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      Deep-learning augmented RNA-seq analysis of transcript splicing

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          Abstract

          A major limitation for RNA-seq analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS ( https://github.com/Xinglab/DARTS), a computational framework that integrates deep learning-based predictions with empirical RNA-seq evidence to infer differential alternative splicing between biological samples. DARTS leverages public RNA-seq big data to provide a knowledge base of splicing regulation via deep learning, helping researchers better characterize alternative splicing using RNA-seq datasets even with modest coverage.

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          An Integrated Encyclopedia of DNA Elements in the Human Genome

          Iakes Ezkurdia (2016)
          Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
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            Near-optimal probabilistic RNA-seq quantification.

            Nicolas Bray, Harold Pimentel, Páll Melsted … (2016)
            We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
            Article 0 comments Cited 2633 times – based on 0 reviews     Review now
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              Integrative analysis of 111 reference human epigenomes

              Anshul Kundaje, Wouter Meuleman, Jason Ernst … (2016)
              The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but a similar reference has lacked for epigenomic studies. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection to-date of human epigenomes for primary cells and tissues. Here, we describe the integrative analysis of 111 reference human epigenomes generated as part of the program, profiled for histone modification patterns, DNA accessibility, DNA methylation, and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically-relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation, and human disease.
              Article 0 comments Cited 1429 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 101215604
                Journal ID (pubmed-jr-id): 32338
                Journal ID (nlm-ta): Nat Methods
                Journal ID (iso-abbrev): Nat Methods
                Title: Nature methods
                ISSN (Print): 1548-7091
                ISSN (Electronic): 1548-7105
                Publication date Nihms-submitted: 29 July 2020
                Publication date (Electronic): 25 March 2019
                Publication date (Print): April 2019
                Publication date PMC-release: 02 November 2020
                Volume: 16
                Issue: 4
                Pages: 307-310
                Affiliations
                [1 ]Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, USA.
                [2 ]Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, USA.
                [3 ]Center for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, Philadelphia, USA.
                [4 ]Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, USA.
                [5 ]Department of Medicine, University of Pennsylvania, Philadelphia, USA.
                [6 ]Department of Statistics, University of California, Los Angeles, Los Angeles, USA.
                [7 ]Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, USA.
                [8 ]These authors contributed equally to this work.
                Author notes

                Author Contributions

                Z.Z. and Y.X. conceived the study; Z.Z., Y.N.W., and Y.X. designed the research; Z.Z., Z.P., Y.Y., S.A., and J.P. performed the research; Z.X., R.P.C., and D.L.B contributed analytic tools; Z.Z. and Y.X. analyzed data; and Z.Z. and Y.X. wrote the paper with input from all authors.

                Article
                Manuscript ID: NIHMS1521091
                DOI: 10.1038/s41592-019-0351-9
                PMC ID: 7605494
                PubMed ID: 30923373
                SO-VID: 4aadacb0-5b5a-4853-b837-dd02e00ce6d4
                License:

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                ScienceOpen disciplines: Life sciences

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