Draft 2n=12 472 Mb 2008 Sanger (Clone-based) ... 315.1 Mb 3-5x Parace...l Genome Assembler 954 110,940 Kazusa Annotation PipelinE for Lotus japonicus (KAPSEL) 37,971 (v2.5) KDRI; http://www.kazusa.or.jp/lotus/ v2.5 v2.5 10.1093/dnares/dsn008 18511435 ...

Genome profiling (GP method based classification of insects: congruence with that of classical phenotype-based one.

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Shamim Ahmed

Full Text Available Ribosomal RNAs have been widely used for identification and classification of species, and have produced data giving new insights into phylogenetic relationships. Recently, multilocus genotyping and even whole genome sequencing-based technologies have been adopted in ambitious comparative biology studies. However, such technologies are still far from routine-use in species classification studies due to their high costs in terms of labor, equipment and consumables.Here, we describe a simple and powerful approach for species classification called genome profiling (GP. The GP method composed of random PCR, temperature gradient gel electrophoresis (TGGE and computer-aided gel image processing is highly informative and less laborious. For demonstration, we classified 26 species of insects using GP and 18S rDNA-sequencing approaches. The GP method was found to give a better correspondence to the classical phenotype-based approach than did 18S rDNA sequencing employing a congruence value. To our surprise, use of a single probe in GP was sufficient to identify the relationships between the insect species, making this approach more straightforward.The data gathered here, together with those of previous studies show that GP is a simple and powerful method that can be applied for actually universally identifying and classifying species. The current success supported our previous proposal that GP-based web database can be constructible and effective for the global identification/classification of species.

A Simulation Tool for the Study of Symmetric Inversions in Bacterial Genomes

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Dias, Ulisses; Dias, Zanoni; Setubal, João C.

We present the tool SIB that simulates genomic inversions in bacterial chromosomes. The tool simulates symmetric inversions but allows the appearance of nonsymmetric inversions by simulating small syntenic blocks frequently observed on bacterial genome comparisons. We evaluate SIB by comparing its results to real genome alignments. We develop measures that allow quantitative comparisons between real pairwise alignments (in terms of dotplots) and simulated ones. These measures allow an evaluation of SIB in terms of dendrograms. We evaluate SIB by comparing its results to whole chromosome alignments and maximum likelihood trees for three bacterial groups (the Pseudomonadaceae family and the Xanthomonas and Shewanella genera). We demonstrate an application of SIB by using it to evaluate the ancestral genome reconstruction tool MGR.

New Markov Model Approaches to Deciphering Microbial Genome Function and Evolution: Comparative Genomics of Laterally Transferred Genes

Energy Technology Data Exchange (ETDEWEB)

Borodovsky, M.

2013-04-11

Algorithmic methods for gene prediction have been developed and successfully applied to many different prokaryotic genome sequences. As the set of genes in a particular genome is not homogeneous with respect to DNA sequence composition features, the GeneMark.hmm program utilizes two Markov models representing distinct classes of protein coding genes denoted "typical" and "atypical". Atypical genes are those whose DNA features deviate significantly from those classified as typical and they represent approximately 10% of any given genome. In addition to the inherent interest of more accurately predicting genes, the atypical status of these genes may also reflect their separate evolutionary ancestry from other genes in that genome. We hypothesize that atypical genes are largely comprised of those genes that have been relatively recently acquired through lateral gene transfer (LGT). If so, what fraction of atypical genes are such bona fide LGTs? We have made atypical gene predictions for all fully completed prokaryotic genomes; we have been able to compare these results to other "surrogate" methods of LGT prediction.

Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes

DEFF Research Database (Denmark)

Nielsen, Henrik Bjørn; Almeida, Mathieu; Juncker, Agnieszka

2014-01-01

of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify...

Bacillus subtilis genome diversity.

Science.gov (United States)

Earl, Ashlee M; Losick, Richard; Kolter, Roberto

2007-02-01

Microarray-based comparative genomic hybridization (M-CGH) is a powerful method for rapidly identifying regions of genome diversity among closely related organisms. We used M-CGH to examine the genome diversity of 17 strains belonging to the nonpathogenic species Bacillus subtilis. Our M-CGH results indicate that there is considerable genetic heterogeneity among members of this species; nearly one-third of Bsu168-specific genes exhibited variability, as measured by the microarray hybridization intensities. The variable loci include those encoding proteins involved in antibiotic production, cell wall synthesis, sporulation, and germination. The diversity in these genes may reflect this organism's ability to survive in diverse natural settings.

Impact of selective genotyping in the training population on accuracy and bias of genomic selection.

Science.gov (United States)

Zhao, Yusheng; Gowda, Manje; Longin, Friedrich H; Würschum, Tobias; Ranc, Nicolas; Reif, Jochen C

2012-08-01

Estimating marker effects based on routinely generated phenotypic data of breeding programs is a cost-effective strategy to implement genomic selection. Truncation selection in breeding populations, however, could have a strong impact on the accuracy to predict genomic breeding values. The main objective of our study was to investigate the influence of phenotypic selection on the accuracy and bias of genomic selection. We used experimental data of 788 testcross progenies from an elite maize breeding program. The testcross progenies were evaluated in unreplicated field trials in ten environments and fingerprinted with 857 SNP markers. Random regression best linear unbiased prediction method was used in combination with fivefold cross-validation based on genotypic sampling. We observed a substantial loss in the accuracy to predict genomic breeding values in unidirectional selected populations. In contrast, estimating marker effects based on bidirectional selected populations led to only a marginal decrease in the prediction accuracy of genomic breeding values. We concluded that bidirectional selection is a valuable approach to efficiently implement genomic selection in applied plant breeding programs.

