Too New for Textbooks: The Biotechnology Discoveries & Applications Guidebook

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Loftin, Madelene; Lamb, Neil E.

2013-01-01

The "Biotechnology Discoveries and Applications" guidebook aims to provide teachers with an overview of the recent advances in genetics and biotechnology, allowing them to share these findings with their students. The annual guidebook introduces a wealth of modern genomic discoveries and provides teachers with tools to integrate exciting…

Immunologic applications of conditional gene modification technology in the mouse.

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Sharma, Suveena; Zhu, Jinfang

2014-04-02

Since the success of homologous recombination in altering mouse genome and the discovery of Cre-loxP system, the combination of these two breakthroughs has created important applications for studying the immune system in the mouse. Here, we briefly summarize the general principles of this technology and its applications in studying immune cell development and responses; such implications include conditional gene knockout and inducible and/or tissue-specific gene over-expression, as well as lineage fate mapping. We then discuss the pros and cons of a few commonly used Cre-expressing mouse lines for studying lymphocyte development and functions. We also raise several general issues, such as efficiency of gene deletion, leaky activity of Cre, and Cre toxicity, all of which may have profound impacts on data interpretation. Finally, we selectively list some useful links to the Web sites as valuable mouse resources. Copyright © 2014 John Wiley & Sons, Inc.

The medical applications of the discoveries of Marie Sklodowska-Curie

International Nuclear Information System (INIS)

Krawczyk, M.

2011-01-01

In this work, the author indicates what have been the applications of the discoveries of Marie Curie in the field of medicine and how these discoveries have contributed in particular to the development of oncologic radiotherapy. (O.M.)

Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer

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Oguzhan Begik

2017-07-01

Full Text Available Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA, a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian compared to normal tissues. Using machine-learning techniques, we further defined specific subsets of APA events to efficiently classify cancer types. Furthermore, APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data, suggesting functional significance. Overall, our study offers a computational approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches.

Maximizing biomarker discovery by minimizing gene signatures

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Chang Chang

2011-12-01

Full Text Available Abstract Background The use of gene signatures can potentially be of considerable value in the field of clinical diagnosis. However, gene signatures defined with different methods can be quite various even when applied the same disease and the same endpoint. Previous studies have shown that the correct selection of subsets of genes from microarray data is key for the accurate classification of disease phenotypes, and a number of methods have been proposed for the purpose. However, these methods refine the subsets by only considering each single feature, and they do not confirm the association between the genes identified in each gene signature and the phenotype of the disease. We proposed an innovative new method termed Minimize Feature's Size (MFS based on multiple level similarity analyses and association between the genes and disease for breast cancer endpoints by comparing classifier models generated from the second phase of MicroArray Quality Control (MAQC-II, trying to develop effective meta-analysis strategies to transform the MAQC-II signatures into a robust and reliable set of biomarker for clinical applications. Results We analyzed the similarity of the multiple gene signatures in an endpoint and between the two endpoints of breast cancer at probe and gene levels, the results indicate that disease-related genes can be preferably selected as the components of gene signature, and that the gene signatures for the two endpoints could be interchangeable. The minimized signatures were built at probe level by using MFS for each endpoint. By applying the approach, we generated a much smaller set of gene signature with the similar predictive power compared with those gene signatures from MAQC-II. Conclusions Our results indicate that gene signatures of both large and small sizes could perform equally well in clinical applications. Besides, consistency and biological significances can be detected among different gene signatures, reflecting the

Discovery of cancer common and specific driver gene sets

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2017-01-01

Abstract Cancer is known as a disease mainly caused by gene alterations. Discovery of mutated driver pathways or gene sets is becoming an important step to understand molecular mechanisms of carcinogenesis. However, systematically investigating commonalities and specificities of driver gene sets among multiple cancer types is still a great challenge, but this investigation will undoubtedly benefit deciphering cancers and will be helpful for personalized therapy and precision medicine in cancer treatment. In this study, we propose two optimization models to de novo discover common driver gene sets among multiple cancer types (ComMDP) and specific driver gene sets of one certain or multiple cancer types to other cancers (SpeMDP), respectively. We first apply ComMDP and SpeMDP to simulated data to validate their efficiency. Then, we further apply these methods to 12 cancer types from The Cancer Genome Atlas (TCGA) and obtain several biologically meaningful driver pathways. As examples, we construct a common cancer pathway model for BRCA and OV, infer a complex driver pathway model for BRCA carcinogenesis based on common driver gene sets of BRCA with eight cancer types, and investigate specific driver pathways of the liquid cancer lymphoblastic acute myeloid leukemia (LAML) versus other solid cancer types. In these processes more candidate cancer genes are also found. PMID:28168295

MAGIC Database and Interfaces: An Integrated Package for Gene Discovery and Expression

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Lee H. Pratt

2006-03-01

Full Text Available The rapidly increasing rate at which biological data is being produced requires a corresponding growth in relational databases and associated tools that can help laboratories contend with that data. With this need in mind, we describe here a Modular Approach to a Genomic, Integrated and Comprehensive (MAGIC Database. This Oracle 9i database derives from an initial focus in our laboratory on gene discovery via production and analysis of expressed sequence tags (ESTs, and subsequently on gene expression as assessed by both EST clustering and microarrays. The MAGIC Gene Discovery portion of the database focuses on information derived from DNA sequences and on its biological relevance. In addition to MAGIC SEQ-LIMS, which is designed to support activities in the laboratory, it contains several additional subschemas. The latter include MAGIC Admin for database administration, MAGIC Sequence for sequence processing as well as sequence and clone attributes, MAGIC Cluster for the results of EST clustering, MAGIC Polymorphism in support of microsatellite and single-nucleotide-polymorphism discovery, and MAGIC Annotation for electronic annotation by BLAST and BLAT. The MAGIC Microarray portion is a MIAME-compliant database with two components at present. These are MAGIC Array-LIMS, which makes possible remote entry of all information into the database, and MAGIC Array Analysis, which provides data mining and visualization. Because all aspects of interaction with the MAGIC Database are via a web browser, it is ideally suited not only for individual research laboratories but also for core facilities that serve clients at any distance.

Gene Regulation, Modulation, and Their Applications in Gene Expression Data Analysis

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Mario Flores

2013-01-01

Full Text Available Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA, into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of “modulation” can adapt to various biological mechanisms to discover the novel gene regulation mechanisms.

Comparative GO: a web application for comparative gene ontology and gene ontology-based gene selection in bacteria.

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Mario Fruzangohar

Full Text Available The primary means of classifying new functions for genes and proteins relies on Gene Ontology (GO, which defines genes/proteins using a controlled vocabulary in terms of their Molecular Function, Biological Process and Cellular Component. The challenge is to present this information to researchers to compare and discover patterns in multiple datasets using visually comprehensible and user-friendly statistical reports. Importantly, while there are many GO resources available for eukaryotes, there are none suitable for simultaneous, graphical and statistical comparison between multiple datasets. In addition, none of them supports comprehensive resources for bacteria. By using Streptococcus pneumoniae as a model, we identified and collected GO resources including genes, proteins, taxonomy and GO relationships from NCBI, UniProt and GO organisations. Then, we designed database tables in PostgreSQL database server and developed a Java application to extract data from source files and loaded into database automatically. We developed a PHP web application based on Model-View-Control architecture, used a specific data structure as well as current and novel algorithms to estimate GO graphs parameters. We designed different navigation and visualization methods on the graphs and integrated these into graphical reports. This tool is particularly significant when comparing GO groups between multiple samples (including those of pathogenic bacteria from different sources simultaneously. Comparing GO protein distribution among up- or down-regulated genes from different samples can improve understanding of biological pathways, and mechanism(s of infection. It can also aid in the discovery of genes associated with specific function(s for investigation as a novel vaccine or therapeutic targets.http://turing.ersa.edu.au/BacteriaGO.

Application of lean manufacturing concepts to drug discovery: rapid analogue library synthesis.

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Weller, Harold N; Nirschl, David S; Petrillo, Edward W; Poss, Michael A; Andres, Charles J; Cavallaro, Cullen L; Echols, Martin M; Grant-Young, Katherine A; Houston, John G; Miller, Arthur V; Swann, R Thomas

2006-01-01

The application of parallel synthesis to lead optimization programs in drug discovery has been an ongoing challenge since the first reports of library synthesis. A number of approaches to the application of parallel array synthesis to lead optimization have been attempted over the years, ranging from widespread deployment by (and support of) individual medicinal chemists to centralization as a service by an expert core team. This manuscript describes our experience with the latter approach, which was undertaken as part of a larger initiative to optimize drug discovery. In particular, we highlight how concepts taken from the manufacturing sector can be applied to drug discovery and parallel synthesis to improve the timeliness and thus the impact of arrays on drug discovery.

Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships.

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Manda, Prashanti; McCarthy, Fiona; Bridges, Susan M

2013-10-01

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

[Application of Imaging Mass Spectrometry for Drug Discovery].

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Hayasaka, Takahiro

2016-01-01

Imaging mass spectrometry (IMS) can reveal the distribution of biomolecules on tissue sections. In this process, the biomolecules are directly ionized within tissue sections using matrix-assisted laser desorption/ionization, and then their distribution is visualized by pseudo-color based on the relative signal intensity. The biomolecules, such as fatty acids, phospholipids, glycolipids, peptides, proteins, and neurotransmitters, have been analyzed at a spatial resolution of 5 μm. A special instrument for IMS analysis was developed by Shimadzu. The IMS analysis does not require the labeling of biomolecules and is capable of analyzing all the ionized biomolecules. Interest in this method has expanded to many research fields, including biology, agriculture, medicine, and pharmacology. The technique is especially relevant to the drug discovery process. As practiced currently, drug discovery is expensive and time consuming, requiring the preparation of probes for each drug and its metabolites, followed by systematic probe tracking in animal models. The IMS technique is expected to overcome these drawbacks by revealing the distribution of drugs and their metabolites using only a single analysis. In this symposium, I introduced the methodology and applications of IMS and discussed the feasibility of its application to drug discovery in the near future.

iSyTE 2.0: a database for expression-based gene discovery in the eye

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Kakrana, Atul; Yang, Andrian; Anand, Deepti; Djordjevic, Djordje; Ramachandruni, Deepti; Singh, Abhyudai; Huang, Hongzhan

2018-01-01

Abstract Although successful in identifying new cataract-linked genes, the previous version of the database iSyTE (integrated Systems Tool for Eye gene discovery) was based on expression information on just three mouse lens stages and was functionally limited to visualization by only UCSC-Genome Browser tracks. To increase its efficacy, here we provide an enhanced iSyTE version 2.0 (URL: http://research.bioinformatics.udel.edu/iSyTE) based on well-curated, comprehensive genome-level lens expression data as a one-stop portal for the effective visualization and analysis of candidate genes in lens development and disease. iSyTE 2.0 includes all publicly available lens Affymetrix and Illumina microarray datasets representing a broad range of embryonic and postnatal stages from wild-type and specific gene-perturbation mouse mutants with eye defects. Further, we developed a new user-friendly web interface for direct access and cogent visualization of the curated expression data, which supports convenient searches and a range of downstream analyses. The utility of these new iSyTE 2.0 features is illustrated through examples of established genes associated with lens development and pathobiology, which serve as tutorials for its application by the end-user. iSyTE 2.0 will facilitate the prioritization of eye development and disease-linked candidate genes in studies involving transcriptomics or next-generation sequencing data, linkage analysis and GWAS approaches. PMID:29036527

Generation of cell lines for drug discovery through random activation of gene expression: application to the human histamine H3 receptor.

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Song, J; Doucette, C; Hanniford, D; Hunady, K; Wang, N; Sherf, B; Harrington, J J; Brunden, K R; Stricker-Krongrad, A

2005-06-01

Target-based high-throughput screening (HTS) plays an integral role in drug discovery. The implementation of HTS assays generally requires high expression levels of the target protein, and this is typically accomplished using recombinant cDNA methodologies. However, the isolated gene sequences to many drug targets have intellectual property claims that restrict the ability to implement drug discovery programs. The present study describes the pharmacological characterization of the human histamine H3 receptor that was expressed using random activation of gene expression (RAGE), a technology that over-expresses proteins by up-regulating endogenous genes rather than introducing cDNA expression vectors into the cell. Saturation binding analysis using [125I]iodoproxyfan and RAGE-H3 membranes revealed a single class of binding sites with a K(D) value of 0.77 nM and a B(max) equal to 756 fmol/mg of protein. Competition binding studies showed that the rank order of potency for H3 agonists was N(alpha)-methylhistamine approximately (R)-alpha- methylhistamine > histamine and that the rank order of potency for H3 antagonists was clobenpropit > iodophenpropit > thioperamide. The same rank order of potency for H3 agonists and antagonists was observed in the functional assays as in the binding assays. The Fluorometic Imaging Plate Reader assays in RAGE-H3 cells gave high Z' values for agonist and antagonist screening, respectively. These results reveal that the human H3 receptor expressed with the RAGE technology is pharmacologically comparable to that expressed through recombinant methods. Moreover, the level of expression of the H3 receptor in the RAGE-H3 cells is suitable for HTS and secondary assays.

Comprehensive Clinical Phenotyping & Genetic Mapping for the Discovery of Autism Susceptibility Genes

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2012-12-05

teaching students with autism spectrum disorders 4.52 Learn strategies for incorporating IEP goals and district standard into daily teaching...W403 Columbus, OH 43205 Final Report Comprehensive Clinical Phenotyping & Genetic Mapping for the Discovery of Autism Susceptibility Genes...QFOXGHDUHDFRGH 1.0 Summary In 2006, the Central Ohio Registry for Autism (CORA) was initiated as a collaboration between Wright-Patterson Air

On reliable discovery of molecular signatures

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Björkegren Johan

2009-01-01

Full Text Available Abstract Background Molecular signatures are sets of genes, proteins, genetic variants or other variables that can be used as markers for a particular phenotype. Reliable signature discovery methods could yield valuable insight into cell biology and mechanisms of human disease. However, it is currently not clear how to control error rates such as the false discovery rate (FDR in signature discovery. Moreover, signatures for cancer gene expression have been shown to be unstable, that is, difficult to replicate in independent studies, casting doubts on their reliability. Results We demonstrate that with modern prediction methods, signatures that yield accurate predictions may still have a high FDR. Further, we show that even signatures with low FDR may fail to replicate in independent studies due to limited statistical power. Thus, neither stability nor predictive accuracy are relevant when FDR control is the primary goal. We therefore develop a general statistical hypothesis testing framework that for the first time provides FDR control for signature discovery. Our method is demonstrated to be correct in simulation studies. When applied to five cancer data sets, the method was able to discover molecular signatures with 5% FDR in three cases, while two data sets yielded no significant findings. Conclusion Our approach enables reliable discovery of molecular signatures from genome-wide data with current sample sizes. The statistical framework developed herein is potentially applicable to a wide range of prediction problems in bioinformatics.

Canonical correlation analysis for gene-based pleiotropy discovery.

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Jose A Seoane

2014-10-01

Full Text Available Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy and for testing multiple variants for association with a single phenotype (gene-based association tests. Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women's Heart and Health Study, we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1 with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.

Gene Discovery in the Apicomplexa as Revealed by EST Sequencing and Assembly of a Comparative Gene Database

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Li, Li; Brunk, Brian P.; Kissinger, Jessica C.; Pape, Deana; Tang, Keliang; Cole, Robert H.; Martin, John; Wylie, Todd; Dante, Mike; Fogarty, Steven J.; Howe, Daniel K.; Liberator, Paul; Diaz, Carmen; Anderson, Jennifer; White, Michael; Jerome, Maria E.; Johnson, Emily A.; Radke, Jay A.; Stoeckert, Christian J.; Waterston, Robert H.; Clifton, Sandra W.; Roos, David S.; Sibley, L. David

2003-01-01

Large-scale EST sequencing projects for several important parasites within the phylum Apicomplexa were undertaken for the purpose of gene discovery. Included were several parasites of medical importance (Plasmodium falciparum, Toxoplasma gondii) and others of veterinary importance (Eimeria tenella, Sarcocystis neurona, and Neospora caninum). A total of 55,192 ESTs, deposited into dbEST/GenBank, were included in the analyses. The resulting sequences have been clustered into nonredundant gene assemblies and deposited into a relational database that supports a variety of sequence and text searches. This database has been used to compare the gene assemblies using BLAST similarity comparisons to the public protein databases to identify putative genes. Of these new entries, ∼15%–20% represent putative homologs with a conservative cutoff of p neurona: , , , , , , , , , , , , , –, –, –, –, –. Eimeria tenella: –, –, –, –, –, –, –, –, – , –, –, –, –, –, –, –, –, –, –, –. Neospora caninum: –, –, , – , –, –.] PMID:12618375

Pine Gene Discovery Project - Final Report - 08/31/1997 - 02/28/2001; FINAL

International Nuclear Information System (INIS)

Whetten, R. W.; Sederoff, R. R.; Kinlaw, C.; Retzel, E.

2001-01-01

Integration of pines into the large scope of plant biology research depends on study of pines in parallel with study of annual plants, and on availability of research materials from pine to plant biologists interested in comparing pine with annual plant systems. The objectives of the Pine Gene Discovery Project were to obtain 10,000 partial DNA sequences of genes expressed in loblolly pine, to determine which of those pine genes were similar to known genes from other organisms, and to make the DNA sequences and isolated pine genes available to plant researchers to stimulate integration of pines into the wider scope of plant biology research. Those objectives have been completed, and the results are available to the public. Requests for pine genes have been received from a number of laboratories that would otherwise not have included pine in their research, indicating that progress is being made toward the goal of integrating pine research into the larger molecular biology research community

Discovery of rare protein-coding genes in model methylotroph Methylobacterium extorquens AM1.

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Kumar, Dhirendra; Mondal, Anupam Kumar; Yadav, Amit Kumar; Dash, Debasis

2014-12-01

Proteogenomics involves the use of MS to refine annotation of protein-coding genes and discover genes in a genome. We carried out comprehensive proteogenomic analysis of Methylobacterium extorquens AM1 (ME-AM1) from publicly available proteomics data with a motive to improve annotation for methylotrophs; organisms capable of surviving in reduced carbon compounds such as methanol. Besides identifying 2482(50%) proteins, 29 new genes were discovered and 66 annotated gene models were revised in ME-AM1 genome. One such novel gene is identified with 75 peptides, lacks homolog in other methylobacteria but has glycosyl transferase and lipopolysaccharide biosynthesis protein domains, indicating its potential role in outer membrane synthesis. Many novel genes are present only in ME-AM1 among methylobacteria. Distant homologs of these genes in unrelated taxonomic classes and low GC-content of few genes suggest lateral gene transfer as a potential mode of their origin. Annotations of methylotrophy related genes were also improved by the discovery of a short gene in methylotrophy gene island and redefining a gene important for pyrroquinoline quinone synthesis, essential for methylotrophy. The combined use of proteogenomics and rigorous bioinformatics analysis greatly enhanced the annotation of protein-coding genes in model methylotroph ME-AM1 genome. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Exome sequencing for gene discovery in lethal fetal disorders--harnessing the value of extreme phenotypes.

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Filges, Isabel; Friedman, Jan M

2015-10-01

Massively parallel sequencing has revolutionized our understanding of Mendelian disorders, and many novel genes have been discovered to cause disease phenotypes when mutant. At the same time, next-generation sequencing approaches have enabled non-invasive prenatal testing of free fetal DNA in maternal blood. However, little attention has been paid to using whole exome and genome sequencing strategies for gene identification in fetal disorders that are lethal in utero, because they can appear to be sporadic and Mendelian inheritance may be missed. We present challenges and advantages of applying next-generation sequencing approaches to gene discovery in fetal malformation phenotypes and review recent successful discovery approaches. We discuss the implication and significance of recessive inheritance and cross-species phenotyping in fetal lethal conditions. Whole exome sequencing can be used in individual families with undiagnosed lethal congenital anomaly syndromes to discover causal mutations, provided that prior to data analysis, the fetal phenotype can be correlated to a particular developmental pathway in embryogenesis. Cross-species phenotyping allows providing further evidence for causality of discovered variants in genes involved in those extremely rare phenotypes and will increase our knowledge about normal and abnormal human developmental processes. Ultimately, families will benefit from the option of early prenatal diagnosis. © 2014 John Wiley & Sons, Ltd.

Data-Centric Knowledge Discovery Strategy for a Safety-Critical Sensor Application

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Nilamadhab Mishra

2014-01-01

Full Text Available In an indoor safety-critical application, sensors and actuators are clustered together to accomplish critical actions within a limited time constraint. The cluster may be controlled by a dedicated programmed autonomous microcontroller device powered with electricity to perform in-network time critical functions, such as data collection, data processing, and knowledge production. In a data-centric sensor network, approximately 3–60% of the sensor data are faulty, and the data collected from the sensor environment are highly unstructured and ambiguous. Therefore, for safety-critical sensor applications, actuators must function intelligently within a hard time frame and have proper knowledge to perform their logical actions. This paper proposes a knowledge discovery strategy and an exploration algorithm for indoor safety-critical industrial applications. The application evidence and discussion validate that the proposed strategy and algorithm can be implemented for knowledge discovery within the operational framework.

Automated discovery of functional generality of human gene expression programs.

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Georg K Gerber

2007-08-01

Full Text Available An important research problem in computational biology is the identification of expression programs, sets of co-expressed genes orchestrating normal or pathological processes, and the characterization of the functional breadth of these programs. The use of human expression data compendia for discovery of such programs presents several challenges including cellular inhomogeneity within samples, genetic and environmental variation across samples, uncertainty in the numbers of programs and sample populations, and temporal behavior. We developed GeneProgram, a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. GeneProgram uses expression data to simultaneously organize tissues into groups and genes into overlapping programs with consistent temporal behavior, to produce maps of expression programs, which are sorted by generality scores that exploit the automatically learned groupings. Using synthetic and real gene expression data, we showed that GeneProgram outperformed several popular expression analysis methods. We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules. GeneProgram produced a map of 104 expression programs, a substantial number of which were significantly enriched for genes involved in key signaling pathways and/or bound by NF-kappaB transcription factors in genome-wide experiments. Further, GeneProgram discovered expression programs that appear to implicate surprising signaling pathways or receptor types in the response to infection, including Wnt signaling and neurotransmitter receptors. We believe the discovered map of expression programs involved in the response to infection will be useful for guiding future biological experiments; genes from programs with low generality scores might serve as new drug targets that exhibit minimal

Recent advances in inkjet dispensing technologies: applications in drug discovery.

Science.gov (United States)

Zhu, Xiangcheng; Zheng, Qiang; Yang, Hu; Cai, Jin; Huang, Lei; Duan, Yanwen; Xu, Zhinan; Cen, Peilin

2012-09-01

Inkjet dispensing technology is a promising fabrication methodology widely applied in drug discovery. The automated programmable characteristics and high-throughput efficiency makes this approach potentially very useful in miniaturizing the design patterns for assays and drug screening. Various custom-made inkjet dispensing systems as well as specialized bio-ink and substrates have been developed and applied to fulfill the increasing demands of basic drug discovery studies. The incorporation of other modern technologies has further exploited the potential of inkjet dispensing technology in drug discovery and development. This paper reviews and discusses the recent developments and practical applications of inkjet dispensing technology in several areas of drug discovery and development including fundamental assays of cells and proteins, microarrays, biosensors, tissue engineering, basic biological and pharmaceutical studies. Progression in a number of areas of research including biomaterials, inkjet mechanical systems and modern analytical techniques as well as the exploration and accumulation of profound biological knowledge has enabled different inkjet dispensing technologies to be developed and adapted for high-throughput pattern fabrication and miniaturization. This in turn presents a great opportunity to propel inkjet dispensing technology into drug discovery.

Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data.

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Yip, Shun H; Sham, Pak Chung; Wang, Junwen

2018-02-21

Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.

Engineering Application Way of Faults Knowledge Discovery Based on Rough Set Theory

International Nuclear Information System (INIS)

Zhao Rongzhen; Deng Linfeng; Li Chao

2011-01-01

For the knowledge acquisition puzzle of intelligence decision-making technology in mechanical industry, to use the Rough Set Theory (RST) as a kind of tool to solve the puzzle was researched. And the way to realize the knowledge discovery in engineering application is explored. A case extracting out the knowledge rules from a concise data table shows out some important information. It is that the knowledge discovery similar to the mechanical faults diagnosis is an item of complicated system engineering project. In where, first of all-important tasks is to preserve the faults knowledge into a table with data mode. And the data must be derived from the plant site and should also be as concise as possible. On the basis of the faults knowledge data obtained so, the methods and algorithms to process the data and extract the knowledge rules from them by means of RST can be processed only. The conclusion is that the faults knowledge discovery by the way is a process of rising upward. But to develop the advanced faults diagnosis technology by the way is a large-scale knowledge engineering project for long time. Every step in which should be designed seriously according to the tool's demands firstly. This is the basic guarantees to make the knowledge rules obtained have the values of engineering application and the studies have scientific significance. So, a general framework is designed for engineering application to go along the route developing the faults knowledge discovery technology.

Application of PBPK modelling in drug discovery and development at Pfizer.

Science.gov (United States)

Jones, Hannah M; Dickins, Maurice; Youdim, Kuresh; Gosset, James R; Attkins, Neil J; Hay, Tanya L; Gurrell, Ian K; Logan, Y Raj; Bungay, Peter J; Jones, Barry C; Gardner, Iain B

2012-01-01

Early prediction of human pharmacokinetics (PK) and drug-drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly.

Privacy-aware knowledge discovery novel applications and new techniques

CERN Document Server

Bonchi, Francesco

2010-01-01

Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations not only cover well-established results-they also explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development. Divided into seve

Genome Enabled Discovery of Carbon Sequestration Genes in Poplar

Energy Technology Data Exchange (ETDEWEB)

Filichkin, Sergei; Etherington, Elizabeth; Ma, Caiping; Strauss, Steve

2007-02-22

The goals of the S.H. Strauss laboratory portion of 'Genome-enabled discovery of carbon sequestration genes in poplar' are (1) to explore the functions of candidate genes using Populus transformation by inserting genes provided by Oakridge National Laboratory (ORNL) and the University of Florida (UF) into poplar; (2) to expand the poplar transformation toolkit by developing transformation methods for important genotypes; and (3) to allow induced expression, and efficient gene suppression, in roots and other tissues. As part of the transformation improvement effort, OSU developed transformation protocols for Populus trichocarpa 'Nisqually-1' clone and an early flowering P. alba clone, 6K10. Complete descriptions of the transformation systems were published (Ma et. al. 2004, Meilan et. al 2004). Twenty-one 'Nisqually-1' and 622 6K10 transgenic plants were generated. To identify root predominant promoters, a set of three promoters were tested for their tissue-specific expression patterns in poplar and in Arabidopsis as a model system. A novel gene, ET304, was identified by analyzing a collection of poplar enhancer trap lines generated at OSU (Filichkin et. al 2006a, 2006b). Other promoters include the pGgMT1 root-predominant promoter from Casuarina glauca and the pAtPIN2 promoter from Arabidopsis root specific PIN2 gene. OSU tested two induction systems, alcohol- and estrogen-inducible, in multiple poplar transgenics. Ethanol proved to be the more efficient when tested in tissue culture and greenhouse conditions. Two estrogen-inducible systems were evaluated in transgenic Populus, neither of which functioned reliably in tissue culture conditions. GATEWAY-compatible plant binary vectors were designed to compare the silencing efficiency of homologous (direct) RNAi vs. heterologous (transitive) RNAi inverted repeats. A set of genes was targeted for post transcriptional silencing in the model Arabidopsis system; these include the floral

Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is.

Science.gov (United States)

Benson, Neil

2015-08-01

Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way. Copyright © 2015 Elsevier Ltd. All rights reserved.

The application of molecular topology for ulcerative colitis drug discovery.

Science.gov (United States)

Bellera, Carolina L; Di Ianni, Mauricio E; Talevi, Alan

2018-01-01

Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.

Genome-wide target profiling of piggyBac and Tol2 in HEK 293: pros and cons for gene discovery and gene therapy

Science.gov (United States)

2011-01-01

Background DNA transposons have emerged as indispensible tools for manipulating vertebrate genomes with applications ranging from insertional mutagenesis and transgenesis to gene therapy. To fully explore the potential of two highly active DNA transposons, piggyBac and Tol2, as mammalian genetic tools, we have conducted a side-by-side comparison of the two transposon systems in the same setting to evaluate their advantages and disadvantages for use in gene therapy and gene discovery. Results We have observed that (1) the Tol2 transposase (but not piggyBac) is highly sensitive to molecular engineering; (2) the piggyBac donor with only the 40 bp 3'-and 67 bp 5'-terminal repeat domain is sufficient for effective transposition; and (3) a small amount of piggyBac transposases results in robust transposition suggesting the piggyBac transpospase is highly active. Performing genome-wide target profiling on data sets obtained by retrieving chromosomal targeting sequences from individual clones, we have identified several piggyBac and Tol2 hotspots and observed that (4) piggyBac and Tol2 display a clear difference in targeting preferences in the human genome. Finally, we have observed that (5) only sites with a particular sequence context can be targeted by either piggyBac or Tol2. Conclusions The non-overlapping targeting preference of piggyBac and Tol2 makes them complementary research tools for manipulating mammalian genomes. PiggyBac is the most promising transposon-based vector system for achieving site-specific targeting of therapeutic genes due to the flexibility of its transposase for being molecularly engineered. Insights from this study will provide a basis for engineering piggyBac transposases to achieve site-specific therapeutic gene targeting. PMID:21447194

Functional Gene Discovery and Characterization of Genes and Alleles Affecting Wood Biomass Yield and Quality in Populus

Energy Technology Data Exchange (ETDEWEB)

Busov, Victor [Michigan Technological Univ., Houghton, MI (United States)

2017-02-12

Adoption of biofuels as economically and environmentally viable alternative to fossil fuels would require development of specialized bioenergy varieties. A major goal in the breeding of such varieties is the improvement of lignocellulosic biomass yield and quality. These are complex traits and understanding the underpinning molecular mechanism can assist and accelerate their improvement. This is particularly important for tree bioenergy crops like poplars (species and hybrids from the genus Populus), for which breeding progress is extremely slow due to long generation cycles. A variety of approaches have been already undertaken to better understand the molecular bases of biomass yield and quality in poplar. An obvious void in these undertakings has been the application of mutagenesis. Mutagenesis has been instrumental in the discovery and characterization of many plant traits including such that affect biomass yield and quality. In this proposal we use activation tagging to discover genes that can significantly affect biomass associated traits directly in poplar, a premier bioenergy crop. We screened a population of 5,000 independent poplar activation tagging lines under greenhouse conditions for a battery of biomass yield traits. These same plants were then analyzed for changes in wood chemistry using pyMBMS. As a result of these screens we have identified nearly 800 mutants, which are significantly (P<0.05) different when compared to wild type. Of these majority (~700) are affected in one of ten different biomass yield traits and 100 in biomass quality traits (e.g., lignin, S/G ration and C6/C5 sugars). We successfully recovered the position of the tag in approximately 130 lines, showed activation in nearly half of them and performed recapitulation experiments with 20 genes prioritized by the significance of the phenotype. Recapitulation experiments are still ongoing for many of the genes but the results are encouraging. For example, we have shown successful

A genomics based discovery of secondary metabolite biosynthetic gene clusters in Aspergillus ustus.

Directory of Open Access Journals (Sweden)

Borui Pi

Full Text Available Secondary metabolites (SMs produced by Aspergillus have been extensively studied for their crucial roles in human health, medicine and industrial production. However, the resulting information is almost exclusively derived from a few model organisms, including A. nidulans and A. fumigatus, but little is known about rare pathogens. In this study, we performed a genomics based discovery of SM biosynthetic gene clusters in Aspergillus ustus, a rare human pathogen. A total of 52 gene clusters were identified in the draft genome of A. ustus 3.3904, such as the sterigmatocystin biosynthesis pathway that was commonly found in Aspergillus species. In addition, several SM biosynthetic gene clusters were firstly identified in Aspergillus that were possibly acquired by horizontal gene transfer, including the vrt cluster that is responsible for viridicatumtoxin production. Comparative genomics revealed that A. ustus shared the largest number of SM biosynthetic gene clusters with A. nidulans, but much fewer with other Aspergilli like A. niger and A. oryzae. These findings would help to understand the diversity and evolution of SM biosynthesis pathways in genus Aspergillus, and we hope they will also promote the development of fungal identification methodology in clinic.

A Genomics Based Discovery of Secondary Metabolite Biosynthetic Gene Clusters in Aspergillus ustus

Science.gov (United States)

Pi, Borui; Yu, Dongliang; Dai, Fangwei; Song, Xiaoming; Zhu, Congyi; Li, Hongye; Yu, Yunsong

2015-01-01

Secondary metabolites (SMs) produced by Aspergillus have been extensively studied for their crucial roles in human health, medicine and industrial production. However, the resulting information is almost exclusively derived from a few model organisms, including A. nidulans and A. fumigatus, but little is known about rare pathogens. In this study, we performed a genomics based discovery of SM biosynthetic gene clusters in Aspergillus ustus, a rare human pathogen. A total of 52 gene clusters were identified in the draft genome of A. ustus 3.3904, such as the sterigmatocystin biosynthesis pathway that was commonly found in Aspergillus species. In addition, several SM biosynthetic gene clusters were firstly identified in Aspergillus that were possibly acquired by horizontal gene transfer, including the vrt cluster that is responsible for viridicatumtoxin production. Comparative genomics revealed that A. ustus shared the largest number of SM biosynthetic gene clusters with A. nidulans, but much fewer with other Aspergilli like A. niger and A. oryzae. These findings would help to understand the diversity and evolution of SM biosynthesis pathways in genus Aspergillus, and we hope they will also promote the development of fungal identification methodology in clinic. PMID:25706180

IMG-ABC: A Knowledge Base To Fuel Discovery of Biosynthetic Gene Clusters and Novel Secondary Metabolites.

Science.gov (United States)

Hadjithomas, Michalis; Chen, I-Min Amy; Chu, Ken; Ratner, Anna; Palaniappan, Krishna; Szeto, Ernest; Huang, Jinghua; Reddy, T B K; Cimermančič, Peter; Fischbach, Michael A; Ivanova, Natalia N; Markowitz, Victor M; Kyrpides, Nikos C; Pati, Amrita

2015-07-14

In the discovery of secondary metabolites, analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of computational platforms that enable such a systematic approach on a large scale. In this work, we present IMG-ABC (https://img.jgi.doe.gov/abc), an atlas of biosynthetic gene clusters within the Integrated Microbial Genomes (IMG) system, which is aimed at harnessing the power of "big" genomic data for discovering small molecules. IMG-ABC relies on IMG's comprehensive integrated structural and functional genomic data for the analysis of biosynthetic gene clusters (BCs) and associated secondary metabolites (SMs). SMs and BCs serve as the two main classes of objects in IMG-ABC, each with a rich collection of attributes. A unique feature of IMG-ABC is the incorporation of both experimentally validated and computationally predicted BCs in genomes as well as metagenomes, thus identifying BCs in uncultured populations and rare taxa. We demonstrate the strength of IMG-ABC's focused integrated analysis tools in enabling the exploration of microbial secondary metabolism on a global scale, through the discovery of phenazine-producing clusters for the first time in Alphaproteobacteria. IMG-ABC strives to fill the long-existent void of resources for computational exploration of the secondary metabolism universe; its underlying scalable framework enables traversal of uncovered phylogenetic and chemical structure space, serving as a doorway to a new era in the discovery of novel molecules. IMG-ABC is the largest publicly available database of predicted and experimental biosynthetic gene clusters and the secondary metabolites they produce. The system also includes powerful search and analysis tools that are integrated with IMG's extensive genomic/metagenomic data and analysis tool kits. As new research on biosynthetic gene clusters and secondary metabolites is published and more genomes are sequenced, IMG-ABC will continue to

Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis.

Science.gov (United States)

Shchetynsky, Klementy; Diaz-Gallo, Lina-Marcella; Folkersen, Lasse; Hensvold, Aase Haj; Catrina, Anca Irinel; Berg, Louise; Klareskog, Lars; Padyukov, Leonid

2017-02-02

Here we integrate verified signals from previous genetic association studies with gene expression and pathway analysis for discovery of new candidate genes and signaling networks, relevant for rheumatoid arthritis (RA). RNA-sequencing-(RNA-seq)-based expression analysis of 377 genes from previously verified RA-associated loci was performed in blood cells from 5 newly diagnosed, non-treated patients with RA, 7 patients with treated RA and 12 healthy controls. Differentially expressed genes sharing a similar expression pattern in treated and untreated RA sub-groups were selected for pathway analysis. A set of "connector" genes derived from pathway analysis was tested for differential expression in the initial discovery cohort and validated in blood cells from 73 patients with RA and in 35 healthy controls. There were 11 qualifying genes selected for pathway analysis and these were grouped into two evidence-based functional networks, containing 29 and 27 additional connector molecules. The expression of genes, corresponding to connector molecules was then tested in the initial RNA-seq data. Differences in the expression of ERBB2, TP53 and THOP1 were similar in both treated and non-treated patients with RA and an additional nine genes were differentially expressed in at least one group of patients compared to healthy controls. The ERBB2, TP53. THOP1 expression profile was successfully replicated in RNA-seq data from peripheral blood mononuclear cells from healthy controls and non-treated patients with RA, in an independent collection of samples. Integration of RNA-seq data with findings from association studies, and consequent pathway analysis implicate new candidate genes, ERBB2, TP53 and THOP1 in the pathogenesis of RA.

Harvest: an open platform for developing web-based biomedical data discovery and reporting applications.

Science.gov (United States)

Pennington, Jeffrey W; Ruth, Byron; Italia, Michael J; Miller, Jeffrey; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; White, Peter S

2014-01-01

Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

Science.gov (United States)

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Discovery of Putative Herbicide Resistance Genes and Its Regulatory Network in Chickpea Using Transcriptome Sequencing

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Mir A. Iquebal

2017-06-01

Full Text Available Background: Chickpea (Cicer arietinum L. contributes 75% of total pulse production. Being cheaper than animal protein, makes it important in dietary requirement of developing countries. Weed not only competes with chickpea resulting into drastic yield reduction but also creates problem of harboring fungi, bacterial diseases and insect pests. Chemical approach having new herbicide discovery has constraint of limited lead molecule options, statutory regulations and environmental clearance. Through genetic approach, transgenic herbicide tolerant crop has given successful result but led to serious concern over ecological safety thus non-transgenic approach like marker assisted selection is desirable. Since large variability in tolerance limit of herbicide already exists in chickpea varieties, thus the genes offering herbicide tolerance can be introgressed in variety improvement programme. Transcriptome studies can discover such associated key genes with herbicide tolerance in chickpea.Results: This is first transcriptomic studies of chickpea or even any legume crop using two herbicide susceptible and tolerant genotypes exposed to imidazoline (Imazethapyr. Approximately 90 million paired-end reads generated from four samples were processed and assembled into 30,803 contigs using reference based assembly. We report 6,310 differentially expressed genes (DEGs, of which 3,037 were regulated by 980 miRNAs, 1,528 transcription factors associated with 897 DEGs, 47 Hub proteins, 3,540 putative Simple Sequence Repeat-Functional Domain Marker (SSR-FDM, 13,778 genic Single Nucleotide Polymorphism (SNP putative markers and 1,174 Indels. Randomly selected 20 DEGs were validated using qPCR. Pathway analysis suggested that xenobiotic degradation related gene, glutathione S-transferase (GST were only up-regulated in presence of herbicide. Down-regulation of DNA replication genes and up-regulation of abscisic acid pathway genes were observed. Study further reveals

IMG-ABC: An Atlas of Biosynthetic Gene Clusters to Fuel the Discovery of Novel Secondary Metabolites

Energy Technology Data Exchange (ETDEWEB)

Chen, I-Min; Chu, Ken; Ratner, Anna; Palaniappan, Krishna; Huang, Jinghua; Reddy, T. B.K.; Cimermancic, Peter; Fischbach, Michael; Ivanova, Natalia; Markowitz, Victor; Kyrpides, Nikos; Pati, Amrita

2014-10-28

In the discovery of secondary metabolites (SMs), large-scale analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of relevant computational resources. We present IMG-ABC (https://img.jgi.doe.gov/abc/) -- An Atlas of Biosynthetic gene Clusters within the Integrated Microbial Genomes (IMG) system1. IMG-ABC is a rich repository of both validated and predicted biosynthetic clusters (BCs) in cultured isolates, single-cells and metagenomes linked with the SM chemicals they produce and enhanced with focused analysis tools within IMG. The underlying scalable framework enables traversal of phylogenetic dark matter and chemical structure space -- serving as a doorway to a new era in the discovery of novel molecules.

An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation

Science.gov (United States)

Ma, Sisi; Kemmeren, Patrick; Aliferis, Constantin F.; Statnikov, Alexander

2016-01-01

Reverse-engineering of causal pathways that implicate diseases and vital cellular functions is a fundamental problem in biomedicine. Discovery of the local causal pathway of a target variable (that consists of its direct causes and direct effects) is essential for effective intervention and can facilitate accurate diagnosis and prognosis. Recent research has provided several active learning methods that can leverage passively observed high-throughput data to draft causal pathways and then refine the inferred relations with a limited number of experiments. The current study provides a comprehensive evaluation of the performance of active learning methods for local causal pathway discovery in real biological data. Specifically, 54 active learning methods/variants from 3 families of algorithms were applied for local causal pathways reconstruction of gene regulation for 5 transcription factors in S. cerevisiae. Four aspects of the methods’ performance were assessed, including adjacency discovery quality, edge orientation accuracy, complete pathway discovery quality, and experimental cost. The results of this study show that some methods provide significant performance benefits over others and therefore should be routinely used for local causal pathway discovery tasks. This study also demonstrates the feasibility of local causal pathway reconstruction in real biological systems with significant quality and low experimental cost. PMID:26939894

Bioluminescent bacteria: lux genes as environmental biosensors

OpenAIRE

Nunes-Halldorson,Vânia da Silva; Duran,Norma Letícia

2003-01-01

Bioluminescent bacteria are widespread in natural environments. Over the years, many researchers have been studying the physiology, biochemistry and genetic control of bacterial bioluminescence. These discoveries have revolutionized the area of Environmental Microbiology through the use of luminescent genes as biosensors for environmental studies. This paper will review the chronology of scientific discoveries on bacterial bioluminescence and the current applications of bioluminescence in env...

Construction of functional linkage gene networks by data integration.

Science.gov (United States)

Linghu, Bolan; Franzosa, Eric A; Xia, Yu

2013-01-01

Networks of functional associations between genes have recently been successfully used for gene function and disease-related research. A typical approach for constructing such functional linkage gene networks (FLNs) is based on the integration of diverse high-throughput functional genomics datasets. Data integration is a nontrivial task due to the heterogeneous nature of the different data sources and their variable accuracy and completeness. The presence of correlations between data sources also adds another layer of complexity to the integration process. In this chapter we discuss an approach for constructing a human FLN from data integration and a subsequent application of the FLN to novel disease gene discovery. Similar approaches can be applied to nonhuman species and other discovery tasks.

Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

Directory of Open Access Journals (Sweden)

Sapna Kumari

Full Text Available BACKGROUND: Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS: In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

The web server of IBM's Bioinformatics and Pattern Discovery group.

Science.gov (United States)

Huynh, Tien; Rigoutsos, Isidore; Parida, Laxmi; Platt, Daniel; Shibuya, Tetsuo

2003-07-01

We herein present and discuss the services and content which are available on the web server of IBM's Bioinformatics and Pattern Discovery group. The server is operational around the clock and provides access to a variety of methods that have been published by the group's members and collaborators. The available tools correspond to applications ranging from the discovery of patterns in streams of events and the computation of multiple sequence alignments, to the discovery of genes in nucleic acid sequences and the interactive annotation of amino acid sequences. Additionally, annotations for more than 70 archaeal, bacterial, eukaryotic and viral genomes are available on-line and can be searched interactively. The tools and code bundles can be accessed beginning at http://cbcsrv.watson.ibm.com/Tspd.html whereas the genomics annotations are available at http://cbcsrv.watson.ibm.com/Annotations/.

Cultivation of hard-to-culture subsurface mercury-resistant bacteria and discovery of new merA gene sequences

DEFF Research Database (Denmark)

Rasmussen, L D; Zawadsky, C; Binnerup, S J

2008-01-01

different 16S rRNA gene sequences were observed, including Alpha-, Beta-, and Gammaproteobacteria; Actinobacteria; Firmicutes; and Bacteroidetes. The diversity of isolates obtained by direct plating included eight different 16S rRNA gene sequences (Alpha- and Betaproteobacteria and Actinobacteria). Partial...... sequencing of merA of selected isolates led to the discovery of new merA sequences. With phylum-specific merA primers, PCR products were obtained for Alpha- and Betaproteobacteria and Actinobacteria but not for Bacteroidetes and Firmicutes. The similarity to known sequences ranged between 89 and 95%. One...

A comparative review of estimates of the proportion unchanged genes and the false discovery rate

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Broberg Per

2005-08-01

Full Text Available Abstract Background In the analysis of microarray data one generally produces a vector of p-values that for each gene give the likelihood of obtaining equally strong evidence of change by pure chance. The distribution of these p-values is a mixture of two components corresponding to the changed genes and the unchanged ones. The focus of this article is how to estimate the proportion unchanged and the false discovery rate (FDR and how to make inferences based on these concepts. Six published methods for estimating the proportion unchanged genes are reviewed, two alternatives are presented, and all are tested on both simulated and real data. All estimates but one make do without any parametric assumptions concerning the distributions of the p-values. Furthermore, the estimation and use of the FDR and the closely related q-value is illustrated with examples. Five published estimates of the FDR and one new are presented and tested. Implementations in R code are available. Results A simulation model based on the distribution of real microarray data plus two real data sets were used to assess the methods. The proposed alternative methods for estimating the proportion unchanged fared very well, and gave evidence of low bias and very low variance. Different methods perform well depending upon whether there are few or many regulated genes. Furthermore, the methods for estimating FDR showed a varying performance, and were sometimes misleading. The new method had a very low error. Conclusion The concept of the q-value or false discovery rate is useful in practical research, despite some theoretical and practical shortcomings. However, it seems possible to challenge the performance of the published methods, and there is likely scope for further developing the estimates of the FDR. The new methods provide the scientist with more options to choose a suitable method for any particular experiment. The article advocates the use of the conjoint information

The in silico drug discovery toolbox: applications in lead discovery and optimization.

