Institution
University of Warwick
Education•Coventry, Warwickshire, United Kingdom•
About: University of Warwick is a education organization based out in Coventry, Warwickshire, United Kingdom. It is known for research contribution in the topics: Population & White dwarf. The organization has 26212 authors who have published 77127 publications receiving 2666552 citations. The organization is also known as: Warwick University & The University of Warwick.
Topics: Population, White dwarf, Politics, Health care, Poison control
Papers published on a yearly basis
Papers
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Virginia Tech1, Joint Genome Institute2, Lawrence Berkeley National Laboratory3, Wageningen University and Research Centre4, University of Warwick5, Imperial College London6, University of California, Berkeley7, Cornell University8, Ohio Agricultural Research and Development Center9, Agriculture and Agri-Food Canada10, Agricultural Research Service11, Lawrence Livermore National Laboratory12, North Carolina State University13, University of Tennessee14, Oak Ridge National Laboratory15, University of California, Merced16, University of Queensland17, Wilkes University18, Bowling Green State University19, Hokkaido University20
TL;DR: Comparison of the two species' genomes reveals a rapid expansion and diversification of many protein families associated with plant infection such as hydrolases, ABC transporters, protein toxins, proteinase inhibitors, and, in particular, a superfamily of 700 proteins with similarity to known oömycete avirulence genes.
Abstract: Draft genome sequences have been determined for the soybean pathogen Phytophthora sojae and the sudden oak death pathogen Phytophthora ramorum. Oomycetes such as these Phytophthora species share the kingdom Stramenopila with photosynthetic algae such as diatoms, and the presence of many Phytophthora genes of probable phototroph origin supports a photosynthetic ancestry for the stramenopiles. Comparison of the two species' genomes reveals a rapid expansion and diversification of many protein families associated with plant infection such as hydrolases, ABC transporters, protein toxins, proteinase inhibitors, and, in particular, a superfamily of 700 proteins with similarity to known oomycete avirulence genes.
1,016 citations
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Kyriaki Michailidou1, Kyriaki Michailidou2, Sara Lindström3, Sara Lindström4 +393 more•Institutions (127)
TL;DR: A genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry finds that heritability of Breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2–5-fold enriched relative to the genome- wide average.
Abstract: Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.
1,014 citations
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TL;DR: Climate change mitigation in transport should benefit public health substantially and policies to increase the acceptability, appeal, and safety of active urban travel, and discourage travel in private motor vehicles would provide larger health benefits than would policies that focus solely on lower-emission motor vehicles.
1,013 citations
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TL;DR: Computational approaches to overcome the challenges that affect both assembly-based and mapping-based metagenomic profiling, particularly of high-complexity samples or environments containing organisms with limited similarity to sequenced genomes, are needed.
Abstract: Diverse microbial communities of bacteria, archaea, viruses and single-celled eukaryotes have crucial roles in the environment and in human health. However, microbes are frequently difficult to culture in the laboratory, which can confound cataloging of members and understanding of how communities function. High-throughput sequencing technologies and a suite of computational pipelines have been combined into shotgun metagenomics methods that have transformed microbiology. Still, computational approaches to overcome the challenges that affect both assembly-based and mapping-based metagenomic profiling, particularly of high-complexity samples or environments containing organisms with limited similarity to sequenced genomes, are needed. Understanding the functions and characterizing specific strains of these communities offers biotechnological promise in therapeutic discovery and innovative ways to synthesize products using microbial factories and can pinpoint the contributions of microorganisms to planetary, animal and human health.
1,007 citations
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University of Iceland1, University of Manchester2, Charité3, University of California, San Diego4, University of Amsterdam5, Netherlands Bioinformatics Centre6, 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 26659 results
Name | H-index | Papers | Citations |
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David Miller | 203 | 2573 | 204840 |
Daniel R. Weinberger | 177 | 879 | 128450 |
Kay-Tee Khaw | 174 | 1389 | 138782 |
Joseph E. Stiglitz | 164 | 1142 | 152469 |
Edmund T. Rolls | 153 | 612 | 77928 |
Thomas J. Smith | 140 | 1775 | 113919 |
Tim Jones | 135 | 1314 | 91422 |
Ian Ford | 134 | 678 | 85769 |
Paul Harrison | 133 | 1400 | 80539 |
Sinead Farrington | 133 | 1422 | 91099 |
Peter Hall | 132 | 1640 | 85019 |
Paul Brennan | 132 | 1221 | 72748 |
G. T. Jones | 131 | 864 | 75491 |
Peter Simmonds | 131 | 823 | 62953 |
Tim Martin | 129 | 878 | 82390 |