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

Manzoni, C; Kia, DA; Vandrovcova, J; Hardy, J; Wood, NW; Lewis, PA; Ferrari, R; (2018) Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Briefings in Bioinformatics , 19 (2) pp. 286-302. 10.1093/bib/bbw114. Green open access

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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.

Type: Article
Title: Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/bib/bbw114
Publisher version: http://doi.org/10.1093/bib/bbw114
Language: English
Additional information: Copyright © 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.
Keywords: omics, bioinformatics, databases, genomics, transcriptomics, proteomics
URI: http://discovery.ucl.ac.uk/id/eprint/10050671
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