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Article: Finding cancer driver mutations in the era of big data research

TitleFinding cancer driver mutations in the era of big data research
Authors
KeywordsBig data
Cancer
Cancer genomics
Driver mutation
Genome
Mutational signatures
Selection
Sequencing
Somatic
Issue Date2019
PublisherSpringer Verlag. The Journal's web site is located at https://www.springer.com/journal/12551
Citation
Biophysical Reviews, 2019, v. 11, p. 21-29 How to Cite?
AbstractIn the last decade, the costs of genome sequencing have decreased considerably. The commencement of large-scale cancer sequencing projects has enabled cancer genomics to join the big data revolution. One of the challenges still facing cancer genomics research is determining which are the driver mutations in an individual cancer, as these contribute only a small subset of the overall mutation profile of a tumour. Focusing primarily on somatic single nucleotide mutations in this review, we consider both coding and non-coding driver mutations, and discuss how such mutations might be identified from cancer sequencing datasets. We describe some of the tools and database that are available for the annotation of somatic variants and the identification of cancer driver genes. We also address the use of genome-wide variation in mutation load to establish background mutation rates from which to identify driver mutations under positive selection. Finally, we describe the ways in which mutational signatures can act as clues for the identification of cancer drivers, as these mutations may cause, or arise from, certain mutational processes. By defining the molecular changes responsible for driving cancer development, new cancer treatment strategies may be developed or novel preventative measures proposed.
Persistent Identifierhttp://hdl.handle.net/10722/267376
ISSN
2023 Impact Factor: 4.9
2023 SCImago Journal Rankings: 1.145

 

DC FieldValueLanguage
dc.contributor.authorPoulos, RC-
dc.contributor.authorWong, WHJ-
dc.date.accessioned2019-02-18T09:00:45Z-
dc.date.available2019-02-18T09:00:45Z-
dc.date.issued2019-
dc.identifier.citationBiophysical Reviews, 2019, v. 11, p. 21-29-
dc.identifier.issn1867-2450-
dc.identifier.urihttp://hdl.handle.net/10722/267376-
dc.description.abstractIn the last decade, the costs of genome sequencing have decreased considerably. The commencement of large-scale cancer sequencing projects has enabled cancer genomics to join the big data revolution. One of the challenges still facing cancer genomics research is determining which are the driver mutations in an individual cancer, as these contribute only a small subset of the overall mutation profile of a tumour. Focusing primarily on somatic single nucleotide mutations in this review, we consider both coding and non-coding driver mutations, and discuss how such mutations might be identified from cancer sequencing datasets. We describe some of the tools and database that are available for the annotation of somatic variants and the identification of cancer driver genes. We also address the use of genome-wide variation in mutation load to establish background mutation rates from which to identify driver mutations under positive selection. Finally, we describe the ways in which mutational signatures can act as clues for the identification of cancer drivers, as these mutations may cause, or arise from, certain mutational processes. By defining the molecular changes responsible for driving cancer development, new cancer treatment strategies may be developed or novel preventative measures proposed.-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at https://www.springer.com/journal/12551-
dc.relation.ispartofBiophysical Reviews-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/[insert DOI]-
dc.subjectBig data-
dc.subjectCancer-
dc.subjectCancer genomics-
dc.subjectDriver mutation-
dc.subjectGenome-
dc.subjectMutational signatures-
dc.subjectSelection-
dc.subjectSequencing-
dc.subjectSomatic-
dc.titleFinding cancer driver mutations in the era of big data research-
dc.typeArticle-
dc.identifier.emailWong, WHJ: jwhwong@hku.hk-
dc.identifier.authorityWong, WHJ=rp02363-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s12551-018-0415-6-
dc.identifier.scopuseid_2-s2.0-85061767647-
dc.identifier.hkuros296868-
dc.identifier.volume11-
dc.identifier.spage21-
dc.identifier.epage29-
dc.publisher.placeGermany-
dc.identifier.issnl1867-2450-

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