Year: 2014
Type: article
Abstract: Uniform processing and detailed annotation of human, worm and fly RNA-sequencing data reveal ancient, conserved features of the transcriptome, shared co-expression modules (many enriched in developmental genes), matched expression patterns across development and similar extent of non-canonical, non-coding transcription; furthermore, the data are used to create a single, universal model to predict gene-expression levels for all three organisms from chromatin features at the promoter. In this paper the modENCODE consortium reports on a comparative analysis of transcriptome data for human, worm and fly, revealing ancient, conserved features such as shared co-expression modules enriched in developmental genes. Expression patterns are used to align the stages in worm and fly development. Gene expression levels, both coding and non-coding, in all three organisms can be quantitatively predicted from chromatin features at the promoter using a model based on a single set of organism-independent parameters. The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features1,2,3,4,5,6. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a ‘universal model’ based on a single set of organism-independent parameters.
Source: Nature
DOI: 10.1038/nature13424
Authors: Mark B. Gerstein, Joel Rozowsky, Koon-Kiu Yan, Daifeng Wang, Chao Cheng, James B. Brown, Carrie A. Davis, LaDeana Hillier, Cristina Sisu, Jingyi Jessica Li, Baikang Pei, Arif O. Harmanci, Michael O. Duff, Sarah Djebali, Roger P. Alexander, Burak H. Alver, Raymond Auerbach, Kimberly Bell, Peter J. Bickel, Max E. Boeck, Nathan P. Boley, Benjamin W. Booth, Lucy Cherbas, Peter Cherbas, Chao Di, Alex Dobin, Jorg Drenkow, Brent Ewing, Gang Fang, Megan Fastuca, Elise A. Feingold, Adam Frankish, Guanjun Gao, Peter J. Good, Roderic Guigó, Ann Hammonds, Jen Harrow, Roger A. Hoskins, Cédric Howald, Long Hu, Haiyan Huang, Tim J. P. Hubbard, Chau Huynh, Sonali Jha, Dionna Kasper, Masaomi Kato, Thomas C. Kaufman, Robert R. Kitchen, Erik Ladewig, Julien Lagarde, Eric Lai, Jing Leng, Zhi Lu, Michael MacCoss, Gemma May, Rebecca McWhirter, Gennifer Merrihew, David M. Miller, Ali Mortazavi, Rabi Murad, Brian Oliver, Sara Olson, Peter J. Park, Michael J. Pazin, Norbert Perrimon, Dmitri Pervouchine, Valerie Reinke, Alexandre Reymond, Garrett Robinson, Anastasia Samsonova, Gary I. Saunders, Felix Schlesinger, Anurag Sethi, Frank J. Slack, William C. Spencer, Marcus H. Stoiber, Pnina Strasbourger, Andrea Tanzer, Owen A. Thompson, Kenneth H. Wan, Guilin Wang, Huaien Wang, Kathie L. Watkins, Jiayu Wen, Kejia Wen, Chenghai Xue, Li Yang, Kevin Yip, Chris Zaleski, Yan Zhang, Henry Zheng, Steven E. Brenner, Brenton R. Graveley, Susan E. Celniker, Thomas R. Gingeras, Robert Waterston
Institutions: Yale University, Dartmouth College, Dartmouth Cancer Center, Lawrence Berkeley National Laboratory, University of California, Berkeley, Cold Spring Harbor Laboratory, University of Washington, University of California, Los Angeles, UConn Health, Universitat Pompeu Fabra, Centre for Genomic Regulation, Harvard University, Indiana University Bloomington, Indiana University, Tsinghua University, National Institutes of Health, National Human Genome Research Institute, Wellcome Sanger Institute, SIB Swiss Institute of Bioinformatics, University of Lausanne, King's College London, Carnegie Mellon University, Vanderbilt University, University of California, Irvine, National Institute of Diabetes and Digestive and Kidney Diseases, Howard Hughes Medical Institute, European Bioinformatics Institute, University of Vienna, Chinese Academy of Sciences, Shanghai Institutes for Biological Sciences, Chinese University of Hong Kong
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
Funders: Howard Hughes Medical Institute, U.S. Department of Energy, Eli Lilly and Company, Yale University, Lilly Endowment, Wellcome Trust, National Institutes of Health, National Human Genome Research Institute, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, U.S. National Library of Medicine, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Genome-Wide Mapping of Chromosomal Proteins in Drosophilia, Landscape of transcription in human and mouse, EDAC: ENCODE Data Analysis Center, G1286236842, G216544311, G2257205304, Patterned Gene Expression in Drosophila Development, Removing statistical bottle-necks in data analysis for the ENCODE Consortium, G3083819904, EDAC: ENCODE Data Analysis Center, G382641700, G42916606, Creating Comprehensive Maps of Worm and Fly Transcription Factor Binding Sites, G4612446324, G464362581, Annotation of the small RNA/microRNA component Drosophila genome, Integrated human genome annotation: generation of a reference gene set, Comprehensive characterization of the Drosophila transcriptome, A Data Analysis Center for integration of fly and worm modENCODE datasets, Nonparametric methods for functional and translational genomics, G5956551949, Comprehensive Discovery of Functional Elements of the Drosphila Transcriptome, G666505512, GENCODE: comprehensive genome annotation for human and mouse, G6713907200, Training Program in Systems Biology of Development, G7146827954, G8437048056, Global Identification of transcribed elements in the C. elegans genome, Biomedical Informatics and Data Science Training at Yale, Cancer Center Support Grant, Drosophila Functional Genomics, CSHL Cancer Center Support Grant, G969889393, Comprhensive analysis of functional RNA elements encoded in the human genome
OpenAlex ID: W2074468058