Science.gov (United States)

Bruno, Agostino; Costantino, Gabriele; Sartori, Luca; Radi, Marco

2017-11-06

Discovery and development of a new drug is a long lasting and expensive journey that takes around 15 years from starting idea to approval and marketing of new medication. Despite the R&D expenditures have been constantly increasing in the last few years, number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. From this point of view, it is clear that if we want to increase drug-discovery success rate and reduce costs associated with development of a new drug, a comprehensive evaluation/prediction of potential safety issues should be conducted as soon as possible during early drug discovery phase. In the present review, we will analyse the early steps of drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Gene Overexpression Resources in Cereals for Functional Genomics and Discovery of Useful Genes

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Kiyomi Abe

2016-09-01

Full Text Available Identification and elucidation of functions of plant genes is valuable for both basic and applied research. In addition to natural variation in model plants, numerous loss-of-function resources have been produced by mutagenesis with chemicals, irradiation, or insertions of transposable elements or T-DNA. However, we may be unable to observe loss-of-function phenotypes for genes with functionally redundant homologs, and for those essential for growth and development. To offset such disadvantages, gain-of-function transgenic resources have been exploited. Activation-tagged lines have been generated using obligatory overexpression of endogenous genes by random insertion of an enhancer. Recent progress in DNA sequencing technology and bioinformatics has enabled the preparation of genomewide collections of full-length cDNAs (fl-cDNAs in some model species. Using the fl-cDNA clones, a novel gain-of-function strategy, Fl-cDNA OvereXpressor gene (FOX-hunting system, has been developed. A mutant phenotype in a FOX line can be directly attributed to the overexpressed fl-cDNA. Investigating a large population of FOX lines could reveal important genes conferring favorable phenotypes for crop breeding. Alternatively, a unique loss-of-function approach Chimeric REpressor gene Silencing Technology (CRES-T has been developed. In CRES-T, overexpression of a chimeric repressor, composed of the coding sequence of a transcription factor (TF and short peptide designated as the repression domain, could interfere with the action of endogenous TF in plants. Although plant TFs usually consist of gene families, CRES-T is effective, in principle, even for the TFs with functional redundancy. In this review, we focus on the current status of the gene-overexpression strategies and resources for identifying and elucidating novel functions of cereal genes. We discuss the potential of these research tools for identifying useful genes and phenotypes for application in crop

Technology development for gene discovery and full-length sequencing

Energy Technology Data Exchange (ETDEWEB)

Marcelo Bento Soares

2004-07-19

In previous years, with support from the U.S. Department of Energy, we developed methods for construction of normalized and subtracted cDNA libraries, and constructed hundreds of high-quality libraries for production of Expressed Sequence Tags (ESTs). Our clones were made widely available to the scientific community through the IMAGE Consortium, and millions of ESTs were produced from our libraries either by collaborators or by our own sequencing laboratory at the University of Iowa. During this grant period, we focused on (1) the development of a method for preferential cloning of tissue-specific and/or rare transcripts, (2) its utilization to expedite EST-based gene discovery for the NIH Mouse Brain Molecular Anatomy Project, (3) further development and optimization of a method for construction of full-length-enriched cDNA libraries, and (4) modification of a plasmid vector to maximize efficiency of full-length cDNA sequencing by the transposon-mediated approach. It is noteworthy that the technology developed for preferential cloning of rare mRNAs enabled identification of over 2,000 mouse transcripts differentially expressed in the hippocampus. In addition, the method that we optimized for construction of full-length-enriched cDNA libraries was successfully utilized for the production of approximately fifty libraries from the developing mouse nervous system, from which over 2,500 full-ORF-containing cDNAs have been identified and accurately sequenced in their entirety either by our group or by the NIH-Mammalian Gene Collection Program Sequencing Team.

TargetMine, an integrated data warehouse for candidate gene prioritisation and target discovery.

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Yi-An Chen

Full Text Available Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/.

The application of DNA microarrays in gene expression analysis.

Science.gov (United States)

van Hal, N L; Vorst, O; van Houwelingen, A M; Kok, E J; Peijnenburg, A; Aharoni, A; van Tunen, A J; Keijer, J

2000-03-31

DNA microarray technology is a new and powerful technology that will substantially increase the speed of molecular biological research. This paper gives a survey of DNA microarray technology and its use in gene expression studies. The technical aspects and their potential improvements are discussed. These comprise array manufacturing and design, array hybridisation, scanning, and data handling. Furthermore, it is discussed how DNA microarrays can be applied in the working fields of: safety, functionality and health of food and gene discovery and pathway engineering in plants.

31 CFR 10.71 - Discovery.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 1 2010-07-01 2010-07-01 false Discovery. 10.71 Section 10.71 Money... SERVICE Rules Applicable to Disciplinary Proceedings § 10.71 Discovery. (a) In general. Discovery may be... relevance, materiality and reasonableness of the requested discovery and subject to the requirements of § 10...

Application of mass spectrometry-based proteomics for biomarker discovery in neurological disorders

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Venugopal Abhilash

2009-01-01

Full Text Available Mass spectrometry-based quantitative proteomics has emerged as a powerful approach that has the potential to accelerate biomarker discovery, both for diagnostic as well as therapeutic purposes. Proteomics has traditionally been synonymous with 2D gels but is increasingly shifting to the use of gel-free systems and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS. Quantitative proteomic approaches have already been applied to investigate various neurological disorders, especially in the context of identifying biomarkers from cerebrospinal fluid and serum. This review highlights the scope of different applications of quantitative proteomics in understanding neurological disorders with special emphasis on biomarker discovery.

Metagenomics as a Tool for Enzyme Discovery: Hydrolytic Enzymes from Marine-Related Metagenomes.

Science.gov (United States)

Popovic, Ana; Tchigvintsev, Anatoly; Tran, Hai; Chernikova, Tatyana N; Golyshina, Olga V; Yakimov, Michail M; Golyshin, Peter N; Yakunin, Alexander F

2015-01-01

This chapter discusses metagenomics and its application for enzyme discovery, with a focus on hydrolytic enzymes from marine metagenomic libraries. With less than one percent of culturable microorganisms in the environment, metagenomics, or the collective study of community genetics, has opened up a rich pool of uncharacterized metabolic pathways, enzymes, and adaptations. This great untapped pool of genes provides the particularly exciting potential to mine for new biochemical activities or novel enzymes with activities tailored to peculiar sets of environmental conditions. Metagenomes also represent a huge reservoir of novel enzymes for applications in biocatalysis, biofuels, and bioremediation. Here we present the results of enzyme discovery for four enzyme activities, of particular industrial or environmental interest, including esterase/lipase, glycosyl hydrolase, protease and dehalogenase.

CLARM: An integrative approach for functional modules discovery

KAUST Repository

Salem, Saeed M.; Alroobi, Rami; Banitaan, Shadi; Seridi, Loqmane; Brewer, James E.; Aljarah, Ibrahim

2011-01-01

Functional module discovery aims to find well-connected subnetworks which can serve as candidate protein complexes. Advances in High-throughput proteomic technologies have enabled the collection of large amount of interaction data as well as gene expression data. We propose, CLARM, a clustering algorithm that integrates gene expression profiles and protein protein interaction network for biological modules discovery. The main premise is that by enriching the interaction network by adding interactions between genes which are highly co-expressed over a wide range of biological and environmental conditions, we can improve the quality of the discovered modules. Protein protein interactions, known protein complexes, and gene expression profiles for diverse environmental conditions from the yeast Saccharomyces cerevisiae were used for evaluate the biological significance of the reported modules. Our experiments show that the CLARM approach is competitive to wellestablished module discovery methods. Copyright © 2011 ACM.

Cell-specific prediction and application of drug-induced gene expression profiles.

Science.gov (United States)

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

2018-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Independent Gene Discovery and Testing

Science.gov (United States)

Palsule, Vrushalee; Coric, Dijana; Delancy, Russell; Dunham, Heather; Melancon, Caleb; Thompson, Dennis; Toms, Jamie; White, Ashley; Shultz, Jeffry

2010-01-01

A clear understanding of basic gene structure is critical when teaching molecular genetics, the central dogma and the biological sciences. We sought to create a gene-based teaching project to improve students' understanding of gene structure and to integrate this into a research project that can be implemented by instructors at the secondary level…

A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing

Science.gov (United States)

Guerrero, Ginés D.; Imbernón, Baldomero; García, José M.

2014-01-01

Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC) platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs) has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO). This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor. PMID:25025055

RNA-Seq analysis and gene discovery of Andrias davidianus using Illumina short read sequencing.

Directory of Open Access Journals (Sweden)

Fenggang Li

Full Text Available The Chinese giant salamander, Andrias davidianus, is an important species in the course of evolution; however, there is insufficient genomic data in public databases for understanding its immunologic mechanisms. High-throughput transcriptome sequencing is necessary to generate an enormous number of transcript sequences from A. davidianus for gene discovery. In this study, we generated more than 40 million reads from samples of spleen and skin tissue using the Illumina paired-end sequencing technology. De novo assembly yielded 87,297 transcripts with a mean length of 734 base pairs (bp. Based on the sequence similarities, searching with known proteins, 38,916 genes were identified. Gene enrichment analysis determined that 981 transcripts were assigned to the immune system. Tissue-specific expression analysis indicated that 443 of transcripts were specifically expressed in the spleen and skin. Among these transcripts, 147 transcripts were found to be involved in immune responses and inflammatory reactions, such as fucolectin, β-defensins and lymphotoxin beta. Eight tissue-specific genes were selected for validation using real time reverse transcription quantitative PCR (qRT-PCR. The results showed that these genes were significantly more expressed in spleen and skin than in other tissues, suggesting that these genes have vital roles in the immune response. This work provides a comprehensive genomic sequence resource for A. davidianus and lays the foundation for future research on the immunologic and disease resistance mechanisms of A. davidianus and other amphibians.

Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks.

Directory of Open Access Journals (Sweden)

Nolan Priedigkeit

2015-02-01

Full Text Available Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC, is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting "disease map" network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.

Feature Issue Introduction: Bio-Optics in Clinical Applications, Nanotechnology, and Drug Discovery

OpenAIRE

Nordstrom, Robert J.; Almutairi, Adah; Hillman, Elizabeth M.C.

2010-01-01

The editors introduce the Biomedical Optics Express feature issue, “Bio-Optics in Clinical Applications, Nanotechnology, and Drug Discovery,” which combines three technical areas from the 2010 Optical Society of America (OSA), Biomedical Optics (BIOMED) Topical Meeting held on 11–14 April in Miami, FL and includes contributions from conference attendees.

Accelerators for Discovery Science and Security applications

Energy Technology Data Exchange (ETDEWEB)

Todd, A.M.M., E-mail: alan_todd@mail.aesys.net; Bluem, H.P.; Jarvis, J.D.; Park, J.H.; Rathke, J.W.; Schultheiss, T.J.

2015-05-01

Several Advanced Energy Systems (AES) accelerator projects that span applications in Discovery Science and Security are described. The design and performance of the IR and THz free electron laser (FEL) at the Fritz-Haber-Institut der Max-Planck-Gesellschaft in Berlin that is now an operating user facility for physical chemistry research in molecular and cluster spectroscopy as well as surface science, is highlighted. The device was designed to meet challenging specifications, including a final energy adjustable in the range of 15–50 MeV, low longitudinal emittance (<50 keV-psec) and transverse emittance (<20 π mm-mrad), at more than 200 pC bunch charge with a micropulse repetition rate of 1 GHz and a macropulse length of up to 15 μs. Secondly, we will describe an ongoing effort to develop an ultrafast electron diffraction (UED) source that is scheduled for completion in 2015 with prototype testing taking place at the Brookhaven National Laboratory (BNL) Accelerator Test Facility (ATF). This tabletop X-band system will find application in time-resolved chemical imaging and as a resource for drug–cell interaction analysis. A third active area at AES is accelerators for security applications where we will cover some top-level aspects of THz and X-ray systems that are under development and in testing for stand-off and portal detection.

Accelerators for Discovery Science and Security applications

International Nuclear Information System (INIS)

Todd, A.M.M.; Bluem, H.P.; Jarvis, J.D.; Park, J.H.; Rathke, J.W.; Schultheiss, T.J.

2015-01-01

Several Advanced Energy Systems (AES) accelerator projects that span applications in Discovery Science and Security are described. The design and performance of the IR and THz free electron laser (FEL) at the Fritz-Haber-Institut der Max-Planck-Gesellschaft in Berlin that is now an operating user facility for physical chemistry research in molecular and cluster spectroscopy as well as surface science, is highlighted. The device was designed to meet challenging specifications, including a final energy adjustable in the range of 15–50 MeV, low longitudinal emittance (<50 keV-psec) and transverse emittance (<20 π mm-mrad), at more than 200 pC bunch charge with a micropulse repetition rate of 1 GHz and a macropulse length of up to 15 μs. Secondly, we will describe an ongoing effort to develop an ultrafast electron diffraction (UED) source that is scheduled for completion in 2015 with prototype testing taking place at the Brookhaven National Laboratory (BNL) Accelerator Test Facility (ATF). This tabletop X-band system will find application in time-resolved chemical imaging and as a resource for drug–cell interaction analysis. A third active area at AES is accelerators for security applications where we will cover some top-level aspects of THz and X-ray systems that are under development and in testing for stand-off and portal detection

43 CFR 4.1130 - Discovery methods.

Science.gov (United States)

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Discovery methods. 4.1130 Section 4.1130... Special Rules Applicable to Surface Coal Mining Hearings and Appeals Discovery § 4.1130 Discovery methods. Parties may obtain discovery by one or more of the following methods— (a) Depositions upon oral...

Bactérias bioluminescentes: os genes lux como biosensores ambientais

OpenAIRE

Nunes-Halldorson, Vânia da Silva; Duran, Norma Letícia

2003-01-01

Bioluminescent bacteria are widespread in natural environments. Over the years, many researchers have been studying the physiology, biochemistry and genetic control of bacterial bioluminescence. These discoveries have revolutionized the area of Environmental Microbiology through the use of luminescent genes as biosensors for environmental studies. This paper will review the chronology of scientific discoveries on bacterial bioluminescence and the current applications of bioluminescence in env...

Designing an intuitive web application for drug discovery scientists.

Science.gov (United States)

Karamanis, Nikiforos; Pignatelli, Miguel; Carvalho-Silva, Denise; Rowland, Francis; Cham, Jennifer A; Dunham, Ian

2018-01-11

We discuss how we designed the Open Targets Platform (www.targetvalidation.org), an intuitive application for bench scientists working in early drug discovery. To meet the needs of our users, we applied lean user experience (UX) design methods: we started engaging with users very early and carried out research, design and evaluation activities within an iterative development process. We also emphasize the collaborative nature of applying lean UX design, which we believe is a foundation for success in this and many other scientific projects. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Exploiting Pre-rRNA Processing in Diamond Blackfan Anemia Gene Discovery and Diagnosis

Science.gov (United States)

Farrar, Jason E.; Quarello, Paola; Fisher, Ross; O’Brien, Kelly A.; Aspesi, Anna; Parrella, Sara; Henson, Adrianna L.; Seidel, Nancy E.; Atsidaftos, Eva; Prakash, Supraja; Bari, Shahla; Garelli, Emanuela; Arceci, Robert J.; Dianzani, Irma; Ramenghi, Ugo; Vlachos, Adrianna; Lipton, Jeffrey M.; Bodine, David M.; Ellis, Steven R.

2014-01-01

Diamond Blackfan anemia (DBA), a syndrome primarily characterized by anemia and physical abnormalities, is one among a group of related inherited bone marrow failure syndromes (IBMFS) which share overlapping clinical features. Heterozygous mutations or single-copy deletions have been identified in 12 ribosomal protein genes in approximately 60% of DBA cases, with the genetic etiology unexplained in most remaining patients. Unlike many IBMFS, for which functional screening assays complement clinical and genetic findings, suspected DBA in the absence of typical alterations of the known genes must frequently be diagnosed after exclusion of other IBMFS. We report here a novel deletion in a child that presented such a diagnostic challenge and prompted development of a novel functional assay that can assist in the diagnosis of a significant fraction of patients with DBA. The ribosomal proteins affected in DBA are required for pre-rRNA processing, a process which can be interrogated to monitor steps in the maturation of 40S and 60S ribosomal subunits. In contrast to prior methods used to assess pre-rRNA processing, the assay reported here, based on capillary electrophoresis measurement of the maturation of rRNA in pre-60S ribosomal subunits, would be readily amenable to use in diagnostic laboratories. In addition to utility as a diagnostic tool, we applied this technique to gene discovery in DBA, resulting in the identification of RPL31 as a novel DBA gene. PMID:25042156

A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing

Directory of Open Access Journals (Sweden)

Ginés D. Guerrero

2014-01-01

Full Text Available Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO. This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor.

Gene discovery in Triatoma infestans

Directory of Open Access Journals (Sweden)

de Burgos Nelia

2011-03-01

Full Text Available Abstract Background Triatoma infestans is the most relevant vector of Chagas disease in the southern cone of South America. Since its genome has not yet been studied, sequencing of Expressed Sequence Tags (ESTs is one of the most powerful tools for efficiently identifying large numbers of expressed genes in this insect vector. Results In this work, we generated 826 ESTs, resulting in an increase of 47% in the number of ESTs available for T. infestans. These ESTs were assembled in 471 unique sequences, 151 of which represent 136 new genes for the Reduviidae family. Conclusions Among the putative new genes for the Reduviidae family, we identified and described an interesting subset of genes involved in development and reproduction, which constitute potential targets for insecticide development.

De-novo discovery of differentially abundant transcription factor binding sites including their positional preference.

Science.gov (United States)

Keilwagen, Jens; Grau, Jan; Paponov, Ivan A; Posch, Stefan; Strickert, Marc; Grosse, Ivo

2011-02-10

Transcription factors are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in promoters. The de-novo discovery of transcription factor binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not been fully solved yet. Here, we present a de-novo motif discovery tool called Dispom for finding differentially abundant transcription factor binding sites that models existing positional preferences of binding sites and adjusts the length of the motif in the learning process. Evaluating Dispom, we find that its prediction performance is superior to existing tools for de-novo motif discovery for 18 benchmark data sets with planted binding sites, and for a metazoan compendium based on experimental data from micro-array, ChIP-chip, ChIP-DSL, and DamID as well as Gene Ontology data. Finally, we apply Dispom to find binding sites differentially abundant in promoters of auxin-responsive genes extracted from Arabidopsis thaliana microarray data, and we find a motif that can be interpreted as a refined auxin responsive element predominately positioned in the 250-bp region upstream of the transcription start site. Using an independent data set of auxin-responsive genes, we find in genome-wide predictions that the refined motif is more specific for auxin-responsive genes than the canonical auxin-responsive element. In general, Dispom can be used to find differentially abundant motifs in sequences of any origin. However, the positional distribution learned by Dispom is especially beneficial if all sequences are aligned to some anchor point like the transcription start site in case of promoter sequences. We demonstrate that the combination of searching for differentially abundant motifs and inferring a position distribution from the data is beneficial for de-novo motif discovery. Hence, we make the tool freely available as a component of the open

Transient transformation meets gene function discovery: the strawberry fruit case

Directory of Open Access Journals (Sweden)

Michela eGuidarelli

2015-06-01

Full Text Available Beside the well known nutritional and health benefits, strawberry (Fragaria X ananassa crop draws increasing attention as plant model system for the Rosaceae family, due to the short generation time, the rapid in vitro regeneration, and to the availability of the genome sequence of F. X ananassa and of the closely related F. vesca species. In the last years, the use of high-throughput sequence technologies provided large amounts of molecular information on the genes possibly related to several biological processes of this crop. Nevertheless, the function of most genes or gene products is still poorly understood and needs investigation. Transient transformation technology provides a powerful tool to study gene function in vivo, avoiding difficult drawbacks that typically affect the stable transformation protocols, such as transformation efficiency, transformants selection and regeneration. In this review we provide an overview of the use of transient expression in the investigation of the function of genes important for strawberry fruit development, defence and nutritional properties. The technical aspects related to an efficient use of this technique are described, and the possible impact and application in strawberry crop improvement are discussed.

Harvest: a web-based biomedical data discovery and reporting application development platform.

Science.gov (United States)

Italia, Michael J; Pennington, Jeffrey W; Ruth, Byron; Wrazien, Stacey; Loutrel, Jennifer G; Crenshaw, E Bryan; Miller, Jeffrey; White, Peter S

2013-01-01

Biomedical researchers share a common challenge of making complex data understandable and accessible. This need is increasingly acute as investigators seek opportunities for discovery amidst an exponential growth in the volume and complexity of laboratory and clinical data. To address this need, we developed Harvest, an open source framework that provides a set of modular components to aid the rapid development and deployment of custom data discovery software applications. Harvest incorporates visual representations of multidimensional data types in an intuitive, web-based interface that promotes a real-time, iterative approach to exploring complex clinical and experimental data. The Harvest architecture capitalizes on standards-based, open source technologies to address multiple functional needs critical to a research and development environment, including domain-specific data modeling, abstraction of complex data models, and a customizable web client.

Bioluminescent bacteria: lux genes as environmental biosensors

Directory of Open Access Journals (Sweden)

Nunes-Halldorson Vânia da Silva

2003-01-01

Full Text Available Bioluminescent bacteria are widespread in natural environments. Over the years, many researchers have been studying the physiology, biochemistry and genetic control of bacterial bioluminescence. These discoveries have revolutionized the area of Environmental Microbiology through the use of luminescent genes as biosensors for environmental studies. This paper will review the chronology of scientific discoveries on bacterial bioluminescence and the current applications of bioluminescence in environmental studies, with special emphasis on the Microtox toxicity bioassay. Also, the general ecological significance of bioluminescence will be addressed.

Repurposed transcriptomic data facilitate discovery of innate immunity toll-like receptor (TLR) Genes across Lophotrochozoa.

Science.gov (United States)

Halanych, Kenneth M; Kocot, Kevin M

2014-10-01

The growing volume of genomic data from across life represents opportunities for deriving valuable biological information from data that were initially collected for another purpose. Here, we use transcriptomes collected for phylogenomic studies to search for toll-like receptor (TLR) genes in poorly sampled lophotrochozoan clades (Annelida, Mollusca, Brachiopoda, Phoronida, and Entoprocta) and one ecdysozoan clade (Priapulida). TLR genes are involved in innate immunity across animals by recognizing potential microbial infection. They have an extracellular leucine-rich repeat (LRR) domain connected to a transmembrane domain and an intracellular toll/interleukin-1 receptor (TIR) domain. Consequently, these genes are important in initiating a signaling pathway to trigger defense. We found at least one TLR ortholog in all but two taxa examined, suggesting that a broad array of lophotrochozoans may have innate immune systems similar to those observed in vertebrates and arthropods. Comparison to the SMART database confirmed the presence of both the LRR and the TIR protein motifs characteristic of TLR genes. Because we looked at only one transcriptome per species, discovery of TLR genes was limited for most taxa. However, several TRL-like genes that vary in the number and placement of LRR domains were found in phoronids. Additionally, several contigs contained LRR domains but lacked TIR domains, suggesting they were not TLRs. Many of these LRR-containing contigs had other domains (e.g., immunoglobin) and are likely involved in innate immunity. © 2014 Marine Biological Laboratory.

Common characteristics of open source software development and applicability for drug discovery: a systematic review.

Science.gov (United States)

Ardal, Christine; Alstadsæter, Annette; Røttingen, John-Arne

2011-09-28

Innovation through an open source model has proven to be successful for software development. This success has led many to speculate if open source can be applied to other industries with similar success. We attempt to provide an understanding of open source software development characteristics for researchers, business leaders and government officials who may be interested in utilizing open source innovation in other contexts and with an emphasis on drug discovery. A systematic review was performed by searching relevant, multidisciplinary databases to extract empirical research regarding the common characteristics and barriers of initiating and maintaining an open source software development project. Common characteristics to open source software development pertinent to open source drug discovery were extracted. The characteristics were then grouped into the areas of participant attraction, management of volunteers, control mechanisms, legal framework and physical constraints. Lastly, their applicability to drug discovery was examined. We believe that the open source model is viable for drug discovery, although it is unlikely that it will exactly follow the form used in software development. Hybrids will likely develop that suit the unique characteristics of drug discovery. We suggest potential motivations for organizations to join an open source drug discovery project. We also examine specific differences between software and medicines, specifically how the need for laboratories and physical goods will impact the model as well as the effect of patents.

Helping Students Understand Gene Regulation with Online Tools: A Review of MEME and Melina II, Motif Discovery Tools for Active Learning in Biology

Directory of Open Access Journals (Sweden)

David Treves

2012-08-01

Full Text Available Review of: MEME and Melina II, which are two free and easy-to-use online motif discovery tools that can be employed to actively engage students in learning about gene regulatory elements.

Applications of fiber-optics-based nanosensors to drug discovery.

Science.gov (United States)

Vo-Dinh, Tuan; Scaffidi, Jonathan; Gregas, Molly; Zhang, Yan; Seewaldt, Victoria

2009-08-01

Fiber-optic nanosensors are fabricated by heating and pulling optical fibers to yield sub-micron diameter tips and have been used for in vitro analysis of individual living mammalian cells. Immobilization of bioreceptors (e.g., antibodies, peptides, DNA) selective to targeting analyte molecules of interest provides molecular specificity. Excitation light can be launched into the fiber, and the resulting evanescent field at the tip of the nanofiber can be used to excite target molecules bound to the bioreceptor molecules. The fluorescence or surface-enhanced Raman scattering produced by the analyte molecules is detected using an ultra-sensitive photodetector. This article provides an overview of the development and application of fiber-optic nanosensors for drug discovery. The nanosensors provide minimally invasive tools to probe subcellular compartments inside single living cells for health effect studies (e.g., detection of benzopyrene adducts) and medical applications (e.g., monitoring of apoptosis in cells treated with anticancer drugs).

Biomimicry as a basis for drug discovery.

Science.gov (United States)

Kolb, V M

1998-01-01

Selected works are discussed which clearly demonstrate that mimicking various aspects of the process by which natural products evolved is becoming a powerful tool in contemporary drug discovery. Natural products are an established and rich source of drugs. The term "natural product" is often used synonymously with "secondary metabolite." Knowledge of genetics and molecular evolution helps us understand how biosynthesis of many classes of secondary metabolites evolved. One proposed hypothesis is termed "inventive evolution." It invokes duplication of genes, and mutation of the gene copies, among other genetic events. The modified duplicate genes, per se or in conjunction with other genetic events, may give rise to new enzymes, which, in turn, may generate new products, some of which may be selected for. Steps of the inventive evolution can be mimicked in several ways for purpose of drug discovery. For example, libraries of chemical compounds of any imaginable structure may be produced by combinatorial synthesis. Out of these libraries new active compounds can be selected. In another example, genetic system can be manipulated to produce modified natural products ("unnatural natural products"), from which new drugs can be selected. In some instances, similar natural products turn up in species that are not direct descendants of each other. This is presumably due to a horizontal gene transfer. The mechanism of this inter-species gene transfer can be mimicked in therapeutic gene delivery. Mimicking specifics or principles of chemical evolution including experimental and test-tube evolution also provides leads for new drug discovery.

Delivery strategies of the CRISPR-Cas9 gene-editing system for therapeutic applications.

Science.gov (United States)

Liu, Chang; Zhang, Li; Liu, Hao; Cheng, Kun

2017-11-28

The CRISPR-Cas9 genome-editing system is a part of the adaptive immune system in archaea and bacteria to defend against invasive nucleic acids from phages and plasmids. The single guide RNA (sgRNA) of the system recognizes its target sequence in the genome, and the Cas9 nuclease of the system acts as a pair of scissors to cleave the double strands of DNA. Since its discovery, CRISPR-Cas9 has become the most robust platform for genome engineering in eukaryotic cells. Recently, the CRISPR-Cas9 system has triggered enormous interest in therapeutic applications. CRISPR-Cas9 can be applied to correct disease-causing gene mutations or engineer T cells for cancer immunotherapy. The first clinical trial using the CRISPR-Cas9 technology was conducted in 2016. Despite the great promise of the CRISPR-Cas9 technology, several challenges remain to be tackled before its successful applications for human patients. The greatest challenge is the safe and efficient delivery of the CRISPR-Cas9 genome-editing system to target cells in human body. In this review, we will introduce the molecular mechanism and different strategies to edit genes using the CRISPR-Cas9 system. We will then highlight the current systems that have been developed to deliver CRISPR-Cas9 in vitro and in vivo for various therapeutic purposes. Copyright © 2017 Elsevier B.V. All rights reserved.

Promzea: a pipeline for discovery of co-regulatory motifs in maize and other plant species and its application to the anthocyanin and phlobaphene biosynthetic pathways and the Maize Development Atlas.

Science.gov (United States)

Liseron-Monfils, Christophe; Lewis, Tim; Ashlock, Daniel; McNicholas, Paul D; Fauteux, François; Strömvik, Martina; Raizada, Manish N

2013-03-15

The discovery of genetic networks and cis-acting DNA motifs underlying their regulation is a major objective of transcriptome studies. The recent release of the maize genome (Zea mays L.) has facilitated in silico searches for regulatory motifs. Several algorithms exist to predict cis-acting elements, but none have been adapted for maize. A benchmark data set was used to evaluate the accuracy of three motif discovery programs: BioProspector, Weeder and MEME. Analysis showed that each motif discovery tool had limited accuracy and appeared to retrieve a distinct set of motifs. Therefore, using the benchmark, statistical filters were optimized to reduce the false discovery ratio, and then remaining motifs from all programs were combined to improve motif prediction. These principles were integrated into a user-friendly pipeline for motif discovery in maize called Promzea, available at http://www.promzea.org and on the Discovery Environment of the iPlant Collaborative website. Promzea was subsequently expanded to include rice and Arabidopsis. Within Promzea, a user enters cDNA sequences or gene IDs; corresponding upstream sequences are retrieved from the maize genome. Predicted motifs are filtered, combined and ranked. Promzea searches the chosen plant genome for genes containing each candidate motif, providing the user with the gene list and corresponding gene annotations. Promzea was validated in silico using a benchmark data set: the Promzea pipeline showed a 22% increase in nucleotide sensitivity compared to the best standalone program tool, Weeder, with equivalent nucleotide specificity. Promzea was also validated by its ability to retrieve the experimentally defined binding sites of transcription factors that regulate the maize anthocyanin and phlobaphene biosynthetic pathways. Promzea predicted additional promoter motifs, and genome-wide motif searches by Promzea identified 127 non-anthocyanin/phlobaphene genes that each contained all five predicted promoter

On the pulse of discovery

Science.gov (United States)

2017-12-01

What started 50 years ago as a `smudge' on paper has flourished into a fundamental field of astrophysics replete with unexpected applications and exciting discoveries. To celebrate the discovery of pulsars, we look at the past, present and future of pulsar astrophysics.

Mammalian synthetic biology: emerging medical applications.

Science.gov (United States)

Kis, Zoltán; Pereira, Hugo Sant'Ana; Homma, Takayuki; Pedrigi, Ryan M; Krams, Rob

2015-05-06

In this review, we discuss new emerging medical applications of the rapidly evolving field of mammalian synthetic biology. We start with simple mammalian synthetic biological components and move towards more complex and therapy-oriented gene circuits. A comprehensive list of ON-OFF switches, categorized into transcriptional, post-transcriptional, translational and post-translational, is presented in the first sections. Subsequently, Boolean logic gates, synthetic mammalian oscillators and toggle switches will be described. Several synthetic gene networks are further reviewed in the medical applications section, including cancer therapy gene circuits, immuno-regulatory networks, among others. The final sections focus on the applicability of synthetic gene networks to drug discovery, drug delivery, receptor-activating gene circuits and mammalian biomanufacturing processes. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Antibiotic discovery throughout the Small World Initiative: A molecular strategy to identify biosynthetic gene clusters involved in antagonistic activity.

Science.gov (United States)

Davis, Elizabeth; Sloan, Tyler; Aurelius, Krista; Barbour, Angela; Bodey, Elijah; Clark, Brigette; Dennis, Celeste; Drown, Rachel; Fleming, Megan; Humbert, Allison; Glasgo, Elizabeth; Kerns, Trent; Lingro, Kelly; McMillin, MacKenzie; Meyer, Aaron; Pope, Breanna; Stalevicz, April; Steffen, Brittney; Steindl, Austin; Williams, Carolyn; Wimberley, Carmen; Zenas, Robert; Butela, Kristen; Wildschutte, Hans

2017-06-01

The emergence of bacterial pathogens resistant to all known antibiotics is a global health crisis. Adding to this problem is that major pharmaceutical companies have shifted away from antibiotic discovery due to low profitability. As a result, the pipeline of new antibiotics is essentially dry and many bacteria now resist the effects of most commonly used drugs. To address this global health concern, citizen science through the Small World Initiative (SWI) was formed in 2012. As part of SWI, students isolate bacteria from their local environments, characterize the strains, and assay for antibiotic production. During the 2015 fall semester at Bowling Green State University, students isolated 77 soil-derived bacteria and genetically characterized strains using the 16S rRNA gene, identified strains exhibiting antagonistic activity, and performed an expanded SWI workflow using transposon mutagenesis to identify a biosynthetic gene cluster involved in toxigenic compound production. We identified one mutant with loss of antagonistic activity and through subsequent whole-genome sequencing and linker-mediated PCR identified a 24.9 kb biosynthetic gene locus likely involved in inhibitory activity in that mutant. Further assessment against human pathogens demonstrated the inhibition of Bacillus cereus, Listeria monocytogenes, and methicillin-resistant Staphylococcus aureus in the presence of this compound, thus supporting our molecular strategy as an effective research pipeline for SWI antibiotic discovery and genetic characterization. © 2017 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.

Effector genomics accelerates discovery and functional profiling of potato disease resistance and phytophthora infestans avirulence genes.

Directory of Open Access Journals (Sweden)

Vivianne G A A Vleeshouwers

Full Text Available Potato is the world's fourth largest food crop yet it continues to endure late blight, a devastating disease caused by the Irish famine pathogen Phytophthora infestans. Breeding broad-spectrum disease resistance (R genes into potato (Solanum tuberosum is the best strategy for genetically managing late blight but current approaches are slow and inefficient. We used a repertoire of effector genes predicted computationally from the P. infestans genome to accelerate the identification, functional characterization, and cloning of potentially broad-spectrum R genes. An initial set of 54 effectors containing a signal peptide and a RXLR motif was profiled for activation of innate immunity (avirulence or Avr activity on wild Solanum species and tentative Avr candidates were identified. The RXLR effector family IpiO induced hypersensitive responses (HR in S. stoloniferum, S. papita and the more distantly related S. bulbocastanum, the source of the R gene Rpi-blb1. Genetic studies with S. stoloniferum showed cosegregation of resistance to P. infestans and response to IpiO. Transient co-expression of IpiO with Rpi-blb1 in a heterologous Nicotiana benthamiana system identified IpiO as Avr-blb1. A candidate gene approach led to the rapid cloning of S. stoloniferum Rpi-sto1 and S. papita Rpi-pta1, which are functionally equivalent to Rpi-blb1. Our findings indicate that effector genomics enables discovery and functional profiling of late blight R genes and Avr genes at an unprecedented rate and promises to accelerate the engineering of late blight resistant potato varieties.

Accelerating scientific discovery : 2007 annual report.

Energy Technology Data Exchange (ETDEWEB)

Beckman, P.; Dave, P.; Drugan, C.

2008-11-14

As a gateway for scientific discovery, the Argonne Leadership Computing Facility (ALCF) works hand in hand with the world's best computational scientists to advance research in a diverse span of scientific domains, ranging from chemistry, applied mathematics, and materials science to engineering physics and life sciences. Sponsored by the U.S. Department of Energy's (DOE) Office of Science, researchers are using the IBM Blue Gene/L supercomputer at the ALCF to study and explore key scientific problems that underlie important challenges facing our society. For instance, a research team at the University of California-San Diego/ SDSC is studying the molecular basis of Parkinson's disease. The researchers plan to use the knowledge they gain to discover new drugs to treat the disease and to identify risk factors for other diseases that are equally prevalent. Likewise, scientists from Pratt & Whitney are using the Blue Gene to understand the complex processes within aircraft engines. Expanding our understanding of jet engine combustors is the secret to improved fuel efficiency and reduced emissions. Lessons learned from the scientific simulations of jet engine combustors have already led Pratt & Whitney to newer designs with unprecedented reductions in emissions, noise, and cost of ownership. ALCF staff members provide in-depth expertise and assistance to those using the Blue Gene/L and optimizing user applications. Both the Catalyst and Applications Performance Engineering and Data Analytics (APEDA) teams support the users projects. In addition to working with scientists running experiments on the Blue Gene/L, we have become a nexus for the broader global community. In partnership with the Mathematics and Computer Science Division at Argonne National Laboratory, we have created an environment where the world's most challenging computational science problems can be addressed. Our expertise in high-end scientific computing enables us to provide

SNP discovery in candidate adaptive genes using exon capture in a free-ranging alpine ungulate

Science.gov (United States)

Roffler, Gretchen H.; Amish, Stephen J.; Smith, Seth; Cosart, Ted F.; Kardos, Marty; Schwartz, Michael K.; Luikart, Gordon

2016-01-01

Identification of genes underlying genomic signatures of natural selection is key to understanding adaptation to local conditions. We used targeted resequencing to identify SNP markers in 5321 candidate adaptive genes associated with known immunological, metabolic and growth functions in ovids and other ungulates. We selectively targeted 8161 exons in protein-coding and nearby 5′ and 3′ untranslated regions of chosen candidate genes. Targeted sequences were taken from bighorn sheep (Ovis canadensis) exon capture data and directly from the domestic sheep genome (Ovis aries v. 3; oviAri3). The bighorn sheep sequences used in the Dall's sheep (Ovis dalli dalli) exon capture aligned to 2350 genes on the oviAri3 genome with an average of 2 exons each. We developed a microfluidic qPCR-based SNP chip to genotype 476 Dall's sheep from locations across their range and test for patterns of selection. Using multiple corroborating approaches (lositan and bayescan), we detected 28 SNP loci potentially under selection. We additionally identified candidate loci significantly associated with latitude, longitude, precipitation and temperature, suggesting local environmental adaptation. The three methods demonstrated consistent support for natural selection on nine genes with immune and disease-regulating functions (e.g. Ovar-DRA, APC, BATF2, MAGEB18), cell regulation signalling pathways (e.g. KRIT1, PI3K, ORRC3), and respiratory health (CYSLTR1). Characterizing adaptive allele distributions from novel genetic techniques will facilitate investigation of the influence of environmental variation on local adaptation of a northern alpine ungulate throughout its range. This research demonstrated the utility of exon capture for gene-targeted SNP discovery and subsequent SNP chip genotyping using low-quality samples in a nonmodel species.

De Novo Discovery of Structured ncRNA Motifs in Genomic Sequences

DEFF Research Database (Denmark)

Ruzzo, Walter L; Gorodkin, Jan

2014-01-01

De novo discovery of "motifs" capturing the commonalities among related noncoding ncRNA structured RNAs is among the most difficult problems in computational biology. This chapter outlines the challenges presented by this problem, together with some approaches towards solving them, with an emphas...... on an approach based on the CMfinder CMfinder program as a case study. Applications to genomic screens for novel de novo structured ncRNA ncRNA s, including structured RNA elements in untranslated portions of protein-coding genes, are presented.......De novo discovery of "motifs" capturing the commonalities among related noncoding ncRNA structured RNAs is among the most difficult problems in computational biology. This chapter outlines the challenges presented by this problem, together with some approaches towards solving them, with an emphasis...

Carbon Nanotubes in Drug and Gene Delivery

Science.gov (United States)

Karimi, Mahdi; Ghasemi, Amir; Mirkiani, Soroush; Moosavi Basri, Seyed Masoud; Hamblin, Michael R.

2017-10-01

Recent important discoveries and developments in nanotechnology have had a remarkable and ever-increasing impact on many industries, especially materials science, pharmaceuticals, and biotechnology. Within this book, the authors describe different features of carbon nanotubes, survey the properties of both the multi-walled and single-walled varieties, and cover their applications in drug and gene delivery.

Model-driven discovery of underground metabolic functions in Escherichia coli

DEFF Research Database (Denmark)

Guzmán, Gabriela I.; Utrilla, José; Nurk, Sergey

2015-01-01

-scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gene essentiality offer an opportunity for targeting biological discovery, suggesting the presence......E, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations....

Discovery of the neutron (to the fiftieth anniversary of neutron discovery)

International Nuclear Information System (INIS)

Pasechnik, M.V.

1984-01-01

Development of neutron physics in the USSR for the recent 50 years from the moment of neutron discovery is considered. History of neutron discovery is presented in brief. Neutron properties and fundamental problems of physics: electric dipole neutron moment, neutron β-decay, neutron interaction with nuclei and potential of nucleon interaction not conserving spatial parity are discussed. Main aspects of neutron physics application in power engineering, nuclear technology and other branches of science and technique are set forth

Ataxin1L is a regulator of HSC function highlighting the utility of cross-tissue comparisons for gene discovery.

Directory of Open Access Journals (Sweden)

Juliette J Kahle

2013-03-01

Full Text Available Hematopoietic stem cells (HSCs are rare quiescent cells that continuously replenish the cellular components of the peripheral blood. Observing that the ataxia-associated gene Ataxin-1-like (Atxn1L was highly expressed in HSCs, we examined its role in HSC function through in vitro and in vivo assays. Mice lacking Atxn1L had greater numbers of HSCs that regenerated the blood more quickly than their wild-type counterparts. Molecular analyses indicated Atxn1L null HSCs had gene expression changes that regulate a program consistent with their higher level of proliferation, suggesting that Atxn1L is a novel regulator of HSC quiescence. To determine if additional brain-associated genes were candidates for hematologic regulation, we examined genes encoding proteins from autism- and ataxia-associated protein-protein interaction networks for their representation in hematopoietic cell populations. The interactomes were found to be highly enriched for proteins encoded by genes specifically expressed in HSCs relative to their differentiated progeny. Our data suggest a heretofore unappreciated similarity between regulatory modules in the brain and HSCs, offering a new strategy for novel gene discovery in both systems.

Biomedical Information Extraction: Mining Disease Associated Genes from Literature

Science.gov (United States)

Huang, Zhong

2014-01-01

Disease associated gene discovery is a critical step to realize the future of personalized medicine. However empirical and clinical validation of disease associated genes are time consuming and expensive. In silico discovery of disease associated genes from literature is therefore becoming the first essential step for biomarker discovery to…

Representation Discovery using Harmonic Analysis

CERN Document Server

Mahadevan, Sridhar

2008-01-01

Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particu

KNODWAT: a scientific framework application for testing knowledge discovery methods for the biomedical domain.

Science.gov (United States)

Holzinger, Andreas; Zupan, Mario

2013-06-13

Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem. A web application, called KNODWAT (KNOwledge Discovery With Advanced Techniques) has been developed, using Java on Spring framework 3.1. and following a user-centered approach. The software runs on Java 1.6 and above and requires a web server such as Apache Tomcat and a database server such as the MySQL Server. For frontend functionality and styling, Twitter Bootstrap was used as well as jQuery for interactive user interface operations. The framework presented is user-centric, highly extensible and flexible. Since it enables methods for testing using existing data to assess suitability and performance, it is especially suitable for inexperienced biomedical researchers, new to the field of knowledge discovery and data mining. For testing purposes two algorithms, CART and C4.5 were implemented using the WEKA data mining framework.

Systematic discovery of unannotated genes in 11 yeast species using a database of orthologous genomic segments

LENUS (Irish Health Repository)

OhEigeartaigh, Sean S

2011-07-26

Abstract Background In standard BLAST searches, no information other than the sequences of the query and the database entries is considered. However, in situations where two genes from different species have only borderline similarity in a BLAST search, the discovery that the genes are located within a region of conserved gene order (synteny) can provide additional evidence that they are orthologs. Thus, for interpreting borderline search results, it would be useful to know whether the syntenic context of a database hit is similar to that of the query. This principle has often been used in investigations of particular genes or genomic regions, but to our knowledge it has never been implemented systematically. Results We made use of the synteny information contained in the Yeast Gene Order Browser database for 11 yeast species to carry out a systematic search for protein-coding genes that were overlooked in the original annotations of one or more yeast genomes but which are syntenic with their orthologs. Such genes tend to have been overlooked because they are short, highly divergent, or contain introns. The key features of our software - called SearchDOGS - are that the database entries are classified into sets of genomic segments that are already known to be orthologous, and that very weak BLAST hits are retained for further analysis if their genomic location is similar to that of the query. Using SearchDOGS we identified 595 additional protein-coding genes among the 11 yeast species, including two new genes in Saccharomyces cerevisiae. We found additional genes for the mating pheromone a-factor in six species including Kluyveromyces lactis. Conclusions SearchDOGS has proven highly successful for identifying overlooked genes in the yeast genomes. We anticipate that our approach can be adapted for study of further groups of species, such as bacterial genomes. More generally, the concept of doing sequence similarity searches against databases to which external

InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data.

Directory of Open Access Journals (Sweden)

Konstantin Okonechnikov

Full Text Available Analysis of fusion transcripts has become increasingly important due to their link with cancer development. Since high-throughput sequencing approaches survey fusion events exhaustively, several computational methods for the detection of gene fusions from RNA-seq data have been developed. This kind of analysis, however, is complicated by native trans-splicing events, the splicing-induced complexity of the transcriptome and biases and artefacts introduced in experiments and data analysis. There are a number of tools available for the detection of fusions from RNA-seq data; however, certain differences in specificity and sensitivity between commonly used approaches have been found. The ability to detect gene fusions of different types, including isoform fusions and fusions involving non-coding regions, has not been thoroughly studied yet. Here, we propose a novel computational toolkit called InFusion for fusion gene detection from RNA-seq data. InFusion introduces several unique features, such as discovery of fusions involving intergenic regions, and detection of anti-sense transcription in chimeric RNAs based on strand-specificity. Our approach demonstrates superior detection accuracy on simulated data and several public RNA-seq datasets. This improved performance was also evident when evaluating data from RNA deep-sequencing of two well-established prostate cancer cell lines. InFusion identified 26 novel fusion events that were validated in vitro, including alternatively spliced gene fusion isoforms and chimeric transcripts that include intergenic regions. The toolkit is freely available to download from http:/bitbucket.org/kokonech/infusion.

Pharmacogenetics in type 2 diabetes: precision medicine or discovery tool?

Science.gov (United States)

Florez, Jose C

2017-05-01

In recent years, technological and analytical advances have led to an explosion in the discovery of genetic loci associated with type 2 diabetes. However, their ability to improve prediction of disease outcomes beyond standard clinical risk factors has been limited. On the other hand, genetic effects on drug response may be stronger than those commonly seen for disease incidence. Pharmacogenetic findings may aid in identifying new drug targets, elucidate pathophysiology, unravel disease heterogeneity, help prioritise specific genes in regions of genetic association, and contribute to personalised or precision treatment. In diabetes, precedent for the successful application of pharmacogenetic concepts exists in its monogenic subtypes, such as MODY or neonatal diabetes. Whether similar insights will emerge for the much more common entity of type 2 diabetes remains to be seen. As genetic approaches advance, the progressive deployment of candidate gene, large-scale genotyping and genome-wide association studies has begun to produce suggestive results that may transform clinical practice. However, many barriers to the translation of diabetes pharmacogenetic discoveries to the clinic still remain. This perspective offers a contemporary overview of the field with a focus on sulfonylureas and metformin, identifies the major uses of pharmacogenetics, and highlights potential limitations and future directions.

Meta4: a web application for sharing and annotating metagenomic gene predictions using web services.

Science.gov (United States)

Richardson, Emily J; Escalettes, Franck; Fotheringham, Ian; Wallace, Robert J; Watson, Mick

2013-01-01

Whole-genome shotgun metagenomics experiments produce DNA sequence data from entire ecosystems, and provide a huge amount of novel information. Gene discovery projects require up-to-date information about sequence homology and domain structure for millions of predicted proteins to be presented in a simple, easy-to-use system. There is a lack of simple, open, flexible tools that allow the rapid sharing of metagenomics datasets with collaborators in a format they can easily interrogate. We present Meta4, a flexible and extensible web application that can be used to share and annotate metagenomic gene predictions. Proteins and predicted domains are stored in a simple relational database, with a dynamic front-end which displays the results in an internet browser. Web services are used to provide up-to-date information about the proteins from homology searches against public databases. Information about Meta4 can be found on the project website, code is available on Github, a cloud image is available, and an example implementation can be seen at.

Maximum Entropy in Drug Discovery

Directory of Open Access Journals (Sweden)

Chih-Yuan Tseng

2014-07-01

Full Text Available Drug discovery applies multidisciplinary approaches either experimentally, computationally or both ways to identify lead compounds to treat various diseases. While conventional approaches have yielded many US Food and Drug Administration (FDA-approved drugs, researchers continue investigating and designing better approaches to increase the success rate in the discovery process. In this article, we provide an overview of the current strategies and point out where and how the method of maximum entropy has been introduced in this area. The maximum entropy principle has its root in thermodynamics, yet since Jaynes’ pioneering work in the 1950s, the maximum entropy principle has not only been used as a physics law, but also as a reasoning tool that allows us to process information in hand with the least bias. Its applicability in various disciplines has been abundantly demonstrated. We give several examples of applications of maximum entropy in different stages of drug discovery. Finally, we discuss a promising new direction in drug discovery that is likely to hinge on the ways of utilizing maximum entropy.

