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      Data integration in the era of omics: current and future challenges

      introduction
      Author(s): 1 , , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 1
      Publication date (Electronic):
      Journal: BMC Systems Biology
      Publisher: BioMed Central
      Conference name: High-Throughput Omics and Data Integration Workshop
      Conference date: 13-15 February 2013

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          Abstract

          To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community.

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            A map of the cis-regulatory sequences in the mouse genome.

            Yin Shen, Feng Yue, David F. McCleary … (2012)
            The laboratory mouse is the most widely used mammalian model organism in biomedical research. The 2.6 × 10(9) bases of the mouse genome possess a high degree of conservation with the human genome, so a thorough annotation of the mouse genome will be of significant value to understanding the function of the human genome. So far, most of the functional sequences in the mouse genome have yet to be found, and the cis-regulatory sequences in particular are still poorly annotated. Comparative genomics has been a powerful tool for the discovery of these sequences, but on its own it cannot resolve their temporal and spatial functions. Recently, ChIP-Seq has been developed to identify cis-regulatory elements in the genomes of several organisms including humans, Drosophila melanogaster and Caenorhabditis elegans. Here we apply the same experimental approach to a diverse set of 19 tissues and cell types in the mouse to produce a map of nearly 300,000 murine cis-regulatory sequences. The annotated sequences add up to 11% of the mouse genome, and include more than 70% of conserved non-coding sequences. We define tissue-specific enhancers and identify potential transcription factors regulating gene expression in each tissue or cell type. Finally, we show that much of the mouse genome is organized into domains of coordinately regulated enhancers and promoters. Our results provide a resource for the annotation of functional elements in the mammalian genome and for the study of mechanisms regulating tissue-specific gene expression.
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              MicroRNA profiling: approaches and considerations.

              Colin C Pritchard, Heather H Cheng, Muneesh Tewari (2012)
              MicroRNAs (miRNAs) are small RNAs that post-transcriptionally regulate the expression of thousands of genes in a broad range of organisms in both normal physiological contexts and in disease contexts. miRNA expression profiling is gaining popularity because miRNAs, as key regulators in gene expression networks, can influence many biological processes and also show promise as biomarkers for disease. Technological advances have spawned a multitude of platforms for miRNA profiling, and an understanding of the strengths and pitfalls of different approaches can aid in their effective use. Here, we review the major considerations for carrying out and interpreting results of miRNA-profiling studies.
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                Author and article information

                Conference
                Journal ID (nlm-ta): BMC Syst Biol
                Journal ID (iso-abbrev): BMC Syst Biol
                Title: BMC Systems Biology
                Publisher: BioMed Central
                ISSN (Electronic): 1752-0509
                Publication date Collection: 2014
                Publication date (Electronic): 13 March 2014
                Volume: 8
                Issue: Suppl 2
                Page: I1
                Affiliations
                [1 ]Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
                [2 ]Biomax Informatics AG, Munich, Germany
                [3 ]Statistical Cancer Genomics, UCL Cancer Institute; Centre for Mathematics and Physics in the Life Sciences and Experimental Biology, University College London, London WC1E 6BT, UK
                [4 ]Lymphocyte Development Group, MRC Clinical Sciences Centre, Imperial College London, London W12 0NN, UK
                [5 ]Istituto di Tecnologie Biomediche (CNR), Unità Organizzativa di Bari, Via Amendola 122/D, 70126 Bari, Italy
                [6 ]Chromatin and Disease Group, Cancer Epigenetics and Biology Program (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
                [7 ]Department of Animal Breeding and Genetics, SLU Global Bioinformatics Centre, Swedish University of Agricultural Sciences, Uppsala, Sweden
                [8 ]Computational Genomics Program, Centro de Investigaciones Príncipe Felipe, Valencia, Spain
                Article
                Publisher ID: 1752-0509-8-S2-I1
                DOI: 10.1186/1752-0509-8-S2-I1
                PMC ID: 4101704
                PubMed ID: 25032990
                SO-VID: 1b292e52-5c80-467b-a14e-4d4059b124ee
                Copyright © Copyright © 2014 Gomez-Cabrero et al; licensee BioMed Central Ltd.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Conference name: High-Throughput Omics and Data Integration Workshop
                Conference location: Barcelona, Spain
                Conference date: 13-15 February 2013
                History
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                Subject: Introduction

                ScienceOpen disciplines: Quantitative & Systems biology
                Data availability:
                ScienceOpen disciplines: Quantitative & Systems biology

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