The international Genome sample resource (IGSR): A worldwide collection of genome variation incorporating the 1000 Genomes Project data.

Science.gov (United States)

Clarke, Laura; Fairley, Susan; Zheng-Bradley, Xiangqun; Streeter, Ian; Perry, Emily; Lowy, Ernesto; Tassé, Anne-Marie; Flicek, Paul

2017-01-04

The International Genome Sample Resource (IGSR; http://www.internationalgenome.org) expands in data type and population diversity the resources from the 1000 Genomes Project. IGSR represents the largest open collection of human variation data and provides easy access to these resources. IGSR was established in 2015 to maintain and extend the 1000 Genomes Project data, which has been widely used as a reference set of human variation and by researchers developing analysis methods. IGSR has mapped all of the 1000 Genomes sequence to the newest human reference (GRCh38), and will release updated variant calls to ensure maximal usefulness of the existing data. IGSR is collecting new structural variation data on the 1000 Genomes samples from long read sequencing and other technologies, and will collect relevant functional data into a single comprehensive resource. IGSR is extending coverage with new populations sequenced by collaborating groups. Here, we present the new data and analysis that IGSR has made available. We have also introduced a new data portal that increases discoverability of our data-previously only browseable through our FTP site-by focusing on particular samples, populations or data sets of interest. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A novel method of genomic DNA extraction for Cactaceae1

Science.gov (United States)

Fehlberg, Shannon D.; Allen, Jessica M.; Church, Kathleen

2013-01-01

• Premise of the study: Genetic studies of Cactaceae can at times be impeded by difficult sampling logistics and/or high mucilage content in tissues. Simplifying sampling and DNA isolation through the use of cactus spines has not previously been investigated. • Methods and Results: Several protocols for extracting DNA from spines were tested and modified to maximize yield, amplification, and sequencing. Sampling of and extraction from spines resulted in a simplified protocol overall and complete avoidance of mucilage as compared to typical tissue extractions. Sequences from one nuclear and three plastid regions were obtained across eight genera and 20 species of cacti using DNA extracted from spines. • Conclusions: Genomic DNA useful for amplification and sequencing can be obtained from cactus spines. The protocols described here are valuable for any cactus species, but are particularly useful for investigators interested in sampling living collections, extensive field sampling, and/or conservation genetic studies. PMID:25202521

Genomic prediction across dairy cattle populations and breeds

DEFF Research Database (Denmark)

Zhou, Lei

Genomic prediction is successful in single breed genetic evaluation. However, there is no achievement in acoress breed prediction until now. This thesis investigated genomic prediction across populations and breeds using Chinese Holsterin, Nordic Holstein, Norwgian Red, and Nordic Red. Nordic Red...

Scrutinizing virus genome termini by high-throughput sequencing.

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Shasha Li

Full Text Available Analysis of genomic terminal sequences has been a major step in studies on viral DNA replication and packaging mechanisms. However, traditional methods to study genome termini are challenging due to the time-consuming protocols and their inefficiency where critical details are lost easily. Recent advances in next generation sequencing (NGS have enabled it to be a powerful tool to study genome termini. In this study, using NGS we sequenced one iridovirus genome and twenty phage genomes and confirmed for the first time that the high frequency sequences (HFSs found in the NGS reads are indeed the terminal sequences of viral genomes. Further, we established a criterion to distinguish the type of termini and the viral packaging mode. We also obtained additional terminal details such as terminal repeats, multi-termini, asymmetric termini. With this approach, we were able to simultaneously detect details of the genome termini as well as obtain the complete sequence of bacteriophage genomes. Theoretically, this application can be further extended to analyze larger and more complicated genomes of plant and animal viruses. This study proposed a novel and efficient method for research on viral replication, packaging, terminase activity, transcription regulation, and metabolism of the host cell.

Separating metagenomic short reads into genomes via clustering

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Tanaseichuk Olga

2012-09-01

Full Text Available Abstract Background The metagenomics approach allows the simultaneous sequencing of all genomes in an environmental sample. This results in high complexity datasets, where in addition to repeats and sequencing errors, the number of genomes and their abundance ratios are unknown. Recently developed next-generation sequencing (NGS technologies significantly improve the sequencing efficiency and cost. On the other hand, they result in shorter reads, which makes the separation of reads from different species harder. Among the existing computational tools for metagenomic analysis, there are similarity-based methods that use reference databases to align reads and composition-based methods that use composition patterns (i.e., frequencies of short words or l-mers to cluster reads. Similarity-based methods are unable to classify reads from unknown species without close references (which constitute the majority of reads. Since composition patterns are preserved only in significantly large fragments, composition-based tools cannot be used for very short reads, which becomes a significant limitation with the development of NGS. A recently proposed algorithm, AbundanceBin, introduced another method that bins reads based on predicted abundances of the genomes sequenced. However, it does not separate reads from genomes of similar abundance levels. Results In this work, we present a two-phase heuristic algorithm for separating short paired-end reads from different genomes in a metagenomic dataset. We use the observation that most of the l-mers belong to unique genomes when l is sufficiently large. The first phase of the algorithm results in clusters of l-mers each of which belongs to one genome. During the second phase, clusters are merged based on l-mer repeat information. These final clusters are used to assign reads. The algorithm could handle very short reads and sequencing errors. It is initially designed for genomes with similar abundance levels and then