Species-independent MicroRNA Gene Discovery

KAUST Repository

Kamanu, Timothy K.

2012-01-01

and other incurable diseases such as autism and Alzheimer’s. Functional miRNAs are excised from hairpin-like sequences that are known as miRNA genes. There are about 21,000 known miRNA genes, most of which have been determined using experimental methods. mi

Gene/QTL discovery for Anthracnose in common bean (Phaseolus vulgaris L.) from North-western Himalayas.

Science.gov (United States)

Choudhary, Neeraj; Bawa, Vanya; Paliwal, Rajneesh; Singh, Bikram; Bhat, Mohd Ashraf; Mir, Javid Iqbal; Gupta, Moni; Sofi, Parvaze A; Thudi, Mahendar; Varshney, Rajeev K; Mir, Reyazul Rouf

2018-01-01

Common bean (Phaseolus vulgaris L.) is one of the most important grain legume crops in the world. The beans grown in north-western Himalayas possess huge diversity for seed color, shape and size but are mostly susceptible to Anthracnose disease caused by seed born fungus Colletotrichum lindemuthianum. Dozens of QTLs/genes have been already identified for this disease in common bean world-wide. However, this is the first report of gene/QTL discovery for Anthracnose using bean germplasm from north-western Himalayas of state Jammu & Kashmir, India. A core set of 96 bean lines comprising 54 indigenous local landraces from 11 hot-spots and 42 exotic lines from 10 different countries were phenotyped at two locations (SKUAST-Jammu and Bhaderwah, Jammu) for Anthracnose resistance. The core set was also genotyped with genome-wide (91) random and trait linked SSR markers. The study of marker-trait associations (MTAs) led to the identification of 10 QTLs/genes for Anthracnose resistance. Among the 10 QTLs/genes identified, two MTAs are stable (BM45 & BM211), two MTAs (PVctt1 & BM211) are major explaining more than 20% phenotypic variation for Anthracnose and one MTA (BM211) is both stable and major. Six (06) genomic regions are reported for the first time, while as four (04) genomic regions validated the already known QTL/gene regions/clusters for Anthracnose. The major, stable and validated markers reported during the present study associated with Anthracnose resistance will prove useful in common bean molecular breeding programs aimed at enhancing Anthracnose resistance of local bean landraces grown in north-western Himalayas of state Jammu and Kashmir.

Synthetic biology of antimicrobial discovery.

Science.gov (United States)

Zakeri, Bijan; Lu, Timothy K

2013-07-19

Antibiotic discovery has a storied history. From the discovery of penicillin by Sir Alexander Fleming to the relentless quest for antibiotics by Selman Waksman, the stories have become like folklore used to inspire future generations of scientists. However, recent discovery pipelines have run dry at a time when multidrug-resistant pathogens are on the rise. Nature has proven to be a valuable reservoir of antimicrobial agents, which are primarily produced by modularized biochemical pathways. Such modularization is well suited to remodeling by an interdisciplinary approach that spans science and engineering. Herein, we discuss the biological engineering of small molecules, peptides, and non-traditional antimicrobials and provide an overview of the growing applicability of synthetic biology to antimicrobials discovery.

Gene Therapy and its applications in Dentistry

Directory of Open Access Journals (Sweden)

Sharma Lakhanpal Manisha

2006-01-01

Full Text Available This era of advanced technology is marked by progress in identifying and understanding the molecular and cellular cause of a disease. With the conventional methods of treatment failing to render satisfactory results, gene therapy is not only being used for the cure of inherited diseases but also the acquired ones. The broad spectrum of gene therapy includes its application in the treatment of oral cancer and precancerous conditions and lesions, treatment of salivary gland diseases, bone repair, autoimmune diseases, DNA vaccination, etc. The aim of this article is to throw light on the history, methodology, applications and future of gene therapy as it would change the nature and face of dentistry in the coming years.

On the antiproton discovery

International Nuclear Information System (INIS)

Piccioni, O.

1989-01-01

The author of this article describes his own role in the discovery of the antiproton. Although Segre and Chamberlain received the Nobel Prize in 1959 for its discovery, the author claims that their experimental method was his idea which he communicated to them informally in December 1954. He describes how his application for citizenship (he was Italian), and other scientists' manipulation, prevented him from being at Berkeley to work on the experiment himself. (UK)

Crowdsourcing the nodulation gene network discovery environment.

Science.gov (United States)

Li, Yupeng; Jackson, Scott A

2016-05-26

The Legumes (Fabaceae) are an economically and ecologically important group of plant species with the conspicuous capacity for symbiotic nitrogen fixation in root nodules, specialized plant organs containing symbiotic microbes. With the aim of understanding the underlying molecular mechanisms leading to nodulation, many efforts are underway to identify nodulation-related genes and determine how these genes interact with each other. In order to accurately and efficiently reconstruct nodulation gene network, a crowdsourcing platform, CrowdNodNet, was created. The platform implements the jQuery and vis.js JavaScript libraries, so that users are able to interactively visualize and edit the gene network, and easily access the information about the network, e.g. gene lists, gene interactions and gene functional annotations. In addition, all the gene information is written on MediaWiki pages, enabling users to edit and contribute to the network curation. Utilizing the continuously updated, collaboratively written, and community-reviewed Wikipedia model, the platform could, in a short time, become a comprehensive knowledge base of nodulation-related pathways. The platform could also be used for other biological processes, and thus has great potential for integrating and advancing our understanding of the functional genomics and systems biology of any process for any species. The platform is available at http://crowd.bioops.info/ , and the source code can be openly accessed at https://github.com/bioops/crowdnodnet under MIT License.

The limits of de novo DNA motif discovery.

Directory of Open Access Journals (Sweden)

David Simcha

Full Text Available A major challenge in molecular biology is reverse-engineering the cis-regulatory logic that plays a major role in the control of gene expression. This program includes searching through DNA sequences to identify "motifs" that serve as the binding sites for transcription factors or, more generally, are predictive of gene expression across cellular conditions. Several approaches have been proposed for de novo motif discovery-searching sequences without prior knowledge of binding sites or nucleotide patterns. However, unbiased validation is not straightforward. We consider two approaches to unbiased validation of discovered motifs: testing the statistical significance of a motif using a DNA "background" sequence model to represent the null hypothesis and measuring performance in predicting membership in gene clusters. We demonstrate that the background models typically used are "too null," resulting in overly optimistic assessments of significance, and argue that performance in predicting TF binding or expression patterns from DNA motifs should be assessed by held-out data, as in predictive learning. Applying this criterion to common motif discovery methods resulted in universally poor performance, although there is a marked improvement when motifs are statistically significant against real background sequences. Moreover, on synthetic data where "ground truth" is known, discriminative performance of all algorithms is far below the theoretical upper bound, with pronounced "over-fitting" in training. A key conclusion from this work is that the failure of de novo discovery approaches to accurately identify motifs is basically due to statistical intractability resulting from the fixed size of co-regulated gene clusters, and thus such failures do not necessarily provide evidence that unfound motifs are not active biologically. Consequently, the use of prior knowledge to enhance motif discovery is not just advantageous but necessary. An implementation of

A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model

Directory of Open Access Journals (Sweden)

Mickael Orgeur

2018-01-01

Full Text Available The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads, and the gene annotation that defines gene features, must also be taken into account. A partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.

Synthetic biology of antimicrobial discovery

Science.gov (United States)

Zakeri, Bijan; Lu, Timothy K.

2012-01-01

Antibiotic discovery has a storied history. From the discovery of penicillin by Sir Alexander Fleming to the relentless quest for antibiotics by Selman Waksman, the stories have become like folklore, used to inspire future generations of scientists. However, recent discovery pipelines have run dry at a time when multidrug resistant pathogens are on the rise. Nature has proven to be a valuable reservoir of antimicrobial agents, which are primarily produced by modularized biochemical pathways. Such modularization is well suited to remodeling by an interdisciplinary approach that spans science and engineering. Herein, we discuss the biological engineering of small molecules, peptides, and non-traditional antimicrobials and provide an overview of the growing applicability of synthetic biology to antimicrobials discovery. PMID:23654251

Efficient strategy for detecting gene × gene joint action and its application in schizophrenia.

Science.gov (United States)

Won, Sungho; Kwon, Min-Seok; Mattheisen, Manuel; Park, Suyeon; Park, Changsoon; Kihara, Daisuke; Cichon, Sven; Ophoff, Roel; Nöthen, Markus M; Rietschel, Marcella; Baur, Max; Uitterlinden, Andre G; Hofmann, A; Lange, Christoph

2014-01-01

We propose a new approach to detect gene × gene joint action in genome-wide association studies (GWASs) for case-control designs. This approach offers an exhaustive search for all two-way joint action (including, as a special case, single gene action) that is computationally feasible at the genome-wide level and has reasonable statistical power under most genetic models. We found that the presence of any gene × gene joint action may imply differences in three types of genetic components: the minor allele frequencies and the amounts of Hardy-Weinberg disequilibrium may differ between cases and controls, and between the two genetic loci the degree of linkage disequilibrium may differ between cases and controls. Using Fisher's method, it is possible to combine the different sources of genetic information in an overall test for detecting gene × gene joint action. The proposed statistical analysis is efficient and its simplicity makes it applicable to GWASs. In the current study, we applied the proposed approach to a GWAS on schizophrenia and found several potential gene × gene interactions. Our application illustrates the practical advantage of the proposed method. © 2013 WILEY PERIODICALS, INC.

Biomarker discovery and applications for foods and beverages: proteomics to nanoproteomics.

Science.gov (United States)

Agrawal, Ganesh Kumar; Timperio, Anna Maria; Zolla, Lello; Bansal, Vipul; Shukla, Ravi; Rakwal, Randeep

2013-11-20

Foods and beverages have been at the heart of our society for centuries, sustaining humankind - health, life, and the pleasures that go with it. The more we grow and develop as a civilization, the more we feel the need to know about the food we eat and beverages we drink. Moreover, with an ever increasing demand for food due to the growing human population food security remains a major concern. Food safety is another growing concern as the consumers prefer varied foods and beverages that are not only traded nationally but also globally. The 21st century science and technology is at a new high, especially in the field of biological sciences. The availability of genome sequences and associated high-throughput sensitive technologies means that foods are being analyzed at various levels. For example and in particular, high-throughput omics approaches are being applied to develop suitable biomarkers for foods and beverages and their applications in addressing quality, technology, authenticity, and safety issues. Proteomics are one of those technologies that are increasingly being utilized to profile expressed proteins in different foods and beverages. Acquired knowledge and protein information have now been translated to address safety of foods and beverages. Very recently, the power of proteomic technology has been integrated with another highly sensitive and miniaturized technology called nanotechnology, yielding a new term nanoproteomics. Nanoproteomics offer a real-time multiplexed analysis performed in a miniaturized assay, with low-sample consumption and high sensitivity. To name a few, nanomaterials - quantum dots, gold nanoparticles, carbon nanotubes, and nanowires - have demonstrated potential to overcome the challenges of sensitivity faced by proteomics for biomarker detection, discovery, and application. In this review, we will discuss the importance of biomarker discovery and applications for foods and beverages, the contribution of proteomic technology in

Development and application of a 6.5 million feature affymetrix genechip® for massively parallel discovery of single position polymorphisms in lettuce (Lactuca spp.)

OpenAIRE

Stoffel, Kevin; van Leeuwen, Hans; Kozik, Alexander; Caldwell, David; Ashrafi, Hamid; Cui, Xinping; Tan, Xiaoping; Hill, Theresa; Reyes-Chin-Wo, Sebastian; Truco, Maria-Jose; Michelmore, Richard W; Van Deynze, Allen

2012-01-01

Abstract Background High-resolution genetic maps are needed in many crops to help characterize the genetic diversity that determines agriculturally important traits. Hybridization to microarrays to detect single feature polymorphisms is a powerful technique for marker discovery and genotyping because of its highly parallel nature. However, microarrays designed for gene expression analysis rarely provide sufficient gene coverage for optimal detection o...

Classification of genes and putative biomarker identification using distribution metrics on expression profiles.

Directory of Open Access Journals (Sweden)

Hung-Chung Huang

Full Text Available BACKGROUND: Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic, and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as 'brain group' and 'non-brain group'; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. CONCLUSIONS/SIGNIFICANCE: The methodology employed here may be used to facilitate disease-specific biomarker discovery.

Using Phenomic Analysis of Photosynthetic Function for Abiotic Stress Response Gene Discovery

KAUST Repository

Rungrat, Tepsuda

2016-09-09

Monitoring the photosynthetic performance of plants is a major key to understanding how plants adapt to their growth conditions. Stress tolerance traits have a high genetic complexity as plants are constantly, and unavoidably, exposed to numerous stress factors, which limits their growth rates in the natural environment. Arabidopsis thaliana, with its broad genetic diversity and wide climatic range, has been shown to successfully adapt to stressful conditions to ensure the completion of its life cycle. As a result, A. thaliana has become a robust and renowned plant model system for studying natural variation and conducting gene discovery studies. Genome wide association studies (GWAS) in restructured populations combining natural and recombinant lines is a particularly effective way to identify the genetic basis of complex traits. As most abiotic stresses affect photosynthetic activity, chlorophyll fluorescence measurements are a potential phenotyping technique for monitoring plant performance under stress conditions. This review focuses on the use of chlorophyll fluorescence as a tool to study genetic variation underlying the stress tolerance responses to abiotic stress in A. thaliana.

Managing Innovation to Maximize Value Along the Discovery-Translation-Application Continuum.

Science.gov (United States)

Waldman, S A; Terzic, A

2017-01-01

Success in pharmaceutical development led to a record 51 drugs approved in the past year, surpassing every previous year since 1950. Technology innovation enabled identification and exploitation of increasingly precise disease targets ensuring next generation diagnostic and therapeutic products for patient management. The expanding biopharmaceutical portfolio stands, however, in contradistinction to the unsustainable costs that reflect remarkable challenges of clinical development programs. This annual Therapeutic Innovations issue juxtaposes advances in translating molecular breakthroughs into transformative therapies with essential considerations for lowering attrition and improving the cost-effectiveness of the drug-development paradigm. Realizing the discovery-translation-application continuum mandates a congruent approval, adoption, and access triad. © 2016 ASCPT.

The Utility of Next Generation Sequencing in Gene Discovery for Mutation-negative Patients with Rett Syndrome

Directory of Open Access Journals (Sweden)

Wendy Anne Gold

2015-07-01

Full Text Available Rett syndrome (RTT is a rare, severe disorder of neuronal plasticity that predominantly affects girls. Girls with RTT usually appear asymptomatic in the first 6-18 months of life, but gradually develop severe motor, cognitive and behavioural abnormalities that persist for life. A predominance of neuronal and synaptic dysfunction, with altered excitatory-inhibitory neuronal synaptic transmission and synaptic plasticity are overarching features of RTT in children and in mouse models. Approximately 95% of patients with classical RTT have mutations in the X-linked methyl-CpG-binding (MECP2 gene, whilst other genes, including cyclin-dependent kinase-like 5 (CDKL5, Forkhead box protein G1 (FOXG1, Myocyte-specific enhancer factor 2C (MEF2C and Transcription factor 4 (TCF4, have been associated with phenotypes overlapping with RTT. However, there remain a proportion of patients who carry a clinical diagnosis of RTT, but who are mutation negative. In recent years, next-generation sequencing (NGS technologies have revolutionized approaches to genetic studies, making whole-exome and even whole-genome sequencing possible strategies for the detection of rare and de novo mutations, aiding the discovery of novel disease genes. Here, we review the recent progress that is emerging in identifying pathogenic variations, specifically from exome sequencing in RTT patients, and emphasize the need for the use of this technology to identify known and new disease genes in RTT patients.

Bioinformatics for discovery of microbiome variation

DEFF Research Database (Denmark)

Brejnrod, Asker Daniel

of various molecular methods to build hypotheses about the impact of a copper contaminated soil. The introduction is a broad introduction to the field of microbiome research with a focus on the technologies that enable these discoveries and how some of the broader issues have related to this thesis......Sequencing based tools have revolutionized microbiology in recent years. Highthroughput DNA sequencing have allowed high-resolution studies on microbial life in many different environments and at unprecedented low cost. These culture-independent methods have helped discovery of novel bacteria...... 1 ,“Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies”, benchmarked the performance of a variety of popular statistical methods for discovering differentially abundant bacteria . between...

Large-scale discovery of promoter motifs in Drosophila melanogaster.

Directory of Open Access Journals (Sweden)

Thomas A Down

2007-01-01

Full Text Available A key step in understanding gene regulation is to identify the repertoire of transcription factor binding motifs (TFBMs that form the building blocks of promoters and other regulatory elements. Identifying these experimentally is very laborious, and the number of TFBMs discovered remains relatively small, especially when compared with the hundreds of transcription factor genes predicted in metazoan genomes. We have used a recently developed statistical motif discovery approach, NestedMICA, to detect candidate TFBMs from a large set of Drosophila melanogaster promoter regions. Of the 120 motifs inferred in our initial analysis, 25 were statistically significant matches to previously reported motifs, while 87 appeared to be novel. Analysis of sequence conservation and motif positioning suggested that the great majority of these discovered motifs are predictive of functional elements in the genome. Many motifs showed associations with specific patterns of gene expression in the D. melanogaster embryo, and we were able to obtain confident annotation of expression patterns for 25 of our motifs, including eight of the novel motifs. The motifs are available through Tiffin, a new database of DNA sequence motifs. We have discovered many new motifs that are overrepresented in D. melanogaster promoter regions, and offer several independent lines of evidence that these are novel TFBMs. Our motif dictionary provides a solid foundation for further investigation of regulatory elements in Drosophila, and demonstrates techniques that should be applicable in other species. We suggest that further improvements in computational motif discovery should narrow the gap between the set of known motifs and the total number of transcription factors in metazoan genomes.

From General Aberrant Alternative Splicing in Cancers and Its Therapeutic Application to the Discovery of an Oncogenic DMTF1 Isoform

Directory of Open Access Journals (Sweden)

Na Tian

2017-03-01

Full Text Available Alternative pre-mRNA splicing is a crucial process that allows the generation of diversified RNA and protein products from a multi-exon gene. In tumor cells, this mechanism can facilitate cancer development and progression through both creating oncogenic isoforms and reducing the expression of normal or controllable protein species. We recently demonstrated that an alternative cyclin D-binding myb-like transcription factor 1 (DMTF1 pre-mRNA splicing isoform, DMTF1β, is increasingly expressed in breast cancer and promotes mammary tumorigenesis in a transgenic mouse model. Aberrant pre-mRNA splicing is a typical event occurring for many cancer-related functional proteins. In this review, we introduce general aberrant pre-mRNA splicing in cancers and discuss its therapeutic application using our recent discovery of the oncogenic DMTF1 isoform as an example. We also summarize new insights in designing novel targeting strategies of cancer therapies based on the understanding of deregulated pre-mRNA splicing mechanisms.

Development and application of a 6.5 million feature Affymetrix Genechip® for massively parallel discovery of single position polymorphisms in lettuce (Lactuca spp.)

OpenAIRE

Stoffel, Kevin; Kozik, Alexander; Ashrafi, Hamid; Cui, Xinping; Tan, Xiaoping; Hill, Theresa; Reyes-Chin-Wo, Sebastian; Truco, Maria-Jose; Michelmore, Richard W; Van Deynze, Allen

2012-01-01

Abstract Background High-resolution genetic maps are needed in many crops to help characterize the genetic diversity that determines agriculturally important traits. Hybridization to microarrays to detect single feature polymorphisms is a powerful technique for marker discovery and genotyping because of its highly parallel nature. However, microarrays designed for gene expression analysis rarely provide sufficient gene coverage for optimal detection of nucleotide polymorphisms, which limits u...

A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection.

Science.gov (United States)

Carey, Michelle; Ramírez, Juan Camilo; Wu, Shuang; Wu, Hulin

2018-07-01

A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.

Mass spectrometry in biomarker applications: from untargeted discovery to targeted verification, and implications for platform convergence and clinical application

Energy Technology Data Exchange (ETDEWEB)

Smith, Richard D.

2012-03-01

It is really only in the last ten years that mass spectrometry (MS) has had a truly significant (but still small) impact on biomedical research. Much of this impact can be attributed to proteomics and its more basic applications. Early biomedical applications have included a number of efforts aimed at developing new biomarkers; however, the success of these endeavors to date have been quite modest - essentially confined to preclinical applications - and have often suffered from combinations of immature technology and hubris. Now that MS-based proteomics is reaching adolescence, it is appropriate to ask if and when biomarker-related applications will extend to the clinical realm, and what developments will be essential for this transition. Biomarker development can be described as a multistage process consisting of discovery, qualification, verification, research assay optimization, validation, and commercialization (1). From a MS perspective, it is possible to 'bin' measurements into 1 of 2 categories - those aimed at discovering potential protein biomarkers and those seeking to verify and validate biomarkers. Approaches in both categories generally involve digesting proteins (e.g., with trypsin) as a first step to yield peptides that can be effectively detected and identified with MS. Discovery-based approaches use broad 'unbiased' or 'undirected' measurements that attempt to cover as many proteins as possible in the hope of revealing promising biomarker candidates. A key challenge with this approach stems from the extremely large dynamic range (i.e., relative stoichiometry) of proteins of potential interest in biofluids such as plasma and the expectation that biomarker proteins of the greatest clinical value for many diseases may very well be present at low relative abundances (2). Protein concentrations in plasma extend from approximately 10{sup 10} pg/mL for albumin to approximately 10 pg/mL and below for interleukins and other

Discovery and industrial applications of lytic polysaccharide mono-oxygenases.

Science.gov (United States)

Johansen, Katja S

2016-02-01

The recent discovery of copper-dependent lytic polysaccharide mono-oxygenases (LPMOs) has opened up a vast area of research covering several fields of application. The biotech company Novozymes A/S holds patents on the use of these enzymes for the conversion of steam-pre-treated plant residues such as straw to free sugars. These patents predate the correct classification of LPMOs and the striking synergistic effect of fungal LPMOs when combined with canonical cellulases was discovered when fractions of fungal secretomes were evaluated in industrially relevant enzyme performance assays. Today, LPMOs are a central component in the Cellic CTec enzyme products which are used in several large-scale plants for the industrial production of lignocellulosic ethanol. LPMOs are characterized by an N-terminal histidine residue which, together with an internal histidine and a tyrosine residue, co-ordinates a single copper atom in a so-called histidine brace. The mechanism by which oxygen binds to the reduced copper atom has been reported and the general mechanism of copper-oxygen-mediated activation of carbon is being investigated in the light of these discoveries. LPMOs are widespread in both the fungal and the bacterial kingdoms, although the range of action of these enzymes remains to be elucidated. However, based on the high abundance of LPMOs expressed by microbes involved in the decomposition of organic matter, the importance of LPMOs in the natural carbon-cycle is predicted to be significant. In addition, it has been suggested that LPMOs play a role in the pathology of infectious diseases such as cholera and to thus be relevant in the field of medicine. © 2016 Authors; published by Portland Press Limited.

Computational methods for 2D materials: discovery, property characterization, and application design.

Science.gov (United States)

Paul, J T; Singh, A K; Dong, Z; Zhuang, H; Revard, B C; Rijal, B; Ashton, M; Linscheid, A; Blonsky, M; Gluhovic, D; Guo, J; Hennig, R G

2017-11-29

The discovery of two-dimensional (2D) materials comes at a time when computational methods are mature and can predict novel 2D materials, characterize their properties, and guide the design of 2D materials for applications. This article reviews the recent progress in computational approaches for 2D materials research. We discuss the computational techniques and provide an overview of the ongoing research in the field. We begin with an overview of known 2D materials, common computational methods, and available cyber infrastructures. We then move onto the discovery of novel 2D materials, discussing the stability criteria for 2D materials, computational methods for structure prediction, and interactions of monolayers with electrochemical and gaseous environments. Next, we describe the computational characterization of the 2D materials' electronic, optical, magnetic, and superconducting properties and the response of the properties under applied mechanical strain and electrical fields. From there, we move on to discuss the structure and properties of defects in 2D materials, and describe methods for 2D materials device simulations. We conclude by providing an outlook on the needs and challenges for future developments in the field of computational research for 2D materials.

Orphan diseases: state of the drug discovery art.

Science.gov (United States)

Volmar, Claude-Henry; Wahlestedt, Claes; Brothers, Shaun P

2017-06-01

Since 1983 more than 300 drugs have been developed and approved for orphan diseases. However, considering the development of novel diagnosis tools, the number of rare diseases vastly outpaces therapeutic discovery. Academic centers and nonprofit institutes are now at the forefront of rare disease R&D, partnering with pharmaceutical companies when academic researchers discover novel drugs or targets for specific diseases, thus reducing the failure risk and cost for pharmaceutical companies. Considerable progress has occurred in the art of orphan drug discovery, and a symbiotic relationship now exists between pharmaceutical industry, academia, and philanthropists that provides a useful framework for orphan disease therapeutic discovery. Here, the current state-of-the-art of drug discovery for orphan diseases is reviewed. Current technological approaches and challenges for drug discovery are considered, some of which can present somewhat unique challenges and opportunities in orphan diseases, including the potential for personalized medicine, gene therapy, and phenotypic screening.

The discovery of radioactivity: the centenary

International Nuclear Information System (INIS)

Patil, S.K.

1995-01-01

In the last decade of the nineteenth century, a number of fundamental discoveries of outstanding importance were made unexpectedly which marked the beginning of a new era in physics. A cascade of spectacular discoveries began with the announcement of the discovery of x-rays by Roentgen followed by the discoveries, in quick succession, of radioactivity by Becquerel, of Zeeman effect, of electron by J.J. Thomson, and of polonium and radium by the Curies. Both x-rays and radioactivity have wide applications in scientific, medical and industrial fields and have made outstanding contribution to the advancement of human knowledge and welfare. Radioactivity is well known and no other discovery in the field of physics or chemistry has had a more profound effect on our fundamental knowledge of nature. Present article, on the occasion of the centenary of the discovery of radioactivity, makes an attempt to describe some glimpses of the history of radioactivity. (author). 59 refs

Gene2Function: An Integrated Online Resource for Gene Function Discovery

Directory of Open Access Journals (Sweden)

Yanhui Hu

2017-08-01

Full Text Available One of the most powerful ways to develop hypotheses regarding the biological functions of conserved genes in a given species, such as humans, is to first look at what is known about their function in another species. Model organism databases and other resources are rich with functional information but difficult to mine. Gene2Function addresses a broad need by integrating information about conserved genes in a single online resource.

Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification

Science.gov (United States)

Book Chapter 18, titled Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification, will be published in the book titled High Performance Liquid Chromatography in Pesticide Residue Analysis (Part of the C...

Leveraging gene-environment interactions and endotypes for asthma gene discovery

DEFF Research Database (Denmark)

Bønnelykke, Klaus; Ober, Carole

2016-01-01

, such as childhood asthma with severe exacerbations, and on relevant exposures that are involved in gene-environment interactions (GEIs), such as rhinovirus infections, will improve detection of asthma genes and our understanding of the underlying mechanisms. We will discuss the challenges of considering GEIs......Asthma is a heterogeneous clinical syndrome that includes subtypes of disease with different underlying causes and disease mechanisms. Asthma is caused by a complex interaction between genes and environmental exposures; early-life exposures in particular play an important role. Asthma is also...... heritable, and a number of susceptibility variants have been discovered in genome-wide association studies, although the known risk alleles explain only a small proportion of the heritability. In this review, we present evidence supporting the hypothesis that focusing on more specific asthma phenotypes...

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

Science.gov (United States)

Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

2010-01-18

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

Computational and Experimental Approaches to Cancer Biomarker Discovery

DEFF Research Database (Denmark)

Krzystanek, Marcin

of a patient’s response to a particular treatment, thus helping to avoid unnecessary treatment and unwanted side effects in non-responding individuals.Currently biomarker discovery is facilitated by recent advances in high-throughput technologies when association between a given biological phenotype...... and the state or level of a large number of molecular entities is investigated. Such associative analysis could be confounded by several factors, leading to false discoveries. For example, it is assumed that with the exception of the true biomarkers most molecular entities such as gene expression levels show...... random distribution in a given cohort. However, gene expression levels may also be affected by technical bias when the actual measurement technology or sample handling may introduce a systematic error. If the distribution of systematic errors correlates with the biological phenotype then the risk...

Delta: the first pion nucleon resonance - its discovery and applications

International Nuclear Information System (INIS)

Nagle, D.E.

1984-07-01

It is attempted to recapture some of the fun and excitement of the pion-scattering work that led to the discovery of what is now called the delta particle. How significant this discovery was became apparent only gradually. That the delta is alive today and thriving at Los Alamos (as well as other places) is described

Emerging techniques for the discovery and validation of therapeutic targets for skeletal diseases.

Science.gov (United States)

Cho, Christine H; Nuttall, Mark E

2002-12-01

Advances in genomics and proteomics have revolutionised the drug discovery process and target validation. Identification of novel therapeutic targets for chronic skeletal diseases is an extremely challenging process based on the difficulty of obtaining high-quality human diseased versus normal tissue samples. The quality of tissue and genomic information obtained from the sample is critical to identifying disease-related genes. Using a genomics-based approach, novel genes or genes with similar homology to existing genes can be identified from cDNA libraries generated from normal versus diseased tissue. High-quality cDNA libraries are prepared from uncontaminated homogeneous cell populations harvested from tissue sections of interest. Localised gene expression analysis and confirmation are obtained through in situ hybridisation or immunohistochemical studies. Cells overexpressing the recombinant protein are subsequently designed for primary cell-based high-throughput assays that are capable of screening large compound banks for potential hits. Afterwards, secondary functional assays are used to test promising compounds. The same overexpressing cells are used in the secondary assay to test protein activity and functionality as well as screen for small-molecule agonists or antagonists. Once a hit is generated, a structure-activity relationship of the compound is optimised for better oral bioavailability and pharmacokinetics allowing the compound to progress into development. Parallel efforts from proteomics, as well as genetics/transgenics, bioinformatics and combinatorial chemistry, and improvements in high-throughput automation technologies, allow the drug discovery process to meet the demands of the medicinal market. This review discusses and illustrates how different approaches are incorporated into the discovery and validation of novel targets and, consequently, the development of potentially therapeutic agents in the areas of osteoporosis and osteoarthritis

Deep Learning in Drug Discovery.

Science.gov (United States)

Gawehn, Erik; Hiss, Jan A; Schneider, Gisbert

2016-01-01

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Radioactivity. Centenary of radioactivity discovery

International Nuclear Information System (INIS)

Charpak, G.; Tubiana, M.; Bimbot, R.

1997-01-01

This small booklet was edited for the occasion of the exhibitions of the celebration of the centenary of radioactivity discovery which took place in various locations in France from 1996 to 1998. It recalls some basic knowledge concerning radioactivity and its applications: history of discovery, atoms and isotopes, radiations, measurement of ionizing radiations, natural and artificial radioactivity, isotope dating and labelling, radiotherapy, nuclear power and reactors, fission and fusion, nuclear wastes, dosimetry, effects and radioprotection. (J.S.)

Incremental discovery of hidden structure: Applications in theory of elementary particles

International Nuclear Information System (INIS)

Zytkow, J.M.; Fischer, P.J.

1996-01-01

Discovering hidden structure is a challenging, universal research task in Physics, Chemistry, Biology, and other disciplines. Not only must the elements of hidden structure be postulated by the discoverer, but they can only be verified by indirect evidence, at the level of observable objects. In this paper we describe a framework for hidden structure discovery, built on a constructive definition of hidden structure. This definition leads to operators that build models of hidden structure step by step, postulating hidden objects, their combinations and properties, reactions described in terms of hidden objects, and mapping between the hidden and the observed structure. We introduce the operator dependency diagram, which shows the order of operator application and model evaluation. Different observational knowledge supports different evaluation criteria, which lead to different search systems with verifiable sequences of operator applications. Isomorph-free structure generation is another issue critical for efficiency of search. We apply our framework in the system GELL-MANN, that hypothesizes hidden structure for elementary particles and we present the results of a large scale search for quark models

Gene therapy imaging in patients for oncological applications

International Nuclear Information System (INIS)

Penuelas, Ivan; Haberkorn, Uwe; Yaghoubi, Shahriar; Gambhir, Sanjiv S.

2005-01-01

Thus far, traditional methods for evaluating gene transfer and expression have been shown to be of limited value in the clinical arena. Consequently there is a real need to develop new methods that could be repeatedly and safely performed in patients for such purposes. Molecular imaging techniques for gene expression monitoring have been developed and successfully used in animal models, but their sensitivity and reproducibility need to be tested and validated in human studies. In this review, we present the current status of gene therapy-based anticancer strategies and show how molecular imaging, and more specifically radionuclide-based approaches, can be used in gene therapy procedures for oncological applications in humans. The basis of gene expression imaging is described and specific uses of these non-invasive procedures for gene therapy monitoring illustrated. Molecular imaging of transgene expression in humans and evaluation of response to gene-based therapeutic procedures are considered. The advantages of molecular imaging for whole-body monitoring of transgene expression as a way to permit measurement of important parameters in both target and non-target organs are also analyzed. The relevance of this technology for evaluation of the necessary vector dose and how it can be used to improve vector design are also examined. Finally, the advantages of designing a gene therapy-based clinical trial with imaging fully integrated from the very beginning are discussed and future perspectives for the development of these applications outlined. (orig.)

DEVELOPING GUIDED DISCOVERY LEARNING MATERIALS USING MATHEMATICS MOBILE LEARNING APPLICATION AS AN ALTERNATIVE MEDIA FOR THE STUDENTS CALCULUS II

Directory of Open Access Journals (Sweden)

Sunismi .

2015-12-01

Full Text Available Abstract: The development research aims to develop guided-discovery learning materials of Calculus II by implementing Mathematics Mobile Learning (MML. The products to develop are MML media of Calculus II using guided discovery model for students and a guide book for lecturers. The study employed used 4-D development model consisting of define, design, develop, and disseminate. The draft of the learning materials was validated by experts and tried-out to a group of students. The data were analyzed qualitatively and quantitatively by using a descriptive technique and t-test. The findings of the research were appropriate to be used ad teaching media for the students. The students responded positively that the MML media of Calculus II using the guided-discovery model was interestingly structured, easily operated through handphones (all JAVA, android, and blackberry-based handphones to be used as their learning guide anytime. The result of the field testing showed that the guided-discovery learning materials of Calculus II using the Mathematics Mobile Learning (MML application was effective to adopt in learning Calculus II. Keywords: learning materials, guided-discovery, mathematics mobile learning (MML, calculus II PENGEMBANGAN BAHAN AJAR MODEL GUIDED DISCOVERY DENGAN APLIKASI MATHEMATICS MOBILE LEARNING SEBAGAI ALTERNATIF MEDIA PEMBELAJARAN MAHASISWA MATAKULIAH KALKULUS II Abstrak: Penelitian pengembangan ini bertujuan untuk mengembangkan bahan ajar matakuliah Kalkulus II model guided discovery dengan aplikasi Mathematics Mobile Learning (MML. Produk yang dikembangkan berupa media MML Kalkulus II dengan model guided discovery untuk mahasiswa dan buku panduan dosen. Model pengembangan menggunakan 4-D yang meliputi tahap define, design, develop, dan dissemination. Draf bahan ajar divalidasi oleh pakar dan diujicobakan kepada sejumlah mahasiswa. Data dianalisis secara kualitatif dan kuantitatif dengan teknik deskriptif dan uji t. Temuan penelitian

Challenges of the information age: the impact of false discovery on pathway identification.

Science.gov (United States)

Rog, Colin J; Chekuri, Srinivasa C; Edgerton, Mary E

2012-11-21

Pathways with members that have known relevance to a disease are used to support hypotheses generated from analyses of gene expression and proteomic studies. Using cancer as an example, the pitfalls of searching pathways databases as support for genes and proteins that could represent false discoveries are explored. The frequency with which networks could be generated from 100 instances each of randomly selected five and ten genes sets as input to MetaCore, a commercial pathways database, was measured. A PubMed search enumerated cancer-related literature published for any gene in the networks. Using three, two, and one maximum intervening step between input genes to populate the network, networks were generated with frequencies of 97%, 77%, and 7% using ten gene sets and 73%, 27%, and 1% using five gene sets. PubMed reported an average of 4225 cancer-related articles per network gene. This can be attributed to the richly populated pathways databases and the interest in the molecular basis of cancer. As information sources become enriched, they are more likely to generate plausible mechanisms for false discoveries.

Resource Discovery in Activity-Based Sensor Networks

DEFF Research Database (Denmark)

Bucur, Doina; Bardram, Jakob

This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networ....... ABSN enhances the generic Extended Zone Routing Protocol with logical sensor grouping and greatly lowers network overhead during the process of discovery, while keeping discovery latency close to optimal.......This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

Resource Discovery in Activity-Based Sensor Networks

DEFF Research Database (Denmark)

Bucur, Doina; Bardram, Jakob

This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

A Metadata Schema for Geospatial Resource Discovery Use Cases

Directory of Open Access Journals (Sweden)

Darren Hardy

2014-07-01

Full Text Available We introduce a metadata schema that focuses on GIS discovery use cases for patrons in a research library setting. Text search, faceted refinement, and spatial search and relevancy are among GeoBlacklight's primary use cases for federated geospatial holdings. The schema supports a variety of GIS data types and enables contextual, collection-oriented discovery applications as well as traditional portal applications. One key limitation of GIS resource discovery is the general lack of normative metadata practices, which has led to a proliferation of metadata schemas and duplicate records. The ISO 19115/19139 and FGDC standards specify metadata formats, but are intricate, lengthy, and not focused on discovery. Moreover, they require sophisticated authoring environments and cataloging expertise. Geographic metadata standards target preservation and quality measure use cases, but they do not provide for simple inter-institutional sharing of metadata for discovery use cases. To this end, our schema reuses elements from Dublin Core and GeoRSS to leverage their normative semantics, community best practices, open-source software implementations, and extensive examples already deployed in discovery contexts such as web search and mapping. Finally, we discuss a Solr implementation of the schema using a "geo" extension to MODS.

Generation of comprehensive transposon insertion mutant library for the model archaeon, Haloferax volcanii, and its use for gene discovery.

Science.gov (United States)

Kiljunen, Saija; Pajunen, Maria I; Dilks, Kieran; Storf, Stefanie; Pohlschroder, Mechthild; Savilahti, Harri

2014-12-09

Archaea share fundamental properties with bacteria and eukaryotes. Yet, they also possess unique attributes, which largely remain poorly characterized. Haloferax volcanii is an aerobic, moderately halophilic archaeon that can be grown in defined media. It serves as an excellent archaeal model organism to study the molecular mechanisms of biological processes and cellular responses to changes in the environment. Studies on haloarchaea have been impeded by the lack of efficient genetic screens that would facilitate the identification of protein functions and respective metabolic pathways. Here, we devised an insertion mutagenesis strategy that combined Mu in vitro DNA transposition and homologous-recombination-based gene targeting in H. volcanii. We generated an insertion mutant library, in which the clones contained a single genomic insertion. From the library, we isolated pigmentation-defective and auxotrophic mutants, and the respective insertions pinpointed a number of genes previously known to be involved in carotenoid and amino acid biosynthesis pathways, thus validating the performance of the methodologies used. We also identified mutants that had a transposon insertion in a gene encoding a protein of unknown or putative function, demonstrating that novel roles for non-annotated genes could be assigned. We have generated, for the first time, a random genomic insertion mutant library for a halophilic archaeon and used it for efficient gene discovery. The library will facilitate the identification of non-essential genes behind any specific biochemical pathway. It represents a significant step towards achieving a more complete understanding of the unique characteristics of halophilic archaea.

Chitosan for gene delivery and orthopedic tissue engineering applications.

Science.gov (United States)

Raftery, Rosanne; O'Brien, Fergal J; Cryan, Sally-Ann

2013-05-15

Gene therapy involves the introduction of foreign genetic material into cells in order exert a therapeutic effect. The application of gene therapy to the field of orthopaedic tissue engineering is extremely promising as the controlled release of therapeutic proteins such as bone morphogenetic proteins have been shown to stimulate bone repair. However, there are a number of drawbacks associated with viral and synthetic non-viral gene delivery approaches. One natural polymer which has generated interest as a gene delivery vector is chitosan. Chitosan is biodegradable, biocompatible and non-toxic. Much of the appeal of chitosan is due to the presence of primary amine groups in its repeating units which become protonated in acidic conditions. This property makes it a promising candidate for non-viral gene delivery. Chitosan-based vectors have been shown to transfect a number of cell types including human embryonic kidney cells (HEK293) and human cervical cancer cells (HeLa). Aside from its use in gene delivery, chitosan possesses a range of properties that show promise in tissue engineering applications; it is biodegradable, biocompatible, has anti-bacterial activity, and, its cationic nature allows for electrostatic interaction with glycosaminoglycans and other proteoglycans. It can be used to make nano- and microparticles, sponges, gels, membranes and porous scaffolds. Chitosan has also been shown to enhance mineral deposition during osteogenic differentiation of MSCs in vitro. The purpose of this review is to critically discuss the use of chitosan as a gene delivery vector with emphasis on its application in orthopedic tissue engineering.

Cross-organism learning method to discover new gene functionalities.

Science.gov (United States)

Domeniconi, Giacomo; Masseroli, Marco; Moro, Gianluca; Pinoli, Pietro

2016-04-01

Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount. Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism. We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones

From Protein Structure to Small-Molecules: Recent Advances and Applications to Fragment-Based Drug Discovery.

Science.gov (United States)

Ferreira, Leonardo G; Andricopulo, Adriano D

2017-01-01

Fragment-based drug discovery (FBDD) is a broadly used strategy in structure-guided ligand design, whereby low-molecular weight hits move from lead-like to drug-like compounds. Over the past 15 years, an increasingly important role of the integration of these strategies into industrial and academic research platforms has been successfully established, allowing outstanding contributions to drug discovery. One important factor for the current prominence of FBDD is the better coverage of the chemical space provided by fragment-like libraries. The development of the field relies on two features: (i) the growing number of structurally characterized drug targets and (ii) the enormous chemical diversity available for experimental and virtual screenings. Indeed, fragment-based campaigns have contributed to address major challenges in lead optimization, such as the appropriate physicochemical profile of clinical candidates. This perspective paper outlines the usefulness and applications of FBDD approaches in medicinal chemistry and drug design. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

Directory of Open Access Journals (Sweden)

Li Min

2012-03-01

Full Text Available Abstract Background Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. Results In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC, Betweenness Centrality (BC, Closeness Centrality (CC, Subgraph Centrality (SC, Eigenvector Centrality (EC, Information Centrality (IC, Bottle Neck (BN, Density of Maximum Neighborhood Component (DMNC, Local Average Connectivity-based method (LAC, Sum of ECC (SoECC, Range-Limited Centrality (RL, L-index (LI, Leader Rank (LR, Normalized α-Centrality (NC, and Moduland-Centrality (MC. Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN is more than 50% when predicting no more than 500 proteins. Conclusions We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.

Applications of lipid nanoparticles in gene therapy.

Science.gov (United States)

Del Pozo-Rodríguez, Ana; Solinís, María Ángeles; Rodríguez-Gascón, Alicia

2016-12-01

Solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been recognized, among the large number of non-viral vectors for gene transfection, as an effective and safety alternative to potentially treat both genetic and not genetic diseases. A key feature is the possibility to be designed to overcome the numerous challenges for successful gene delivery. Lipid nanoparticles (LNs) are able to overcome the main biological barriers for cell transfection, including degradation by nucleases, cell internalization intracellular trafficking, and selectively targeting to a specific cell type. Additionally, they present important advantages: from a safety point of view LNs are prepared with well tolerated components, and from a technological point of view, they can be easily produced at large-scale, can be subjected to sterilization and lyophilization, and have shown good storage stability. This review focuses on the potential of SLNs and NLCs for gene therapy, including the main advances in their application for the treatment of ocular diseases, infectious diseases, lysosomal storage disorders and cancer, and current research for their future clinical application. Copyright © 2016 Elsevier B.V. All rights reserved.

IMG-ABC: new features for bacterial secondary metabolism analysis and targeted biosynthetic gene cluster discovery in thousands of microbial genomes.

Science.gov (United States)

Hadjithomas, Michalis; Chen, I-Min A; Chu, Ken; Huang, Jinghua; Ratner, Anna; Palaniappan, Krishna; Andersen, Evan; Markowitz, Victor; Kyrpides, Nikos C; Ivanova, Natalia N

2017-01-04

Secondary metabolites produced by microbes have diverse biological functions, which makes them a great potential source of biotechnologically relevant compounds with antimicrobial, anti-cancer and other activities. The proteins needed to synthesize these natural products are often encoded by clusters of co-located genes called biosynthetic gene clusters (BCs). In order to advance the exploration of microbial secondary metabolism, we developed the largest publically available database of experimentally verified and predicted BCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc/). Here, we describe an update of IMG-ABC, which includes ClusterScout, a tool for targeted identification of custom biosynthetic gene clusters across 40 000 isolate microbial genomes, and a new search capability to query more than 700 000 BCs from isolate genomes for clusters with similar Pfam composition. Additional features enable fast exploration and analysis of BCs through two new interactive visualization features, a BC function heatmap and a BC similarity network graph. These new tools and features add to the value of IMG-ABC's vast body of BC data, facilitating their in-depth analysis and accelerating secondary metabolite discovery. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Applying Hierarchical Task Analysis Method to Discovery Layer Evaluation

Directory of Open Access Journals (Sweden)

Marlen Promann

2015-03-01

Full Text Available Libraries are implementing discovery layers to offer better user experiences. While usability tests have been helpful in evaluating the success or failure of implementing discovery layers in the library context, the focus has remained on its relative interface benefits over the traditional federated search. The informal site- and context specific usability tests have offered little to test the rigor of the discovery layers against the user goals, motivations and workflow they have been designed to support. This study proposes hierarchical task analysis (HTA as an important complementary evaluation method to usability testing of discovery layers. Relevant literature is reviewed for the discovery layers and the HTA method. As no previous application of HTA to the evaluation of discovery layers was found, this paper presents the application of HTA as an expert based and workflow centered (e.g. retrieving a relevant book or a journal article method to evaluating discovery layers. Purdue University’s Primo by Ex Libris was used to map eleven use cases as HTA charts. Nielsen’s Goal Composition theory was used as an analytical framework to evaluate the goal carts from two perspectives: a users’ physical interactions (i.e. clicks, and b user’s cognitive steps (i.e. decision points for what to do next. A brief comparison of HTA and usability test findings is offered as a way of conclusion.

Antisense gene silencing

DEFF Research Database (Denmark)

Nielsen, Troels T; Nielsen, Jørgen E

2013-01-01

Since the first reports that double-stranded RNAs can efficiently silence gene expression in C. elegans, the technology of RNA interference (RNAi) has been intensively exploited as an experimental tool to study gene function. With the subsequent discovery that RNAi could also be applied...

Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

Directory of Open Access Journals (Sweden)

Guo Zheng

2006-01-01

Full Text Available Abstract Background It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. Results In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network to address the underlying regulations of genes that can span any unit(s of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. Conclusion We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex

Culture-independent discovery of natural products from soil metagenomes.

