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Weighill D, Tschaplinski TJ, Tuskan GA, Jacobson D. Data Integration in Poplar: 'Omics Layers and Integration Strategies. Front Genet 2019; 10:874. [PMID: 31608114 PMCID: PMC6773870 DOI: 10.3389/fgene.2019.00874] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Accepted: 08/20/2019] [Indexed: 12/20/2022] Open
Abstract
Populus trichocarpa is an important biofuel feedstock that has been the target of extensive research and is emerging as a model organism for plants, especially woody perennials. This research has generated several large ‘omics datasets. However, only few studies in Populus have attempted to integrate various data types. This review will summarize various ‘omics data layers, focusing on their application in Populus species. Subsequently, network and signal processing techniques for the integration and analysis of these data types will be discussed, with particular reference to examples in Populus.
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Affiliation(s)
- Deborah Weighill
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Timothy J Tschaplinski
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Gerald A Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Daniel Jacobson
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
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Choobdar S, Ahsen ME, Crawford J, Tomasoni M, Fang T, Lamparter D, Lin J, Hescott B, Hu X, Mercer J, Natoli T, Narayan R, Subramanian A, Zhang JD, Stolovitzky G, Kutalik Z, Lage K, Slonim DK, Saez-Rodriguez J, Cowen LJ, Bergmann S, Marbach D. Assessment of network module identification across complex diseases. Nat Methods 2019; 16:843-852. [PMID: 31471613 PMCID: PMC6719725 DOI: 10.1038/s41592-019-0509-5] [Citation(s) in RCA: 140] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 07/10/2019] [Indexed: 12/11/2022]
Abstract
Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.
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Affiliation(s)
- Sarvenaz Choobdar
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Mehmet E Ahsen
- Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jake Crawford
- Department of Computer Science, Tufts University, Medford, MA, USA
| | - Mattia Tomasoni
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tao Fang
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - David Lamparter
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Verge Genomics, San Francisco, CA, USA
| | - Junyuan Lin
- Department of Mathematics, Tufts University, Medford, MA, USA
| | - Benjamin Hescott
- College of Computer and Information Science, Northeastern University, Boston, MA, USA
| | - Xiaozhe Hu
- Department of Mathematics, Tufts University, Medford, MA, USA
| | - Johnathan Mercer
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ted Natoli
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rajiv Narayan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Jitao D Zhang
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Gustavo Stolovitzky
- Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Institute of Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Kasper Lage
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center at the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Biological Psychiatry, Mental Health Center Sct. Hans, University of Copenhagen, Roskilde, Denmark
| | - Donna K Slonim
- Department of Computer Science, Tufts University, Medford, MA, USA
- Department of Immunology, Tufts University School of Medicine, Boston, MA, USA
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, Bioquant, Heidelberg, Germany
- RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine, Aachen, Germany
| | - Lenore J Cowen
- Department of Computer Science, Tufts University, Medford, MA, USA
- Department of Mathematics, Tufts University, Medford, MA, USA
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa.
| | - Daniel Marbach
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland.
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Dozmorov MG. Disease classification: from phenotypic similarity to integrative genomics and beyond. Brief Bioinform 2019; 20:1769-1780. [DOI: 10.1093/bib/bby049] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 05/01/2018] [Indexed: 02/06/2023] Open
Abstract
Abstract
A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).
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Affiliation(s)
- Mikhail G Dozmorov
- Department of Biostatistics, Virginia Commonwealth University, 830 East Main Street, Richmond, VA, USA
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Arneson D, Shu L, Tsai B, Barrere-Cain R, Sun C, Yang X. Multidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease. Front Cardiovasc Med 2017; 4:8. [PMID: 28289683 PMCID: PMC5327355 DOI: 10.3389/fcvm.2017.00008] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 02/09/2017] [Indexed: 12/19/2022] Open
Abstract
Elucidating the mechanisms of complex diseases such as cardiovascular disease (CVD) remains a significant challenge due to multidimensional alterations at molecular, cellular, tissue, and organ levels. To better understand CVD and offer insights into the underlying mechanisms and potential therapeutic strategies, data from multiple omics types (genomics, epigenomics, transcriptomics, metabolomics, proteomics, microbiomics) from both humans and model organisms have become available. However, individual omics data types capture only a fraction of the molecular mechanisms. To address this challenge, there have been numerous efforts to develop integrative genomics methods that can leverage multidimensional information from diverse data types to derive comprehensive molecular insights. In this review, we summarize recent methodological advances in multidimensional omics integration, exemplify their applications in cardiovascular research, and pinpoint challenges and future directions in this incipient field.
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Affiliation(s)
- Douglas Arneson
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Le Shu
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA; Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Brandon Tsai
- Department of Integrative Biology and Physiology, University of California Los Angeles , Los Angeles, CA , USA
| | - Rio Barrere-Cain
- Department of Integrative Biology and Physiology, University of California Los Angeles , Los Angeles, CA , USA
| | - Christine Sun
- Department of Integrative Biology and Physiology, University of California Los Angeles , Los Angeles, CA , USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA, USA; Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA, USA
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