Institution
Virginia Bioinformatics Institute
About: Virginia Bioinformatics Institute is a based out in . It is known for research contribution in the topics: Population & Gene. The organization has 574 authors who have published 1119 publications receiving 56101 citations.
Topics: Population, Gene, Genome, Immune system, Cellular Potts model
Papers published on a yearly basis
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California Institute of Technology1, University of Hertfordshire2, University of California, Berkeley3, Jet Propulsion Laboratory4, University of Cambridge5, Centre national de la recherche scientifique6, University of Auckland7, GlaxoSmithKline8, Max Planck Society9, Stellenbosch University10, University of Connecticut Health Center11, Virginia Bioinformatics Institute12, University of California, Irvine13, Keio University14, Princeton University15
TL;DR: This work summarizes the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks, a software-independent language for describing models common to research in many areas of computational biology.
Abstract: Motivation: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. Results: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. ∗ To whom correspondence should be addressed. Availability: The specification of SBML Level 1 is freely available from http://www.sbml.org/.
3,205 citations
TL;DR: COPASI is presented, a platform-independent and user-friendly biochemical simulator that offers several unique features, and numerical issues with these features are discussed; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in Stochastic simulation.
Abstract: Motivation: Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods.
Results: Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic--stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation.
Availability: The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org.
Contact: mendes@vbi.vt.edu
2,351 citations
TL;DR: The Drosophila melanogaster Genetic Reference Panel is described, a community resource for analysis of population genomics and quantitative traits, which reveals reduced polymorphism in centromeric autosomal regions and the X chromosomes, evidence for positive and negative selection, and rapid evolution of the X chromosome.
Abstract: A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype-phenotype map were based on incomplete genotypic information. Here, we describe the Drosophila melanogaster Genetic Reference Panel (DGRP), a community resource for analysis of population genomics and quantitative traits. The DGRP consists of fully sequenced inbred lines derived from a natural population. Population genomic analyses reveal reduced polymorphism in centromeric autosomal regions and the X chromosome, evidence for positive and negative selection, and rapid evolution of the X chromosome. Many variants in novel genes, most at low frequency, are associated with quantitative traits and explain a large fraction of the phenotypic variance. The DGRP facilitates genotype-phenotype mapping using the power of Drosophila genetics.
1,568 citations
Broad Institute1, Ohio Agricultural Research and Development Center2, Sainsbury Laboratory3, Uppsala University4, Wageningen University and Research Centre5, Virginia Bioinformatics Institute6, University of California, Riverside7, University of Aberdeen8, Scottish Crop Research Institute9, University of Warwick10, Agricultural Research Service11, Royal Institute of Technology12, Cornell University13, Oregon State University14, Lafayette College15, University of Glasgow16, Harvard University17, Delaware Biotechnology Institute18, North Carolina State University19, University of Delaware20, University of Tennessee21, University of Maryland, Baltimore22, Vanderbilt University23, College of Wooster24, Bowling Green State University25, Edinburgh Cancer Research Centre26, J. Craig Venter Institute27, Tel Aviv University28, University of Wisconsin-Madison29, University of Hohenheim30, University of Dundee31
TL;DR: The sequence of the P. infestans genome is reported, which at ∼240 megabases (Mb) is by far the largest and most complex genome sequenced so far in the chromalveolates and probably plays a crucial part in the rapid adaptability of the pathogen to host plants and underpins its evolutionary potential.
Abstract: Phytophthora infestans is the most destructive pathogen of potato and a model organism for the oomycetes, a distinct lineage of fungus-like eukaryotes that are related to organisms such as brown algae and diatoms. As the agent of the Irish potato famine in the mid-nineteenth century, P. infestans has had a tremendous effect on human history, resulting in famine and population displacement(1). To this day, it affects world agriculture by causing the most destructive disease of potato, the fourth largest food crop and a critical alternative to the major cereal crops for feeding the world's population(1). Current annual worldwide potato crop losses due to late blight are conservatively estimated at $6.7 billion(2). Management of this devastating pathogen is challenged by its remarkable speed of adaptation to control strategies such as genetically resistant cultivars(3,4). Here we report the sequence of the P. infestans genome, which at similar to 240 megabases (Mb) is by far the largest and most complex genome sequenced so far in the chromalveolates. Its expansion results from a proliferation of repetitive DNA accounting for similar to 74% of the genome. Comparison with two other Phytophthora genomes showed rapid turnover and extensive expansion of specific families of secreted disease effector proteins, including many genes that are induced during infection or are predicted to have activities that alter host physiology. These fast-evolving effector genes are localized to highly dynamic and expanded regions of the P. infestans genome. This probably plays a crucial part in the rapid adaptability of the pathogen to host plants and underpins its evolutionary potential.
1,341 citations
University of Iceland1, University of Manchester2, Charité3, University of California, San Diego4, Netherlands Bioinformatics Centre5, University of Amsterdam6, Chalmers University of Technology7, University of Virginia8, University of Sheffield9, Central Manchester University Hospitals NHS Foundation Trust10, University of Vienna11, University of North Texas12, California Institute of Technology13, European Bioinformatics Institute14, Babraham Institute15, University of Warwick16, University of Edinburgh17, Institute for Systems Biology18, University of Luxembourg19, Jacobs University Bremen20, Russian Academy of Sciences21, VU University Amsterdam22, Virginia Bioinformatics Institute23
TL;DR: Recon 2, a community-driven, consensus 'metabolic reconstruction', is described, which is the most comprehensive representation of human metabolism that is applicable to computational modeling and has improved topological and functional features.
Abstract: Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
1,002 citations
Authors
Showing all 574 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Alessandro Vespignani | 118 | 419 | 63824 |
| Hans V. Westerhoff | 90 | 566 | 29104 |
| John S. Brownstein | 79 | 376 | 31346 |
| Zhangjun Fei | 73 | 318 | 19906 |
| John J. Tyson | 69 | 297 | 20008 |
| Alessandro Flammini | 62 | 171 | 18848 |
| Brett M. Tyler | 58 | 196 | 12840 |
| Stephen C. Jacobson | 58 | 170 | 13826 |
| Jeffrey H. Reed | 57 | 448 | 16371 |
| Santo Fortunato | 57 | 178 | 42502 |
| Pedro Mendes | 56 | 161 | 21001 |
| Vittoria Colizza | 54 | 180 | 15305 |
| Madhav V. Marathe | 53 | 315 | 13493 |
| James A. Glazier | 53 | 182 | 10223 |
| Vladimir Shulaev | 52 | 108 | 16505 |