Science.gov (United States)

Katz, Micah; Hover, Bradley M; Brady, Sean F

2016-03-01

Bacterial natural products have proven to be invaluable starting points in the development of many currently used therapeutic agents. Unfortunately, traditional culture-based methods for natural product discovery have been deemphasized by pharmaceutical companies due in large part to high rediscovery rates. Culture-independent, or "metagenomic," methods, which rely on the heterologous expression of DNA extracted directly from environmental samples (eDNA), have the potential to provide access to metabolites encoded by a large fraction of the earth's microbial biosynthetic diversity. As soil is both ubiquitous and rich in bacterial diversity, it is an appealing starting point for culture-independent natural product discovery efforts. This review provides an overview of the history of soil metagenome-driven natural product discovery studies and elaborates on the recent development of new tools for sequence-based, high-throughput profiling of environmental samples used in discovering novel natural product biosynthetic gene clusters. We conclude with several examples of these new tools being employed to facilitate the recovery of novel secondary metabolite encoding gene clusters from soil metagenomes and the subsequent heterologous expression of these clusters to produce bioactive small molecules.

GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data.

Science.gov (United States)

Ben-Ari Fuchs, Shani; Lieder, Iris; Stelzer, Gil; Mazor, Yaron; Buzhor, Ella; Kaplan, Sergey; Bogoch, Yoel; Plaschkes, Inbar; Shitrit, Alina; Rappaport, Noa; Kohn, Asher; Edgar, Ron; Shenhav, Liraz; Safran, Marilyn; Lancet, Doron; Guan-Golan, Yaron; Warshawsky, David; Shtrichman, Ronit

2016-03-01

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics

Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery.

Science.gov (United States)

Nowotka, Michał M; Gaulton, Anna; Mendez, David; Bento, A Patricia; Hersey, Anne; Leach, Andrew

2017-08-01

ChEMBL is a manually curated database of bioactivity data on small drug-like molecules, used by drug discovery scientists. Among many access methods, a REST API provides programmatic access, allowing the remote retrieval of ChEMBL data and its integration into other applications. This approach allows scientists to move from a world where they go to the ChEMBL web site to search for relevant data, to one where ChEMBL data can be simply integrated into their everyday tools and work environment. Areas covered: This review highlights some of the audiences who may benefit from using the ChEMBL API, and the goals they can address, through the description of several use cases. The examples cover a team communication tool (Slack), a data analytics platform (KNIME), batch job management software (Luigi) and Rich Internet Applications. Expert opinion: The advent of web technologies, cloud computing and micro services oriented architectures have made REST APIs an essential ingredient of modern software development models. The widespread availability of tools consuming RESTful resources have made them useful for many groups of users. The ChEMBL API is a valuable resource of drug discovery bioactivity data for professional chemists, chemistry students, data scientists, scientific and web developers.

Gene Discovery through Genomic Sequencing of Brucella abortus

Science.gov (United States)

Sánchez, Daniel O.; Zandomeni, Ruben O.; Cravero, Silvio; Verdún, Ramiro E.; Pierrou, Ester; Faccio, Paula; Diaz, Gabriela; Lanzavecchia, Silvia; Agüero, Fernán; Frasch, Alberto C. C.; Andersson, Siv G. E.; Rossetti, Osvaldo L.; Grau, Oscar; Ugalde, Rodolfo A.

2001-01-01

Brucella abortus is the etiological agent of brucellosis, a disease that affects bovines and human. We generated DNA random sequences from the genome of B. abortus strain 2308 in order to characterize molecular targets that might be useful for developing immunological or chemotherapeutic strategies against this pathogen. The partial sequencing of 1,899 clones allowed the identification of 1,199 genomic sequence surveys (GSSs) with high homology (BLAST expect value < 10−5) to sequences deposited in the GenBank databases. Among them, 925 represent putative novel genes for the Brucella genus. Out of 925 nonredundant GSSs, 470 were classified in 15 categories based on cellular function. Seven hundred GSSs showed no significant database matches and remain available for further studies in order to identify their function. A high number of GSSs with homology to Agrobacterium tumefaciens and Rhizobium meliloti proteins were observed, thus confirming their close phylogenetic relationship. Among them, several GSSs showed high similarity with genes related to nodule nitrogen fixation, synthesis of nod factors, nodulation protein symbiotic plasmid, and nodule bacteroid differentiation. We have also identified several B. abortus homologs of virulence and pathogenesis genes from other pathogens, including a homolog to both the Shda gene from Salmonella enterica serovar Typhimurium and the AidA-1 gene from Escherichia coli. Other GSSs displayed significant homologies to genes encoding components of the type III and type IV secretion machineries, suggesting that Brucella might also have an active type III secretion machinery. PMID:11159979

The Matchmaker Exchange: a platform for rare disease gene discovery

NARCIS (Netherlands)

Philippakis, A.A.; Azzariti, D.R.; Beltran, S.; Brookes, A.J.; Brownstein, C.A.; Brudno, M.; Brunner, H.G.; Buske, O.J.; Carey, K.; Doll, C.; Dumitriu, S.; Dyke, S.O.M.; Dunnen, J.T. den; Firth, H.V.; Gibbs, R.A.; Girdea, M.; Gonzalez, M.; Haendel, M.A.; Hamosh, A.; Holm, I.A.; Huang, L.; Hurles, M.E.; Hutton, B.; Krier, J.B.; Misyura, A.; Mungall, C.J.; Paschall, J.; Paten, B.; Robinson, P.N.; Schiettecatte, F.; Sobreira, N.L.; Swaminathan, G.J.; Taschner, P.E.M.; Terry, S.F.; Washington, N.L.; Zuchner, S.; Boycott, K.M.; Rehm, H.L.

2015-01-01

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of

Hierarchical virtual screening approaches in small molecule drug discovery.

Science.gov (United States)

Kumar, Ashutosh; Zhang, Kam Y J

2015-01-01

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery. Copyright © 2014 Elsevier Inc. All rights reserved.

Predicting future discoveries from current scientific literature.

Science.gov (United States)

Petrič, Ingrid; Cestnik, Bojan

2014-01-01

Knowledge discovery in biomedicine is a time-consuming process starting from the basic research, through preclinical testing, towards possible clinical applications. Crossing of conceptual boundaries is often needed for groundbreaking biomedical research that generates highly inventive discoveries. We demonstrate the ability of a creative literature mining method to advance valuable new discoveries based on rare ideas from existing literature. When emerging ideas from scientific literature are put together as fragments of knowledge in a systematic way, they may lead to original, sometimes surprising, research findings. If enough scientific evidence is already published for the association of such findings, they can be considered as scientific hypotheses. In this chapter, we describe a method for the computer-aided generation of such hypotheses based on the existing scientific literature. Our literature-based discovery of NF-kappaB with its possible connections to autism was recently approved by scientific community, which confirms the ability of our literature mining methodology to accelerate future discoveries based on rare ideas from existing literature.

Cracking the regulatory code of biosynthetic gene clusters as a strategy for natural product discovery.

Science.gov (United States)

Rigali, Sébastien; Anderssen, Sinaeda; Naômé, Aymeric; van Wezel, Gilles P

2018-01-05

The World Health Organization (WHO) describes antibiotic resistance as "one of the biggest threats to global health, food security, and development today", as the number of multi- and pan-resistant bacteria is rising dangerously. Acquired resistance phenomena also impair antifungals, antivirals, anti-cancer drug therapy, while herbicide resistance in weeds threatens the crop industry. On the positive side, it is likely that the chemical space of natural products goes far beyond what has currently been discovered. This idea is fueled by genome sequencing of microorganisms which unveiled numerous so-called cryptic biosynthetic gene clusters (BGCs), many of which are transcriptionally silent under laboratory culture conditions, and by the fact that most bacteria cannot yet be cultivated in the laboratory. However, brute force antibiotic discovery does not yield the same results as it did in the past, and researchers have had to develop creative strategies in order to unravel the hidden potential of microorganisms such as Streptomyces and other antibiotic-producing microorganisms. Identifying the cis elements and their corresponding transcription factors(s) involved in the control of BGCs through bioinformatic approaches is a promising strategy. Theoretically, we are a few 'clicks' away from unveiling the culturing conditions or genetic changes needed to activate the production of cryptic metabolites or increase the production yield of known compounds to make them economically viable. In this opinion article, we describe and illustrate the idea beyond 'cracking' the regulatory code for natural product discovery, by presenting a series of proofs of concept, and discuss what still should be achieved to increase the rate of success of this strategy. Copyright © 2018 Elsevier Inc. All rights reserved.

Towards evolution-guided microbial engineering - tools development and applications

DEFF Research Database (Denmark)

Genee, Hans Jasper

is thedevelopment of highly robust biosensor-based synthetic selection systemsthat enable high-throughput functional interrogation of complexphenotypic libraries. Using the model organism Escherichia coli as a host, Ideploy these systems to i) perform metagenome wide sequenceindependentidentification of novel...... for microbial engineering anddemonstrates direct applications to gene discovery, protein engineering andcell factory development....

Current perspectives in fragment-based lead discovery (FBLD)

Science.gov (United States)

Lamoree, Bas; Hubbard, Roderick E.

2017-01-01

It is over 20 years since the first fragment-based discovery projects were disclosed. The methods are now mature for most ‘conventional’ targets in drug discovery such as enzymes (kinases and proteases) but there has also been growing success on more challenging targets, such as disruption of protein–protein interactions. The main application is to identify tractable chemical startpoints that non-covalently modulate the activity of a biological molecule. In this essay, we overview current practice in the methods and discuss how they have had an impact in lead discovery – generating a large number of fragment-derived compounds that are in clinical trials and two medicines treating patients. In addition, we discuss some of the more recent applications of the methods in chemical biology – providing chemical tools to investigate biological molecules, mechanisms and systems. PMID:29118093

Scientific workflows as productivity tools for drug discovery.

Science.gov (United States)

Shon, John; Ohkawa, Hitomi; Hammer, Juergen

2008-05-01

Large pharmaceutical companies annually invest tens to hundreds of millions of US dollars in research informatics to support their early drug discovery processes. Traditionally, most of these investments are designed to increase the efficiency of drug discovery. The introduction of do-it-yourself scientific workflow platforms has enabled research informatics organizations to shift their efforts toward scientific innovation, ultimately resulting in a possible increase in return on their investments. Unlike the handling of most scientific data and application integration approaches, researchers apply scientific workflows to in silico experimentation and exploration, leading to scientific discoveries that lie beyond automation and integration. This review highlights some key requirements for scientific workflow environments in the pharmaceutical industry that are necessary for increasing research productivity. Examples of the application of scientific workflows in research and a summary of recent platform advances are also provided.

Sex-specific associations between particulate matter exposure and gene expression in independent discovery and validation cohorts of middle-aged men and women

DEFF Research Database (Denmark)

Vrijens, Karen; Winckelmans, Ellen; Tsamou, Maria

2017-01-01

Background: Particulate matter (PM) exposure leads to premature death, mainly due to respiratory and cardiovascular diseases. Objectives: Identification of transcriptomic biomarkers of air pollution exposure and effect in a healthy adult population. Methods: Microarray analyses were performed in 98...... healthy volunteers (48 men, 50 women). The expression of eight sex-specific candidate biomarker genes (significantly associated with PM10 in the discovery cohort and with a reported link to air pollution-related disease) was measured with qPCR in an independent validation cohort (75 men, 94 women...

Using the TIGR gene index databases for biological discovery.

Science.gov (United States)

Lee, Yuandan; Quackenbush, John

2003-11-01

The TIGR Gene Index web pages provide access to analyses of ESTs and gene sequences for nearly 60 species, as well as a number of resources derived from these. Each species-specific database is presented using a common format with a homepage. A variety of methods exist that allow users to search each species-specific database. Methods implemented currently include nucleotide or protein sequence queries using WU-BLAST, text-based searches using various sequence identifiers, searches by gene, tissue and library name, and searches using functional classes through Gene Ontology assignments. This protocol provides guidance for using the Gene Index Databases to extract information.

The Emerging Field of Quantitative Blood Metabolomics for Biomarker Discovery in Critical Illnesses

Science.gov (United States)

Serkova, Natalie J.; Standiford, Theodore J.

2011-01-01

Metabolomics, a science of systems biology, is the global assessment of endogenous metabolites within a biologic system and represents a “snapshot” reading of gene function, enzyme activity, and the physiological landscape. Metabolite detection, either individual or grouped as a metabolomic profile, is usually performed in cells, tissues, or biofluids by either nuclear magnetic resonance spectroscopy or mass spectrometry followed by sophisticated multivariate data analysis. Because loss of metabolic homeostasis is common in critical illness, the metabolome could have many applications, including biomarker and drug target identification. Metabolomics could also significantly advance our understanding of the complex pathophysiology of acute illnesses, such as sepsis and acute lung injury/acute respiratory distress syndrome. Despite this potential, the clinical community is largely unfamiliar with the field of metabolomics, including the methodologies involved, technical challenges, and, most importantly, clinical uses. Although there is evidence of successful preclinical applications, the clinical usefulness and application of metabolomics in critical illness is just beginning to emerge, the advancement of which hinges on linking metabolite data to known and validated clinically relevant indices. In addition, other important aspects, such as patient selection, sample collection, and processing, as well as the needed multivariate data analysis, have to be taken into consideration before this innovative approach to biomarker discovery can become a reliable tool in the intensive care unit. The purpose of this review is to begin to familiarize clinicians with the field of metabolomics and its application for biomarker discovery in critical illnesses such as sepsis. PMID:21680948

SNP discovery in the bovine milk transcriptome using RNA-Seq technology.

Science.gov (United States)

Cánovas, Angela; Rincon, Gonzalo; Islas-Trejo, Alma; Wickramasinghe, Saumya; Medrano, Juan F

2010-12-01

High-throughput sequencing of RNA (RNA-Seq) was developed primarily to analyze global gene expression in different tissues. However, it also is an efficient way to discover coding SNPs. The objective of this study was to perform a SNP discovery analysis in the milk transcriptome using RNA-Seq. Seven milk samples from Holstein cows were analyzed by sequencing cDNAs using the Illumina Genome Analyzer system. We detected 19,175 genes expressed in milk samples corresponding to approximately 70% of the total number of genes analyzed. The SNP detection analysis revealed 100,734 SNPs in Holstein samples, and a large number of those corresponded to differences between the Holstein breed and the Hereford bovine genome assembly Btau4.0. The number of polymorphic SNPs within Holstein cows was 33,045. The accuracy of RNA-Seq SNP discovery was tested by comparing SNPs detected in a set of 42 candidate genes expressed in milk that had been resequenced earlier using Sanger sequencing technology. Seventy of 86 SNPs were detected using both RNA-Seq and Sanger sequencing technologies. The KASPar Genotyping System was used to validate unique SNPs found by RNA-Seq but not observed by Sanger technology. Our results confirm that analyzing the transcriptome using RNA-Seq technology is an efficient and cost-effective method to identify SNPs in transcribed regions. This study creates guidelines to maximize the accuracy of SNP discovery and prevention of false-positive SNP detection, and provides more than 33,000 SNPs located in coding regions of genes expressed during lactation that can be used to develop genotyping platforms to perform marker-trait association studies in Holstein cattle.

Computational method for discovery of estrogen responsive genes

DEFF Research Database (Denmark)

Tang, Suisheng; Tan, Sin Lam; Ramadoss, Suresh Kumar

2004-01-01

Estrogen has a profound impact on human physiology and affects numerous genes. The classical estrogen reaction is mediated by its receptors (ERs), which bind to the estrogen response elements (EREs) in target gene's promoter region. Due to tedious and expensive experiments, a limited number of hu...

The application of mass-spectrometry-based protein biomarker discovery to theragnostics

OpenAIRE

Street, Jonathan M; Dear, James W

2010-01-01

Over the last decade rapid developments in mass spectrometry have allowed the identification of multiple proteins in complex biological samples. This proteomic approach has been applied to biomarker discovery in the context of clinical pharmacology (the combination of biomarker and drug now being termed ‘theragnostics’). In this review we provide a roadmap for early protein biomarker discovery studies, focusing on some key questions that regularly confront researchers.

Recent development of computational resources for new antibiotics discovery

DEFF Research Database (Denmark)

Kim, Hyun Uk; Blin, Kai; Lee, Sang Yup

2017-01-01

Understanding a complex working mechanism of biosynthetic gene clusters (BGCs) encoding secondary metabolites is a key to discovery of new antibiotics. Computational resources continue to be developed in order to better process increasing volumes of genome and chemistry data, and thereby better...

Discovery and Selection of Semantic Web Services

CERN Document Server

Wang, Xia

2013-01-01

For advanced web search engines to be able not only to search for semantically related information dispersed over different web pages, but also for semantic services providing certain functionalities, discovering semantic services is the key issue. Addressing four problems of current solution, this book presents the following contributions. A novel service model independent of semantic service description models is proposed, which clearly defines all elements necessary for service discovery and selection. It takes service selection as its gist and improves efficiency. Corresponding selection algorithms and their implementation as components of the extended Semantically Enabled Service-oriented Architecture in the Web Service Modeling Environment are detailed. Many applications of semantic web services, e.g. discovery, composition and mediation, can benefit from a general approach for building application ontologies. With application ontologies thus built, services are discovered in the same way as with single...

Some Applications of Fourier's Great Discovery for Beginners

Science.gov (United States)

Kraftmakher, Yaakov

2012-01-01

Nearly two centuries ago, Fourier discovered that any periodic function of period T can be presented as a sum of sine waveforms of frequencies equal to an integer times the fundamental frequency [omega] = 2[pi]/T (Fourier's series). It is impossible to overestimate the importance of Fourier's discovery, and all physics or engineering students…

Applied metabolomics in drug discovery.

Science.gov (United States)

Cuperlovic-Culf, M; Culf, A S

2016-08-01

The metabolic profile is a direct signature of phenotype and biochemical activity following any perturbation. Metabolites are small molecules present in a biological system including natural products as well as drugs and their metabolism by-products depending on the biological system studied. Metabolomics can provide activity information about possible novel drugs and drug scaffolds, indicate interesting targets for drug development and suggest binding partners of compounds. Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Metabolomics methodology, both experimental and analytical is fast developing. At the same time, databases of compounds are ever growing with the inclusion of more molecular and spectral information. An increasing number of systems are being represented by very detailed metabolic network models. Combining these experimental and computational tools with high throughput drug testing and drug discovery techniques can provide new promising compounds and leads.

Integration of Antibody Array Technology into Drug Discovery and Development.

Science.gov (United States)

Huang, Wei; Whittaker, Kelly; Zhang, Huihua; Wu, Jian; Zhu, Si-Wei; Huang, Ruo-Pan

Antibody arrays represent a high-throughput technique that enables the parallel detection of multiple proteins with minimal sample volume requirements. In recent years, antibody arrays have been widely used to identify new biomarkers for disease diagnosis or prognosis. Moreover, many academic research laboratories and commercial biotechnology companies are starting to apply antibody arrays in the field of drug discovery. In this review, some technical aspects of antibody array development and the various platforms currently available will be addressed; however, the main focus will be on the discussion of antibody array technologies and their applications in drug discovery. Aspects of the drug discovery process, including target identification, mechanisms of drug resistance, molecular mechanisms of drug action, drug side effects, and the application in clinical trials and in managing patient care, which have been investigated using antibody arrays in recent literature will be examined and the relevance of this technology in progressing this process will be discussed. Protein profiling with antibody array technology, in addition to other applications, has emerged as a successful, novel approach for drug discovery because of the well-known importance of proteins in cell events and disease development.

A new approach to the rationale discovery of polymeric biomaterials

Science.gov (United States)

Kohn, Joachim; Welsh, William J.; Knight, Doyle

2007-01-01

This paper attempts to illustrate both the need for new approaches to biomaterials discovery as well as the significant promise inherent in the use of combinatorial and computational design strategies. The key observation of this Leading Opinion Paper is that the biomaterials community has been slow to embrace advanced biomaterials discovery tools such as combinatorial methods, high throughput experimentation, and computational modeling in spite of the significant promise shown by these discovery tools in materials science, medicinal chemistry and the pharmaceutical industry. It seems that the complexity of living cells and their interactions with biomaterials has been a conceptual as well as a practical barrier to the use of advanced discovery tools in biomaterials science. However, with the continued increase in computer power, the goal of predicting the biological response of cells in contact with biomaterials surfaces is within reach. Once combinatorial synthesis, high throughput experimentation, and computational modeling are integrated into the biomaterials discovery process, a significant acceleration is possible in the pace of development of improved medical implants, tissue regeneration scaffolds, and gene/drug delivery systems. PMID:17644176

Use of combinatorial chemistry to speed drug discovery.

Science.gov (United States)

Rádl, S

1998-10-01

IBC's International Conference on Integrating Combinatorial Chemistry into the Discovery Pipeline was held September 14-15, 1998. The program started with a pre-conference workshop on High-Throughput Compound Characterization and Purification. The agenda of the main conference was divided into sessions of Synthesis, Automation and Unique Chemistries; Integrating Combinatorial Chemistry, Medicinal Chemistry and Screening; Combinatorial Chemistry Applications for Drug Discovery; and Information and Data Management. This meeting was an excellent opportunity to see how big pharma, biotech and service companies are addressing the current bottlenecks in combinatorial chemistry to speed drug discovery. (c) 1998 Prous Science. All rights reserved.

Gene discovery for the carcinogenic human liver fluke, Opisthorchis viverrini

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Gasser Robin B

2007-06-01

Full Text Available Abstract Background Cholangiocarcinoma (CCA – cancer of the bile ducts – is associated with chronic infection with the liver fluke, Opisthorchis viverrini. Despite being the only eukaryote that is designated as a 'class I carcinogen' by the International Agency for Research on Cancer, little is known about its genome. Results Approximately 5,000 randomly selected cDNAs from the adult stage of O. viverrini were characterized and accounted for 1,932 contigs, representing ~14% of the entire transcriptome, and, presently, the largest sequence dataset for any species of liver fluke. Twenty percent of contigs were assigned GO classifications. Abundantly represented protein families included those involved in physiological functions that are essential to parasitism, such as anaerobic respiration, reproduction, detoxification, surface maintenance and feeding. GO assignments were well conserved in relation to other parasitic flukes, however, some categories were over-represented in O. viverrini, such as structural and motor proteins. An assessment of evolutionary relationships showed that O. viverrini was more similar to other parasitic (Clonorchis sinensis and Schistosoma japonicum than to free-living (Schmidtea mediterranea flatworms, and 105 sequences had close homologues in both parasitic species but not in S. mediterranea. A total of 164 O. viverrini contigs contained ORFs with signal sequences, many of which were platyhelminth-specific. Examples of convergent evolution between host and parasite secreted/membrane proteins were identified as were homologues of vaccine antigens from other helminths. Finally, ORFs representing secreted proteins with known roles in tumorigenesis were identified, and these might play roles in the pathogenesis of O. viverrini-induced CCA. Conclusion This gene discovery effort for O. viverrini should expedite molecular studies of cholangiocarcinogenesis and accelerate research focused on developing new interventions

Wide-Area Publish/Subscribe Mobile Resource Discovery Based on IPv6 GeoNetworking

OpenAIRE

Noguchi, Satoru; Matsuura, Satoshi; Inomata, Atsuo; Fujikawa, Kazutoshi; Sunahara, Hideki

2013-01-01

Resource discovery is an essential function for distributed mobile applications integrated in vehicular communication systems. Key requirements of the mobile resource discovery are wide-area geographic-based discovery and scalable resource discovery not only inside a vehicular ad-hoc network but also through the Internet. While a number of resource discovery solutions have been proposed, most of them have focused on specific scale of network. Furthermore, managing a large number of mobile res...

Speeding disease gene discovery by sequence based candidate prioritization

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Porteous David J

2005-03-01

Full Text Available Abstract Background Regions of interest identified through genetic linkage studies regularly exceed 30 centimorgans in size and can contain hundreds of genes. Traditionally this number is reduced by matching functional annotation to knowledge of the disease or phenotype in question. However, here we show that disease genes share patterns of sequence-based features that can provide a good basis for automatic prioritization of candidates by machine learning. Results We examined a variety of sequence-based features and found that for many of them there are significant differences between the sets of genes known to be involved in human hereditary disease and those not known to be involved in disease. We have created an automatic classifier called PROSPECTR based on those features using the alternating decision tree algorithm which ranks genes in the order of likelihood of involvement in disease. On average, PROSPECTR enriches lists for disease genes two-fold 77% of the time, five-fold 37% of the time and twenty-fold 11% of the time. Conclusion PROSPECTR is a simple and effective way to identify genes involved in Mendelian and oligogenic disorders. It performs markedly better than the single existing sequence-based classifier on novel data. PROSPECTR could save investigators looking at large regions of interest time and effort by prioritizing positional candidate genes for mutation detection and case-control association studies.

Evaluating Discovery Services Architectures in the Context of the Internet of Things

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Polytarchos, Elias; Eliakis, Stelios; Bochtis, Dimitris; Pramatari, Katerina

As the "Internet of Things" is expected to grow rapidly in the following years, the need to develop and deploy efficient and scalable Discovery Services in this context is very important for its success. Thus, the ability to evaluate and compare the performance of different Discovery Services architectures is vital if we want to allege that a given design is better at meeting requirements of a specific application. The purpose of this chapter is to provide a paradigm for the evaluation of different Discovery Services for the Internet of Things in terms of efficiency, scalability and performance through the use of simulations. The methodology presented uses the application of Discovery Services to a supply chain with the Service Lookup Service Discovery Service using OMNeT++, an open source network simulation suite. Then, we delve into the simulation design and the details of our findings.

[Artificial Intelligence in Drug Discovery].

Science.gov (United States)

Fujiwara, Takeshi; Kamada, Mayumi; Okuno, Yasushi

2018-04-01

According to the increase of data generated from analytical instruments, application of artificial intelligence(AI)technology in medical field is indispensable. In particular, practical application of AI technology is strongly required in "genomic medicine" and "genomic drug discovery" that conduct medical practice and novel drug development based on individual genomic information. In our laboratory, we have been developing a database to integrate genome data and clinical information obtained by clinical genome analysis and a computational support system for clinical interpretation of variants using AI. In addition, with the aim of creating new therapeutic targets in genomic drug discovery, we have been also working on the development of a binding affinity prediction system for mutated proteins and drugs by molecular dynamics simulation using supercomputer "Kei". We also have tackled for problems in a drug virtual screening. Our developed AI technology has successfully generated virtual compound library, and deep learning method has enabled us to predict interaction between compound and target protein.

Enhanced gene ranking approaches using modified trace ratio algorithm for gene expression data

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Shruti Mishra

Full Text Available Microarray technology enables the understanding and investigation of gene expression levels by analyzing high dimensional datasets that contain few samples. Over time, microarray expression data have been collected for studying the underlying biological mechanisms of disease. One such application for understanding the mechanism is by constructing a gene regulatory network (GRN. One of the foremost key criteria for GRN discovery is gene selection. Choosing a generous set of genes for the structure of the network is highly desirable. For this role, two suitable methods were proposed for selection of appropriate genes. The first approach comprises a gene selection method called Information gain, where the dataset is reformed and fused with another distinct algorithm called Trace Ratio (TR. Our second method is the implementation of our projected modified TR algorithm, where the scoring base for finding weight matrices has been re-designed. Both the methods' efficiency was shown with different classifiers that include variants of the Artificial Neural Network classifier, such as Resilient Propagation, Quick Propagation, Back Propagation, Manhattan Propagation and Radial Basis Function Neural Network and also the Support Vector Machine (SVM classifier. In the study, it was confirmed that both of the proposed methods worked well and offered high accuracy with a lesser number of iterations as compared to the original Trace Ratio algorithm. Keywords: Gene regulatory network, Gene selection, Information gain, Trace ratio, Canonical correlation analysis, Classification

Fragment-based drug discovery and its application to challenging drug targets.

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Price, Amanda J; Howard, Steven; Cons, Benjamin D

2017-11-08

Fragment-based drug discovery (FBDD) is a technique for identifying low molecular weight chemical starting points for drug discovery. Since its inception 20 years ago, FBDD has grown in popularity to the point where it is now an established technique in industry and academia. The approach involves the biophysical screening of proteins against collections of low molecular weight compounds (fragments). Although fragments bind to proteins with relatively low affinity, they form efficient, high quality binding interactions with the protein architecture as they have to overcome a significant entropy barrier to bind. Of the biophysical methods available for fragment screening, X-ray protein crystallography is one of the most sensitive and least prone to false positives. It also provides detailed structural information of the protein-fragment complex at the atomic level. Fragment-based screening using X-ray crystallography is therefore an efficient method for identifying binding hotspots on proteins, which can then be exploited by chemists and biologists for the discovery of new drugs. The use of FBDD is illustrated here with a recently published case study of a drug discovery programme targeting the challenging protein-protein interaction Kelch-like ECH-associated protein 1:nuclear factor erythroid 2-related factor 2. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

Live Cell in Vitro and in Vivo Imaging Applications: Accelerating Drug Discovery

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Neil O Carragher

2011-04-01

Full Text Available Dynamic regulation of specific molecular processes and cellular phenotypes in live cell systems reveal unique insights into cell fate and drug pharmacology that are not gained from traditional fixed endpoint assays. Recent advances in microscopic imaging platform technology combined with the development of novel optical biosensors and sophisticated image analysis solutions have increased the scope of live cell imaging applications in drug discovery. We highlight recent literature examples where live cell imaging has uncovered novel insight into biological mechanism or drug mode-of-action. We survey distinct types of optical biosensors and associated analytical methods for monitoring molecular dynamics, in vitro and in vivo. We describe the recent expansion of live cell imaging into automated target validation and drug screening activities through the development of dedicated brightfield and fluorescence kinetic imaging platforms. We provide specific examples of how temporal profiling of phenotypic response signatures using such kinetic imaging platforms can increase the value of in vitro high-content screening. Finally, we offer a prospective view of how further application and development of live cell imaging technology and reagents can accelerate preclinical lead optimization cycles and enhance the in vitro to in vivo translation of drug candidates.

Some historical glimpses on the discovery of x-rays and radioactivity

International Nuclear Information System (INIS)

Patil, S.K.

1996-01-01

In the last decade of the nineteenth century, a cascade of a number of scientific discoveries of great importance took place. The first of these was the discovery of x-rays which marked the beginning of a new era in physics and this was soon followed by the discovery of radioactivity. Both x-rays and radioactivity have wide applications in basic science, technology and medicine. Some glimpses on the historical aspects of the discovery of x-rays and radioactivity are presented. (author). 72 refs., 1 fig

An online conserved SSR discovery through cross-species comparison

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Tun-Wen Pai

2009-02-01

Full Text Available Tun-Wen Pai1, Chien-Ming Chen1, Meng-Chang Hsiao1, Ronshan Cheng2, Wen-Shyong Tzou3, Chin-Hua Hu31Department of Computer Science and Engineering; 2Department of Aquaculture, 3Institute of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan, Republic of ChinaAbstract: Simple sequence repeats (SSRs play important roles in gene regulation and genome evolution. Although there exist several online resources for SSR mining, most of them only extract general SSR patterns without providing functional information. Here, an online search tool, CG-SSR (Comparative Genomics SSR discovery, has been developed for discovering potential functional SSRs from vertebrate genomes through cross-species comparison. In addition to revealing SSR candidates in conserved regions among various species, it also combines accurate coordinate and functional genomics information. CG-SSR is the first comprehensive and efficient online tool for conserved SSR discovery.Keywords: microsatellites, genome, comparative genomics, functional SSR, gene ontology, conserved region

Bioinformatics and biomarker discovery "Omic" data analysis for personalized medicine

CERN Document Server

Azuaje, Francisco

2010-01-01

This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided w

Reconstruction of ribosomal RNA genes from metagenomic data.

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Lu Fan

Full Text Available Direct sequencing of environmental DNA (metagenomics has a great potential for describing the 16S rRNA gene diversity of microbial communities. However current approaches using this 16S rRNA gene information to describe community diversity suffer from low taxonomic resolution or chimera problems. Here we describe a new strategy that involves stringent assembly and data filtering to reconstruct full-length 16S rRNA genes from metagenomicpyrosequencing data. Simulations showed that reconstructed 16S rRNA genes provided a true picture of the community diversity, had minimal rates of chimera formation and gave taxonomic resolution down to genus level. The strategy was furthermore compared to PCR-based methods to determine the microbial diversity in two marine sponges. This showed that about 30% of the abundant phylotypes reconstructed from metagenomic data failed to be amplified by PCR. Our approach is readily applicable to existing metagenomic datasets and is expected to lead to the discovery of new microbial phylotypes.

Translational medicine and drug discovery

National Research Council Canada - National Science Library

Littman, Bruce H; Krishna, Rajesh

2011-01-01

..., and examples of their application to real-life drug discovery and development. The latest thinking is presented by researchers from many of the world's leading pharmaceutical companies, including Pfizer, Merck, Eli Lilly, Abbott, and Novartis, as well as from academic institutions and public- private partnerships that support translational research...

Directional genomic hybridization for chromosomal inversion discovery and detection.

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Ray, F Andrew; Zimmerman, Erin; Robinson, Bruce; Cornforth, Michael N; Bedford, Joel S; Goodwin, Edwin H; Bailey, Susan M

2013-04-01

Chromosomal rearrangements are a source of structural variation within the genome that figure prominently in human disease, where the importance of translocations and deletions is well recognized. In principle, inversions-reversals in the orientation of DNA sequences within a chromosome-should have similar detrimental potential. However, the study of inversions has been hampered by traditional approaches used for their detection, which are not particularly robust. Even with significant advances in whole genome approaches, changes in the absolute orientation of DNA remain difficult to detect routinely. Consequently, our understanding of inversions is still surprisingly limited, as is our appreciation for their frequency and involvement in human disease. Here, we introduce the directional genomic hybridization methodology of chromatid painting-a whole new way of looking at structural features of the genome-that can be employed with high resolution on a cell-by-cell basis, and demonstrate its basic capabilities for genome-wide discovery and targeted detection of inversions. Bioinformatics enabled development of sequence- and strand-specific directional probe sets, which when coupled with single-stranded hybridization, greatly improved the resolution and ease of inversion detection. We highlight examples of the far-ranging applicability of this cytogenomics-based approach, which include confirmation of the alignment of the human genome database and evidence that individuals themselves share similar sequence directionality, as well as use in comparative and evolutionary studies for any species whose genome has been sequenced. In addition to applications related to basic mechanistic studies, the information obtainable with strand-specific hybridization strategies may ultimately enable novel gene discovery, thereby benefitting the diagnosis and treatment of a variety of human disease states and disorders including cancer, autism, and idiopathic infertility.

Bayesian centroid estimation for motif discovery.

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Carvalho, Luis

2013-01-01

Biological sequences may contain patterns that signal important biomolecular functions; a classical example is regulation of gene expression by transcription factors that bind to specific patterns in genomic promoter regions. In motif discovery we are given a set of sequences that share a common motif and aim to identify not only the motif composition, but also the binding sites in each sequence of the set. We propose a new centroid estimator that arises from a refined and meaningful loss function for binding site inference. We discuss the main advantages of centroid estimation for motif discovery, including computational convenience, and how its principled derivation offers further insights about the posterior distribution of binding site configurations. We also illustrate, using simulated and real datasets, that the centroid estimator can differ from the traditional maximum a posteriori or maximum likelihood estimators.

Bayesian centroid estimation for motif discovery.

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Luis Carvalho

Full Text Available Biological sequences may contain patterns that signal important biomolecular functions; a classical example is regulation of gene expression by transcription factors that bind to specific patterns in genomic promoter regions. In motif discovery we are given a set of sequences that share a common motif and aim to identify not only the motif composition, but also the binding sites in each sequence of the set. We propose a new centroid estimator that arises from a refined and meaningful loss function for binding site inference. We discuss the main advantages of centroid estimation for motif discovery, including computational convenience, and how its principled derivation offers further insights about the posterior distribution of binding site configurations. We also illustrate, using simulated and real datasets, that the centroid estimator can differ from the traditional maximum a posteriori or maximum likelihood estimators.

Genetic barcoding with fluorescent proteins for multiplexed applications.

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Smurthwaite, Cameron A; Williams, Wesley; Fetsko, Alexandra; Abbadessa, Darin; Stolp, Zachary D; Reed, Connor W; Dharmawan, Andre; Wolkowicz, Roland

2015-04-14

Fluorescent proteins, fluorescent dyes and fluorophores in general have revolutionized the field of molecular cell biology. In particular, the discovery of fluorescent proteins and their genes have enabled the engineering of protein fusions for localization, the analysis of transcriptional activation and translation of proteins of interest, or the general tracking of individual cells and cell populations. The use of fluorescent protein genes in combination with retroviral technology has further allowed the expression of these proteins in mammalian cells in a stable and reliable manner. Shown here is how one can utilize these genes to give cells within a population of cells their own biosignature. As the biosignature is achieved with retroviral technology, cells are barcoded 'indefinitely'. As such, they can be individually tracked within a mixture of barcoded cells and utilized in more complex biological applications. The tracking of distinct populations in a mixture of cells is ideal for multiplexed applications such as discovery of drugs against a multitude of targets or the activation profile of different promoters. The protocol describes how to elegantly develop and amplify barcoded mammalian cells with distinct genetic fluorescent markers, and how to use several markers at once or one marker at different intensities. Finally, the protocol describes how the cells can be further utilized in combination with cell-based assays to increase the power of analysis through multiplexing.

Gene discovery and molecular marker development, based on high-throughput transcript sequencing of Paspalum dilatatum Poir.

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Andrea Giordano

Full Text Available BACKGROUND: Paspalum dilatatum Poir. (common name dallisgrass is a native grass species of South America, with special relevance to dairy and red meat production. P. dilatatum exhibits higher forage quality than other C4 forage grasses and is tolerant to frost and water stress. This species is predominantly cultivated in an apomictic monoculture, with an inherent high risk that biotic and abiotic stresses could potentially devastate productivity. Therefore, advanced breeding strategies that characterise and use available genetic diversity, or assess germplasm collections effectively are required to deliver advanced cultivars for production systems. However, there are limited genomic resources available for this forage grass species. RESULTS: Transcriptome sequencing using second-generation sequencing platforms has been employed using pooled RNA from different tissues (stems, roots, leaves and inflorescences at the final reproductive stage of P. dilatatum cultivar Primo. A total of 324,695 sequence reads were obtained, corresponding to c. 102 Mbp. The sequences were assembled, generating 20,169 contigs of a combined length of 9,336,138 nucleotides. The contigs were BLAST analysed against the fully sequenced grass species of Oryza sativa subsp. japonica, Brachypodium distachyon, the closely related Sorghum bicolor and foxtail millet (Setaria italica genomes as well as against the UniRef 90 protein database allowing a comprehensive gene ontology analysis to be performed. The contigs generated from the transcript sequencing were also analysed for the presence of simple sequence repeats (SSRs. A total of 2,339 SSR motifs were identified within 1,989 contigs and corresponding primer pairs were designed. Empirical validation of a cohort of 96 SSRs was performed, with 34% being polymorphic between sexual and apomictic biotypes. CONCLUSIONS: The development of genetic and genomic resources for P. dilatatum will contribute to gene discovery and expression

Science of the science, drug discovery and artificial neural networks.

Science.gov (United States)

Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Patent border wars: defining the boundary between scientific discoveries and patentable inventions.

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Holman, Christopher M

2007-12-01

Drawing an appropriate boundary between unpatentable natural phenomena and patentable inventions is crucial in preventing the patent laws from unduly restricting access to fundamental scientific discoveries. Some would argue that, particularly in the U.S., patents are being issued that purport to claim a novel product or process but that, in effect, encompass any practical application of a fundamental biological principle. Examples include gene patents, which Congress is considering banning, and patents relating to biological correlations and pathways, such as the patents at issue in the headline-grabbing LabCorp v. Metabolite and Ariad v. Eli Lilly litigations. In view of the mounting concern, it seems likely that Congress and/or the courts will address the issue, and perhaps substantially shift the boundary.

A constrained polynomial regression procedure for estimating the local False Discovery Rate

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Broët Philippe

2007-06-01

Full Text Available Abstract Background In the context of genomic association studies, for which a large number of statistical tests are performed simultaneously, the local False Discovery Rate (lFDR, which quantifies the evidence of a specific gene association with a clinical or biological variable of interest, is a relevant criterion for taking into account the multiple testing problem. The lFDR not only allows an inference to be made for each gene through its specific value, but also an estimate of Benjamini-Hochberg's False Discovery Rate (FDR for subsets of genes. Results In the framework of estimating procedures without any distributional assumption under the alternative hypothesis, a new and efficient procedure for estimating the lFDR is described. The results of a simulation study indicated good performances for the proposed estimator in comparison to four published ones. The five different procedures were applied to real datasets. Conclusion A novel and efficient procedure for estimating lFDR was developed and evaluated.

Applicability of bioanalysis of multiple analytes in drug discovery and development: review of select case studies including assay development considerations.

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Srinivas, Nuggehally R

2006-05-01

The development of sound bioanalytical method(s) is of paramount importance during the process of drug discovery and development culminating in a marketing approval. Although the bioanalytical procedure(s) originally developed during the discovery stage may not necessarily be fit to support the drug development scenario, they may be suitably modified and validated, as deemed necessary. Several reviews have appeared over the years describing analytical approaches including various techniques, detection systems, automation tools that are available for an effective separation, enhanced selectivity and sensitivity for quantitation of many analytes. The intention of this review is to cover various key areas where analytical method development becomes necessary during different stages of drug discovery research and development process. The key areas covered in this article with relevant case studies include: (a) simultaneous assay for parent compound and metabolites that are purported to display pharmacological activity; (b) bioanalytical procedures for determination of multiple drugs in combating a disease; (c) analytical measurement of chirality aspects in the pharmacokinetics, metabolism and biotransformation investigations; (d) drug monitoring for therapeutic benefits and/or occupational hazard; (e) analysis of drugs from complex and/or less frequently used matrices; (f) analytical determination during in vitro experiments (metabolism and permeability related) and in situ intestinal perfusion experiments; (g) determination of a major metabolite as a surrogate for the parent molecule; (h) analytical approaches for universal determination of CYP450 probe substrates and metabolites; (i) analytical applicability to prodrug evaluations-simultaneous determination of prodrug, parent and metabolites; (j) quantitative determination of parent compound and/or phase II metabolite(s) via direct or indirect approaches; (k) applicability in analysis of multiple compounds in select

Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information.

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Li, Jin; Wang, Limei; Guo, Maozu; Zhang, Ruijie; Dai, Qiguo; Liu, Xiaoyan; Wang, Chunyu; Teng, Zhixia; Xuan, Ping; Zhang, Mingming

2015-01-01

In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.

Integrative subtype discovery in glioblastoma using iCluster.

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Ronglai Shen

Full Text Available Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.

Applications of Dynamic Clamp to Cardiac Arrhythmia Research: Role in Drug Target Discovery and Safety Pharmacology Testing

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Francis A. Ortega

2018-01-01

Full Text Available Dynamic clamp, a hybrid-computational-experimental technique that has been used to elucidate ionic mechanisms underlying cardiac electrophysiology, is emerging as a promising tool in the discovery of potential anti-arrhythmic targets and in pharmacological safety testing. Through the injection of computationally simulated conductances into isolated cardiomyocytes in a real-time continuous loop, dynamic clamp has greatly expanded the capabilities of patch clamp outside traditional static voltage and current protocols. Recent applications include fine manipulation of injected artificial conductances to identify promising drug targets in the prevention of arrhythmia and the direct testing of model-based hypotheses. Furthermore, dynamic clamp has been used to enhance existing experimental models by addressing their intrinsic limitations, which increased predictive power in identifying pro-arrhythmic pharmacological compounds. Here, we review the recent advances of the dynamic clamp technique in cardiac electrophysiology with a focus on its future role in the development of safety testing and discovery of anti-arrhythmic drugs.

Working with Data: Discovering Knowledge through Mining and Analysis; Systematic Knowledge Management and Knowledge Discovery; Text Mining; Methodological Approach in Discovering User Search Patterns through Web Log Analysis; Knowledge Discovery in Databases Using Formal Concept Analysis; Knowledge Discovery with a Little Perspective.

Science.gov (United States)

Qin, Jian; Jurisica, Igor; Liddy, Elizabeth D.; Jansen, Bernard J; Spink, Amanda; Priss, Uta; Norton, Melanie J.

2000-01-01

These six articles discuss knowledge discovery in databases (KDD). Topics include data mining; knowledge management systems; applications of knowledge discovery; text and Web mining; text mining and information retrieval; user search patterns through Web log analysis; concept analysis; data collection; and data structure inconsistency. (LRW)

Microscale High-Throughput Experimentation as an Enabling Technology in Drug Discovery: Application in the Discovery of (Piperidinyl)pyridinyl-1H-benzimidazole Diacylglycerol Acyltransferase 1 Inhibitors.

Science.gov (United States)

Cernak, Tim; Gesmundo, Nathan J; Dykstra, Kevin; Yu, Yang; Wu, Zhicai; Shi, Zhi-Cai; Vachal, Petr; Sperbeck, Donald; He, Shuwen; Murphy, Beth Ann; Sonatore, Lisa; Williams, Steven; Madeira, Maria; Verras, Andreas; Reiter, Maud; Lee, Claire Heechoon; Cuff, James; Sherer, Edward C; Kuethe, Jeffrey; Goble, Stephen; Perrotto, Nicholas; Pinto, Shirly; Shen, Dong-Ming; Nargund, Ravi; Balkovec, James; DeVita, Robert J; Dreher, Spencer D

2017-05-11

Miniaturization and parallel processing play an important role in the evolution of many technologies. We demonstrate the application of miniaturized high-throughput experimentation methods to resolve synthetic chemistry challenges on the frontlines of a lead optimization effort to develop diacylglycerol acyltransferase (DGAT1) inhibitors. Reactions were performed on ∼1 mg scale using glass microvials providing a miniaturized high-throughput experimentation capability that was used to study a challenging S N Ar reaction. The availability of robust synthetic chemistry conditions discovered in these miniaturized investigations enabled the development of structure-activity relationships that ultimately led to the discovery of soluble, selective, and potent inhibitors of DGAT1.

Genetic correction using engineered nucleases for gene therapy applications.

Science.gov (United States)

Li, Hongmei Lisa; Nakano, Takao; Hotta, Akitsu

2014-01-01

Genetic mutations in humans are associated with congenital disorders and phenotypic traits. Gene therapy holds the promise to cure such genetic disorders, although it has suffered from several technical limitations for decades. Recent progress in gene editing technology using tailor-made nucleases, such as meganucleases (MNs), zinc finger nucleases (ZFNs), TAL effector nucleases (TALENs) and, more recently, CRISPR/Cas9, has significantly broadened our ability to precisely modify target sites in the human genome. In this review, we summarize recent progress in gene correction approaches of the human genome, with a particular emphasis on the clinical applications of gene therapy. © 2013 The Authors Development, Growth & Differentiation © 2013 Japanese Society of Developmental Biologists.

Systems Biology Modeling of the Radiation Sensitivity Network: A Biomarker Discovery Platform

International Nuclear Information System (INIS)

Eschrich, Steven; Zhang Hongling; Zhao Haiyan; Boulware, David; Lee, Ji-Hyun; Bloom, Gregory; Torres-Roca, Javier F.

2009-01-01

Purpose: The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers. Methods and Materials: Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt). Results: The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers. Conclusion: We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.

Development and application of a 6.5 million feature Affymetrix Genechip® for massively parallel discovery of single position polymorphisms in lettuce (Lactuca spp.).

