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      Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

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          Abstract

          Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

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          Most cited references74

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          Gene Ontology: tool for the unification of biology

          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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            FunRich: An open access standalone functional enrichment and interaction network analysis tool.

            As high-throughput techniques including proteomics become more accessible to individual laboratories, there is an urgent need for a user-friendly bioinformatics analysis system. Here, we describe FunRich, an open access, standalone functional enrichment and network analysis tool. FunRich is designed to be used by biologists with minimal or no support from computational and database experts. Using FunRich, users can perform functional enrichment analysis on background databases that are integrated from heterogeneous genomic and proteomic resources (>1.5 million annotations). Besides default human specific FunRich database, users can download data from the UniProt database, which currently supports 20 different taxonomies against which enrichment analysis can be performed. Moreover, the users can build their own custom databases and perform the enrichment analysis irrespective of organism. In addition to proteomics datasets, the custom database allows for the tool to be used for genomics, lipidomics and metabolomics datasets. Thus, FunRich allows for complete database customization and thereby permits for the tool to be exploited as a skeleton for enrichment analysis irrespective of the data type or organism used. FunRich (http://www.funrich.org) is user-friendly and provides graphical representation (Venn, pie charts, bar graphs, column, heatmap and doughnuts) of the data with customizable font, scale and color (publication quality).
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              MicroRNA profiling: approaches and considerations.

              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

                Journal
                Brief Bioinform
                Brief. Bioinformatics
                bib
                Briefings in Bioinformatics
                Oxford University Press
                1467-5463
                1477-4054
                March 2018
                22 November 2016
                22 November 2016
                : 19
                : 2
                : 286-302
                Affiliations
                [1 ]School of Pharmacy, University of Reading, Whiteknights, Reading, United Kingdom
                [2 ]Department Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom
                Author notes
                Corresponding author: Claudia Manzoni, School of Pharmacy, University of Reading, Whiteknights, Reading, RG6 6AP, United Kingdom. Tel.: +44 (0) 118 378 4561; E-mail: c.manzoni@ 123456reading.ac.uk

                Claudia Manzoni and Demis A. Kia authors contributed equally to this work.

                Article
                bbw114
                10.1093/bib/bbw114
                6018996
                27881428
                1bbc68e2-2fa1-4ed0-9c4b-0715e37e8361
                © The Author 2016. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 July 2016
                : 12 October 2016
                Page count
                Pages: 17
                Funding
                Funded by: Wellcome Trust 10.13039/100004440
                Funded by: MRC 10.13039/501100000265
                Award ID: WT089698
                Funded by: University of Sheffield 10.13039/501100000858
                Funded by: MRC 10.13039/501100000265
                Funded by: Medical Research Council 10.13039/501100000265
                Funded by: Wellcome Trust 10.13039/100004440
                Award ID: 089698/Z/09/Z
                Award ID: 091673/Z/10/Z
                Funded by: Medical Research Council 10.13039/501100000265
                Award ID: MR/N026004/1
                Award ID: MR/L010933/1
                Funded by: University College London 10.13039/501100000765
                Funded by: NIHR 10.13039/100006662
                Categories
                Papers

                Bioinformatics & Computational biology
                omics,bioinformatics,databases,genomics,transcriptomics,proteomics

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