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High-Throughput 2019, 8(1), 1; https://doi.org/10.3390/ht8010001

The Identification and Interpretation of cis-Regulatory Noncoding Mutations in Cancer

Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, UK
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Received: 6 November 2018 / Revised: 11 December 2018 / Accepted: 14 December 2018 / Published: 20 December 2018
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Abstract

In the need to characterise the genomic landscape of cancers and to establish novel biomarkers and therapeutic targets, studies have largely focused on the identification of driver mutations within the protein-coding gene regions, where the most pathogenic alterations are known to occur. However, the noncoding genome is significantly larger than its protein-coding counterpart, and evidence reveals that regulatory sequences also harbour functional mutations that significantly affect the regulation of genes and pathways implicated in cancer. Due to the sheer number of noncoding mutations (NCMs) and the limited knowledge of regulatory element functionality in cancer genomes, differentiating pathogenic mutations from background passenger noise is particularly challenging technically and computationally. Here we review various up-to-date high-throughput sequencing data/studies and in silico methods that can be employed to interrogate the noncoding genome. We aim to provide an overview of available data resources as well as computational and molecular techniques that can help and guide the search for functional NCMs in cancer genomes. View Full-Text
Keywords: NCMs; cis-regulatory; high-throughput sequencing; computational analysis; cancer NCMs; cis-regulatory; high-throughput sequencing; computational analysis; cancer
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Patel, M.B.; Wang, J. The Identification and Interpretation of cis-Regulatory Noncoding Mutations in Cancer. High-Throughput 2019, 8, 1.

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