Science.gov (United States)

Stoffel, Kevin; van Leeuwen, Hans; Kozik, Alexander; Caldwell, David; Ashrafi, Hamid; Cui, Xinping; Tan, Xiaoping; Hill, Theresa; Reyes-Chin-Wo, Sebastian; Truco, Maria-Jose; Michelmore, Richard W; Van Deynze, Allen

2012-05-14

High-resolution genetic maps are needed in many crops to help characterize the genetic diversity that determines agriculturally important traits. Hybridization to microarrays to detect single feature polymorphisms is a powerful technique for marker discovery and genotyping because of its highly parallel nature. However, microarrays designed for gene expression analysis rarely provide sufficient gene coverage for optimal detection of nucleotide polymorphisms, which limits utility in species with low rates of polymorphism such as lettuce (Lactuca sativa). We developed a 6.5 million feature Affymetrix GeneChip® for efficient polymorphism discovery and genotyping, as well as for analysis of gene expression in lettuce. Probes on the microarray were designed from 26,809 unigenes from cultivated lettuce and an additional 8,819 unigenes from four related species (L. serriola, L. saligna, L. virosa and L. perennis). Where possible, probes were tiled with a 2 bp stagger, alternating on each DNA strand; providing an average of 187 probes covering approximately 600 bp for each of over 35,000 unigenes; resulting in up to 13 fold redundancy in coverage per nucleotide. We developed protocols for hybridization of genomic DNA to the GeneChip® and refined custom algorithms that utilized coverage from multiple, high quality probes to detect single position polymorphisms in 2 bp sliding windows across each unigene. This allowed us to detect greater than 18,000 polymorphisms between the parental lines of our core mapping population, as well as numerous polymorphisms between cultivated lettuce and wild species in the lettuce genepool. Using marker data from our diversity panel comprised of 52 accessions from the five species listed above, we were able to separate accessions by species using both phylogenetic and principal component analyses. Additionally, we estimated the diversity between different types of cultivated lettuce and distinguished morphological types. By hybridizing

Volatility Discovery

DEFF Research Database (Denmark)

Dias, Gustavo Fruet; Scherrer, Cristina; Papailias, Fotis

The price discovery literature investigates how homogenous securities traded on different markets incorporate information into prices. We take this literature one step further and investigate how these markets contribute to stochastic volatility (volatility discovery). We formally show...... that the realized measures from homogenous securities share a fractional stochastic trend, which is a combination of the price and volatility discovery measures. Furthermore, we show that volatility discovery is associated with the way that market participants process information arrival (market sensitivity......). Finally, we compute volatility discovery for 30 actively traded stocks in the U.S. and report that Nyse and Arca dominate Nasdaq....

Discovery of Intrinsic Primitives on Triangle Meshes

KAUST Repository

Solomon, Justin; Ben-Chen, Mirela; Butscher, Adrian; Guibas, Leonidas

2011-01-01

The discovery of meaningful parts of a shape is required for many geometry processing applications, such as parameterization, shape correspondence, and animation. It is natural to consider primitives such as spheres, cylinders and cones

Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems

Directory of Open Access Journals (Sweden)

Chung I-Fang

2008-10-01

Full Text Available Abstract Background The Signal-to-Noise-Ratio (SNR is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs. These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems. Results and discussion To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer. For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC, Nearest Mean Classifier (NMC, Support Vector Machine (SVM classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA and one-vs-one (OVO strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the

[Sequencing technology in gene diagnosis and its application].

Science.gov (United States)

Yibin, Guo

2014-11-01

The study of gene mutation is one of the hot topics in the field of life science nowadays, and the related detection methods and diagnostic technology have been developed rapidly. Sequencing technology plays an indispensable role in the definite diagnosis and classification of genetic diseases. In this review, we summarize the research progress in sequencing technology, evaluate the advantages and disadvantages of 1(st) ~3(rd) generation of sequencing technology, and describe its application in gene diagnosis. Also we made forecasts and prospects on its development trend.

GENOME-ENABLED DISCOVERY OF CARBON SEQUESTRATION GENES IN POPLAR

Energy Technology Data Exchange (ETDEWEB)

DAVIS J M

2007-10-11

Plants utilize carbon by partitioning the reduced carbon obtained through photosynthesis into different compartments and into different chemistries within a cell and subsequently allocating such carbon to sink tissues throughout the plant. Since the phytohormones auxin and cytokinin are known to influence sink strength in tissues such as roots (Skoog & Miller 1957, Nordstrom et al. 2004), we hypothesized that altering the expression of genes that regulate auxin-mediated (e.g., AUX/IAA or ARF transcription factors) or cytokinin-mediated (e.g., RR transcription factors) control of root growth and development would impact carbon allocation and partitioning belowground (Fig. 1 - Renewal Proposal). Specifically, the ARF, AUX/IAA and RR transcription factor gene families mediate the effects of the growth regulators auxin and cytokinin on cell expansion, cell division and differentiation into root primordia. Invertases (IVR), whose transcript abundance is enhanced by both auxin and cytokinin, are critical components of carbon movement and therefore of carbon allocation. Thus, we initiated comparative genomic studies to identify the AUX/IAA, ARF, RR and IVR gene families in the Populus genome that could impact carbon allocation and partitioning. Bioinformatics searches using Arabidopsis gene sequences as queries identified regions with high degrees of sequence similarities in the Populus genome. These Populus sequences formed the basis of our transgenic experiments. Transgenic modification of gene expression involving members of these gene families was hypothesized to have profound effects on carbon allocation and partitioning.

HiGate (High Grade Anti-Tamper Equipment Prototype and Application to e-Discovery

Directory of Open Access Journals (Sweden)

Yui Sakurai

2010-06-01

Full Text Available These days, most data is digitized and processed in various ways by computers. In the past, computer owners were free to process data as desired and to observe the inputted data as well as the interim results. However, the unrestricted processing of data and accessing of interim results even by computer users is associated with an increasing number of adverse events. These adverse events often occur when sensitive data such as personal or confidential business information must be handled by two or more parties, such as in the case of e-Discovery, used in legal proceedings, or epidemiologic studies. To solve this problem, providers encrypt data, and the owner of the computer performs decoding in the memory for encrypted data. The computer owner can be limited to performing only certain processing of data and to observing only the final results. As an implementation that uses existing technology to realize this solution, the processing of data contained in a smart card was considered, but such an implementation would not be practical due to issues related to computer capacity and processing speed. Accordingly, the authors present the concept of PC-based High Grade Anti-Tamper Equipment (HiGATE, which allows data to be handled without revealing the data content to administrators or users. To verify this concept, an e-Discovery application on a prototype was executed and the results are reported here.

Service-oriented discovery of knowledge : foundations, implementations and applications

NARCIS (Netherlands)

Bruin, Jeroen Sebastiaan de

2010-01-01

In this thesis we will investigate how a popular new way of distributed computing called service orientation can be used within the field of Knowledge Discovery. We critically investigate its principles and present models for developing withing this paradigm. We then apply this model to create a web

The need for operating guidelines and a decision making framework applicable to the discovery of non-intelligent extraterrestrial life

Science.gov (United States)

Race, Margaret S.; Randolph, Richard O.

While formal principles have been adopted for the eventuality of detecting intelligent life in our galaxy (SETI Principles), no such guidelines exist for the discovery of non-intelligent extraterrestrial life within the solar system. Current scientifically based planetary protection policies for solar system exploration address how to undertake exploration, but do not provide clear guidance on what to do if and when life is detected. Considering that martian life could be detected under several different robotic and human exploration scenarios in the coming decades, it is appropriate to anticipate how detection of non-intelligent, microbial life could impact future exploration missions and activities, especially on Mars. This paper discusses a proposed set of interim guidelines based loosely on the SETI Principles and addresses issues extending from the time of discovery through future handling and treatment of extraterrestrial life on Mars or elsewhere. Based on an analysis of both scientific and ethical considerations, there is a clear need for developing operating protocols applicable at the time of discovery and a decision making framework that anticipates future missions and activities, both robotic and human. There is growing scientific confidence that the discovery of extraterrestrial life in some form is nearly inevitable. If and when life is discovered beyond Earth, non-scientific dimensions may strongly influence decisions about the nature and scope of future missions and activities. It is appropriate to encourage international discussion and consideration of the issues prior to an event of such historical significance.

The influence of discovery learning model application to the higher order thinking skills student of Srijaya Negara Senior High School Palembang on the animal kingdom subject matter

Science.gov (United States)

Riandari, F.; Susanti, R.; Suratmi

2018-05-01

This study aimed to find out the information in concerning the influence of discovery learning model application to the higher order thinking skills at the tenth grade students of Srijaya Negara senior high school Palembang on the animal kingdom subject matter. The research method used was pre-experimental with one-group pretest-posttest design. The researchconducted at Srijaya Negara senior high school Palembang academic year 2016/2017. The population sample of this research was tenth grade students of natural science 2. Purposive sampling techniquewas applied in this research. Data was collected by(1) the written test, consist of pretest to determine the initial ability and posttest to determine higher order thinking skills of students after learning by using discovery learning models. (2) Questionnaire sheet, aimed to investigate the response of the students during the learning process by using discovery learning models. The t-test result indicated there was significant increasement of higher order thinking skills students. Thus, it can be concluded that the application of discovery learning modelhad a significant effect and increased to higher order thinking skills students of Srijaya Negara senior high school Palembang on the animal kingdom subject matter.

Applications of the Preclinical Molecular Imaging in Biomedicine: Gene Therapy

International Nuclear Information System (INIS)

Collantes, M.; Peñuelas, I.

2014-01-01

Gene therapy constitutes a promising option for efficient and targeted treatment of several inherited disorders. Imaging techniques using ionizing radiation as PET or SPECT are used for non-invasive monitoring of the distribution and kinetics of vector-mediated gene expression. In this review the main reporter gene/reporter probe strategies are summarized, as well as the contribution of preclinical models to the development of this new imaging modality previously to its application in clinical arena. [es

Literature-related discovery techniques applied to ocular disease : a vitreous restoration example

NARCIS (Netherlands)

Kostoff, Ronald N.; Los, Leonoor I.

2013-01-01

Purpose of reviewLiterature-related discovery and innovation (LRDI) is a text mining approach for bridging unconnected disciplines to hypothesize radical discovery. Application to medical problems involves identifying key disease symptoms, and identifying causes and treatments for those symptoms

Gene discovery in EST sequences from the wheat leaf rust fungus Puccinia triticina sexual spores, asexual spores and haustoria, compared to other rust and corn smut fungi

Science.gov (United States)

2011-01-01

Background Rust fungi are biotrophic basidiomycete plant pathogens that cause major diseases on plants and trees world-wide, affecting agriculture and forestry. Their biotrophic nature precludes many established molecular genetic manipulations and lines of research. The generation of genomic resources for these microbes is leading to novel insights into biology such as interactions with the hosts and guiding directions for breakthrough research in plant pathology. Results To support gene discovery and gene model verification in the genome of the wheat leaf rust fungus, Puccinia triticina (Pt), we have generated Expressed Sequence Tags (ESTs) by sampling several life cycle stages. We focused on several spore stages and isolated haustorial structures from infected wheat, generating 17,684 ESTs. We produced sequences from both the sexual (pycniospores, aeciospores and teliospores) and asexual (germinated urediniospores) stages of the life cycle. From pycniospores and aeciospores, produced by infecting the alternate host, meadow rue (Thalictrum speciosissimum), 4,869 and 1,292 reads were generated, respectively. We generated 3,703 ESTs from teliospores produced on the senescent primary wheat host. Finally, we generated 6,817 reads from haustoria isolated from infected wheat as well as 1,003 sequences from germinated urediniospores. Along with 25,558 previously generated ESTs, we compiled a database of 13,328 non-redundant sequences (4,506 singlets and 8,822 contigs). Fungal genes were predicted using the EST version of the self-training GeneMarkS algorithm. To refine the EST database, we compared EST sequences by BLASTN to a set of 454 pyrosequencing-generated contigs and Sanger BAC-end sequences derived both from the Pt genome, and to ESTs and genome reads from wheat. A collection of 6,308 fungal genes was identified and compared to sequences of the cereal rusts, Puccinia graminis f. sp. tritici (Pgt) and stripe rust, P. striiformis f. sp. tritici (Pst), and poplar

Gene discovery in EST sequences from the wheat leaf rust fungus Puccinia triticina sexual spores, asexual spores and haustoria, compared to other rust and corn smut fungi

Directory of Open Access Journals (Sweden)

Wynhoven Brian

2011-03-01

Full Text Available Abstract Background Rust fungi are biotrophic basidiomycete plant pathogens that cause major diseases on plants and trees world-wide, affecting agriculture and forestry. Their biotrophic nature precludes many established molecular genetic manipulations and lines of research. The generation of genomic resources for these microbes is leading to novel insights into biology such as interactions with the hosts and guiding directions for breakthrough research in plant pathology. Results To support gene discovery and gene model verification in the genome of the wheat leaf rust fungus, Puccinia triticina (Pt, we have generated Expressed Sequence Tags (ESTs by sampling several life cycle stages. We focused on several spore stages and isolated haustorial structures from infected wheat, generating 17,684 ESTs. We produced sequences from both the sexual (pycniospores, aeciospores and teliospores and asexual (germinated urediniospores stages of the life cycle. From pycniospores and aeciospores, produced by infecting the alternate host, meadow rue (Thalictrum speciosissimum, 4,869 and 1,292 reads were generated, respectively. We generated 3,703 ESTs from teliospores produced on the senescent primary wheat host. Finally, we generated 6,817 reads from haustoria isolated from infected wheat as well as 1,003 sequences from germinated urediniospores. Along with 25,558 previously generated ESTs, we compiled a database of 13,328 non-redundant sequences (4,506 singlets and 8,822 contigs. Fungal genes were predicted using the EST version of the self-training GeneMarkS algorithm. To refine the EST database, we compared EST sequences by BLASTN to a set of 454 pyrosequencing-generated contigs and Sanger BAC-end sequences derived both from the Pt genome, and to ESTs and genome reads from wheat. A collection of 6,308 fungal genes was identified and compared to sequences of the cereal rusts, Puccinia graminis f. sp. tritici (Pgt and stripe rust, P. striiformis f. sp

Pattern Discovery in Time-Ordered Data; TOPICAL

International Nuclear Information System (INIS)

CONRAD, GREGORY N.; BRITANIK, JOHN M.; DELAND, SHARON M.; JENKIN, CHRISTINA L.

2002-01-01

This report describes the results of a Laboratory-Directed Research and Development project on techniques for pattern discovery in discrete event time series data. In this project, we explored two different aspects of the pattern matching/discovery problem. The first aspect studied was the use of Dynamic Time Warping for pattern matching in continuous data. In essence, DTW is a technique for aligning time series along the time axis to optimize the similarity measure. The second aspect studied was techniques for discovering patterns in discrete event data. We developed a pattern discovery tool based on adaptations of the A-priori and GSP (Generalized Sequential Pattern mining) algorithms. We then used the tool on three different application areas-unattended monitoring system data from a storage magazine, computer network intrusion detection, and analysis of robot training data

dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface

Science.gov (United States)

Rot, Gregor; Parikh, Anup; Curk, Tomaz; Kuspa, Adam; Shaulsky, Gad; Zupan, Blaz

2009-01-01

Background Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability. Results We have developed dictyExpress, a web application that features a graphical, highly interactive explorative interface to our database that consists of more than 1000 Dictyostelium discoideum gene expression experiments. In dictyExpress, the user can select experiments and genes, perform gene clustering, view gene expression profiles across time, view gene co-expression networks, perform analyses of Gene Ontology term enrichment, and simultaneously display expression profiles for a selected gene in various experiments. Most importantly, these tasks are achieved through web applications whose components are seamlessly interlinked and immediately respond to events triggered by the user, thus providing a powerful explorative data analysis environment. Conclusion dictyExpress is a precursor for a new generation of web-based bioinformatics applications with simple but powerful interactive interfaces that resemble that of the modern desktop. While dictyExpress serves mainly the Dictyostelium research community, it is relatively easy to adapt it to other datasets. We propose that the design ideas behind dictyExpress will influence the development of similar applications for other model organisms. PMID:19706156

Semantic Service Discovery Techniques for the composable web

OpenAIRE

Fernández Villamor, José Ignacio

2013-01-01

This PhD thesis contributes to the problem of resource and service discovery in the context of the composable web. In the current web, mashup technologies allow developers reusing services and contents to build new web applications. However, developers face a problem of information flood when searching for appropriate services or resources for their combination. To contribute to overcoming this problem, a framework is defined for the discovery of services and resources. In this framework, thr...

miRvestigator: web application to identify miRNAs responsible for co-regulated gene expression patterns discovered through transcriptome profiling.

Science.gov (United States)

Plaisier, Christopher L; Bare, J Christopher; Baliga, Nitin S

2011-07-01

Transcriptome profiling studies have produced staggering numbers of gene co-expression signatures for a variety of biological systems. A significant fraction of these signatures will be partially or fully explained by miRNA-mediated targeted transcript degradation. miRvestigator takes as input lists of co-expressed genes from Caenorhabditis elegans, Drosophila melanogaster, G. gallus, Homo sapiens, Mus musculus or Rattus norvegicus and identifies the specific miRNAs that are likely to bind to 3' un-translated region (UTR) sequences to mediate the observed co-regulation. The novelty of our approach is the miRvestigator hidden Markov model (HMM) algorithm which systematically computes a similarity P-value for each unique miRNA seed sequence from the miRNA database miRBase to an overrepresented sequence motif identified within the 3'-UTR of the query genes. We have made this miRNA discovery tool accessible to the community by integrating our HMM algorithm with a proven algorithm for de novo discovery of miRNA seed sequences and wrapping these algorithms into a user-friendly interface. Additionally, the miRvestigator web server also produces a list of putative miRNA binding sites within 3'-UTRs of the query transcripts to facilitate the design of validation experiments. The miRvestigator is freely available at http://mirvestigator.systemsbiology.net.

Discovery Mondays

CERN Multimedia

2003-01-01

Many people don't realise quite how much is going on at CERN. Would you like to gain first-hand knowledge of CERN's scientific and technological activities and their many applications? Try out some experiments for yourself, or pick the brains of the people in charge? If so, then the «Lundis Découverte» or Discovery Mondays, will be right up your street. Starting on May 5th, on every first Monday of the month you will be introduced to a different facet of the Laboratory. CERN staff, non-scientists, and members of the general public, everyone is welcome. So tell your friends and neighbours and make sure you don't miss this opportunity to satisfy your curiosity and enjoy yourself at the same time. You won't have to listen to a lecture, as the idea is to have open exchange with the expert in question and for each subject to be illustrated with experiments and demonstrations. There's no need to book, as Microcosm, CERN's interactive museum, will be open non-stop from 7.30 p.m. to 9 p.m. On the first Discovery M...

Induced pluripotency with endogenous and inducible genes

International Nuclear Information System (INIS)

Duinsbergen, Dirk; Eriksson, Malin; Hoen, Peter A.C. 't; Frisen, Jonas; Mikkers, Harald

2008-01-01

The recent discovery that two partly overlapping sets of four genes induce nuclear reprogramming of mouse and even human cells has opened up new possibilities for cell replacement therapies. Although the combination of genes that induce pluripotency differs to some extent, Oct4 and Sox2 appear to be a prerequisite. The introduction of four genes, several of which been linked with cancer, using retroviral approaches is however unlikely to be suitable for future clinical applications. Towards developing a safer reprogramming protocol, we investigated whether cell types that express one of the most critical reprogramming genes endogenously are predisposed to reprogramming. We show here that three of the original four pluripotency transcription factors (Oct4, Klf4 and c-Myc or MYCER TAM ) induced reprogramming of mouse neural stem (NS) cells exploiting endogenous SoxB1 protein levels in these cells. The reprogrammed neural stem cells differentiated into cells of each germ layer in vitro and in vivo, and contributed to mouse development in vivo. Thus a combinatorial approach taking advantage of endogenously expressed genes and inducible transgenes may contribute to the development of improved reprogramming protocols

Advances in Chance Discovery : Extended Selection from International Workshops

CERN Document Server

Abe, Akinori

2013-01-01

Since year 2000, scientists on artificial and natural intelligences started to study chance discovery - methods for discovering events/situations that significantly affect decision making. Partially because the editors Ohsawa and Abe are teaching at schools of Engineering and of Literature with sharing the interest in chance discovery, this book reflects interdisciplinary aspects of progress: First, as an interdisciplinary melting pot of cognitive science, computational intelligence, data mining/visualization, collective intelligence, … etc, chance discovery came to reach new application domains e.g. health care, aircraft control, energy plant, management of technologies, product designs, innovations, marketing, finance etc. Second, basic technologies and sciences including sensor technologies, medical sciences, communication technologies etc. joined this field and interacted with cognitive/computational scientists in workshops on chance discovery, to obtain breakthroughs by stimulating each other. Third, �...

Glycoscience aids in biomarker discovery

Directory of Open Access Journals (Sweden)

Serenus Hua1,2 & Hyun Joo An1,2,*

2012-06-01

Full Text Available The glycome consists of all glycans (or carbohydrates within abiological system, and modulates a wide range of important biologicalactivities, from protein folding to cellular communications.The mining of the glycome for disease markers representsa new paradigm for biomarker discovery; however, this effortis severely complicated by the vast complexity and structuraldiversity of glycans. This review summarizes recent developmentsin analytical technology and methodology as applied tothe fields of glycomics and glycoproteomics. Mass spectrometricstrategies for glycan compositional profiling are described, as arepotential refinements which allow structure-specific profiling.Analytical methods that can discern protein glycosylation at aspecific site of modification are also discussed in detail.Biomarker discovery applications are shown at each level ofanalysis, highlighting the key role that glycoscience can play inhelping scientists understand disease biology.

Advances in phage display technology for drug discovery.

Science.gov (United States)

Omidfar, Kobra; Daneshpour, Maryam

2015-06-01

Over the past decade, several library-based methods have been developed to discover ligands with strong binding affinities for their targets. These methods mimic the natural evolution for screening and identifying ligand-target interactions with specific functional properties. Phage display technology is a well-established method that has been applied to many technological challenges including novel drug discovery. This review describes the recent advances in the use of phage display technology for discovering novel bioactive compounds. Furthermore, it discusses the application of this technology to produce proteins and peptides as well as minimize the use of antibodies, such as antigen-binding fragment, single-chain fragment variable or single-domain antibody fragments like VHHs. Advances in screening, manufacturing and humanization technologies demonstrate that phage display derived products can play a significant role in the diagnosis and treatment of disease. The effects of this technology are inevitable in the development pipeline for bringing therapeutics into the market, and this number is expected to rise significantly in the future as new advances continue to take place in display methods. Furthermore, a widespread application of this methodology is predicted in different medical technological areas, including biosensing, monitoring, molecular imaging, gene therapy, vaccine development and nanotechnology.

Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery.

Science.gov (United States)

Jia, Zhilong; Liu, Ying; Guan, Naiyang; Bo, Xiaochen; Luo, Zhigang; Barnes, Michael R

2016-05-27

Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context. We demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena . In conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and

Neutron Diffraction and Inorganic Materials Discovery

International Nuclear Information System (INIS)

Rosseinsky, M.J.

2005-01-01

Full text: The discovery of complex inorganic materials is an important academic and technological challenge because of the opportunities these systems offer for observation of new phenomena, and the questions they pose for fundamental understanding. This presentation will illustrate the key role of neutron powder diffraction in enabling the discovery of new classes of materials, and in evaluating their properties and the conditions under which they need to be processed to optimise their behaviour in devices for applications. New chemistry is illustrated by the transition metal oxide hydrides, where both structure and ionic mobility required neutron scattering characterisation. The relationship between chemistry, structure and properties will be addressed by considering the difficulties in inducing superconductivity in analogues of magnesium diboride. The role of both neutron and X-ray diffraction in evaluating the processing of microwave dielectric ceramics will be highlighted, with the discovery of new phases shown to be a useful bonus in this type of in-situ study. (author)

Symbiosis-inspired approaches to antibiotic discovery.

Science.gov (United States)

Adnani, Navid; Rajski, Scott R; Bugni, Tim S

2017-07-06

Covering: 2010 up to 2017Life on Earth is characterized by a remarkable abundance of symbiotic and highly refined relationships among life forms. Defined as any kind of close, long-term association between two organisms, symbioses can be mutualistic, commensalistic or parasitic. Historically speaking, selective pressures have shaped symbioses in which one organism (typically a bacterium or fungus) generates bioactive small molecules that impact the host (and possibly other symbionts); the symbiosis is driven fundamentally by the genetic machineries available to the small molecule producer. The human microbiome is now integral to the most recent chapter in animal-microbe symbiosis studies and plant-microbe symbioses have significantly advanced our understanding of natural products biosynthesis; this also is the case for studies of fungal-microbe symbioses. However, much less is known about microbe-microbe systems involving interspecies interactions. Microbe-derived small molecules (i.e. antibiotics and quorum sensing molecules, etc.) have been shown to regulate transcription in microbes within the same environmental niche, suggesting interspecies interactions whereas, intraspecies interactions, such as those that exploit autoinducing small molecules, also modulate gene expression based on environmental cues. We, and others, contend that symbioses provide almost unlimited opportunities for the discovery of new bioactive compounds whose activities and applications have been evolutionarily optimized. Particularly intriguing is the possibility that environmental effectors can guide laboratory expression of secondary metabolites from "orphan", or silent, biosynthetic gene clusters (BGCs). Notably, many of the studies summarized here result from advances in "omics" technologies and highlight how symbioses have given rise to new anti-bacterial and antifungal natural products now being discovered.

Engineered nonviral nanocarriers for intracellular gene delivery applications

International Nuclear Information System (INIS)

Ojea-Jiménez, Isaac; Puntes, Victor F; Tort, Olivia; Lorenzo, Julia

2012-01-01

The efficient delivery of nucleic acids into mammalian cells is a central aspect of cell biology and of medical applications, including cancer therapy and tissue engineering. Non-viral chemical methods have been received with great interest for transfecting cells. However, further development of nanocarriers that are biocompatible, efficient and suitable for clinical applications is still required. In this paper, the different material platforms for gene delivery are comparatively addressed, and the mechanisms of interaction with biological systems are discussed carefully. (paper)

GeneDig: a web application for accessing genomic and bioinformatics knowledge.

Science.gov (United States)

Suciu, Radu M; Aydin, Emir; Chen, Brian E

2015-02-28

With the exponential increase and widespread availability of genomic, transcriptomic, and proteomic data, accessing these '-omics' data is becoming increasingly difficult. The current resources for accessing and analyzing these data have been created to perform highly specific functions intended for specialists, and thus typically emphasize functionality over user experience. We have developed a web-based application, GeneDig.org, that allows any general user access to genomic information with ease and efficiency. GeneDig allows for searching and browsing genes and genomes, while a dynamic navigator displays genomic, RNA, and protein information simultaneously for co-navigation. We demonstrate that our application allows more than five times faster and efficient access to genomic information than any currently available methods. We have developed GeneDig as a platform for bioinformatics integration focused on usability as its central design. This platform will introduce genomic navigation to broader audiences while aiding the bioinformatics analyses performed in everyday biology research.

In vitro membrane binding and protein binding (IAM MB/PB technology to estimate in vivo distribution: applications in early drug discovery

Directory of Open Access Journals (Sweden)

Klara Livia Valko

2017-03-01

Full Text Available The drug discovery process can be accelerated by chromatographic profiling of the analogs to model in vivo distribution and the major non-specific binding. A balanced potency and chromatographically determined membrane and protein binding (IAM MB/PB data enable selecting drug discovery compounds for further analysis that have the highest probability to show the desired in vivo distribution behavior for efficacy and reduced chance for toxicity. Although the basic principles of the technology have already appeared in numerous publications, the lack of standardized procedures limited its widespread applications especially in academia and small drug discovery biotech companies. In this paper, the standardized procedures are described that has been trademarked as Regis IAM MB/PB Technology®. Comparison between the Drug Efficiency Index (DEI=pIC50-logVdu+2 and generally used Ligand Lipophilicity Efficiency (LLE has been made, demonstrating the advantage of measured IAM and HSA binding over calculated log P. The power of the proposed chromatographic technology is demonstrated using the data of marketed drugs.

Using directed information for influence discovery in interconnected dynamical systems

Science.gov (United States)

Rao, Arvind; Hero, Alfred O.; States, David J.; Engel, James Douglas

2008-08-01

Structure discovery in non-linear dynamical systems is an important and challenging problem that arises in various applications such as computational neuroscience, econometrics, and biological network discovery. Each of these systems have multiple interacting variables and the key problem is the inference of the underlying structure of the systems (which variables are connected to which others) based on the output observations (such as multiple time trajectories of the variables). Since such applications demand the inference of directed relationships among variables in these non-linear systems, current methods that have a linear assumption on structure or yield undirected variable dependencies are insufficient. Hence, in this work, we present a methodology for structure discovery using an information-theoretic metric called directed time information (DTI). Using both synthetic dynamical systems as well as true biological datasets (kidney development and T-cell data), we demonstrate the utility of DTI in such problems.

SNP Discovery for mapping alien introgressions in wheat

Science.gov (United States)

2014-01-01

Background Monitoring alien introgressions in crop plants is difficult due to the lack of genetic and molecular mapping information on the wild crop relatives. The tertiary gene pool of wheat is a very important source of genetic variability for wheat improvement against biotic and abiotic stresses. By exploring the 5Mg short arm (5MgS) of Aegilops geniculata, we can apply chromosome genomics for the discovery of SNP markers and their use for monitoring alien introgressions in wheat (Triticum aestivum L). Results The short arm of chromosome 5Mg of Ae. geniculata Roth (syn. Ae. ovata L.; 2n = 4x = 28, UgUgMgMg) was flow-sorted from a wheat line in which it is maintained as a telocentric chromosome. DNA of the sorted arm was amplified and sequenced using an Illumina Hiseq 2000 with ~45x coverage. The sequence data was used for SNP discovery against wheat homoeologous group-5 assemblies. A total of 2,178 unique, 5MgS-specific SNPs were discovered. Randomly selected samples of 59 5MgS-specific SNPs were tested (44 by KASPar assay and 15 by Sanger sequencing) and 84% were validated. Of the selected SNPs, 97% mapped to a chromosome 5Mg addition to wheat (the source of t5MgS), and 94% to 5Mg introgressed from a different accession of Ae. geniculata substituting for chromosome 5D of wheat. The validated SNPs also identified chromosome segments of 5MgS origin in a set of T5D-5Mg translocation lines; eight SNPs (25%) mapped to TA5601 [T5DL · 5DS-5MgS(0.75)] and three (8%) to TA5602 [T5DL · 5DS-5MgS (0.95)]. SNPs (gsnp_5ms83 and gsnp_5ms94), tagging chromosome T5DL · 5DS-5MgS(0.95) with the smallest introgression carrying resistance to leaf rust (Lr57) and stripe rust (Yr40), were validated in two released germplasm lines with Lr57 and Yr40 genes. Conclusion This approach should be widely applicable for the identification of species/genome-specific SNPs. The development of a large number of SNP markers will facilitate the precise introgression and

SNP Discovery for mapping alien introgressions in wheat.

Science.gov (United States)

Tiwari, Vijay K; Wang, Shichen; Sehgal, Sunish; Vrána, Jan; Friebe, Bernd; Kubaláková, Marie; Chhuneja, Praveen; Doležel, Jaroslav; Akhunov, Eduard; Kalia, Bhanu; Sabir, Jamal; Gill, Bikram S

2014-04-10

Monitoring alien introgressions in crop plants is difficult due to the lack of genetic and molecular mapping information on the wild crop relatives. The tertiary gene pool of wheat is a very important source of genetic variability for wheat improvement against biotic and abiotic stresses. By exploring the 5Mg short arm (5MgS) of Aegilops geniculata, we can apply chromosome genomics for the discovery of SNP markers and their use for monitoring alien introgressions in wheat (Triticum aestivum L). The short arm of chromosome 5Mg of Ae. geniculata Roth (syn. Ae. ovata L.; 2n = 4x = 28, UgUgMgMg) was flow-sorted from a wheat line in which it is maintained as a telocentric chromosome. DNA of the sorted arm was amplified and sequenced using an Illumina Hiseq 2000 with ~45x coverage. The sequence data was used for SNP discovery against wheat homoeologous group-5 assemblies. A total of 2,178 unique, 5MgS-specific SNPs were discovered. Randomly selected samples of 59 5MgS-specific SNPs were tested (44 by KASPar assay and 15 by Sanger sequencing) and 84% were validated. Of the selected SNPs, 97% mapped to a chromosome 5Mg addition to wheat (the source of t5MgS), and 94% to 5Mg introgressed from a different accession of Ae. geniculata substituting for chromosome 5D of wheat. The validated SNPs also identified chromosome segments of 5MgS origin in a set of T5D-5Mg translocation lines; eight SNPs (25%) mapped to TA5601 [T5DL · 5DS-5MgS(0.75)] and three (8%) to TA5602 [T5DL · 5DS-5MgS (0.95)]. SNPs (gsnp_5ms83 and gsnp_5ms94), tagging chromosome T5DL · 5DS-5MgS(0.95) with the smallest introgression carrying resistance to leaf rust (Lr57) and stripe rust (Yr40), were validated in two released germplasm lines with Lr57 and Yr40 genes. This approach should be widely applicable for the identification of species/genome-specific SNPs. The development of a large number of SNP markers will facilitate the precise introgression and monitoring of alien segments in crop

Characteristics and Application of a Novel Species of Bacillus: Bacillus velezensis.

Science.gov (United States)

Ye, Miao; Tang, Xiangfang; Yang, Ru; Zhang, Hongfu; Li, Fangshu; Tao, Fangzheng; Li, Fei; Wang, Zaigui

2018-03-16

Bacillus velezensis has been investigated and applied more and more widely recently because it can inhibit fungi and bacteria and become a potential biocontrol agent. In order to provide more clear and comprehensive understanding of B. velezensis for researchers, we collected the recent relevant articles systematically and reviewed the discovery and taxonomy, secondary metabolites, characteristics and application, gene function, and molecular research of B. velezensis. This review will give some direction to the research and application of this strain for the future.

Application of nanomaterials in the bioanalytical detection of disease-related genes.

Science.gov (United States)

Zhu, Xiaoqian; Li, Jiao; He, Hanping; Huang, Min; Zhang, Xiuhua; Wang, Shengfu

2015-12-15

In the diagnosis of genetic diseases and disorders, nanomaterials-based gene detection systems have significant advantages over conventional diagnostic systems in terms of simplicity, sensitivity, specificity, and portability. In this review, we describe the application of nanomaterials for disease-related genes detection in different methods excluding PCR-related method, such as colorimetry, fluorescence-based methods, electrochemistry, microarray methods, surface-enhanced Raman spectroscopy (SERS), quartz crystal microbalance (QCM) methods, and dynamic light scattering (DLS). The most commonly used nanomaterials are gold, silver, carbon and semiconducting nanoparticles. Various nanomaterials-based gene detection methods are introduced, their respective advantages are discussed, and selected examples are provided to illustrate the properties of these nanomaterials and their emerging applications for the detection of specific nucleic acid sequences. Copyright © 2015. Published by Elsevier B.V.

Construction and evaluation of normalized cDNA libraries enriched with full-length sequences for rapid discovery of new genes from Sisal (Agave sisalana Perr.) different developmental stages.

Science.gov (United States)

Zhou, Wen-Zhao; Zhang, Yan-Mei; Lu, Jun-Ying; Li, Jun-Feng

2012-10-12

To provide a resource of sisal-specific expressed sequence data and facilitate this powerful approach in new gene research, the preparation of normalized cDNA libraries enriched with full-length sequences is necessary. Four libraries were produced with RNA pooled from Agave sisalana multiple tissues to increase efficiency of normalization and maximize the number of independent genes by SMART™ method and the duplex-specific nuclease (DSN). This procedure kept the proportion of full-length cDNAs in the subtracted/normalized libraries and dramatically enhanced the discovery of new genes. Sequencing of 3875 cDNA clones of libraries revealed 3320 unigenes with an average insert length about 1.2 kb, indicating that the non-redundancy of libraries was about 85.7%. These unigene functions were predicted by comparing their sequences to functional domain databases and extensively annotated with Gene Ontology (GO) terms. Comparative analysis of sisal unigenes and other plant genomes revealed that four putative MADS-box genes and knotted-like homeobox (knox) gene were obtained from a total of 1162 full-length transcripts. Furthermore, real-time PCR showed that the characteristics of their transcripts mainly depended on the tight expression regulation of a number of genes during the leaf and flower development. Analysis of individual library sequence data indicated that the pooled-tissue approach was highly effective in discovering new genes and preparing libraries for efficient deep sequencing.

Gene Fusion Markup Language: a prototype for exchanging gene fusion data.

Science.gov (United States)

Kalyana-Sundaram, Shanker; Shanmugam, Achiraman; Chinnaiyan, Arul M

2012-10-16

An avalanche of next generation sequencing (NGS) studies has generated an unprecedented amount of genomic structural variation data. These studies have also identified many novel gene fusion candidates with more detailed resolution than previously achieved. However, in the excitement and necessity of publishing the observations from this recently developed cutting-edge technology, no community standardization approach has arisen to organize and represent the data with the essential attributes in an interchangeable manner. As transcriptome studies have been widely used for gene fusion discoveries, the current non-standard mode of data representation could potentially impede data accessibility, critical analyses, and further discoveries in the near future. Here we propose a prototype, Gene Fusion Markup Language (GFML) as an initiative to provide a standard format for organizing and representing the significant features of gene fusion data. GFML will offer the advantage of representing the data in a machine-readable format to enable data exchange, automated analysis interpretation, and independent verification. As this database-independent exchange initiative evolves it will further facilitate the formation of related databases, repositories, and analysis tools. The GFML prototype is made available at http://code.google.com/p/gfml-prototype/. The Gene Fusion Markup Language (GFML) presented here could facilitate the development of a standard format for organizing, integrating and representing the significant features of gene fusion data in an inter-operable and query-able fashion that will enable biologically intuitive access to gene fusion findings and expedite functional characterization. A similar model is envisaged for other NGS data analyses.

Gene discovery from Jatropha curcas by sequencing of ESTs from normalized and full-length enriched cDNA library from developing seeds

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Sugantham Priyanka Annabel

2010-10-01

Full Text Available Abstract Background Jatropha curcas L. is promoted as an important non-edible biodiesel crop worldwide. Jatropha oil, which is a triacylglycerol, can be directly blended with petro-diesel or transesterified with methanol and used as biodiesel. Genetic improvement in jatropha is needed to increase the seed yield, oil content, drought and pest resistance, and to modify oil composition so that it becomes a technically and economically preferred source for biodiesel production. However, genetic improvement efforts in jatropha could not take advantage of genetic engineering methods due to lack of cloned genes from this species. To overcome this hurdle, the current gene discovery project was initiated with an objective of isolating as many functional genes as possible from J. curcas by large scale sequencing of expressed sequence tags (ESTs. Results A normalized and full-length enriched cDNA library was constructed from developing seeds of J. curcas. The cDNA library contained about 1 × 106 clones and average insert size of the clones was 2.1 kb. Totally 12,084 ESTs were sequenced to average high quality read length of 576 bp. Contig analysis revealed 2258 contigs and 4751 singletons. Contig size ranged from 2-23 and there were 7333 ESTs in the contigs. This resulted in 7009 unigenes which were annotated by BLASTX. It showed 3982 unigenes with significant similarity to known genes and 2836 unigenes with significant similarity to genes of unknown, hypothetical and putative proteins. The remaining 191 unigenes which did not show similarity with any genes in the public database may encode for unique genes. Functional classification revealed unigenes related to broad range of cellular, molecular and biological functions. Among the 7009 unigenes, 6233 unigenes were identified to be potential full-length genes. Conclusions The high quality normalized cDNA library was constructed from developing seeds of J. curcas for the first time and 7009 unigenes coding

48 CFR 22.1015 - Discovery of errors by the Department of Labor.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 1 2010-10-01 2010-10-01 false Discovery of errors by the... REGULATION SOCIOECONOMIC PROGRAMS APPLICATION OF LABOR LAWS TO GOVERNMENT ACQUISITIONS Service Contract Act of 1965, as Amended 22.1015 Discovery of errors by the Department of Labor. If the Department of...

Development and application of a 6.5 million feature Affymetrix Genechip® for massively parallel discovery of single position polymorphisms in lettuce (Lactuca spp.

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Stoffel Kevin

2012-05-01

Full Text Available Abstract Background High-resolution genetic maps are needed in many crops to help characterize the genetic diversity that determines agriculturally important traits. Hybridization to microarrays to detect single feature polymorphisms is a powerful technique for marker discovery and genotyping because of its highly parallel nature. However, microarrays designed for gene expression analysis rarely provide sufficient gene coverage for optimal detection of nucleotide polymorphisms, which limits utility in species with low rates of polymorphism such as lettuce (Lactuca sativa. Results We developed a 6.5 million feature Affymetrix GeneChip® for efficient polymorphism discovery and genotyping, as well as for analysis of gene expression in lettuce. Probes on the microarray were designed from 26,809 unigenes from cultivated lettuce and an additional 8,819 unigenes from four related species (L. serriola, L. saligna, L. virosa and L. perennis. Where possible, probes were tiled with a 2 bp stagger, alternating on each DNA strand; providing an average of 187 probes covering approximately 600 bp for each of over 35,000 unigenes; resulting in up to 13 fold redundancy in coverage per nucleotide. We developed protocols for hybridization of genomic DNA to the GeneChip® and refined custom algorithms that utilized coverage from multiple, high quality probes to detect single position polymorphisms in 2 bp sliding windows across each unigene. This allowed us to detect greater than 18,000 polymorphisms between the parental lines of our core mapping population, as well as numerous polymorphisms between cultivated lettuce and wild species in the lettuce genepool. Using marker data from our diversity panel comprised of 52 accessions from the five species listed above, we were able to separate accessions by species using both phylogenetic and principal component analyses. Additionally, we estimated the diversity between different types of cultivated lettuce and

Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy.

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Kirk, Jon; Shah, Nirav; Noll, Braxton; Stevens, Craig B; Lawler, Marshall; Mougeot, Farah B; Mougeot, Jean-Luc C

2018-08-01

Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy. Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways. OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM. Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success. Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

Applications of Gene Editing Technologies to Cellular Therapies.

Science.gov (United States)

Rein, Lindsay A M; Yang, Haeyoon; Chao, Nelson J

2018-03-27

Hematologic malignancies are characterized by genetic heterogeneity, making classic gene therapy with a goal of correcting 1 genetic defect ineffective in many of these diseases. Despite initial tribulations, gene therapy, as a field, has grown by leaps and bounds with the recent development of gene editing techniques including zinc finger nucleases, transcription activator-like effector nucleases, and clustered regularly interspaced short palindromic repeat (CRISPR) sequences and CRISPR-associated protein-9 (Cas9) nuclease or CRISPR/Cas9. These novel technologies have been applied to efficiently and specifically modify genetic information in target and effector cells. In particular, CRISPR/Cas9 technology has been applied to various hematologic malignancies and has also been used to modify and improve chimeric antigen receptor-modified T cells for the purpose of providing effective cellular therapies. Although gene editing is in its infancy in malignant hematologic diseases, there is much room for growth and application in the future. Copyright © 2018 The American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Chirality - The forthcoming 160th Anniversary of Pasteur's Discovery

OpenAIRE

Molčanov, K.; Kojić-Prodić., B.

2007-01-01

The presented review on chirality is dedicated to the centennial birth anniversary of Nobel laureate Vladimir Prelog and 160 years of Pasteur's discovery of chirality on tartrates. Chirality has been recognized in nature by artists and architects, who have used it for decorations and basic constructions, as shown in the Introduction. The progress of science through history has enabled the gathering of knowledge on chirality and its many ways of application. The key historical discoveries abou...

Discovery of a novel gene involved in autolysis of Clostridium cells.

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Yang, Liejian; Bao, Guanhui; Zhu, Yan; Dong, Hongjun; Zhang, Yanping; Li, Yin

2013-06-01

Cell autolysis plays important physiological roles in the life cycle of clostridial cells. Understanding the genetic basis of the autolysis phenomenon of pathogenic Clostridium or solvent producing Clostridium cells might provide new insights into this important species. Genes that might be involved in autolysis of Clostridium acetobutylicum, a model clostridial species, were investigated in this study. Twelve putative autolysin genes were predicted in C. acetobutylicum DSM 1731 genome through bioinformatics analysis. Of these 12 genes, gene SMB_G3117 was selected for testing the in tracellular autolysin activity, growth profile, viable cell numbers, and cellular morphology. We found that overexpression of SMB_G3117 gene led to earlier ceased growth, significantly increased number of dead cells, and clear electrolucent cavities, while disruption of SMB_G3117 gene exhibited remarkably reduced intracellular autolysin activity. These results indicate that SMB_G3117 is a novel gene involved in cellular autolysis of C. acetobutylicum.

MINER: exploratory analysis of gene interaction networks by machine learning from expression data

Directory of Open Access Journals (Sweden)

Sivieng Jane

2009-12-01

Full Text Available Abstract Background The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies. Results We have developed MINER (Microarray Interactive Network Exploration and Representation, an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation. Conclusion Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.

The cloud chamber. A wonderful instrument for discoveries

International Nuclear Information System (INIS)

Fadel, Kamil

2012-01-01

The author proposes an overview of the various applications and discoveries based on the use of the cloud chamber or Wilson chamber: blood flow rate measurements, investigation of alpha radiation (interaction of an alpha particle with gas atoms), investigation of beta radioactivity with the evidence of the existence of the neutrino, confirmation of a relativistic effect, discovery of the neutron in the 1930's, uranium fission, evidence of the cosmic origin of a ionizing radiation in the 1930's. The author briefly evokes the technological evolutions of these cloud chambers

OSIRIS, an entirely in-house developed drug discovery informatics system.

Science.gov (United States)

Sander, Thomas; Freyss, Joel; von Korff, Modest; Reich, Jacqueline Renée; Rufener, Christian

2009-02-01

We present OSIRIS, an entirely in-house developed drug discovery informatics system. Its components cover all information handling aspects from compound synthesis via biological testing to preclinical development. Its design principles are platform and vendor independence, a consistent look and feel, and complete coverage of the drug discovery process by custom tailored applications. These include electronic laboratory notebook applications for biology and chemistry, tools for high-throughput and secondary screening evaluation, chemistry-aware data visualization, physicochemical property prediction, 3D-pharmacophore comparisons, interactive modeling, computing grid based ligand-protein docking, and more. Most applications are developed in Java and are built on top of a Java library layer that provides reusable cheminformatics functionality and GUI components such as chemical editors, structure canonicalization, substructure search, combinatorial enumeration, enhanced stereo perception, force field minimization, and conformation generation.

Fluorescence lifetime assays: current advances and applications in drug discovery.

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Pritz, Stephan; Doering, Klaus; Woelcke, Julian; Hassiepen, Ulrich

2011-06-01

Fluorescence lifetime assays complement the portfolio of established assay formats available in drug discovery, particularly with the recent advances in microplate readers and the commercial availability of novel fluorescent labels. Fluorescence lifetime assists in lowering complexity of compound screening assays, affording a modular, toolbox-like approach to assay development and yielding robust homogeneous assays. To date, materials and procedures have been reported for biochemical assays on proteases, as well as on protein kinases and phosphatases. This article gives an overview of two assay families, distinguished by the origin of the fluorescence signal modulation. The pharmaceutical industry demands techniques with a robust, integrated compound profiling process and short turnaround times. Fluorescence lifetime assays have already helped the drug discovery field, in this sense, by enhancing productivity during the hit-to-lead and lead optimization phases. Future work will focus on covering other biochemical molecular modifications by investigating the detailed photo-physical mechanisms underlying the fluorescence signal.

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.

Science.gov (United States)

Verbist, Bie; Klambauer, Günter; Vervoort, Liesbet; Talloen, Willem; Shkedy, Ziv; Thas, Olivier; Bender, Andreas; Göhlmann, Hinrich W H; Hochreiter, Sepp

2015-05-01

The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Determination of performance characteristics of scientific applications on IBM Blue Gene/Q

Energy Technology Data Exchange (ETDEWEB)

Evangelinos, C. [IBM Research Division, Cambridge, MA (United States); Walkup, R. E. [IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center; Sachdeva, V. [IBM Research Division, Cambridge, MA (United States); Jordan, K. E. [IBM Research Division, Cambridge, MA (United States); Gahvari, H. [Univ. of Illinois, Urbana-Champaign, IL (United States). Computer Science Dept.; Chung, I. -H. [IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center; Perrone, M. P. [IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center; Lu, L. [IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center; Liu, L. -K. [IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center; Magerlein, K. [IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center

2013-02-13

The IBM Blue Gene®/Q platform presents scientists and engineers with a rich set of hardware features such as 16 cores per chip sharing a Level 2 cache, a wide SIMD (single-instruction, multiple-data) unit, a five-dimensional torus network, and hardware support for collective operations. Especially important is the feature related to cores that have four “hardware threads,” which makes it possible to hide latencies and obtain a high fraction of the peak issue rate from each core. All of these hardware resources present unique performance-tuning opportunities on Blue Gene/Q. We provide an overview of several important applications and solvers and study them on Blue Gene/Q using performance counters and Message Passing Interface profiles. We also discuss how Blue Gene/Q tools help us understand the interaction of the application with the hardware and software layers and provide guidance for optimization. Furthermore, on the basis of our analysis, we discuss code improvement strategies targeting Blue Gene/Q. Information about how these algorithms map to the Blue Gene® architecture is expected to have an impact on future system design as we move to the exascale era.

Theory and in vivo application of electroporative gene delivery.

Science.gov (United States)

Somiari, S; Glasspool-Malone, J; Drabick, J J; Gilbert, R A; Heller, R; Jaroszeski, M J; Malone, R W

2000-09-01

Efficient and safe methods for delivering exogenous genetic material into tissues must be developed before the clinical potential of gene therapy will be realized. Recently, in vivo electroporation has emerged as a leading technology for developing nonviral gene therapies and nucleic acid vaccines (NAV). Electroporation (EP) involves the application of pulsed electric fields to cells to enhance cell permeability, resulting in exogenous polynucleotide transit across the cytoplasmic membrane. Similar pulsed electrical field treatments are employed in a wide range of biotechnological processes including in vitro EP, hybridoma production, development of transgenic animals, and clinical electrochemotherapy. Electroporative gene delivery studies benefit from well-developed literature that may be used to guide experimental design and interpretation. Both theory and experimental analysis predict that the critical parameters governing EP efficacy include cell size and field strength, duration, frequency, and total number of applied pulses. These parameters must be optimized for each tissue in order to maximize gene delivery while minimizing irreversible cell damage. By providing an overview of the theory and practice of electroporative gene transfer, this review intends to aid researchers that wish to employ the method for preclinical and translational gene therapy, NAV, and functional genomic research.

Characterization of Capsicum annuum genetic diversity and population structure based on parallel polymorphism discovery with a 30K unigene Pepper GeneChip.

Science.gov (United States)

Hill, Theresa A; Ashrafi, Hamid; Reyes-Chin-Wo, Sebastian; Yao, JiQiang; Stoffel, Kevin; Truco, Maria-Jose; Kozik, Alexander; Michelmore, Richard W; Van Deynze, Allen

2013-01-01

The widely cultivated pepper, Capsicum spp., important as a vegetable and spice crop world-wide, is one of the most diverse crops. To enhance breeding programs, a detailed characterization of Capsicum diversity including morphological, geographical and molecular data is required. Currently, molecular data characterizing Capsicum genetic diversity is limited. The development and application of high-throughput genome-wide markers in Capsicum will facilitate more detailed molecular characterization of germplasm collections, genetic relationships, and the generation of ultra-high density maps. We have developed the Pepper GeneChip® array from Affymetrix for polymorphism detection and expression analysis in Capsicum. Probes on the array were designed from 30,815 unigenes assembled from expressed sequence tags (ESTs). Our array design provides a maximum redundancy of 13 probes per base pair position allowing integration of multiple hybridization values per position to detect single position polymorphism (SPP). Hybridization of genomic DNA from 40 diverse C. annuum lines, used in breeding and research programs, and a representative from three additional cultivated species (C. frutescens, C. chinense and C. pubescens) detected 33,401 SPP markers within 13,323 unigenes. Among the C. annuum lines, 6,426 SPPs covering 3,818 unigenes were identified. An estimated three-fold reduction in diversity was detected in non-pungent compared with pungent lines, however, we were able to detect 251 highly informative markers across these C. annuum lines. In addition, an 8.7 cM region without polymorphism was detected around Pun1 in non-pungent C. annuum. An analysis of genetic relatedness and diversity using the software Structure revealed clustering of the germplasm which was confirmed with statistical support by principle components analysis (PCA) and phylogenetic analysis. This research demonstrates the effectiveness of parallel high-throughput discovery and application of genome

Characterization of Capsicum annuum genetic diversity and population structure based on parallel polymorphism discovery with a 30K unigene Pepper GeneChip.

Directory of Open Access Journals (Sweden)

Theresa A Hill

Full Text Available The widely cultivated pepper, Capsicum spp., important as a vegetable and spice crop world-wide, is one of the most diverse crops. To enhance breeding programs, a detailed characterization of Capsicum diversity including morphological, geographical and molecular data is required. Currently, molecular data characterizing Capsicum genetic diversity is limited. The development and application of high-throughput genome-wide markers in Capsicum will facilitate more detailed molecular characterization of germplasm collections, genetic relationships, and the generation of ultra-high density maps. We have developed the Pepper GeneChip® array from Affymetrix for polymorphism detection and expression analysis in Capsicum. Probes on the array were designed from 30,815 unigenes assembled from expressed sequence tags (ESTs. Our array design provides a maximum redundancy of 13 probes per base pair position allowing integration of multiple hybridization values per position to detect single position polymorphism (SPP. Hybridization of genomic DNA from 40 diverse C. annuum lines, used in breeding and research programs, and a representative from three additional cultivated species (C. frutescens, C. chinense and C. pubescens detected 33,401 SPP markers within 13,323 unigenes. Among the C. annuum lines, 6,426 SPPs covering 3,818 unigenes were identified. An estimated three-fold reduction in diversity was detected in non-pungent compared with pungent lines, however, we were able to detect 251 highly informative markers across these C. annuum lines. In addition, an 8.7 cM region without polymorphism was detected around Pun1 in non-pungent C. annuum. An analysis of genetic relatedness and diversity using the software Structure revealed clustering of the germplasm which was confirmed with statistical support by principle components analysis (PCA and phylogenetic analysis. This research demonstrates the effectiveness of parallel high-throughput discovery and

Application of data mining and artificial intelligence techniques to mass spectrometry data for knowledge discovery

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Hugo López-Fernández

2016-05-01

Full Text Available Mass spectrometry using matrix assisted laser desorption ionization coupled to time of flight analyzers (MALDI-TOF MS has become popular during the last decade due to its high speed, sensitivity and robustness for detecting proteins and peptides. This allows quickly analyzing large sets of samples are in one single batch and doing high-throughput proteomics. In this scenario, bioinformatics methods and computational tools play a key role in MALDI-TOF data analysis, as they are able handle the large amounts of raw data generated in order to extract new knowledge and useful conclusions. A typical MALDI-TOF MS data analysis workflow has three main stages: data acquisition, preprocessing and analysis. Although the most popular use of this technology is to identify proteins through their peptides, analyses that make use of artificial intelligence (AI, machine learning (ML, and statistical methods can be also carried out in order to perform biomarker discovery, automatic diagnosis, and knowledge discovery. In this research work, this workflow is deeply explored and new solutions based on the application of AI, ML, and statistical methods are proposed. In addition, an integrated software platform that supports the full MALDI-TOF MS data analysis workflow that facilitate the work of proteomics researchers without advanced bioinformatics skills has been developed and released to the scientific community.

Empirical study of supervised gene screening

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Ma Shuangge

2006-12-01

Full Text Available Abstract Background Microarray studies provide a way of linking variations of phenotypes with their genetic causations. Constructing predictive models using high dimensional microarray measurements usually consists of three steps: (1 unsupervised gene screening; (2 supervised gene screening; and (3 statistical model building. Supervised gene screening based on marginal gene ranking is commonly used to reduce the number of genes in the model building. Various simple statistics, such as t-statistic or signal to noise ratio, have been used to rank genes in the supervised screening. Despite of its extensive usage, statistical study of supervised gene screening remains scarce. Our study is partly motivated by the differences in gene discovery results caused by using different supervised gene screening methods. Results We investigate concordance and reproducibility of supervised gene screening based on eight commonly used marginal statistics. Concordance is assessed by the relative fractions of overlaps between top ranked genes screened using different marginal statistics. We propose a Bootstrap Reproducibility Index, which measures reproducibility of individual genes under the supervised screening. Empirical studies are based on four public microarray data. We consider the cases where the top 20%, 40% and 60% genes are screened. Conclusion From a gene discovery point of view, the effect of supervised gene screening based on different marginal statistics cannot be ignored. Empirical studies show that (1 genes passed different supervised screenings may be considerably different; (2 concordance may vary, depending on the underlying data structure and percentage of selected genes; (3 evaluated with the Bootstrap Reproducibility Index, genes passed supervised screenings are only moderately reproducible; and (4 concordance cannot be improved by supervised screening based on reproducibility.

RNA Editing and Drug Discovery for Cancer Therapy

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Wei-Hsuan Huang

2013-01-01

Full Text Available RNA editing is vital to provide the RNA and protein complexity to regulate the gene expression. Correct RNA editing maintains the cell function and organism development. Imbalance of the RNA editing machinery may lead to diseases and cancers. Recently, RNA editing has been recognized as a target for drug discovery although few studies targeting RNA editing for disease and cancer therapy were reported in the field of natural products. Therefore, RNA editing may be a potential target for therapeutic natural products. In this review, we provide a literature overview of the biological functions of RNA editing on gene expression, diseases, cancers, and drugs. The bioinformatics resources of RNA editing were also summarized.

The biological knowledge discovery by PCCF measure and PCA-F projection.

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Jia, Xingang; Zhu, Guanqun; Han, Qiuhong; Lu, Zuhong

2017-01-01

In the process of biological knowledge discovery, PCA is commonly used to complement the clustering analysis, but PCA typically gives the poor visualizations for most gene expression data sets. Here, we propose a PCCF measure, and use PCA-F to display clusters of PCCF, where PCCF and PCA-F are modeled from the modified cumulative probabilities of genes. From the analysis of simulated and experimental data sets, we demonstrate that PCCF is more appropriate and reliable for analyzing gene expression data compared to other commonly used distances or similarity measures, and PCA-F is a good visualization technique for identifying clusters of PCCF, where we aim at such data sets that the expression values of genes are collected at different time points.

Topology Discovery Using Cisco Discovery Protocol

OpenAIRE

Rodriguez, Sergio R.

2009-01-01

In this paper we address the problem of discovering network topology in proprietary networks. Namely, we investigate topology discovery in Cisco-based networks. Cisco devices run Cisco Discovery Protocol (CDP) which holds information about these devices. We first compare properties of topologies that can be obtained from networks deploying CDP versus Spanning Tree Protocol (STP) and Management Information Base (MIB) Forwarding Database (FDB). Then we describe a method of discovering topology ...

Class B Gene Expression and the Modified ABC Model in Nongrass Monocots

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Akira Kanno

2007-01-01

Full Text Available The discovery of the MADS-box genes and the study of model plants such as Arabidopsis thaliana and Antirrhinum majus have greatly improved our understanding of the molecular mechanisms driving the diversity in floral development. The class B genes, which belong to the MADS-box gene family, are important regulators of the development of petals and stamens in flowering plants. Many nongrass monocot flowers have two whorls of petaloid organs, which are called tepals. To explain this floral morphology, the modified ABC model was proposed. This model was exemplified by the tulip, in which expansion and restriction of class B gene expression is linked to the transition of floral morphologies in whorl 1. The expression patterns of class B genes from many monocot species nicely fit this model; however, those from some species, such as asparagus, do not. In this review, we summarize the relationship between class B gene expression and floral morphology in nongrass monocots, such as Liliales (Liliaceae and Asparagales species, and discuss the applicability of the modified ABC model to monocot flowers.

Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks

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Kohane Isaac S

2005-09-01

Full Text Available Abstract Background Biological processes are carried out by coordinated modules of interacting molecules. As clustering methods demonstrate that genes with similar expression display increased likelihood of being associated with a common functional module, networks of coexpressed genes provide one framework for assigning gene function. This has informed the guilt-by-association (GBA heuristic, widely invoked in functional genomics. Yet although the idea of GBA is accepted, the breadth of GBA applicability is uncertain. Results We developed methods to systematically explore the breadth of GBA across a large and varied corpus of expression data to answer the following question: To what extent is the GBA heuristic broadly applicable to the transcriptome and conversely how broadly is GBA captured by a priori knowledge represented in the Gene Ontology (GO? Our study provides an investigation of the functional organization of five coexpression networks using data from three mammalian organisms. Our method calculates a probabilistic score between each gene and each Gene Ontology category that reflects coexpression enrichment of a GO module. For each GO category we use Receiver Operating Curves to assess whether these probabilistic scores reflect GBA. This methodology applied to five different coexpression networks demonstrates that the signature of guilt-by-association is ubiquitous and reproducible and that the GBA heuristic is broadly applicable across the population of nine hundred Gene Ontology categories. We also demonstrate the existence of highly reproducible patterns of coexpression between some pairs of GO categories. Conclusion We conclude that GBA has universal value and that transcriptional control may be more modular than previously realized. Our analyses also suggest that methodologies combining coexpression measurements across multiple genes in a biologically-defined module can aid in characterizing gene function or in characterizing

Ten years since the discovery of iPS cells: The current state of their clinical application.

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Aznar, J; Tudela, J

On the 10-year anniversary of the discovery of induced pluripotent stem cells, we review the main results from their various fields of application, the obstacles encountered during experimentation and the potential applications in clinical practice. The efficacy of induced pluripotent cells in clinical experimentation can be equated to that of human embryonic stem cells; however, unlike stem cells, induced pluripotent cells do not involve the severe ethical difficulties entailed by the need to destroy human embryos to obtain them. The finding of these cells, which was in its day a true scientific milestone worthy of a Nobel Prize in Medicine, is currently enveloped by light and shadow: high hopes for regenerative medicine versus the, as of yet, poorly controlled risks of unpredictable reactions, both in the processes of dedifferentiation and subsequent differentiation to the cell strains employed for therapeutic or experimentation goals. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Medicina Interna (SEMI). All rights reserved.

GSEH: A Novel Approach to Select Prostate Cancer-Associated Genes Using Gene Expression Heterogeneity.

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Kim, Hyunjin; Choi, Sang-Min; Park, Sanghyun

2018-01-01

When a gene shows varying levels of expression among normal people but similar levels in disease patients or shows similar levels of expression among normal people but different levels in disease patients, we can assume that the gene is associated with the disease. By utilizing this gene expression heterogeneity, we can obtain additional information that abets discovery of disease-associated genes. In this study, we used collaborative filtering to calculate the degree of gene expression heterogeneity between classes and then scored the genes on the basis of the degree of gene expression heterogeneity to find "differentially predicted" genes. Through the proposed method, we discovered more prostate cancer-associated genes than 10 comparable methods. The genes prioritized by the proposed method are potentially significant to biological processes of a disease and can provide insight into them.

A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective

OpenAIRE

Mishra, Nilamadhab; Lin, Chung-Chih; Chang, Hsien-Tsung

2015-01-01

In future IoT big-data management and knowledge discovery for large scale industrial automation application, the importance of industrial internet is increasing day by day. Several diversified technologies such as IoT (Internet of Things), computational intelligence, machine type communication, big-data, and sensor technology can be incorporated together to improve the data management and knowledge discovery efficiency of large scale automation applications. So in this work, we need to propos...

Theory and Applications of Covalent Docking in Drug Discovery: Merits and Pitfalls

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Hezekiel Mathambo Kumalo

2015-01-01

Full Text Available he present art of drug discovery and design of new drugs is based on suicidal irreversible inhibitors. Covalent inhibition is the strategy that is used to achieve irreversible inhibition. Irreversible inhibitors interact with their targets in a time-dependent fashion, and the reaction proceeds to completion rather than to equilibrium. Covalent inhibitors possessed some significant advantages over non-covalent inhibitors such as covalent warheads can target rare, non-conserved residue of a particular target protein and thus led to development of highly selective inhibitors, covalent inhibitors can be effective in targeting proteins with shallow binding cleavage which will led to development of novel inhibitors with increased potency than non-covalent inhibitors. Several computational approaches have been developed to simulate covalent interactions; however, this is still a challenging area to explore. Covalent molecular docking has been recently implemented in the computer-aided drug design workflows to describe covalent interactions between inhibitors and biological targets. In this review we highlight: (i covalent interactions in biomolecular systems; (ii the mathematical framework of covalent molecular docking; (iii implementation of covalent docking protocol in drug design workflows; (iv applications covalent docking: case studies and (v shortcomings and future perspectives of covalent docking. To the best of our knowledge; this review is the first account that highlights different aspects of covalent docking with its merits and pitfalls. We believe that the method and applications highlighted in this study will help future efforts towards the design of irreversible inhibitors.

Mathematical modeling for novel cancer drug discovery and development.

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Zhang, Ping; Brusic, Vladimir

2014-10-01

Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

May I Cut in? Gene Editing Approaches in Human Induced Pluripotent Stem Cells.

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Brookhouser, Nicholas; Raman, Sreedevi; Potts, Christopher; Brafman, David A

2017-02-06

In the decade since Yamanaka and colleagues described methods to reprogram somatic cells into a pluripotent state, human induced pluripotent stem cells (hiPSCs) have demonstrated tremendous promise in numerous disease modeling, drug discovery, and regenerative medicine applications. More recently, the development and refinement of advanced gene transduction and editing technologies have further accelerated the potential of hiPSCs. In this review, we discuss the various gene editing technologies that are being implemented with hiPSCs. Specifically, we describe the emergence of technologies including zinc-finger nuclease (ZFN), transcription activator-like effector nuclease (TALEN), and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 that can be used to edit the genome at precise locations, and discuss the strengths and weaknesses of each of these technologies. In addition, we present the current applications of these technologies in elucidating the mechanisms of human development and disease, developing novel and effective therapeutic molecules, and engineering cell-based therapies. Finally, we discuss the emerging technological advances in targeted gene editing methods.

Global Change Master Directory (GCMD) Keywords and Their Applications in Earth Science Data Discovery

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Aleman, A.

2017-12-01

This presentation will provide an overview and discussion of the Global Change Master Directory (GCMD) Keywords and their applications in Earth science data discovery. The GCMD Keywords are a hierarchical set of controlled keywords covering the Earth science disciplines, including: science keywords, service keywords, data centers, projects, location, data resolution, instruments and platforms. Controlled vocabularies (keywords) help users accurately, consistently and comprehensively categorize their data and also allow for the precise search and subsequent retrieval of data. The GCMD Keywords are a community resource and are developed collaboratively with input from various stakeholders, including GCMD staff, keyword users and metadata providers. The GCMD Keyword Landing Page and GCMD Keyword Community Forum provide access to keyword resources and an area for discussion of topics related to the GCMD Keywords. See https://earthdata.nasa.gov/about/gcmd/global-change-master-directory-gcmd-keywords

cudaMap: a GPU accelerated program for gene expression connectivity mapping.

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McArt, Darragh G; Bankhead, Peter; Dunne, Philip D; Salto-Tellez, Manuel; Hamilton, Peter; Zhang, Shu-Dong

2013-10-11

Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take > 2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping. cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance. Emerging 'omics' technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.

Improved accuracy of supervised CRM discovery with interpolated Markov models and cross-species comparison.

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Kazemian, Majid; Zhu, Qiyun; Halfon, Marc S; Sinha, Saurabh

2011-12-01

Despite recent advances in experimental approaches for identifying transcriptional cis-regulatory modules (CRMs, 'enhancers'), direct empirical discovery of CRMs for all genes in all cell types and environmental conditions is likely to remain an elusive goal. Effective methods for computational CRM discovery are thus a critically needed complement to empirical approaches. However, existing computational methods that search for clusters of putative binding sites are ineffective if the relevant TFs and/or their binding specificities are unknown. Here, we provide a significantly improved method for 'motif-blind' CRM discovery that does not depend on knowledge or accurate prediction of TF-binding motifs and is effective when limited knowledge of functional CRMs is available to 'supervise' the search. We propose a new statistical method, based on 'Interpolated Markov Models', for motif-blind, genome-wide CRM discovery. It captures the statistical profile of variable length words in known CRMs of a regulatory network and finds candidate CRMs that match this profile. The method also uses orthologs of the known CRMs from closely related genomes. We perform in silico evaluation of predicted CRMs by assessing whether their neighboring genes are enriched for the expected expression patterns. This assessment uses a novel statistical test that extends the widely used Hypergeometric test of gene set enrichment to account for variability in intergenic lengths. We find that the new CRM prediction method is superior to existing methods. Finally, we experimentally validate 12 new CRM predictions by examining their regulatory activity in vivo in Drosophila; 10 of the tested CRMs were found to be functional, while 6 of the top 7 predictions showed the expected activity patterns. We make our program available as downloadable source code, and as a plugin for a genome browser installed on our servers. © The Author(s) 2011. Published by Oxford University Press.

Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application.

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Roubelakis, Maria G; Zotos, Pantelis; Papachristoudis, Georgios; Michalopoulos, Ioannis; Pappa, Kalliopi I; Anagnou, Nicholas P; Kossida, Sophia

2009-06-16

microRNAs (miRNAs) are single-stranded RNA molecules of about 20-23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance. GOmir is a novel stand-alone application consisting of two separate tools: JTarget and TAGGO. JTarget integrates miRNA target prediction and functional analysis by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar computational tools as well as the experimentally supported targets from TarBase and also providing a full gene description and functional analysis for each target gene. On the other hand, TAGGO application is designed to automatically group gene ontology annotations, taking advantage of the Gene Ontology (GO), in order to extract the main attributes of sets of proteins. GOmir represents a new tool incorporating two separate Java applications integrated into one stand-alone Java application. GOmir (by using up to five different databases) introduces miRNA predicted targets accompanied by (a) full gene description, (b) functional analysis and (c) detailed gene ontology clustering. Additionally, a reverse search initiated by a potential target can also be conducted. GOmir can freely be downloaded BRFAA.

Discovery of seven novel Mammalian and avian coronaviruses in the genus deltacoronavirus supports bat coronaviruses as the gene source of alphacoronavirus and betacoronavirus and avian coronaviruses as the gene source of gammacoronavirus and deltacoronavirus.

Science.gov (United States)

Woo, Patrick C Y; Lau, Susanna K P; Lam, Carol S F; Lau, Candy C Y; Tsang, Alan K L; Lau, John H N; Bai, Ru; Teng, Jade L L; Tsang, Chris C C; Wang, Ming; Zheng, Bo-Jian; Chan, Kwok-Hung; Yuen, Kwok-Yung

2012-04-01

Recently, we reported the discovery of three novel coronaviruses, bulbul coronavirus HKU11, thrush coronavirus HKU12, and munia coronavirus HKU13, which were identified as representatives of a novel genus, Deltacoronavirus, in the subfamily Coronavirinae. In this territory-wide molecular epidemiology study involving 3,137 mammals and 3,298 birds, we discovered seven additional novel deltacoronaviruses in pigs and birds, which we named porcine coronavirus HKU15, white-eye coronavirus HKU16, sparrow coronavirus HKU17, magpie robin coronavirus HKU18, night heron coronavirus HKU19, wigeon coronavirus HKU20, and common moorhen coronavirus HKU21. Complete genome sequencing and comparative genome analysis showed that the avian and mammalian deltacoronaviruses have similar genome characteristics and structures. They all have relatively small genomes (25.421 to 26.674 kb), the smallest among all coronaviruses. They all have a single papain-like protease domain in the nsp3 gene; an accessory gene, NS6 open reading frame (ORF), located between the M and N genes; and a variable number of accessory genes (up to four) downstream of the N gene. Moreover, they all have the same putative transcription regulatory sequence of ACACCA. Molecular clock analysis showed that the most recent common ancestor of all coronaviruses was estimated at approximately 8100 BC, and those of Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and Deltacoronavirus were at approximately 2400 BC, 3300 BC, 2800 BC, and 3000 BC, respectively. From our studies, it appears that bats and birds, the warm blooded flying vertebrates, are ideal hosts for the coronavirus gene source, bats for Alphacoronavirus and Betacoronavirus and birds for Gammacoronavirus and Deltacoronavirus, to fuel coronavirus evolution and dissemination.

A novel algorithm for simplification of complex gene classifiers in cancer

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Wilson, Raphael A.; Teng, Ling; Bachmeyer, Karen M.; Bissonnette, Mei Lin Z.; Husain, Aliya N.; Parham, David M.; Triche, Timothy J.; Wing, Michele R.; Gastier-Foster, Julie M.; Barr, Frederic G.; Hawkins, Douglas S.; Anderson, James R.; Skapek, Stephen X.; Volchenboum, Samuel L.

2013-01-01

The clinical application of complex molecular classifiers as diagnostic or prognostic tools has been limited by the time and cost needed to apply them to patients. Using an existing fifty-gene expression signature known to separate two molecular subtypes of the pediatric cancer rhabdomyosarcoma, we show that an exhaustive iterative search algorithm can distill this complex classifier down to two or three features with equal discrimination. We validated the two-gene signatures using three separate and distinct data sets, including one that uses degraded RNA extracted from formalin-fixed, paraffin-embedded material. Finally, to demonstrate the generalizability of our algorithm, we applied it to a lung cancer data set to find minimal gene signatures that can distinguish survival. Our approach can easily be generalized and coupled to existing technical platforms to facilitate the discovery of simplified signatures that are ready for routine clinical use. PMID:23913937

Bioinformatics Tools for the Discovery of New Nonribosomal Peptides

DEFF Research Database (Denmark)

Leclère, Valérie; Weber, Tilmann; Jacques, Philippe

2016-01-01

-dimensional structure of the peptides can be compared with the structural patterns of all known NRPs. The presented workflow leads to an efficient and rapid screening of genomic data generated by high throughput technologies. The exploration of such sequenced genomes may lead to the discovery of new drugs (i......This chapter helps in the use of bioinformatics tools relevant to the discovery of new nonribosomal peptides (NRPs) produced by microorganisms. The strategy described can be applied to draft or fully assembled genome sequences. It relies on the identification of the synthetase genes...... and the deciphering of the domain architecture of the nonribosomal peptide synthetases (NRPSs). In the next step, candidate peptides synthesized by these NRPSs are predicted in silico, considering the specificity of incorporated monomers together with their isomery. To assess their novelty, the two...

Discovery of a phosphor for light emitting diode applications and its structural determination, Ba(Si,Al)5(O,N)8:Eu2+.

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Park, Woon Bae; Singh, Satendra Pal; Sohn, Kee-Sun

2014-02-12

Most of the novel phosphors that appear in the literature are either a variant of well-known materials or a hybrid material consisting of well-known materials. This situation has actually led to intellectual property (IP) complications in industry and several lawsuits have been the result. Therefore, the definition of a novel phosphor for use in light-emitting diodes should be clarified. A recent trend in phosphor-related IP applications has been to focus on the novel crystallographic structure, so that a slight composition variance and/or the hybrid of a well-known material would not qualify from either a scientific or an industrial point of view. In our previous studies, we employed a systematic materials discovery strategy combining heuristics optimization and a high-throughput process to secure the discovery of genuinely novel and brilliant phosphors that would be immediately ready for use in light emitting diodes. Despite such an achievement, this strategy requires further refinement to prove its versatility under any circumstance. To accomplish such demands, we improved our discovery strategy by incorporating an elitism-involved nondominated sorting genetic algorithm (NSGA-II) that would guarantee the discovery of truly novel phosphors in the present investigation. Using the improved discovery strategy, we discovered an Eu(2+)-doped AB5X8 (A = Sr or Ba, B = Si and Al, X = O and N) phosphor in an orthorhombic structure (A21am) with lattice parameters a = 9.48461(3) Å, b = 13.47194(6) Å, c = 5.77323(2) Å, α = β = γ = 90°, which cannot be found in any of the existing inorganic compound databases.

Down-Regulation of Gene Expression by RNA-Induced Gene Silencing

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Travella, Silvia; Keller, Beat

Down-regulation of endogenous genes via post-transcriptional gene silencing (PTGS) is a key to the characterization of gene function in plants. Many RNA-based silencing mechanisms such as post-transcriptional gene silencing, co-suppression, quelling, and RNA interference (RNAi) have been discovered among species of different kingdoms (plants, fungi, and animals). One of the most interesting discoveries was RNAi, a sequence-specific gene-silencing mechanism initiated by the introduction of double-stranded RNA (dsRNA), homologous in sequence to the silenced gene, which triggers degradation of mRNA. Infection of plants with modified viruses can also induce RNA silencing and is referred to as virus-induced gene silencing (VIGS). In contrast to insertional mutagenesis, these emerging new reverse genetic approaches represent a powerful tool for exploring gene function and for manipulating gene expression experimentally in cereal species such as barley and wheat. We examined how RNAi and VIGS have been used to assess gene function in barley and wheat, including molecular mechanisms involved in the process and available methodological elements, such as vectors, inoculation procedures, and analysis of silenced phenotypes.

Gene discovery and transcript analyses in the corn smut pathogen Ustilago maydis: expressed sequence tag and genome sequence comparison

Directory of Open Access Journals (Sweden)

Saville Barry J

2007-09-01

Full Text Available Abstract Background Ustilago maydis is the basidiomycete fungus responsible for common smut of corn and is a model organism for the study of fungal phytopathogenesis. To aid in the annotation of the genome sequence of this organism, several expressed sequence tag (EST libraries were generated from a variety of U. maydis cell types. In addition to utility in the context of gene identification and structure annotation, the ESTs were analyzed to identify differentially abundant transcripts and to detect evidence of alternative splicing and anti-sense transcription. Results Four cDNA libraries were constructed using RNA isolated from U. maydis diploid teliospores (U. maydis strains 518 × 521 and haploid cells of strain 521 grown under nutrient rich, carbon starved, and nitrogen starved conditions. Using the genome sequence as a scaffold, the 15,901 ESTs were assembled into 6,101 contiguous expressed sequences (contigs; among these, 5,482 corresponded to predicted genes in the MUMDB (MIPS Ustilago maydis database, while 619 aligned to regions of the genome not yet designated as genes in MUMDB. A comparison of EST abundance identified numerous genes that may be regulated in a cell type or starvation-specific manner. The transcriptional response to nitrogen starvation was assessed using RT-qPCR. The results of this suggest that there may be cross-talk between the nitrogen and carbon signalling pathways in U. maydis. Bioinformatic analysis identified numerous examples of alternative splicing and anti-sense transcription. While intron retention was the predominant form of alternative splicing in U. maydis, other varieties were also evident (e.g. exon skipping. Selected instances of both alternative splicing and anti-sense transcription were independently confirmed using RT-PCR. Conclusion Through this work: 1 substantial sequence information has been provided for U. maydis genome annotation; 2 new genes were identified through the discovery of 619

Serial Analysis of Gene Expression: Applications in Human Studies

Directory of Open Access Journals (Sweden)

Tuteja Renu

2004-01-01

Full Text Available Serial analysis of gene expression (SAGE is a powerful tool, which provides quantitative and comprehensive expression profile of genes in a given cell population. It works by isolating short fragments of genetic information from the expressed genes that are present in the cell being studied. These short sequences, called SAGE tags, are linked together for efficient sequencing. The frequency of each SAGE tag in the cloned multimers directly reflects the transcript abundance. Therefore, SAGE results in an accurate picture of gene expression at both the qualitative and the quantitative levels. It does not require a hybridization probe for each transcript and allows new genes to be discovered. This technique has been applied widely in human studies and various SAGE tags/SAGE libraries have been generated from different cells/tissues such as dendritic cells, lung fibroblast cells, oocytes, thyroid tissue, B-cell lymphoma, cultured keratinocytes, muscles, brain tissues, sciatic nerve, cultured Schwann cells, cord blood-derived mast cells, retina, macula, retinal pigment epithelial cells, skin cells, and so forth. In this review we present the updated information on the applications of SAGE technology mainly to human studies.

Motif trie: An efficient text index for pattern discovery with don't cares

DEFF Research Database (Denmark)

Grossi, Roberto; Menconi, Giulia; Pisanti, Nadia

2017-01-01

We introduce the motif trie data structure, which has applications in pattern matching and discovery in genomic analysis, plagiarism detection, data mining, intrusion detection, spam fighting and time series analysis, to name a few. Here the extraction of recurring patterns in sequential and text......We introduce the motif trie data structure, which has applications in pattern matching and discovery in genomic analysis, plagiarism detection, data mining, intrusion detection, spam fighting and time series analysis, to name a few. Here the extraction of recurring patterns in sequential...

The development of high-content screening (HCS) technology and its importance to drug discovery.

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Fraietta, Ivan; Gasparri, Fabio

2016-01-01

High-content screening (HCS) was introduced about twenty years ago as a promising analytical approach to facilitate some critical aspects of drug discovery. Its application has spread progressively within the pharmaceutical industry and academia to the point that it today represents a fundamental tool in supporting drug discovery and development. Here, the authors review some of significant progress in the HCS field in terms of biological models and assay readouts. They highlight the importance of high-content screening in drug discovery, as testified by its numerous applications in a variety of therapeutic areas: oncology, infective diseases, cardiovascular and neurodegenerative diseases. They also dissect the role of HCS technology in different phases of the drug discovery pipeline: target identification, primary compound screening, secondary assays, mechanism of action studies and in vitro toxicology. Recent advances in cellular assay technologies, such as the introduction of three-dimensional (3D) cultures, induced pluripotent stem cells (iPSCs) and genome editing technologies (e.g., CRISPR/Cas9), have tremendously expanded the potential of high-content assays to contribute to the drug discovery process. Increasingly predictive cellular models and readouts, together with the development of more sophisticated and affordable HCS readers, will further consolidate the role of HCS technology in drug discovery.

14 CFR 406.143 - Discovery.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Discovery. 406.143 Section 406.143... Transportation Adjudications § 406.143 Discovery. (a) Initiation of discovery. Any party may initiate discovery... after a complaint has been filed. (b) Methods of discovery. The following methods of discovery are...

Reconstructing Sessions from Data Discovery and Access Logs to Build a Semantic Knowledge Base for Improving Data Discovery

Directory of Open Access Journals (Sweden)

Yongyao Jiang

2016-04-01

Full Text Available Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mining the user knowledge from log files. Specifically, user web session reconstruction is focused upon in this paper as a critical step for extracting usage patterns. However, reconstructing user sessions from raw web logs has always been difficult, as a session identifier tends to be missing in most data portals. To address this problem, we propose two session identification methods, including time-clustering-based and time-referrer-based methods. We also present the workflow of session reconstruction and discuss the approach of selecting appropriate thresholds for relevant steps in the workflow. The proposed session identification methods and workflow are proven to be able to extract data access patterns for further pattern analyses of user behavior and improvement of data discovery for more relevancy data ranking, suggestion, and navigation.

The fragile x mental retardation syndrome 20 years after the FMR1 gene discovery: an expanding universe of knowledge.

Science.gov (United States)

Rousseau, François; Labelle, Yves; Bussières, Johanne; Lindsay, Carmen

2011-08-01

The fragile X mental retardation (FXMR) syndrome is one of the most frequent causes of mental retardation. Affected individuals display a wide range of additional characteristic features including behavioural and physical phenotypes, and the extent to which individuals are affected is highly variable. For these reasons, elucidation of the pathophysiology of this disease has been an important challenge to the scientific community. 1991 marks the year of the discovery of both the FMR1 gene mutations involved in this disease, and of their dynamic nature. Although a mouse model for the disease has been available for 16 years and extensive research has been performed on the FMR1 protein (FMRP), we still understand little about how the disease develops, and no treatment has yet been shown to be effective. In this review, we summarise current knowledge on FXMR with an emphasis on the technical challenges of molecular diagnostics, on its prevalence and dynamics among populations, and on the potential of screening for FMR1 mutations.

The Fragile X Mental Retardation Syndrome 20 Years After the FMR1 Gene Discovery: an Expanding Universe of Knowledge

Science.gov (United States)

Rousseau, François; Labelle, Yves; Bussières, Johanne; Lindsay, Carmen

2011-01-01

The fragile X mental retardation (FXMR) syndrome is one of the most frequent causes of mental retardation. Affected individuals display a wide range of additional characteristic features including behavioural and physical phenotypes, and the extent to which individuals are affected is highly variable. For these reasons, elucidation of the pathophysiology of this disease has been an important challenge to the scientific community. 1991 marks the year of the discovery of both the FMR1 gene mutations involved in this disease, and of their dynamic nature. Although a mouse model for the disease has been available for 16 years and extensive research has been performed on the FMR1 protein (FMRP), we still understand little about how the disease develops, and no treatment has yet been shown to be effective. In this review, we summarise current knowledge on FXMR with an emphasis on the technical challenges of molecular diagnostics, on its prevalence and dynamics among populations, and on the potential of screening for FMR1 mutations. PMID:21912443

Bead-based screening in chemical biology and drug discovery

DEFF Research Database (Denmark)

Komnatnyy, Vitaly V.; Nielsen, Thomas Eiland; Qvortrup, Katrine

2018-01-01

libraries for early drug discovery. Among the various library forms, the one-bead-one-compound (OBOC) library, where each bead carries many copies of a single compound, holds the greatest potential for the rapid identification of novel hits against emerging drug targets. However, this potential has not yet...... been fully realized due to a number of technical obstacles. In this feature article, we review the progress that has been made towards bead-based library screening and applications to the discovery of bioactive compounds. We identify the key challenges of this approach and highlight key steps needed......High-throughput screening is an important component of the drug discovery process. The screening of libraries containing hundreds of thousands of compounds requires assays amanable to miniaturisation and automization. Combinatorial chemistry holds a unique promise to deliver structural diverse...

Porting Ordinary Applications to Blue Gene/Q Supercomputers

Energy Technology Data Exchange (ETDEWEB)

Maheshwari, Ketan C.; Wozniak, Justin M.; Armstrong, Timothy; Katz, Daniel S.; Binkowski, T. Andrew; Zhong, Xiaoliang; Heinonen, Olle; Karpeyev, Dmitry; Wilde, Michael

2015-08-31

Efficiently porting ordinary applications to Blue Gene/Q supercomputers is a significant challenge. Codes are often originally developed without considering advanced architectures and related tool chains. Science needs frequently lead users to want to run large numbers of relatively small jobs (often called many-task computing, an ensemble, or a workflow), which can conflict with supercomputer configurations. In this paper, we discuss techniques developed to execute ordinary applications over leadership class supercomputers. We use the high-performance Swift parallel scripting framework and build two workflow execution techniques-sub-jobs and main-wrap. The sub-jobs technique, built on top of the IBM Blue Gene/Q resource manager Cobalt's sub-block jobs, lets users submit multiple, independent, repeated smaller jobs within a single larger resource block. The main-wrap technique is a scheme that enables C/C++ programs to be defined as functions that are wrapped by a high-performance Swift wrapper and that are invoked as a Swift script. We discuss the needs, benefits, technicalities, and current limitations of these techniques. We further discuss the real-world science enabled by these techniques and the results obtained.

Higgs Discovery

DEFF Research Database (Denmark)

Sannino, Francesco

2013-01-01

has been challenged by the discovery of a not-so-heavy Higgs-like state. I will therefore review the recent discovery \\cite{Foadi:2012bb} that the standard model top-induced radiative corrections naturally reduce the intrinsic non-perturbative mass of the composite Higgs state towards the desired...... via first principle lattice simulations with encouraging results. The new findings show that the recent naive claims made about new strong dynamics at the electroweak scale being disfavoured by the discovery of a not-so-heavy composite Higgs are unwarranted. I will then introduce the more speculative......I discuss the impact of the discovery of a Higgs-like state on composite dynamics starting by critically examining the reasons in favour of either an elementary or composite nature of this state. Accepting the standard model interpretation I re-address the standard model vacuum stability within...

Traditional Chinese Medicine-Based Network Pharmacology Could Lead to New Multicompound Drug Discovery

Directory of Open Access Journals (Sweden)

Jian Li

2012-01-01

Full Text Available Current strategies for drug discovery have reached a bottleneck where the paradigm is generally “one gene, one drug, one disease.” However, using holistic and systemic views, network pharmacology may be the next paradigm in drug discovery. Based on network pharmacology, a combinational drug with two or more compounds could offer beneficial synergistic effects for complex diseases. Interestingly, traditional chinese medicine (TCM has been practicing holistic views for over 3,000 years, and its distinguished feature is using herbal formulas to treat diseases based on the unique pattern classification. Though TCM herbal formulas are acknowledged as a great source for drug discovery, no drug discovery strategies compatible with the multidimensional complexities of TCM herbal formulas have been developed. In this paper, we highlighted some novel paradigms in TCM-based network pharmacology and new drug discovery. A multiple compound drug can be discovered by merging herbal formula-based pharmacological networks with TCM pattern-based disease molecular networks. Herbal formulas would be a source for multiple compound drug candidates, and the TCM pattern in the disease would be an indication for a new drug.

Discovery and replication of gene influences on brain structure using LASSO regression

Directory of Open Access Journals (Sweden)

Omid eKohannim

2012-08-01

Full Text Available We implemented LASSO (least absolute shrinkage and selection operator regression to evaluate gene effects in genome-wide association studies (GWAS of brain images, using an MRI-derived temporal lobe volume measure from 729 subjects scanned as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI. Sparse groups of SNPs in individual genes were selected by LASSO, which identifies efficient sets of variants influencing the data. These SNPs were considered jointly when assessing their association with neuroimaging measures. We discovered 22 genes that passed genome-wide significance for influencing temporal lobe volume. This was a substantially greater number of significant genes compared to those found with standard, univariate GWAS. These top genes are all expressed in the brain and include genes previously related to brain function or neuropsychiatric disorders such as MACROD2, SORCS2, GRIN2B, MAGI2, NPAS3, CLSTN2, GABRG3, NRXN3, PRKAG2, GAS7, RBFOX1, ADARB2, CHD4 and CDH13. The top genes we identified with this method also displayed significant and widespread post-hoc effects on voxelwise, tensor-based morphometry (TBM maps of the temporal lobes. The most significantly associated gene was an autism susceptibility gene known as MACROD2. We were able to successfully replicate the effect of the MACROD2 gene in an independent cohort of 564 young, Australian healthy adult twins and siblings scanned with MRI (mean age: 23.8±2.2 SD years. In exploratory analyses, three selected SNPs in the MACROD2 gene were also significantly associated with performance intelligence quotient (PIQ. Our approach powerfully complements univariate techniques in detecting influences of genes on the living brain.

Semantic Approaches for Knowledge Discovery and Retrieval in Biomedicine

DEFF Research Database (Denmark)

Wilkowski, Bartlomiej

This thesis discusses potential applications of semantics to the recent literaturebased informatics systems to facilitate knowledge discovery, hypothesis generation, and literature retrieval in the domain of biomedicine. The approaches presented herein make use of semantic information extracted...

Lessons from hot spot analysis for fragment-based drug discovery

Science.gov (United States)

Hall, David R.; Vajda, Sandor

2015-01-01

Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high affinity, druglike ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from protein three-dimensional structure, and how their strength, number and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery. PMID:26538314

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework.

Science.gov (United States)

Chen, Yi-An; Tripathi, Lokesh P; Mizuguchi, Kenji

2016-01-01

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org. © The Author(s) 2016. Published by Oxford University Press.

Integrative Sparse K-Means With Overlapping Group Lasso in Genomic Applications for Disease Subtype Discovery.

Science.gov (United States)

Huo, Zhiguang; Tseng, George

2017-06-01

Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K -means (is- K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is- K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency.

Barriers to Liposomal Gene Delivery: from Application Site to the Target.

Science.gov (United States)

Saffari, Mostafa; Moghimi, Hamid Reza; Dass, Crispin R

2016-01-01

Gene therapy is a therapeutic approach to deliver genetic material into cells to alter their function in entire organism. One promising form of gene delivery system (DDS) is liposomes. The success of liposome-mediated gene delivery is a multifactorial issue and well-designed liposomal systems might lead to optimized gene transfection particularly in vivo. Liposomal gene delivery systems face different barriers from their site of application to their target, which is inside the cells. These barriers include presystemic obstacles (epithelial barriers), systemic barriers in blood circulation and cellular barriers. Epithelial barriers differ depending on the route of administration. Systemic barriers include enzymatic degradation, binding and opsonisation. Both of these barriers can act as limiting hurdles that genetic material and their vector should overcome before reaching the cells. Finally liposomes should overcome cellular barriers that include cell entrance, endosomal escape and nuclear uptake. These barriers and their impact on liposomal gene delivery will be discussed in this review.

Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, protein-protein interactions, and human pancreatic islet gene expression

DEFF Research Database (Denmark)

Bergholdt, Regine; Brorsson, Caroline; Palleja, Albert

2012-01-01

Genome-wide association studies (GWAS) have heralded a new era in susceptibility locus discovery in complex diseases. For type 1 diabetes, >40 susceptibility loci have been discovered. However, GWAS do not inevitably lead to identification of the gene or genes in a given locus associated with dis......-cells. Our results provide novel insight to the mechanisms behind type 1 diabetes pathogenesis and, thus, may provide the basis for the design of novel treatment strategies.......Genome-wide association studies (GWAS) have heralded a new era in susceptibility locus discovery in complex diseases. For type 1 diabetes, >40 susceptibility loci have been discovered. However, GWAS do not inevitably lead to identification of the gene or genes in a given locus associated...... with disease, and they do not typically inform the broader context in which the disease genes operate. Here, we integrated type 1 diabetes GWAS data with protein-protein interactions to construct biological networks of relevance for disease. A total of 17 networks were identified. To prioritize...

Mass Spectrometry–Based Biomarker Discovery: Toward a Global Proteome Index of Individuality

Science.gov (United States)

Hawkridge, Adam M.; Muddiman, David C.

2011-01-01

Biomarker discovery and proteomics have become synonymous with mass spectrometry in recent years. Although this conflation is an injustice to the many essential biomolecular techniques widely used in biomarker-discovery platforms, it underscores the power and potential of contemporary mass spectrometry. Numerous novel and powerful technologies have been developed around mass spectrometry, proteomics, and biomarker discovery over the past 20 years to globally study complex proteomes (e.g., plasma). However, very few large-scale longitudinal studies have been carried out using these platforms to establish the analytical variability relative to true biological variability. The purpose of this review is not to cover exhaustively the applications of mass spectrometry to biomarker discovery, but rather to discuss the analytical methods and strategies that have been developed for mass spectrometry–based biomarker-discovery platforms and to place them in the context of the many challenges and opportunities yet to be addressed. PMID:20636062

Can Full Duplex reduce the discovery time in D2D Communication?

DEFF Research Database (Denmark)

Gatnau, Marta; Berardinelli, Gilberto; Mahmood, Nurul Huda

2016-01-01

Device-to-device (D2D) communication is considered as one of the key technologies to support new types of services, such as public safety and proximity-based applications. D2D communication requires a discovery phase, i.e., the node awareness procedure prior to the communication phase. Conventional...... half duplex transmission may not be sufficient to provide fast discovery and cope with the strict latency targets of future 5G services. On the other hand, in-band full duplex, by allowing simultaneous transmission and reception, may complete the discovery phase faster. In this paper, the potential...... of full duplex in providing fast discovery for the next 5th generation (5G) system supporting D2D communication is investigated. A design for such system is presented and evaluated via simulations, showing that full duplex can accelerate the discovery phase by supporting a higher transmission probability...

Glycosyltransferase Gene Expression Profiles Classify Cancer Types and Propose Prognostic Subtypes

Science.gov (United States)

Ashkani, Jahanshah; Naidoo, Kevin J.

2016-05-01

Aberrant glycosylation in tumours stem from altered glycosyltransferase (GT) gene expression but can the expression profiles of these signature genes be used to classify cancer types and lead to cancer subtype discovery? The differential structural changes to cellular glycan structures are predominantly regulated by the expression patterns of GT genes and are a hallmark of neoplastic cell metamorphoses. We found that the expression of 210 GT genes taken from 1893 cancer patient samples in The Cancer Genome Atlas (TCGA) microarray data are able to classify six cancers; breast, ovarian, glioblastoma, kidney, colon and lung. The GT gene expression profiles are used to develop cancer classifiers and propose subtypes. The subclassification of breast cancer solid tumour samples illustrates the discovery of subgroups from GT genes that match well against basal-like and HER2-enriched subtypes and correlates to clinical, mutation and survival data. This cancer type glycosyltransferase gene signature finding provides foundational evidence for the centrality of glycosylation in cancer.

A hybrid computational method for the discovery of novel reproduction-related genes.

Science.gov (United States)

Chen, Lei; Chu, Chen; Kong, Xiangyin; Huang, Guohua; Huang, Tao; Cai, Yu-Dong

2015-01-01

Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations.

The discovery of the periodic table as a case of simultaneous discovery.

Science.gov (United States)

Scerri, Eric

2015-03-13

The article examines the question of priority and simultaneous discovery in the context of the discovery of the periodic system. It is argued that rather than being anomalous, simultaneous discovery is the rule. Moreover, I argue that the discovery of the periodic system by at least six authors in over a period of 7 years represents one of the best examples of a multiple discovery. This notion is supported by a new view of the evolutionary development of science through a mechanism that is dubbed Sci-Gaia by analogy with Lovelock's Gaia hypothesis. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

[Fragment-based drug discovery: concept and aim].

Science.gov (United States)

Tanaka, Daisuke

2010-03-01

Fragment-Based Drug Discovery (FBDD) has been recognized as a newly emerging lead discovery methodology that involves biophysical fragment screening and chemistry-driven fragment-to-lead stages. Although fragments, defined as structurally simple and small compounds (typically FBDD primarily turns our attention to weakly but specifically binding fragments (hit fragments) as the starting point of medicinal chemistry. Hit fragments are then promoted to more potent lead compounds through linking or merging with another hit fragment and/or attaching functional groups. Another positive aspect of FBDD is ligand efficiency. Ligand efficiency is a useful guide in screening hit selection and hit-to-lead phases to achieve lead-likeness. Owing to these features, a number of successful applications of FBDD to "undruggable targets" (where HTS and other lead identification methods failed to identify useful lead compounds) have been reported. As a result, FBDD is now expected to complement more conventional methodologies. This review, as an introduction of the following articles, will summarize the fundamental concepts of FBDD and will discuss its advantages over other conventional drug discovery approaches.

Beyond Discovery

DEFF Research Database (Denmark)

Korsgaard, Steffen; Sassmannshausen, Sean Patrick

2017-01-01

In this chapter we explore four alternatives to the dominant discovery view of entrepreneurship; the development view, the construction view, the evolutionary view, and the Neo-Austrian view. We outline the main critique points of the discovery presented in these four alternatives, as well...

Cell and small animal models for phenotypic drug discovery

Directory of Open Access Journals (Sweden)

Szabo M

2017-06-01

Full Text Available Mihaly Szabo,1 Sara Svensson Akusjärvi,1 Ankur Saxena,1 Jianping Liu,2 Gayathri Chandrasekar,1 Satish S Kitambi1 1Department of Microbiology Tumor, and Cell Biology, 2Department of Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden Abstract: The phenotype-based drug discovery (PDD approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery. Keywords: phenotype, screening, PDD, discovery, zebrafish, drug

microCOMB web application for the identification of gene expression components

OpenAIRE

Skok, Boštjan

2016-01-01

The goal of this thesis is to develop a web application that functions as user interface for microCOMB and manages it's gene expression database. The main functions of the application are to enable the user to upload expression profiles to be analyzed and show it's result, store user history of completed analyses and keep the public database up to date. In the thesis we describe the technologies used, architecture, development process and application functionality. During the development and ...

Key drivers of biomedical innovation in cancer drug discovery

OpenAIRE

Huber, Margit A; Kraut, Norbert

2014-01-01

Discovery and translational research has led to the identification of a series of ?cancer drivers??genes that, when mutated or otherwise misregulated, can drive malignancy. An increasing number of drugs that directly target such drivers have demonstrated activity in clinical trials and are shaping a new landscape for molecularly targeted cancer therapies. Such therapies rely on molecular and genetic diagnostic tests to detect the presence of a biomarker that predicts response. Here, we highli...

Discovery of genomic intervals that underlie nematode responses to benzimidazoles.

Science.gov (United States)

Zamanian, Mostafa; Cook, Daniel E; Zdraljevic, Stefan; Brady, Shannon C; Lee, Daehan; Lee, Junho; Andersen, Erik C

2018-03-01

Parasitic nematodes impose a debilitating health and economic burden across much of the world. Nematode resistance to anthelmintic drugs threatens parasite control efforts in both human and veterinary medicine. Despite this threat, the genetic landscape of potential resistance mechanisms to these critical drugs remains largely unexplored. Here, we exploit natural variation in the model nematodes Caenorhabditis elegans and Caenorhabditis briggsae to discover quantitative trait loci (QTL) that control sensitivity to benzimidazoles widely used in human and animal medicine. High-throughput phenotyping of albendazole, fenbendazole, mebendazole, and thiabendazole responses in panels of recombinant lines led to the discovery of over 15 QTL in C. elegans and four QTL in C. briggsae associated with divergent responses to these anthelmintics. Many of these QTL are conserved across benzimidazole derivatives, but others show drug and dose specificity. We used near-isogenic lines to recapitulate and narrow the C. elegans albendazole QTL of largest effect and identified candidate variants correlated with the resistance phenotype. These QTL do not overlap with known benzimidazole target resistance genes from parasitic nematodes and present specific new leads for the discovery of novel mechanisms of nematode benzimidazole resistance. Analyses of orthologous genes reveal conservation of candidate benzimidazole resistance genes in medically important parasitic nematodes. These data provide a basis for extending these approaches to other anthelmintic drug classes and a pathway towards validating new markers for anthelmintic resistance that can be deployed to improve parasite disease control.

"Eureka, Eureka!" Discoveries in Science

Science.gov (United States)

Agarwal, Pankaj

2011-01-01

Accidental discoveries have been of significant value in the progress of science. Although accidental discoveries are more common in pharmacology and chemistry, other branches of science have also benefited from such discoveries. While most discoveries are the result of persistent research, famous accidental discoveries provide a fascinating…

Serious limitations of the QTL/Microarray approach for QTL gene discovery

Directory of Open Access Journals (Sweden)

Warden Craig H

2010-07-01

Full Text Available Abstract Background It has been proposed that the use of gene expression microarrays in nonrecombinant parental or congenic strains can accelerate the process of isolating individual genes underlying quantitative trait loci (QTL. However, the effectiveness of this approach has not been assessed. Results Thirty-seven studies that have implemented the QTL/microarray approach in rodents were reviewed. About 30% of studies showed enrichment for QTL candidates, mostly in comparisons between congenic and background strains. Three studies led to the identification of an underlying QTL gene. To complement the literature results, a microarray experiment was performed using three mouse congenic strains isolating the effects of at least 25 biometric QTL. Results show that genes in the congenic donor regions were preferentially selected. However, within donor regions, the distribution of differentially expressed genes was homogeneous once gene density was accounted for. Genes within identical-by-descent (IBD regions were less likely to be differentially expressed in chromosome 2, but not in chromosomes 11 and 17. Furthermore, expression of QTL regulated in cis (cis eQTL showed higher expression in the background genotype, which was partially explained by the presence of single nucleotide polymorphisms (SNP. Conclusions The literature shows limited successes from the QTL/microarray approach to identify QTL genes. Our own results from microarray profiling of three congenic strains revealed a strong tendency to select cis-eQTL over trans-eQTL. IBD regions had little effect on rate of differential expression, and we provide several reasons why IBD should not be used to discard eQTL candidates. In addition, mismatch probes produced false cis-eQTL that could not be completely removed with the current strains genotypes and low probe density microarrays. The reviewed studies did not account for lack of coverage from the platforms used and therefore removed genes

30 CFR 44.24 - Discovery.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Discovery. 44.24 Section 44.24 Mineral... Discovery. Parties shall be governed in their conduct of discovery by appropriate provisions of the Federal... discovery. Alternative periods of time for discovery may be prescribed by the presiding administrative law...

19 CFR 356.20 - Discovery.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 3 2010-04-01 2010-04-01 false Discovery. 356.20 Section 356.20 Customs Duties... § 356.20 Discovery. (a) Voluntary discovery. All parties are encouraged to engage in voluntary discovery... sanctions proceeding. (b) Limitations on discovery. The administrative law judge shall place such limits...

Chemical Discovery

Science.gov (United States)

Brown, Herbert C.

1974-01-01

The role of discovery in the advance of the science of chemistry and the factors that are currently operating to handicap that function are considered. Examples are drawn from the author's work with boranes. The thesis that exploratory research and discovery should be encouraged is stressed. (DT)

The University of New Mexico Center for Molecular Discovery

Science.gov (United States)

Edwards, Bruce S.; Gouveia, Kristine; Oprea, Tudor I.; Sklar, Larry A.

2015-01-01

The University of New Mexico Center for Molecular Discovery (UNMCMD) is an academic research center that specializes in discovery using high throughput flow cytometry (HTFC) integrated with virtual screening, as well as knowledge mining and drug informatics. With a primary focus on identifying small molecules that can be used as chemical probes and as leads for drug discovery, it is a central core resource for research and translational activities at UNM that supports implementation and management of funded screening projects as well as “up-front” services such as consulting for project design and implementation, assistance in assay development and generation of preliminary data for pilot projects in support of competitive grant applications. The HTFC platform in current use represents advanced, proprietary technology developed at UNM that is now routinely capable of processing bioassays arrayed in 96-, 384- and 1536-well formats at throughputs of 60,000 or more wells per day. Key programs at UNMCMD include screening of research targets submitted by the international community through NIH’s Molecular Libraries Program; a multi-year effort involving translational partnerships at UNM directed towards drug repurposing - identifying new uses for clinically approved drugs; and a recently established personalized medicine initiative for advancing cancer therapy by the application of “smart” oncology drugs in selected patients based on response patterns of their cancer cells in vitro. UNMCMD discoveries, innovation, and translation have contributed to a wealth of inventions, patents, licenses and publications, as well as startup companies, clinical trials and a multiplicity of domestic and international collaborative partnerships to further the research enterprise. PMID:24409953

Gene expression profiling of human breast tissue samples using SAGE-Seq.

Science.gov (United States)

Wu, Zhenhua Jeremy; Meyer, Clifford A; Choudhury, Sibgat; Shipitsin, Michail; Maruyama, Reo; Bessarabova, Marina; Nikolskaya, Tatiana; Sukumar, Saraswati; Schwartzman, Armin; Liu, Jun S; Polyak, Kornelia; Liu, X Shirley

2010-12-01

We present a powerful application of ultra high-throughput sequencing, SAGE-Seq, for the accurate quantification of normal and neoplastic mammary epithelial cell transcriptomes. We develop data analysis pipelines that allow the mapping of sense and antisense strands of mitochondrial and RefSeq genes, the normalization between libraries, and the identification of differentially expressed genes. We find that the diversity of cancer transcriptomes is significantly higher than that of normal cells. Our analysis indicates that transcript discovery plateaus at 10 million reads/sample, and suggests a minimum desired sequencing depth around five million reads. Comparison of SAGE-Seq and traditional SAGE on normal and cancerous breast tissues reveals higher sensitivity of SAGE-Seq to detect less-abundant genes, including those encoding for known breast cancer-related transcription factors and G protein-coupled receptors (GPCRs). SAGE-Seq is able to identify genes and pathways abnormally activated in breast cancer that traditional SAGE failed to call. SAGE-Seq is a powerful method for the identification of biomarkers and therapeutic targets in human disease.

24 CFR 180.500 - Discovery.

Science.gov (United States)

2010-04-01

... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Discovery. 180.500 Section 180.500... OPPORTUNITY CONSOLIDATED HUD HEARING PROCEDURES FOR CIVIL RIGHTS MATTERS Discovery § 180.500 Discovery. (a) In general. This subpart governs discovery in aid of administrative proceedings under this part. Discovery in...

22 CFR 224.21 - Discovery.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Discovery. 224.21 Section 224.21 Foreign....21 Discovery. (a) The following types of discovery are authorized: (1) Requests for production of... parties, discovery is available only as ordered by the ALJ. The ALJ shall regulate the timing of discovery...

Computer-aided drug discovery [v1; ref status: indexed, http://f1000r.es/5ij

Directory of Open Access Journals (Sweden)

Jürgen Bajorath

2015-08-01

Full Text Available Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

Technical Improvement and Application of Hydrodynamic Gene Delivery in Study of Liver Diseases

Directory of Open Access Journals (Sweden)

Mei Huang

2017-08-01

Full Text Available Development of an safe and efficient in vivo gene delivery method is indispensable for molecular biology research and the progress in the following gene therapy. Over the past few years, hydrodynamic gene delivery (HGD with naked DNA has drawn increasing interest in both research and potential clinic applications due to its high efficiency and low risk in triggering immune responses and carcinogenesis in comparison to viral vectors. This method, involving intravenous injection (i.v. of massive DNA in a short duration, gives a transient but high in vivo gene expression especially in the liver of small animals. In addition to DNA, it has also been shown to deliver other substance such as RNA, proteins, synthetic small compounds and even viruses in vivo. Given its ability to robustly mimic in vivo hepatitis B virus (HBV production in liver, HGD has become a fundamental and important technology on HBV studies in our group and many other groups. Recently, there have been interesting reports about the applications and further improvement of this technology in other liver research. Here, we review the principle, safety, current application and development of hydrodynamic delivery in liver disease studies, and discuss its future prospects, clinical potential and challenges.

19 CFR 207.109 - Discovery.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 3 2010-04-01 2010-04-01 false Discovery. 207.109 Section 207.109 Customs Duties... and Committee Proceedings § 207.109 Discovery. (a) Discovery methods. All parties may obtain discovery under such terms and limitations as the administrative law judge may order. Discovery may be by one or...

15 CFR 25.21 - Discovery.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Discovery. 25.21 Section 25.21... Discovery. (a) The following types of discovery are authorized: (1) Requests for production of documents for..., discovery is available only as ordered by the ALJ. The ALJ shall regulate the timing of discovery. (d...

The discovery, development and future of GMR: The Nobel Prize 2007

International Nuclear Information System (INIS)

Thompson, Sarah M

2008-01-01

One hundred and one years after J J Thomson was awarded the Nobel Prize for the discovery of the electron, the 2007 Nobel Prize for Physics was awarded to Professors Peter Gruenberg and Albert Fert for the discovery of giant magnetoresistance (GMR) in which the spin as well as the charge of the electron is manipulated and exploited in nanoscale magnetic materials. The journey to GMR started with Lord Kelvin who 150 years ago in 1857 made the first observations of anisotropic magnetoresistance and includes Sir Neville Mott who in 1936 realized that electric current in metals could be considered as two independent spin channels. Modern technology also has a significant role to play in the award of this Nobel Prize: GMR is only manifest in nanoscale materials, and the development of nanotechnology growth techniques was a necessary pre-requisite; further, the considerable demands of the magnetic data storage industry to drive up the data density stored on a hard disk fuelled an enormous international research effort following the initial discovery with the result that more than 5 billion GMR read heads have been manufactured since 1997, ubiquitous in hard disks today. This technology drive continues to inspire exploration of the spin current in the field now known as spintronics, generating new ideas and applications. This review explores the science underpinning GMR and spintronics, the different routes to its discovery taken by Professors Gruenberg and Fert, the new science, materials and applications that the discovery has triggered and the considerable potential for the future. (topical review)

The discovery, development and future of GMR: The Nobel Prize 2007

Energy Technology Data Exchange (ETDEWEB)

Thompson, Sarah M [Department of Physics, University of York, York, YO10 5DD (United Kingdom)

2008-05-07

One hundred and one years after J J Thomson was awarded the Nobel Prize for the discovery of the electron, the 2007 Nobel Prize for Physics was awarded to Professors Peter Gruenberg and Albert Fert for the discovery of giant magnetoresistance (GMR) in which the spin as well as the charge of the electron is manipulated and exploited in nanoscale magnetic materials. The journey to GMR started with Lord Kelvin who 150 years ago in 1857 made the first observations of anisotropic magnetoresistance and includes Sir Neville Mott who in 1936 realized that electric current in metals could be considered as two independent spin channels. Modern technology also has a significant role to play in the award of this Nobel Prize: GMR is only manifest in nanoscale materials, and the development of nanotechnology growth techniques was a necessary pre-requisite; further, the considerable demands of the magnetic data storage industry to drive up the data density stored on a hard disk fuelled an enormous international research effort following the initial discovery with the result that more than 5 billion GMR read heads have been manufactured since 1997, ubiquitous in hard disks today. This technology drive continues to inspire exploration of the spin current in the field now known as spintronics, generating new ideas and applications. This review explores the science underpinning GMR and spintronics, the different routes to its discovery taken by Professors Gruenberg and Fert, the new science, materials and applications that the discovery has triggered and the considerable potential for the future. (topical review)

[Discovery of the target genes inhibited by formic acid in Candida shehatae].

Science.gov (United States)

Cai, Peng; Xiong, Xujie; Xu, Yong; Yong, Qiang; Zhu, Junjun; Shiyuan, Yu

2014-01-04

At transcriptional level, the inhibitory effects of formic acid was investigated on Candida shehatae, a model yeast strain capable of fermenting xylose to ethanol. Thereby, the target genes were regulated by formic acid and the transcript profiles were discovered. On the basis of the transcriptome data of C. shehatae metabolizing glucose and xylose, the genes responsible for ethanol fermentation were chosen as candidates by the combined method of yeast metabolic pathway analysis and manual gene BLAST search. These candidates were then quantitatively detected by RQ-PCR technique to find the regulating genes under gradient doses of formic acid. By quantitative analysis of 42 candidate genes, we finally identified 10 and 5 genes as markedly down-regulated and up-regulated targets by formic acid, respectively. With regard to gene transcripts regulated by formic acid in C. shehatae, the markedly down-regulated genes ranking declines as follows: xylitol dehydrogenase (XYL2), acetyl-CoA synthetase (ACS), ribose-5-phosphate isomerase (RKI), transaldolase (TAL), phosphogluconate dehydrogenase (GND1), transketolase (TKL), glucose-6-phosphate dehydrogenase (ZWF1), xylose reductase (XYL1), pyruvate dehydrogenase (PDH) and pyruvate decarboxylase (PDC); and a declining rank for up-regulated gens as follows: fructose-bisphosphate aldolase (ALD), glucokinase (GLK), malate dehydrogenase (MDH), 6-phosphofructokinase (PFK) and alcohol dehydrogenase (ADH).

Lessons from Hot Spot Analysis for Fragment-Based Drug Discovery.

Science.gov (United States)

Hall, David R; Kozakov, Dima; Whitty, Adrian; Vajda, Sandor

2015-11-01

Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high-affinity, drug-like ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from a protein 3D structure, and how their strength, number, and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery. Copyright © 2015 Elsevier Ltd. All rights reserved.

39 CFR 963.14 - Discovery.

Science.gov (United States)

2010-07-01

... 39 Postal Service 1 2010-07-01 2010-07-01 false Discovery. 963.14 Section 963.14 Postal Service... PANDERING ADVERTISEMENTS STATUTE, 39 U.S.C. 3008 § 963.14 Discovery. Discovery is to be conducted on a... such discovery as he or she deems reasonable and necessary. Discovery may include one or more of the...

In silico discovery of transcription regulatory elements in Plasmodium falciparum

Directory of Open Access Journals (Sweden)

Le Roch Karine G

2008-02-01

Full Text Available Abstract Background With the sequence of the Plasmodium falciparum genome and several global mRNA and protein life cycle expression profiling projects now completed, elucidating the underlying networks of transcriptional control important for the progression of the parasite life cycle is highly pertinent to the development of new anti-malarials. To date, relatively little is known regarding the specific mechanisms the parasite employs to regulate gene expression at the mRNA level, with studies of the P. falciparum genome sequence having revealed few cis-regulatory elements and associated transcription factors. Although it is possible the parasite may evoke mechanisms of transcriptional control drastically different from those used by other eukaryotic organisms, the extreme AT-rich nature of P. falciparum intergenic regions (~90% AT presents significant challenges to in silico cis-regulatory element discovery. Results We have developed an algorithm called Gene Enrichment Motif Searching (GEMS that uses a hypergeometric-based scoring function and a position-weight matrix optimization routine to identify with high-confidence regulatory elements in the nucleotide-biased and repeat sequence-rich P. falciparum genome. When applied to promoter regions of genes contained within 21 co-expression gene clusters generated from P. falciparum life cycle microarray data using the semi-supervised clustering algorithm Ontology-based Pattern Identification, GEMS identified 34 putative cis-regulatory elements associated with a variety of parasite processes including sexual development, cell invasion, antigenic variation and protein biosynthesis. Among these candidates were novel motifs, as well as many of the elements for which biological experimental evidence already exists in the Plasmodium literature. To provide evidence for the biological relevance of a cell invasion-related element predicted by GEMS, reporter gene and electrophoretic mobility shift assays

Discovery of Approximate Differential Dependencies

OpenAIRE

Liu, Jixue; Kwashie, Selasi; Li, Jiuyong; Ye, Feiyue; Vincent, Millist

2013-01-01

Differential dependencies (DDs) capture the relationships between data columns of relations. They are more general than functional dependencies (FDs) and and the difference is that DDs are defined on the distances between values of two tuples, not directly on the values. Because of this difference, the algorithms for discovering FDs from data find only special DDs, not all DDs and therefore are not applicable to DD discovery. In this paper, we propose an algorithm to discover DDs from data fo...

EASY-HIT: HIV full-replication technology for broad discovery of multiple classes of HIV inhibitors.

Science.gov (United States)

Kremb, Stephan; Helfer, Markus; Heller, Werner; Hoffmann, Dieter; Wolff, Horst; Kleinschmidt, Andrea; Cepok, Sabine; Hemmer, Bernhard; Durner, Jörg; Brack-Werner, Ruth

2010-12-01

HIV replication assays are important tools for HIV drug discovery efforts. Here, we present a full HIV replication system (EASY-HIT) for the identification and analysis of HIV inhibitors. This technology is based on adherently growing HIV-susceptible cells, with a stable fluorescent reporter gene activated by HIV Tat and Rev. A fluorescence-based assay was designed that measures HIV infection by two parameters relating to the early and the late phases of HIV replication, respectively. Validation of the assay with a panel of nine reference inhibitors yielded effective inhibitory concentrations consistent with published data and allowed discrimination between inhibitors of early and late phases of HIV replication. Finer resolution of the effects of reference drugs on different steps of HIV replication was achieved in secondary time-of-addition assays. The EASY-HIT assay yielded high Z' scores (>0.9) and signal stabilities, confirming its robustness. Screening of the LOPAC(1280) library identified 10 compounds (0.8%), of which eight were known to inhibit HIV, validating the suitability of this assay for screening applications. Studies evaluating anti-HIV activities of natural products with the EASY-HIT technology led to the identification of three novel inhibitory compounds that apparently act at different steps of HIV-1 replication. Furthermore, we demonstrate successful evaluation of plant extracts for HIV-inhibitory activities, suggesting application of this technology for the surveillance of biological extracts with anti-HIV activities. We conclude that the EASY-HIT technology is a versatile tool for the discovery and characterization of HIV inhibitors.

[Application of gene chip technology for acupuncture research over the past 15 years].

Science.gov (United States)

Jia, Wenrui; Zhang, Yue; Guo, Qiying; Sun, Qisheng; Guo, Qiulei; Ji, Zhi; Yang, Fangyuan; Zhan, He; Wang, He; Sui, Minghe; Hou, Zhongwei; Wang, Chaoyang; Liu, Qingguo

2017-12-12

To explore the application of gene chip technology in the acupuncture research so as to provide evidences for the mechanism of acupuncture for regulating bodies. The literature on the application of gene chip technology in the acupuncture field from 2001 to 2016 was collected in PubMed, Springer, CNKI and WANFANG databases, which was analyzed and summarized. There were some achievements of the technology for acupuncture research, focusing on the five aspects, including the study of the relationship between meridian-point and viscera, the influencing factors of acupuncture effect, the effect and mechanism of acupuncture analgesia, the mechanism of acupuncture anti-aging, the effect and mechanism of acupuncture for diseases of each system. Gene chip technology plays an important role in researching acupuncture mechanism. It is an important technology for genomics study of acupuncture. However, there are also some disadvantages such as high cost, deficient data mining, non-uniform observation objects, deficient professionals, etc. All those need further resolution so as to promote the application of this technology in the acupuncture researching field.

FDA Regulation of Clinical Applications of CRISPR-CAS Gene-Editing Technology.

Science.gov (United States)

Grant, Evita V

Scientists have repurposed an adaptive immune system of single cell organisms to create a new type of gene-editing tool: CRISPR (clustered regularly interspaced short palindromic repeats)-Cas technology. Scientists in China have reported its use in the genome modification of non-viable human embryos. This has ignited a spirited debate about the moral, ethical, scientific, and social implications of human germline genome engineering. There have also been calls for regulations; however, FDA has yet to formally announce its oversight of clinical applications of CRISPR-Cas systems. This paper reviews FDA regulation of previously controversial biotechnology breakthroughs, recombinant DNA and human cloning. It then shows that FDA is well positioned to regulate CRISPR-Cas clinical applications, due to its legislative mandates, its existing regulatory frameworks for gene therapies and assisted reproductive technologies, and other considerations.

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

Energy Technology Data Exchange (ETDEWEB)

Wang, Hui; Shi, Tujin; Qian, Wei-Jun; Liu, Tao; Kagan, Jacob; Srivastava, Sudhir; Smith, Richard D.; Rodland, Karin D.; Camp, David G.

2015-12-04

Mass spectrometry-based proteomics has become an indispensable tool in biomedical research with broad applications ranging from fundamental biology, systems biology, and biomarker discovery. Recent advances in LC-MS have made it become a major technology in clinical applications, especially in cancer biomarker discovery and verification. To overcome the challenges associated with the analysis of clinical samples, such as extremely wide dynamic range of protein concentrations in biofluids and the need to perform high throughput and accurate quantification, significant efforts have been devoted to improve the overall performance of LC-MS bases clinical proteomics. In this review, we summarize the recent advances in LC-MS in the aspect of cancer biomarker discovery and quantification, and discuss its potentials, limitations, and future perspectives.

The web server of IBM's Bioinformatics and Pattern Discovery group: 2004 update.

Science.gov (United States)

Huynh, Tien; Rigoutsos, Isidore

2004-07-01

In this report, we provide an update on the services and content which are available on the web server of IBM's Bioinformatics and Pattern Discovery group. The server, which is operational around the clock, provides access to a large number of methods that have been developed and published by the group's members. There is an increasing number of problems that these tools can help tackle; these problems range from the discovery of patterns in streams of events and the computation of multiple sequence alignments, to the discovery of genes in nucleic acid sequences, the identification--directly from sequence--of structural deviations from alpha-helicity and the annotation of amino acid sequences for antimicrobial activity. Additionally, annotations for more than 130 archaeal, bacterial, eukaryotic and viral genomes are now available on-line and can be searched interactively. The tools and code bundles continue to be accessible from http://cbcsrv.watson.ibm.com/Tspd.html whereas the genomics annotations are available at http://cbcsrv.watson.ibm.com/Annotations/.

GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists

Directory of Open Access Journals (Sweden)

Steinfeld Israel

2009-02-01

Full Text Available Abstract Background Since the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database. In particular, a variety of tools that perform GO enrichment analysis are currently available. Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set. A few tools also exist that support analyzing ranked lists. The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results. Results GOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets. This is particularly useful in many typical cases where genomic data may be naturally represented as a ranked list of genes (e.g. by level of expression or of differential expression. GOrilla employs a flexible threshold statistical approach to discover GO terms that are significantly enriched at the top of a ranked gene list. Building on a complete theoretical characterization of the underlying distribution, called mHG, GOrilla computes an exact p-value for the observed enrichment, taking threshold multiple testing into account without the need for simulations. This enables rigorous statistical analysis of thousand of genes and thousands of GO terms in order of seconds. The output of the enrichment analysis is visualized as a hierarchical structure, providing a clear view of the relations between enriched GO terms. Conclusion GOrilla is an efficient GO analysis tool with unique features that make a useful addition to the existing repertoire of GO enrichment tools. GOrilla's unique features and advantages over other threshold free enrichment tools include rigorous statistics, fast running time and an effective graphical representation. GOrilla is publicly available at: http://cbl-gorilla.cs.technion.ac.il

Deep data: discovery and visualization Application to hyperspectral ALMA imagery

Science.gov (United States)

Merényi, Erzsébet; Taylor, Joshua; Isella, Andrea

2017-06-01

Leading-edge telescopes such as the Atacama Large Millimeter and sub-millimeter Array (ALMA), and near-future ones, are capable of imaging the same sky area at hundreds-to-thousands of frequencies with both high spectral and spatial resolution. This provides unprecedented opportunities for discovery about the spatial, kinematical and compositional structure of sources such as molecular clouds or protoplanetary disks, and more. However, in addition to enormous volume, the data also exhibit unprecedented complexity, mandating new approaches for extracting and summarizing relevant information. Traditional techniques such as examining images at selected frequencies become intractable while tools that integrate data across frequencies or pixels (like moment maps) can no longer fully exploit and visualize the rich information. We present a neural map-based machine learning approach that can handle all spectral channels simultaneously, utilizing the full depth of these data for discovery and visualization of spectrally homogeneous spatial regions (spectral clusters) that characterize distinct kinematic behaviors. We demonstrate the effectiveness on an ALMA image cube of the protoplanetary disk HD142527. The tools we collectively name ``NeuroScope'' are efficient for ``Big Data'' due to intelligent data summarization that results in significant sparsity and noise reduction. We also demonstrate a new approach to automate our clustering for fast distillation of large data cubes.

Semiconductor technology in protein kinase research and drug discovery: sensing a revolution.

Science.gov (United States)

Bhalla, Nikhil; Di Lorenzo, Mirella; Estrela, Pedro; Pula, Giordano

2017-02-01

Since the discovery of protein kinase activity in 1954, close to 600 kinases have been discovered that have crucial roles in cell physiology. In several pathological conditions, aberrant protein kinase activity leads to abnormal cell and tissue physiology. Therefore, protein kinase inhibitors are investigated as potential treatments for several diseases, including dementia, diabetes, cancer and autoimmune and cardiovascular disease. Modern semiconductor technology has recently been applied to accelerate the discovery of novel protein kinase inhibitors that could become the standard-of-care drugs of tomorrow. Here, we describe current techniques and novel applications of semiconductor technologies in protein kinase inhibitor drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Thomson, his discovery of the electron and the twentieth century science and technology

International Nuclear Information System (INIS)

Ahmad, N.

1997-01-01

Sir J. J. Thomson was the first to discover a subatomic particle i. e. electron. Due to this discovery he is remembered in the history as T he Atom Smasher . He was a great experimentalists and a devoted physicist. He himself, his son and his seven pupils earned Noble prizes on the basis of their scientific discoveries. The discovery of electron by Sir Thomson in 1897, at Cavendish Laboratory, has rewritten the entire physical science. Although electron has wide spread applications in almost every field, yet its exact nature is not fully known. This article briefly describes the life of Sir Thomson, his achievements and the impact of his discovery of electron on the twentieth century science and technology. (author)

Usability of Discovery Portals

OpenAIRE

Bulens, J.D.; Vullings, L.A.E.; Houtkamp, J.M.; Vanmeulebrouk, B.

2013-01-01

As INSPIRE progresses to be implemented in the EU, many new discovery portals are built to facilitate finding spatial data. Currently the structure of the discovery portals is determined by the way spatial data experts like to work. However, we argue that the main target group for discovery portals are not spatial data experts but professionals with limited spatial knowledge, and a focus outside the spatial domain. An exploratory usability experiment was carried out in which three discovery p...

Gene discovery for the bark beetle-vectored fungal tree pathogen Grosmannia clavigera

Directory of Open Access Journals (Sweden)

Robertson Gordon

2010-10-01

Full Text Available Abstract Background Grosmannia clavigera is a bark beetle-vectored fungal pathogen of pines that causes wood discoloration and may kill trees by disrupting nutrient and water transport. Trees respond to attacks from beetles and associated fungi by releasing terpenoid and phenolic defense compounds. It is unclear which genes are important for G. clavigera's ability to overcome antifungal pine terpenoids and phenolics. Results We constructed seven cDNA libraries from eight G. clavigera isolates grown under various culture conditions, and Sanger sequenced the 5' and 3' ends of 25,000 cDNA clones, resulting in 44,288 high quality ESTs. The assembled dataset of unique transcripts (unigenes consists of 6,265 contigs and 2,459 singletons that mapped to 6,467 locations on the G. clavigera reference genome, representing ~70% of the predicted G. clavigera genes. Although only 54% of the unigenes matched characterized proteins at the NCBI database, this dataset extensively covers major metabolic pathways, cellular processes, and genes necessary for response to environmental stimuli and genetic information processing. Furthermore, we identified genes expressed in spores prior to germination, and genes involved in response to treatment with lodgepole pine phloem extract (LPPE. Conclusions We provide a comprehensively annotated EST dataset for G. clavigera that represents a rich resource for gene characterization in this and other ophiostomatoid fungi. Genes expressed in response to LPPE treatment are indicative of fungal oxidative stress response. We identified two clusters of potentially functionally related genes responsive to LPPE treatment. Furthermore, we report a simple method for identifying contig misassemblies in de novo assembled EST collections caused by gene overlap on the genome.

A high-density transcript linkage map with 1,845 expressed genes positioned by microarray-based Single Feature Polymorphisms (SFP) in Eucalyptus

Science.gov (United States)

2011-01-01

Background Technological advances are progressively increasing the application of genomics to a wider array of economically and ecologically important species. High-density maps enriched for transcribed genes facilitate the discovery of connections between genes and phenotypes. We report the construction of a high-density linkage map of expressed genes for the heterozygous genome of Eucalyptus using Single Feature Polymorphism (SFP) markers. Results SFP discovery and mapping was achieved using pseudo-testcross screening and selective mapping to simultaneously optimize linkage mapping and microarray costs. SFP genotyping was carried out by hybridizing complementary RNA prepared from 4.5 year-old trees xylem to an SFP array containing 103,000 25-mer oligonucleotide probes representing 20,726 unigenes derived from a modest size expressed sequence tags collection. An SFP-mapping microarray with 43,777 selected candidate SFP probes representing 15,698 genes was subsequently designed and used to genotype SFPs in a larger subset of the segregating population drawn by selective mapping. A total of 1,845 genes were mapped, with 884 of them ordered with high likelihood support on a framework map anchored to 180 microsatellites with average density of 1.2 cM. Using more probes per unigene increased by two-fold the likelihood of detecting segregating SFPs eventually resulting in more genes mapped. In silico validation showed that 87% of the SFPs map to the expected location on the 4.5X draft sequence of the Eucalyptus grandis genome. Conclusions The Eucalyptus 1,845 gene map is the most highly enriched map for transcriptional information for any forest tree species to date. It represents a major improvement on the number of genes previously positioned on Eucalyptus maps and provides an initial glimpse at the gene space for this global tree genome. A general protocol is proposed to build high-density transcript linkage maps in less characterized plant species by SFP genotyping

Network-Guided Key Gene Discovery for a Given Cellular Process

DEFF Research Database (Denmark)

He, Feng Q; Ollert, Markus

2018-01-01

Identification of key genes for a given physiological or pathological process is an essential but still very challenging task for the entire biomedical research community. Statistics-based approaches, such as genome-wide association study (GWAS)- or quantitative trait locus (QTL)-related analysis...... have already made enormous contributions to identifying key genes associated with a given disease or phenotype, the success of which is however very much dependent on a huge number of samples. Recent advances in network biology, especially network inference directly from genome-scale data...

Using the iPlant collaborative discovery environment.

Science.gov (United States)

Oliver, Shannon L; Lenards, Andrew J; Barthelson, Roger A; Merchant, Nirav; McKay, Sheldon J

2013-06-01

The iPlant Collaborative is an academic consortium whose mission is to develop an informatics and social infrastructure to address the "grand challenges" in plant biology. Its cyberinfrastructure supports the computational needs of the research community and facilitates solving major challenges in plant science. The Discovery Environment provides a powerful and rich graphical interface to the iPlant Collaborative cyberinfrastructure by creating an accessible virtual workbench that enables all levels of expertise, ranging from students to traditional biology researchers and computational experts, to explore, analyze, and share their data. By providing access to iPlant's robust data-management system and high-performance computing resources, the Discovery Environment also creates a unified space in which researchers can access scalable tools. Researchers can use available Applications (Apps) to execute analyses on their data, as well as customize or integrate their own tools to better meet the specific needs of their research. These Apps can also be used in workflows that automate more complicated analyses. This module describes how to use the main features of the Discovery Environment, using bioinformatics workflows for high-throughput sequence data as examples. © 2013 by John Wiley & Sons, Inc.

First discovery of two polyketide synthase genes for mitorubrinic acid and mitorubrinol yellow pigment biosynthesis and implications in virulence of Penicillium marneffei.

Directory of Open Access Journals (Sweden)

Patrick C Y Woo

Full Text Available BACKGROUND: The genome of P. marneffei, the most important thermal dimorphic fungus causing respiratory, skin and systemic mycosis in China and Southeast Asia, possesses 23 polyketide synthase (PKS genes and 2 polyketide synthase nonribosomal peptide synthase hybrid (PKS-NRPS genes, which is of high diversity compared to other thermal dimorphic pathogenic fungi. We hypothesized that the yellow pigment in the mold form of P. marneffei could also be synthesized by one or more PKS genes. METHODOLOGY/PRINCIPAL FINDINGS: All 23 PKS and 2 PKS-NRPS genes of P. marneffei were systematically knocked down. A loss of the yellow pigment was observed in the mold form of the pks11 knockdown, pks12 knockdown and pks11pks12 double knockdown mutants. Sequence analysis showed that PKS11 and PKS12 are fungal non-reducing PKSs. Ultra high performance liquid chromatography-photodiode array detector/electrospray ionization-quadruple time of flight-mass spectrometry (MS and MS/MS analysis of the culture filtrates of wild type P. marneffei and the pks11 knockdown, pks12 knockdown and pks11pks12 double knockdown mutants showed that the yellow pigment is composed of mitorubrinic acid and mitorubrinol. The survival of mice challenged with the pks11 knockdown, pks12 knockdown and pks11pks12 double knockdown mutants was significantly better than those challenged with wild type P. marneffei (P<0.05. There was also statistically significant decrease in survival of pks11 knockdown, pks12 knockdown and pks11pks12 double knockdown mutants compared to wild type P. marneffei in both J774 and THP1 macrophages (P<0.05. CONCLUSIONS/SIGNIFICANCE: The yellow pigment of the mold form of P. marneffei is composed of mitorubrinol and mitorubrinic acid. This represents the first discovery of PKS genes responsible for mitorubrinol and mitorubrinic acid biosynthesis. pks12 and pks11 are probably responsible for sequential use in the biosynthesis of mitorubrinol and mitorubrinic acid

19 CFR 354.10 - Discovery.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 3 2010-04-01 2010-04-01 false Discovery. 354.10 Section 354.10 Customs Duties... ANTIDUMPING OR COUNTERVAILING DUTY ADMINISTRATIVE PROTECTIVE ORDER § 354.10 Discovery. (a) Voluntary discovery. All parties are encouraged to engage in voluntary discovery procedures regarding any matter, not...

36 CFR 1150.63 - Discovery.

Science.gov (United States)

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false Discovery. 1150.63 Section... PRACTICE AND PROCEDURES FOR COMPLIANCE HEARINGS Prehearing Conferences and Discovery § 1150.63 Discovery. (a) Parties are encouraged to engage in voluntary discovery procedures. For good cause shown under...

37 CFR 11.52 - Discovery.

Science.gov (United States)

2010-07-01

... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Discovery. 11.52 Section 11... Disciplinary Proceedings; Jurisdiction, Sanctions, Investigations, and Proceedings § 11.52 Discovery. Discovery... establishes that discovery is reasonable and relevant, the hearing officer, under such conditions as he or she...

Comprehensive Analysis of MILE Gene Expression Data Set Advances Discovery of Leukaemia Type and Subtype Biomarkers.

Science.gov (United States)

Labaj, Wojciech; Papiez, Anna; Polanski, Andrzej; Polanska, Joanna

2017-03-01

Large collections of data in studies on cancer such as leukaemia provoke the necessity of applying tailored analysis algorithms to ensure supreme information extraction. In this work, a custom-fit pipeline is demonstrated for thorough investigation of the voluminous MILE gene expression data set. Three analyses are accomplished, each for gaining a deeper understanding of the processes underlying leukaemia types and subtypes. First, the main disease groups are tested for differential expression against the healthy control as in a standard case-control study. Here, the basic knowledge on molecular mechanisms is confirmed quantitatively and by literature references. Second, pairwise comparison testing is performed for juxtaposing the main leukaemia types among each other. In this case by means of the Dice coefficient similarity measure the general relations are pointed out. Moreover, lists of candidate main leukaemia group biomarkers are proposed. Finally, with this approach being successful, the third analysis provides insight into all of the studied subtypes, followed by the emergence of four leukaemia subtype biomarkers. In addition, the class enhanced DEG signature obtained on the basis of novel pipeline processing leads to significantly better classification power of multi-class data classifiers. The developed methodology consisting of batch effect adjustment, adaptive noise and feature filtration coupled with adequate statistical testing and biomarker definition proves to be an effective approach towards knowledge discovery in high-throughput molecular biology experiments.

Computer-Aided Drug Discovery in Plant Pathology.

Science.gov (United States)

Shanmugam, Gnanendra; Jeon, Junhyun

2017-12-01

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides .

Usability of Discovery Portals

NARCIS (Netherlands)

Bulens, J.D.; Vullings, L.A.E.; Houtkamp, J.M.; Vanmeulebrouk, B.

2013-01-01

As INSPIRE progresses to be implemented in the EU, many new discovery portals are built to facilitate finding spatial data. Currently the structure of the discovery portals is determined by the way spatial data experts like to work. However, we argue that the main target group for discovery portals

A Wavelet-Based Approach to Pattern Discovery in Melodies

DEFF Research Database (Denmark)

Velarde, Gissel; Meredith, David; Weyde, Tillman

2016-01-01

We present a computational method for pattern discovery based on the application of the wavelet transform to symbolic representations of melodies or monophonic voices. We model the importance of a discovered pattern in terms of the compression ratio that can be achieved by using it to describe...

Emerging trends in the discovery of natural product antibacterials

DEFF Research Database (Denmark)

Bologa, Cristian G; Ursu, Oleg; Oprea, Tudor

2013-01-01

This article highlights current trends and advances in exploiting natural sources for the deployment of novel and potent anti-infective countermeasures. The key challenge is to therapeutically target bacterial pathogens that exhibit a variety of puzzling and evolutionarily complex resistance...... mechanisms. Special emphasis is given to the strengths, weaknesses, and opportunities in the natural product antibacterial drug discovery arena, and to emerging applications driven by advances in bioinformatics, chemical biology, and synthetic biology in concert with exploiting bacterial phenotypes....... These efforts have identified a critical mass of natural product antibacterial lead compounds and discovery technologies with high probability of successful implementation against emerging bacterial pathogens....

14 CFR 16.213 - Discovery.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Discovery. 16.213 Section 16.213... PRACTICE FOR FEDERALLY-ASSISTED AIRPORT ENFORCEMENT PROCEEDINGS Hearings § 16.213 Discovery. (a) Discovery... discovery permitted by this section if a party shows that— (1) The information requested is cumulative or...

28 CFR 76.21 - Discovery.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Discovery. 76.21 Section 76.21 Judicial... POSSESSION OF CERTAIN CONTROLLED SUBSTANCES § 76.21 Discovery. (a) Scope. Discovery under this part covers... as a general guide for discovery practices in proceedings before the Judge. However, unless otherwise...

Some applications of Fourier's great discovery for beginners

International Nuclear Information System (INIS)

Kraftmakher, Yaakov

2012-01-01

Nearly two centuries ago, Fourier discovered that any periodic function of period T can be presented as a sum of sine waveforms of frequencies equal to an integer times the fundamental frequency ω = 2π/T (Fourier's series). It is impossible to overestimate the importance of Fourier's discovery, and all physics or engineering students should be familiar with this subject. A suitable device for demonstrating spectra of electrical signals is a digital storage oscilloscope. Spectra of various waveforms and of AM and FM signals are demonstrated, as well as AM signals from a broadcasting station. Changes in the signals filtered by frequency-selective circuits are seen by comparing the spectra of the input and output voltages. All the experiments are suitable for undergraduate laboratories and usable as classroom demonstrations. (paper)

Mining biological databases for candidate disease genes

Science.gov (United States)

Braun, Terry A.; Scheetz, Todd; Webster, Gregg L.; Casavant, Thomas L.

2001-07-01

The publicly-funded effort to sequence the complete nucleotide sequence of the human genome, the Human Genome Project (HGP), has currently produced more than 93% of the 3 billion nucleotides of the human genome into a preliminary `draft' format. In addition, several valuable sources of information have been developed as direct and indirect results of the HGP. These include the sequencing of model organisms (rat, mouse, fly, and others), gene discovery projects (ESTs and full-length), and new technologies such as expression analysis and resources (micro-arrays or gene chips). These resources are invaluable for the researchers identifying the functional genes of the genome that transcribe and translate into the transcriptome and proteome, both of which potentially contain orders of magnitude more complexity than the genome itself. Preliminary analyses of this data identified approximately 30,000 - 40,000 human `genes.' However, the bulk of the effort still remains -- to identify the functional and structural elements contained within the transcriptome and proteome, and to associate function in the transcriptome and proteome to genes. A fortuitous consequence of the HGP is the existence of hundreds of databases containing biological information that may contain relevant data pertaining to the identification of disease-causing genes. The task of mining these databases for information on candidate genes is a commercial application of enormous potential. We are developing a system to acquire and mine data from specific databases to aid our efforts to identify disease genes. A high speed cluster of Linux of workstations is used to analyze sequence and perform distributed sequence alignments as part of our data mining and processing. This system has been used to mine GeneMap99 sequences within specific genomic intervals to identify potential candidate disease genes associated with Bardet-Biedle Syndrome (BBS).

40 CFR 27.21 - Discovery.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 1 2010-07-01 2010-07-01 false Discovery. 27.21 Section 27.21... Discovery. (a) The following types of discovery are authorized: (1) Requests for production of documents for..., discovery is available only as ordered by the presiding officer. The presiding officer shall regulate the...

37 CFR 41.150 - Discovery.

Science.gov (United States)

2010-07-01

... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Discovery. 41.150 Section 41... COMMERCE PRACTICE BEFORE THE BOARD OF PATENT APPEALS AND INTERFERENCES Contested Cases § 41.150 Discovery. (a) Limited discovery. A party is not entitled to discovery except as authorized in this subpart. The...

14 CFR 13.220 - Discovery.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Discovery. 13.220 Section 13.220... INVESTIGATIVE AND ENFORCEMENT PROCEDURES Rules of Practice in FAA Civil Penalty Actions § 13.220 Discovery. (a) Initiation of discovery. Any party may initiate discovery described in this section, without the consent or...

49 CFR 604.38 - Discovery.

Science.gov (United States)

2010-10-01

... 49 Transportation 7 2010-10-01 2010-10-01 false Discovery. 604.38 Section 604.38 Transportation... TRANSPORTATION CHARTER SERVICE Hearings. § 604.38 Discovery. (a) Permissible forms of discovery shall be within the discretion of the PO. (b) The PO shall limit the frequency and extent of discovery permitted by...

15 CFR 719.10 - Discovery.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Discovery. 719.10 Section 719.10... Discovery. (a) General. The parties are encouraged to engage in voluntary discovery regarding any matter... the Federal Rules of Civil Procedure relating to discovery apply to the extent consistent with this...

24 CFR 26.18 - Discovery.

Science.gov (United States)

2010-04-01

... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Discovery. 26.18 Section 26.18... PROCEDURES Hearings Before Hearing Officers Discovery § 26.18 Discovery. (a) General. The parties are encouraged to engage in voluntary discovery procedures, which may commence at any time after an answer has...

42 CFR 426.532 - Discovery.

Science.gov (United States)

2010-10-01

... 42 Public Health 3 2010-10-01 2010-10-01 false Discovery. 426.532 Section 426.532 Public Health... § 426.532 Discovery. (a) General rule. If the Board orders discovery, the Board must establish a reasonable timeframe for discovery. (b) Protective order—(1) Request for a protective order. Any party...

49 CFR 1503.633 - Discovery.

Science.gov (United States)

2010-10-01

... 49 Transportation 9 2010-10-01 2010-10-01 false Discovery. 1503.633 Section 1503.633... Rules of Practice in TSA Civil Penalty Actions § 1503.633 Discovery. (a) Initiation of discovery. Any party may initiate discovery described in this section, without the consent or approval of the ALJ, at...

14 CFR 1264.120 - Discovery.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Discovery. 1264.120 Section 1264.120... PENALTIES ACT OF 1986 § 1264.120 Discovery. (a) The following types of discovery are authorized: (1..., discovery is available only as ordered by the presiding officer. The presiding officer shall regulate the...

22 CFR 128.6 - Discovery.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Discovery. 128.6 Section 128.6 Foreign... Discovery. (a) Discovery by the respondent. The respondent, through the Administrative Law Judge, may... discovery if the interests of national security or foreign policy so require, or if necessary to comply with...

24 CFR 26.42 - Discovery.

Science.gov (United States)

2010-04-01

... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Discovery. 26.42 Section 26.42... PROCEDURES Hearings Pursuant to the Administrative Procedure Act Discovery § 26.42 Discovery. (a) General. The parties are encouraged to engage in voluntary discovery procedures, which may commence at any time...

49 CFR 386.37 - Discovery.

Science.gov (United States)

2010-10-01

... 49 Transportation 5 2010-10-01 2010-10-01 false Discovery. 386.37 Section 386.37 Transportation... and Hearings § 386.37 Discovery. (a) Parties may obtain discovery by one or more of the following...; and requests for admission. (b) Discovery may not commence until the matter is pending before the...

29 CFR 1955.32 - Discovery.

Science.gov (United States)

2010-07-01

... 29 Labor 9 2010-07-01 2010-07-01 false Discovery. 1955.32 Section 1955.32 Labor Regulations...) PROCEDURES FOR WITHDRAWAL OF APPROVAL OF STATE PLANS Preliminary Conference and Discovery § 1955.32 Discovery... allow discovery by any other appropriate procedure, such as by interrogatories upon a party or request...

Organic synthesis provides opportunities to transform drug discovery

Science.gov (United States)

Blakemore, David C.; Castro, Luis; Churcher, Ian; Rees, David C.; Thomas, Andrew W.; Wilson, David M.; Wood, Anthony

2018-03-01

Despite decades of ground-breaking research in academia, organic synthesis is still a rate-limiting factor in drug-discovery projects. Here we present some current challenges in synthetic organic chemistry from the perspective of the pharmaceutical industry and highlight problematic steps that, if overcome, would find extensive application in the discovery of transformational medicines. Significant synthesis challenges arise from the fact that drug molecules typically contain amines and N-heterocycles, as well as unprotected polar groups. There is also a need for new reactions that enable non-traditional disconnections, more C-H bond activation and late-stage functionalization, as well as stereoselectively substituted aliphatic heterocyclic ring synthesis, C-X or C-C bond formation. We also emphasize that syntheses compatible with biomacromolecules will find increasing use, while new technologies such as machine-assisted approaches and artificial intelligence for synthesis planning have the potential to dramatically accelerate the drug-discovery process. We believe that increasing collaboration between academic and industrial chemists is crucial to address the challenges outlined here.

Preclinical experimental models of drug metabolism and disposition in drug discovery and development

Directory of Open Access Journals (Sweden)

Donglu Zhang

2012-12-01

Full Text Available Drug discovery and development involve the utilization of in vitro and in vivo experimental models. Different models, ranging from test tube experiments to cell cultures, animals, healthy human subjects, and even small numbers of patients that are involved in clinical trials, are used at different stages of drug discovery and development for determination of efficacy and safety. The proper selection and applications of correct models, as well as appropriate data interpretation, are critically important in decision making and successful advancement of drug candidates. In this review, we discuss strategies in the applications of both in vitro and in vivo experimental models of drug metabolism and disposition.

An integration of genome-wide association study and gene expression profiling to prioritize the discovery of novel susceptibility Loci for osteoporosis-related traits.

Directory of Open Access Journals (Sweden)

Yi-Hsiang Hsu

2010-06-01

Full Text Available Osteoporosis is a complex disorder and commonly leads to fractures in elderly persons. Genome-wide association studies (GWAS have become an unbiased approach to identify variations in the genome that potentially affect health. However, the genetic variants identified so far only explain a small proportion of the heritability for complex traits. Due to the modest genetic effect size and inadequate power, true association signals may not be revealed based on a stringent genome-wide significance threshold. Here, we take advantage of SNP and transcript arrays and integrate GWAS and expression signature profiling relevant to the skeletal system in cellular and animal models to prioritize the discovery of novel candidate genes for osteoporosis-related traits, including bone mineral density (BMD at the lumbar spine (LS and femoral neck (FN, as well as geometric indices of the hip (femoral neck-shaft angle, NSA; femoral neck length, NL; and narrow-neck width, NW. A two-stage meta-analysis of GWAS from 7,633 Caucasian women and 3,657 men, revealed three novel loci associated with osteoporosis-related traits, including chromosome 1p13.2 (RAP1A, p = 3.6x10(-8, 2q11.2 (TBC1D8, and 18q11.2 (OSBPL1A, and confirmed a previously reported region near TNFRSF11B/OPG gene. We also prioritized 16 suggestive genome-wide significant candidate genes based on their potential involvement in skeletal metabolism. Among them, 3 candidate genes were associated with BMD in women. Notably, 2 out of these 3 genes (GPR177, p = 2.6x10(-13; SOX6, p = 6.4x10(-10 associated with BMD in women have been successfully replicated in a large-scale meta-analysis of BMD, but none of the non-prioritized candidates (associated with BMD did. Our results support the concept of our prioritization strategy. In the absence of direct biological support for identified genes, we highlighted the efficiency of subsequent functional characterization using publicly available expression profiling relevant

In silico pharmacology for a multidisciplinary drug discovery process.

Science.gov (United States)

Ortega, Santiago Schiaffino; Cara, Luisa Carlota López; Salvador, María Kimatrai

2012-01-01

The process of bringing new and innovative drugs, from conception and synthesis through to approval on the market can take the pharmaceutical industry 8-15 years and cost approximately $1.8 billion. Two key technologies are improving the hit-to-drug timeline: high-throughput screening (HTS) and rational drug design. In the latter case, starting from some known ligand-based or target-based information, a lead structure will be rationally designed to be tested in vitro or in vivo. Computational methods are part of many drug discovery programs, including the assessment of ADME (absorption-distribution-metabolism-excretion) and toxicity (ADMET) properties of compounds at the early stages of discovery/development with impressive results. The aim of this paper is to review, in a simple way, some of the most popular strategies used by modelers and some successful applications on computational chemistry to raise awareness of its importance and potential for an actual multidisciplinary drug discovery process.

42 CFR 426.432 - Discovery.

Science.gov (United States)

2010-10-01

... 42 Public Health 3 2010-10-01 2010-10-01 false Discovery. 426.432 Section 426.432 Public Health... § 426.432 Discovery. (a) General rule. If the ALJ orders discovery, the ALJ must establish a reasonable timeframe for discovery. (b) Protective order—(1) Request for a protective order. Any party receiving a...

10 CFR 13.21 - Discovery.

Science.gov (United States)

2010-01-01

... 10 Energy 1 2010-01-01 2010-01-01 false Discovery. 13.21 Section 13.21 Energy NUCLEAR REGULATORY COMMISSION PROGRAM FRAUD CIVIL REMEDIES § 13.21 Discovery. (a) The following types of discovery are...) Unless mutually agreed to by the parties, discovery is available only as ordered by the ALJ. The ALJ...

49 CFR 1121.2 - Discovery.

Science.gov (United States)

2010-10-01

... 49 Transportation 8 2010-10-01 2010-10-01 false Discovery. 1121.2 Section 1121.2 Transportation... TRANSPORTATION RULES OF PRACTICE RAIL EXEMPTION PROCEDURES § 1121.2 Discovery. Discovery shall follow the procedures set forth at 49 CFR part 1114, subpart B. Discovery may begin upon the filing of the petition for...

38 CFR 42.21 - Discovery.

Science.gov (United States)

2010-07-01

... 38 Pensions, Bonuses, and Veterans' Relief 2 2010-07-01 2010-07-01 false Discovery. 42.21 Section... IMPLEMENTING THE PROGRAM FRAUD CIVIL REMEDIES ACT § 42.21 Discovery. (a) The following types of discovery are... creation of a document. (c) Unless mutually agreed to by the parties, discovery is available only as...

22 CFR 521.21 - Discovery.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 2 2010-04-01 2010-04-01 true Discovery. 521.21 Section 521.21 Foreign... Discovery. (a) The following types of discovery are authorized: (1) Requests for production of documents for... interpreted to require the creation of a document. (c) Unless mutually agreed to by the parties, discovery is...

39 CFR 955.15 - Discovery.

Science.gov (United States)

2010-07-01

... 39 Postal Service 1 2010-07-01 2010-07-01 false Discovery. 955.15 Section 955.15 Postal Service... APPEALS § 955.15 Discovery. (a) The parties are encouraged to engage in voluntary discovery procedures. In connection with any deposition or other discovery procedure, the Board may issue any order which justice...

43 CFR 35.21 - Discovery.

Science.gov (United States)

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Discovery. 35.21 Section 35.21 Public... AND STATEMENTS § 35.21 Discovery. (a) The following types of discovery are authorized: (1) Requests...) Unless mutually agreed to by the parties, discovery is available only as ordered by the ALJ. The ALJ...

15 CFR 766.9 - Discovery.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Discovery. 766.9 Section 766.9... PROCEEDINGS § 766.9 Discovery. (a) General. The parties are encouraged to engage in voluntary discovery... provisions of the Federal Rules of Civil Procedure relating to discovery apply to the extent consistent with...

Discovery and Development of ATP-Competitive mTOR Inhibitors Using Computational Approaches.

Science.gov (United States)

Luo, Yao; Wang, Ling

2017-11-16

The mammalian target of rapamycin (mTOR) is a central controller of cell growth, proliferation, metabolism, and angiogenesis. This protein is an attractive target for new anticancer drug development. Significant progress has been made in hit discovery, lead optimization, drug candidate development and determination of the three-dimensional (3D) structure of mTOR. Computational methods have been applied to accelerate the discovery and development of mTOR inhibitors helping to model the structure of mTOR, screen compound databases, uncover structure-activity relationship (SAR) and optimize the hits, mine the privileged fragments and design focused libraries. Besides, computational approaches were also applied to study protein-ligand interactions mechanisms and in natural product-driven drug discovery. Herein, we survey the most recent progress on the application of computational approaches to advance the discovery and development of compounds targeting mTOR. Future directions in the discovery of new mTOR inhibitors using computational methods are also discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Blueprint for antimicrobial hit discovery targeting metabolic networks.

Science.gov (United States)

Shen, Y; Liu, J; Estiu, G; Isin, B; Ahn, Y-Y; Lee, D-S; Barabási, A-L; Kapatral, V; Wiest, O; Oltvai, Z N

2010-01-19

Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy.

Get Involved in Planetary Discoveries through New Worlds, New Discoveries

Science.gov (United States)

Shupla, Christine; Shipp, S. S.; Halligan, E.; Dalton, H.; Boonstra, D.; Buxner, S.; SMD Planetary Forum, NASA

2013-01-01

"New Worlds, New Discoveries" is a synthesis of NASA’s 50-year exploration history which provides an integrated picture of our new understanding of our solar system. As NASA spacecraft head to and arrive at key locations in our solar system, "New Worlds, New Discoveries" provides an integrated picture of our new understanding of the solar system to educators and the general public! The site combines the amazing discoveries of past NASA planetary missions with the most recent findings of ongoing missions, and connects them to the related planetary science topics. "New Worlds, New Discoveries," which includes the "Year of the Solar System" and the ongoing celebration of the "50 Years of Exploration," includes 20 topics that share thematic solar system educational resources and activities, tied to the national science standards. This online site and ongoing event offers numerous opportunities for the science community - including researchers and education and public outreach professionals - to raise awareness, build excitement, and make connections with educators, students, and the public about planetary science. Visitors to the site will find valuable hands-on science activities, resources and educational materials, as well as the latest news, to engage audiences in planetary science topics and their related mission discoveries. The topics are tied to the big questions of planetary science: how did the Sun’s family of planets and bodies originate and how have they evolved? How did life begin and evolve on Earth, and has it evolved elsewhere in our solar system? Scientists and educators are encouraged to get involved either directly or by sharing "New Worlds, New Discoveries" and its resources with educators, by conducting presentations and events, sharing their resources and events to add to the site, and adding their own public events to the site’s event calendar! Visit to find quality resources and ideas. Connect with educators, students and the public to

13 CFR 134.213 - Discovery.

Science.gov (United States)

2010-01-01

... 13 Business Credit and Assistance 1 2010-01-01 2010-01-01 false Discovery. 134.213 Section 134.213... OFFICE OF HEARINGS AND APPEALS Rules of Practice for Most Cases § 134.213 Discovery. (a) Motion. A party may obtain discovery only upon motion, and for good cause shown. (b) Forms. The forms of discovery...

31 CFR 16.21 - Discovery.

Science.gov (United States)

2010-07-01

... 31 Money and Finance: Treasury 1 2010-07-01 2010-07-01 false Discovery. 16.21 Section 16.21 Money... FRAUD CIVIL REMEDIES ACT OF 1986 § 16.21 Discovery. (a) The following types of discovery are authorized... to require the creation of a document. (c) Unless mutually agreed to by the parties, discovery is...

Identifying candidate driver genes by integrative ovarian cancer genomics data

Science.gov (United States)

Lu, Xinguo; Lu, Jibo

2017-08-01

Integrative analysis of molecular mechanics underlying cancer can distinguish interactions that cannot be revealed based on one kind of data for the appropriate diagnosis and treatment of cancer patients. Tumor samples exhibit heterogeneity in omics data, such as somatic mutations, Copy Number Variations CNVs), gene expression profiles and so on. In this paper we combined gene co-expression modules and mutation modulators separately in tumor patients to obtain the candidate driver genes for resistant and sensitive tumor from the heterogeneous data. The final list of modulators identified are well known in biological processes associated with ovarian cancer, such as CCL17, CACTIN, CCL16, CCL22, APOB, KDF1, CCL11, HNF1B, LRG1, MED1 and so on, which can help to facilitate the discovery of biomarkers, molecular diagnostics, and drug discovery.

Knowledge discovery from data streams

CERN Document Server

Gama, Joao

2010-01-01

Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams.The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks,

Discovery and annotation of small proteins using genomics, proteomics and computational approaches

Energy Technology Data Exchange (ETDEWEB)

Yang, Xiaohan; Tschaplinski, Timothy J.; Hurst, Gregory B.; Jawdy, Sara; Abraham, Paul E.; Lankford, Patricia K.; Adams, Rachel M.; Shah, Manesh B.; Hettich, Robert L.; Lindquist, Erika; Kalluri, Udaya C.; Gunter, Lee E.; Pennacchio, Christa; Tuskan, Gerald A.

2011-03-02

Small proteins (10 200 amino acids aa in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained 2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10 200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) codingpotential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.

Analysis of non-TIR NBS-LRR resistance gene analogs in Musa acuminata Colla: Isolation, RFLP marker development, and physical mapping

Directory of Open Access Journals (Sweden)

Souza Manoel T

2008-01-01

deposited in GenBank and assigned numbers ER935972 – ER936023. RGA sequences and isolated BACs are a valuable resource for R-gene discovery, and in future applications will provide insight into the organization and evolution of NBS-LRR R-genes in the Musa A and B genome. The developed RFLP-RGA markers are applicable for genetic map development and marker assisted selection for defined traits such as pest and disease resistance.

A hundred years later. Radioactivity, the influence of a discovery

International Nuclear Information System (INIS)

Bimbot, R.; Charpak, G.; Tubiana, M.

1999-01-01

This document brings together short but significant citations about radioactivity and its overall applications. Illustrated with historical and ancient or actual documents and photographies, these texts were written by scientists, doctors, politicians and journalists, know from the public or from the scientific community for their implication in this domain. It comprises also an evocation of the celebration of the centenary of the discovery of radioactivity and was written as a conclusion of this event. It represents a unique document, with a substantial bibliography, which summarizes a century of discoveries and considerations about this topic with multiple parts. (J.S.)

Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate

DEFF Research Database (Denmark)

Andreassen, Ole A; Thompson, Wesley K; Schork, Andrew J

2013-01-01

are currently lacking. Here, we use a genetic pleiotropy-informed conditional false discovery rate (FDR) method on GWAS summary statistics data to identify new loci associated with schizophrenia (SCZ) and bipolar disorders (BD), two highly heritable disorders with significant missing heritability...... associated with both SCZ and BD (conjunction FDR). Together, these findings show the feasibility of genetic pleiotropy-informed methods to improve gene discovery in SCZ and BD and indicate overlapping genetic mechanisms between these two disorders....

Unlocking the treasure trove: from genes to schizophrenia biology.

Science.gov (United States)

McCarthy, Shane E; McCombie, W Richard; Corvin, Aiden

2014-05-01

Significant progress is being made in defining the genetic etiology of schizophrenia. As the list of implicated genes grows, parallel developments in gene editing technology provide new methods to investigate gene function in model systems. The confluence of these two research fields--gene discovery and functional biology--may offer novel insights into schizophrenia etiology. We review recent advances in these fields, consider the likely obstacles to progress, and consider strategies as to how these can be overcome.

Antioxidant response elements: Discovery, classes, regulation and potential applications

Directory of Open Access Journals (Sweden)

Azhwar Raghunath

2018-07-01

Full Text Available Exposure to antioxidants and xenobiotics triggers the expression of a myriad of genes encoding antioxidant proteins, detoxifying enzymes, and xenobiotic transporters to offer protection against oxidative stress. This articulated universal mechanism is regulated through the cis-acting elements in an array of Nrf2 target genes called antioxidant response elements (AREs, which play a critical role in redox homeostasis. Though the Keap1/Nrf2/ARE system involves many players, AREs hold the key in transcriptional regulation of cytoprotective genes. ARE-mediated reporter constructs have been widely used, including xenobiotics profiling and Nrf2 activator screening. The complexity of AREs is brought by the presence of other regulatory elements within the AREs. The diversity in the ARE sequences not only bring regulatory selectivity of diverse transcription factors, but also confer functional complexity in the Keap1/Nrf2/ARE pathway. The different transcription factors either homodimerize or heterodimerize to bind the AREs. Depending on the nature of partners, they may activate or suppress the transcription. Attention is required for deeper mechanistic understanding of ARE-mediated gene regulation. The computational methods of identification and analysis of AREs are still in their infancy. Investigations are required to know whether epigenetics mechanism plays a role in the regulation of genes mediated through AREs. The polymorphisms in the AREs leading to oxidative stress related diseases are warranted. A thorough understanding of AREs will pave the way for the development of therapeutic agents against cancer, neurodegenerative, cardiovascular, metabolic and other diseases with oxidative stress. Keywords: Antioxidant response elements, Antioxidant genes, ARE-reporter constructs, ARE SNPs, Keap1/Nrf2/ARE pathway, Oxidative stress

Next-Generation DNA Sequencing of VH/VL Repertoires: A Primer and Guide to Applications in Single-Domain Antibody Discovery.

Science.gov (United States)

Henry, Kevin A

2018-01-01

Immunogenetic analyses of expressed antibody repertoires are becoming increasingly common experimental investigations and are critical to furthering our understanding of autoimmunity, infectious disease, and cancer. Next-generation DNA sequencing (NGS) technologies have now made it possible to interrogate antibody repertoires to unprecedented depths, typically by sequencing of cDNAs encoding immunoglobulin variable domains. In this chapter, we describe simple, fast, and reliable methods for producing and sequencing multiplex PCR amplicons derived from the variable regions (V H , V H H or V L ) of rearranged immunoglobulin heavy and light chain genes using the Illumina MiSeq platform. We include complete protocols and primer sets for amplicon sequencing of V H /V H H/V L repertoires directly from human, mouse, and llama lymphocytes as well as from phage-displayed V H /V H H/V L libraries; these can be easily be adapted to other types of amplicons with little modification. The resulting amplicons are diverse and representative, even using as few as 10 3 input B cells, and their generation is relatively inexpensive, requiring no special equipment and only a limited set of primers. In the absence of heavy-light chain pairing, single-domain antibodies are uniquely amenable to NGS analyses. We present a number of applications of NGS technology useful in discovery of single-domain antibodies from phage display libraries, including: (i) assessment of library functionality; (ii) confirmation of desired library randomization; (iii) estimation of library diversity; and (iv) monitoring the progress of panning experiments. While the case studies presented here are of phage-displayed single-domain antibody libraries, the principles extend to other types of in vitro display libraries.

New Generation Discovery: A Systematic View for Its Development, Issues and Future

KAUST Repository

Yu, Yi

2012-11-01

Collecting, storing, discovering, and locating are integral parts of the composition of the library. To fully utilize the library and achieve its ultimate value, the construction and production of discovery has always been a central part of the library’s practice and identity. That is the reason why the new generation (also called the next-generation discovery) discovery gets such striking effect since it came into library automation arena. However, when we talk about the new generation of discovery in the library domain, we should see it in the entirety of the library as one of its organic parts and consider its progress along with the evolution of the whole library world. We should have a deeper understanding about its relationship and interaction with the internet, the rapidly changing digital environment, and the elements and the chain of library services. To address above issues, this paper overviews the different versions of the definition for the new generation discovery by combining our own understanding. The paper also gives our own description for its properties and characteristics. The paper points out what challenges, which extends the technology domain to commercial interests and business strategy, are faced by the discovery applications, and how library and library professionals deal with those challenges. Finally, the paper elaborates on the promise brought by the new discovery development and what the next exploration might be for its future.

A Population of Deletion Mutants and an Integrated Mapping and Exome-seq Pipeline for Gene Discovery in Maize

Science.gov (United States)

Jia, Shangang; Li, Aixia; Morton, Kyla; Avoles-Kianian, Penny; Kianian, Shahryar F.; Zhang, Chi; Holding, David

2016-01-01

To better understand maize endosperm filling and maturation, we used γ-irradiation of the B73 maize reference line to generate mutants with opaque endosperm and reduced kernel fill phenotypes, and created a population of 1788 lines including 39 Mo17 × F2s showing stable, segregating, and viable kernel phenotypes. For molecular characterization of the mutants, we developed a novel functional genomics platform that combined bulked segregant RNA and exome sequencing (BSREx-seq) to map causative mutations and identify candidate genes within mapping intervals. To exemplify the utility of the mutants and provide proof-of-concept for the bioinformatics platform, we present detailed characterization of line 937, an opaque mutant harboring a 6203 bp in-frame deletion covering six exons within the Opaque-1 gene. In addition, we describe mutant line 146 which contains a 4.8 kb intragene deletion within the Sugary-1 gene and line 916 in which an 8.6 kb deletion knocks out a Cyclin A2 gene. The publically available algorithm developed in this work improves the identification of causative deletions and its corresponding gaps within mapping peaks. This study demonstrates the utility of γ-irradiation for forward genetics in large nondense genomes such as maize since deletions often affect single genes. Furthermore, we show how this classical mutagenesis method becomes applicable for functional genomics when combined with state-of-the-art genomics tools. PMID:27261000

Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

Science.gov (United States)

Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object

Data Science and Optimal Learning for Material Discovery and Design

Science.gov (United States)

; Optimal Learning for Material Discovery & Design Data Science and Optimal Learning for Material inference and optimization methods that can constrain predictions using insights and results from theory directions in the application of information theoretic tools to materials problems related to learning from

Discovery and Characterization of Two Novel Salt-Tolerance Genes in Puccinellia tenuiflora

Directory of Open Access Journals (Sweden)

Ying Li

2014-09-01

Full Text Available Puccinellia tenuiflora is a monocotyledonous halophyte that is able to survive in extreme saline soil environments at an alkaline pH range of 9–10. In this study, we transformed full-length cDNAs of P. tenuiflora into Saccharomyces cerevisiae by using the full-length cDNA over-expressing gene-hunting system to identify novel salt-tolerance genes. In all, 32 yeast clones overexpressing P. tenuiflora cDNA were obtained by screening under NaCl stress conditions; of these, 31 clones showed stronger tolerance to NaCl and were amplified using polymerase chain reaction (PCR and sequenced. Four novel genes encoding proteins with unknown function were identified; these genes had no homology with genes from higher plants. Of the four isolated genes, two that encoded proteins with two transmembrane domains showed the strongest resistance to 1.3 M NaCl. RT-PCR and northern blot analysis of P. tenuiflora cultured cells confirmed the endogenous NaCl-induced expression of the two proteins. Both of the proteins conferred better tolerance in yeasts to high salt, alkaline and osmotic conditions, some heavy metals and H2O2 stress. Thus, we inferred that the two novel proteins might alleviate oxidative and other stresses in P. tenuiflora.

Computational discovery of picomolar Q(o) site inhibitors of cytochrome bc1 complex.

Science.gov (United States)

Hao, Ge-Fei; Wang, Fu; Li, Hui; Zhu, Xiao-Lei; Yang, Wen-Chao; Huang, Li-Shar; Wu, Jia-Wei; Berry, Edward A; Yang, Guang-Fu

2012-07-11

A critical challenge to the fragment-based drug discovery (FBDD) is its low-throughput nature due to the necessity of biophysical method-based fragment screening. Herein, a method of pharmacophore-linked fragment virtual screening (PFVS) was successfully developed. Its application yielded the first picomolar-range Q(o) site inhibitors of the cytochrome bc(1) complex, an important membrane protein for drug and fungicide discovery. Compared with the original hit compound 4 (K(i) = 881.80 nM, porcine bc(1)), the most potent compound 4f displayed 20 507-fold improved binding affinity (K(i) = 43.00 pM). Compound 4f was proved to be a noncompetitive inhibitor with respect to the substrate cytochrome c, but a competitive inhibitor with respect to the substrate ubiquinol. Additionally, we determined the crystal structure of compound 4e (K(i) = 83.00 pM) bound to the chicken bc(1) at 2.70 Å resolution, providing a molecular basis for understanding its ultrapotency. To our knowledge, this study is the first application of the FBDD method in the discovery of picomolar inhibitors of a membrane protein. This work demonstrates that the novel PFVS approach is a high-throughput drug discovery method, independent of biophysical screening techniques.

Target genes discovery through copy number alteration analysis in human hepatocellular carcinoma.

Science.gov (United States)

Gu, De-Leung; Chen, Yen-Hsieh; Shih, Jou-Ho; Lin, Chi-Hung; Jou, Yuh-Shan; Chen, Chian-Feng

2013-12-21

High-throughput short-read sequencing of exomes and whole cancer genomes in multiple human hepatocellular carcinoma (HCC) cohorts confirmed previously identified frequently mutated somatic genes, such as TP53, CTNNB1 and AXIN1, and identified several novel genes with moderate mutation frequencies, including ARID1A, ARID2, MLL, MLL2, MLL3, MLL4, IRF2, ATM, CDKN2A, FGF19, PIK3CA, RPS6KA3, JAK1, KEAP1, NFE2L2, C16orf62, LEPR, RAC2, and IL6ST. Functional classification of these mutated genes suggested that alterations in pathways participating in chromatin remodeling, Wnt/β-catenin signaling, JAK/STAT signaling, and oxidative stress play critical roles in HCC tumorigenesis. Nevertheless, because there are few druggable genes used in HCC therapy, the identification of new therapeutic targets through integrated genomic approaches remains an important task. Because a large amount of HCC genomic data genotyped by high density single nucleotide polymorphism arrays is deposited in the public domain, copy number alteration (CNA) analyses of these arrays is a cost-effective way to reveal target genes through profiling of recurrent and overlapping amplicons, homozygous deletions and potentially unbalanced chromosomal translocations accumulated during HCC progression. Moreover, integration of CNAs with other high-throughput genomic data, such as aberrantly coding transcriptomes and non-coding gene expression in human HCC tissues and rodent HCC models, provides lines of evidence that can be used to facilitate the identification of novel HCC target genes with the potential of improving the survival of HCC patients.

The first set of EST resource for gene discovery and marker development in pigeonpea (Cajanus cajan L.

Directory of Open Access Journals (Sweden)

Byregowda Munishamappa

2010-03-01

.8% in molecular function. Further, 19 genes were identified differentially expressed between FW- responsive genotypes and 20 between SMD- responsive genotypes. Generated ESTs were compiled together with 908 ESTs available in public domain, at the time of analysis, and a set of 5,085 unigenes were defined that were used for identification of molecular markers in pigeonpea. For instance, 3,583 simple sequence repeat (SSR motifs were identified in 1,365 unigenes and 383 primer pairs were designed. Assessment of a set of 84 primer pairs on 40 elite pigeonpea lines showed polymorphism with 15 (28.8% markers with an average of four alleles per marker and an average polymorphic information content (PIC value of 0.40. Similarly, in silico mining of 133 contigs with ≥ 5 sequences detected 102 single nucleotide polymorphisms (SNPs in 37 contigs. As an example, a set of 10 contigs were used for confirming in silico predicted SNPs in a set of four genotypes using wet lab experiments. Occurrence of SNPs were confirmed for all the 6 contigs for which scorable and sequenceable amplicons were generated. PCR amplicons were not obtained in case of 4 contigs. Recognition sites for restriction enzymes were identified for 102 SNPs in 37 contigs that indicates possibility of assaying SNPs in 37 genes using cleaved amplified polymorphic sequences (CAPS assay. Conclusion The pigeonpea EST dataset generated here provides a transcriptomic resource for gene discovery and development of functional markers associated with biotic stress resistance. Sequence analyses of this dataset have showed conservation of a considerable number of pigeonpea transcripts across legume and model plant species analysed as well as some putative pigeonpea specific genes. Validation of identified biotic stress responsive genes should provide candidate genes for allele mining as well as candidate markers for molecular breeding.

Single-Feature Polymorphism Discovery in the Transcriptome of Tetraploid Alfalfa

Directory of Open Access Journals (Sweden)

S. Samuel Yang

2009-11-01

Full Text Available Advances in alfalfa [ (L. subsp. ] breeding, molecular genetics, and genomics have been slow because this crop is an allogamous autotetraploid (2n = 4x = 32 with complex polysomic inheritance and few genomic resources. Increasing cellulose and decreasing lignin in alfalfa stem cell walls would improve this crop as a cellulosic ethanol feedstock. We conducted genome-wide analysis of single-feature polymorphisms (SFPs of two alfalfa genotypes (252, 1283 that differ in stem cell wall lignin and cellulose concentrations. SFP analysis was conducted using the GeneChip (Affymetrix, Santa Clara, CA as a cross-species platform. Analysis of GeneChip expression data files of alfalfa stem internodes of genotypes 252 and 1283 at two growth stages (elongating, post-elongation revealed 10,890 SFPs in 8230 probe sets. Validation analysis by polymerase chain reaction (PCR-sequencing of a random sample of SFPs indicated a 17% false discovery rate. Functional classification and over-representation analysis showed that genes involved in photosynthesis, stress response and cell wall biosynthesis were highly enriched among SFP-harboring genes. The GeneChip is a suitable cross-species platform for detecting SFPs in tetraploid alfalfa.

Decades of Discovery

Science.gov (United States)

2011-06-01

For the past two-and-a-half decades, the Office of Science at the U.S. Department of Energy has been at the forefront of scientific discovery. Over 100 important discoveries supported by the Office of Science are represented in this document.

Computational design and application of endogenous promoters for transcriptionally targeted gene therapy for rheumatoid arthritis.

NARCIS (Netherlands)

Geurts, J.; Joosten, L.A.B.; Takahashi, N.; Arntz, O.J.; Gluck, A.; Bennink, M.B.; Berg, W.B. van den; Loo, F.A.J. van de

2009-01-01

The promoter regions of genes that are differentially regulated in the synovial membrane during the course of rheumatoid arthritis (RA) represent attractive candidates for application in transcriptionally targeted gene therapy. In this study, we applied an unbiased computational approach to define

Visualization of gene expression in the live subject using the Na/I symporter as a reporter gene: applications in biotherapy.

Science.gov (United States)

Baril, Patrick; Martin-Duque, Pilar; Vassaux, Georges

2010-02-01

Biotherapies involve the utilization of antibodies, genetically modified viruses, bacteria or cells for therapeutic purposes. Molecular imaging has the potential to provide unique information that will guarantee their biosafety in humans and provide a rationale for the future development of new generations of reagents. In this context, non-invasive imaging of gene expression is an attractive prospect, allowing precise, spacio-temporal measurements of gene expression in longitudinal studies involving gene transfer vectors. With the emergence of cell therapies in regenerative medicine, it is also possible to track cells injected into subjects. In this context, the Na/I symporter (NIS) has been used in preclinical studies. Associated with a relevant radiotracer ((123)I(-), (124)I(-), (99m)TcO4(-)), NIS can be used to monitor gene transfer and the spread of selectively replicative viruses in tumours as well as in cells with a therapeutic potential. In addition to its imaging potential, NIS can be used as a therapeutic transgene through its ability to concentrate therapeutic doses of radionuclides in target cells. This dual property has applications in cancer treatment and could also be used to eradicate cells with therapeutic potential in the case of adverse events. Through experience acquired in preclinical studies, we can expect that non-invasive molecular imaging using NIS as a transgene will be pivotal for monitoring in vivo the exact distribution and pharmacodynamics of gene expression in a precise and quantitative way. This review highlights the applications of NIS in biotherapy, with a particular emphasis on image-guided radiotherapy, monitoring of gene and vector biodistribution and trafficking of stem cells.

Detecting Role Errors in the Gene Hierarchy of the NCI Thesaurus

Directory of Open Access Journals (Sweden)

Yehoshua Perl

2008-01-01

Full Text Available Gene terminologies are playing an increasingly important role in the ever-growing field of genomic research. While errors in large, complex terminologies are inevitable, gene terminologies are even more susceptible to them due to the rapid growth of genomic knowledge and the nature of its discovery. It is therefore very important to establish quality- assurance protocols for such genomic-knowledge repositories. Different kinds of terminologies oftentimes require auditing methodologies adapted to their particular structures. In light of this, an auditing methodology tailored to the characteristics of the NCI Thesaurus’s (NCIT’s Gene hierarchy is presented. The Gene hierarchy is of particular interest to the NCIT’s designers due to the primary role of genomics in current cancer research. This multiphase methodology focuses on detecting role-errors, such as missing roles or roles with incorrect or incomplete target structures, occurring within that hierarchy. The methodology is based on two kinds of abstraction networks, called taxonomies, that highlight the role distribution among concepts within the IS-A (subsumption hierarchy. These abstract views tend to highlight portions of the hierarchy having a higher concentration of errors. The errors found during an application of the methodology

Identifying potential maternal genes of Bombyx mori using digital gene expression profiling

Science.gov (United States)

Xu, Pingzhen

2018-01-01

Maternal genes present in mature oocytes play a crucial role in the early development of silkworm. Although maternal genes have been widely studied in many other species, there has been limited research in Bombyx mori. High-throughput next generation sequencing provides a practical method for gene discovery on a genome-wide level. Herein, a transcriptome study was used to identify maternal-related genes from silkworm eggs. Unfertilized eggs from five different stages of early development were used to detect the changing situation of gene expression. The expressed genes showed different patterns over time. Seventy-six maternal genes were annotated according to homology analysis with Drosophila melanogaster. More than half of the differentially expressed maternal genes fell into four expression patterns, while the expression patterns showed a downward trend over time. The functional annotation of these material genes was mainly related to transcription factor activity, growth factor activity, nucleic acid binding, RNA binding, ATP binding, and ion binding. Additionally, twenty-two gene clusters including maternal genes were identified from 18 scaffolds. Altogether, we plotted a profile for the maternal genes of Bombyx mori using a digital gene expression profiling method. This will provide the basis for maternal-specific signature research and improve the understanding of the early development of silkworm. PMID:29462160

Discovery and the atom

International Nuclear Information System (INIS)

1989-01-01

''Discovery and the Atom'' tells the story of the founding of nuclear physics. This programme looks at nuclear physics up to the discovery of the neutron in 1932. Animation explains the science of the classic experiments, such as the scattering of alpha particles by Rutherford and the discovery of the nucleus. Archive film shows the people: Lord Rutherford, James Chadwick, Marie Curie. (author)

Scientific Knowledge Discovery in Complex Semantic Networks of Geophysical Systems

Science.gov (United States)

Fox, P.

2012-04-01

The vast majority of explorations of the Earth's systems are limited in their ability to effectively explore the most important (often most difficult) problems because they are forced to interconnect at the data-element, or syntactic, level rather than at a higher scientific, or semantic, level. Recent successes in the application of complex network theory and algorithms to climate data, raise expectations that more general graph-based approaches offer the opportunity for new discoveries. In the past ~ 5 years in the natural sciences there has substantial progress in providing both specialists and non-specialists the ability to describe in machine readable form, geophysical quantities and relations among them in meaningful and natural ways, effectively breaking the prior syntax barrier. The corresponding open-world semantics and reasoning provide higher-level interconnections. That is, semantics provided around the data structures, using semantically-equipped tools, and semantically aware interfaces between science application components allowing for discovery at the knowledge level. More recently, formal semantic approaches to continuous and aggregate physical processes are beginning to show promise and are soon likely to be ready to apply to geoscientific systems. To illustrate these opportunities, this presentation presents two application examples featuring domain vocabulary (ontology) and property relations (named and typed edges in the graphs). First, a climate knowledge discovery pilot encoding and exploration of CMIP5 catalog information with the eventual goal to encode and explore CMIP5 data. Second, a multi-stakeholder knowledge network for integrated assessments in marine ecosystems, where the data is highly inter-disciplinary.

Biotechnological Applications of the Roseobacter Clade

DEFF Research Database (Denmark)

Bentzon-Tilia, Mikkel; Gram, Lone

2017-01-01

spectrum of Gram-positive and Gram-negative bacteria in which resistance towards the compound does not arise easily. Mining the genomes of roseobacters also reveal that they are likely capable of producing other compounds than hitherto discovered by classical bio-assay guided fractionation, since...... the genomes contain genes/gene clusters probably encoding unknown bioactive secondary metabolites. Therefore, bacteria of the Roseobacter clade may serve as potential sources of novel bioactive compounds, including novel antibiotics, which is of paramount importance in the battle against antibiotic resistant...... pathogenic bacteria. The discovery of new antibiotic compounds is not the only means by which we can counter the spread of antibiotic resistance. Development of sustainable alternatives to the application of antibiotics in agri- and aquaculture may be equally important. Attributable to their inherent...

Superconducting hot-electron bolometer: from the discovery of hot-electron phenomena to practical applications

International Nuclear Information System (INIS)

Shurakov, A; Lobanov, Y; Goltsman, G

2016-01-01

The discovery of hot-electron phenomena in a thin superconducting film in the last century was followed by numerous experimental studies of its appearance in different materials aiming for a better understanding of the phenomena and consequent implementation of terahertz detection systems for practical applications. In contrast to the competitors such as superconductor-insulator-superconductor tunnel junctions and Schottky diodes, the hot electron bolometer (HEB) did not demonstrate any frequency limitation of the detection mechanism. The latter, in conjunction with a decent performance, rapidly made the HEB mixer the most attractive candidate for heterodyne observations at frequencies above 1 THz. The successful operation of practical instruments (the Heinrich Hertz Telescope, the Receiver Lab Telescope, APEX, SOFIA, Hershel) ensures the importance of the HEB technology despite the lack of rigorous theoretical routine for predicting the performance. In this review, we provide a summary of experimental and theoretical studies devoted to understanding the HEB physics, and an overview of various fabrication routes and materials. (topical review)

Gene Module Identification from Microarray Data Using Nonnegative Independent Component Analysis

Directory of Open Access Journals (Sweden)

Ting Gong

2007-01-01

Full Text Available Genes mostly interact with each other to form transcriptional modules for performing single or multiple functions. It is important to unravel such transcriptional modules and to determine how disturbances in them may lead to disease. Here, we propose a non-negative independent component analysis (nICA approach for transcriptional module discovery. nICA method utilizes the non-negativity constraint to enforce the independence of biological processes within the participated genes. In such, nICA decomposes the observed gene expression into positive independent components, which fi ts better to the reality of corresponding putative biological processes. In conjunction with nICA modeling, visual statistical data analyzer (VISDA is applied to group genes into modules in latent variable space. We demonstrate the usefulness of the approach through the identification of composite modules from yeast data and the discovery of pathway modules in muscle regeneration.

The discovery of radioactivity: a bend in sciences history

International Nuclear Information System (INIS)

Dautray, R.

1997-01-01

One hundred years after the discovery of radioactivity, it is possible to see what are the consequences of this discovery for the science. Four consequences are studied in this article: the acquisition of a new knowledge about matter and universe. Secondly, the observation that the radioactivity has given a clock of world history and open to us the past and how this past forged the present world. Thirdly, the fact that radioactivity gave tracers, markers which allow to sound the internal structure of the human body as well as these one of earth and solar system and to unveil the mechanisms. The fourth consequence, is all the applications, electro-nuclear energy, national defence, nuclear medicine. (N.C.)

Reanalysis of RNA-sequencing data reveals several additional fusion genes with multiple isoforms.

Science.gov (United States)

Kangaspeska, Sara; Hultsch, Susanne; Edgren, Henrik; Nicorici, Daniel; Murumägi, Astrid; Kallioniemi, Olli

2012-01-01

RNA-sequencing and tailored bioinformatic methodologies have paved the way for identification of expressed fusion genes from the chaotic genomes of solid tumors. We have recently successfully exploited RNA-sequencing for the discovery of 24 novel fusion genes in breast cancer. Here, we demonstrate the importance of continuous optimization of the bioinformatic methodology for this purpose, and report the discovery and experimental validation of 13 additional fusion genes from the same samples. Integration of copy number profiling with the RNA-sequencing results revealed that the majority of the gene fusions were promoter-donating events that occurred at copy number transition points or involved high-level DNA-amplifications. Sequencing of genomic fusion break points confirmed that DNA-level rearrangements underlie selected fusion transcripts. Furthermore, a significant portion (>60%) of the fusion genes were alternatively spliced. This illustrates the importance of reanalyzing sequencing data as gene definitions change and bioinformatic methods improve, and highlights the previously unforeseen isoform diversity among fusion transcripts.

Reanalysis of RNA-sequencing data reveals several additional fusion genes with multiple isoforms.

Directory of Open Access Journals (Sweden)

Sara Kangaspeska

Full Text Available RNA-sequencing and tailored bioinformatic methodologies have paved the way for identification of expressed fusion genes from the chaotic genomes of solid tumors. We have recently successfully exploited RNA-sequencing for the discovery of 24 novel fusion genes in breast cancer. Here, we demonstrate the importance of continuous optimization of the bioinformatic methodology for this purpose, and report the discovery and experimental validation of 13 additional fusion genes from the same samples. Integration of copy number profiling with the RNA-sequencing results revealed that the majority of the gene fusions were promoter-donating events that occurred at copy number transition points or involved high-level DNA-amplifications. Sequencing of genomic fusion break points confirmed that DNA-level rearrangements underlie selected fusion transcripts. Furthermore, a significant portion (>60% of the fusion genes were alternatively spliced. This illustrates the importance of reanalyzing sequencing data as gene definitions change and bioinformatic methods improve, and highlights the previously unforeseen isoform diversity among fusion transcripts.

29 CFR 2700.56 - Discovery; general.

Science.gov (United States)

2010-07-01

...(c) or 111 of the Act has been filed. 30 U.S.C. 815(c) and 821. (e) Completion of discovery... 29 Labor 9 2010-07-01 2010-07-01 false Discovery; general. 2700.56 Section 2700.56 Labor... Hearings § 2700.56 Discovery; general. (a) Discovery methods. Parties may obtain discovery by one or more...

Promoter sequence of 3-phosphoglycerate kinase gene 1 of lactic acid-producing fungus rhizopus oryzae and a method of expressing a gene of interest in fungal species

Energy Technology Data Exchange (ETDEWEB)

Gao, Johnway [Richland, WA; Skeen, Rodney S [Pendleton, OR

2002-10-15

The present invention provides the promoter clone discovery of phosphoglycerate kinase gene 1 of a lactic acid-producing filamentous fungal strain, Rhizopus oryzae. The isolated promoter can constitutively regulate gene expression under various carbohydrate conditions. In addition, the present invention also provides a design of an integration vector for the transformation of a foreign gene in Rhizopus oryzae.

Promoter sequence of 3-phosphoglycerate kinase gene 2 of lactic acid-producing fungus rhizopus oryzae and a method of expressing a gene of interest in fungal species

Energy Technology Data Exchange (ETDEWEB)

Gao, Johnway [Richland, WA; Skeen, Rodney S [Pendleton, OR

2003-03-04

The present invention provides the promoter clone discovery of phosphoglycerate kinase gene 2 of a lactic acid-producing filamentous fungal strain, Rhizopus oryzae. The isolated promoter can constitutively regulate gene expression under various carbohydrate conditions. In addition, the present invention also provides a design of an integration vector for the transformation of a foreign gene in Rhizopus oryzae.

Discovery of convoys in trajectory databases

DEFF Research Database (Denmark)

Jeung, Hoyoung; Yiu, Man Lung; Zhou, Xiaofang

2008-01-01

a group of objects that have traveled together for some time. More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for real-life applications in throughput planning...... convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the paper's proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data....

Drug Discovery Gets a Boost from Data Science.

Science.gov (United States)

Amaro, Rommie E

2016-08-02

In this issue of Structure, Schiebel et al. (2016) describe a workflow-driven approach to high-throughput X-ray crystallographic fragment screening and refinement. In doing so, they extend the applicability of X-ray crystallography as a primary fragment-screening tool and show how data science techniques can favorably impact drug discovery efforts. Copyright © 2016 Elsevier Ltd. All rights reserved.

Robust variable selection method for nonparametric differential equation models with application to nonlinear dynamic gene regulatory network analysis.

Science.gov (United States)

Lu, Tao

2016-01-01

The gene regulation network (GRN) evaluates the interactions between genes and look for models to describe the gene expression behavior. These models have many applications; for instance, by characterizing the gene expression mechanisms that cause certain disorders, it would be possible to target those genes to block the progress of the disease. Many biological processes are driven by nonlinear dynamic GRN. In this article, we propose a nonparametric differential equation (ODE) to model the nonlinear dynamic GRN. Specially, we address following questions simultaneously: (i) extract information from noisy time course gene expression data; (ii) model the nonlinear ODE through a nonparametric smoothing function; (iii) identify the important regulatory gene(s) through a group smoothly clipped absolute deviation (SCAD) approach; (iv) test the robustness of the model against possible shortening of experimental duration. We illustrate the usefulness of the model and associated statistical methods through a simulation and a real application examples.