Literature DB >> 24870132

Studying cancer genomics through next-generation DNA sequencing and bioinformatics.

Maria A Doyle1, Jason Li, Ken Doig, Andrew Fellowes, Stephen Q Wong.   

Abstract

Cancer is a complex disease driven by multiple mutations acquired over the lifetime of the cancer cells. These alterations, termed somatic mutations to distinguish them from inherited germline mutations, can include single-nucleotide substitutions, insertions, deletions, copy number alterations, and structural rearrangements. A patient's cancer can contain a combination of these aberrations, and the ability to generate a comprehensive genetic profile should greatly improve patient diagnosis and treatment. Next-generation sequencing has become the tool of choice to uncover multiple cancer mutations from a single tumor source, and the falling costs of this rapid high-throughput technology are encouraging its transition from basic research into a clinical setting. However, the detection of mutations in sequencing data is still an evolving area and cancer genomic data requires some special considerations. This chapter discusses these aspects and gives an overview of current bioinformatics methods for the detection of somatic mutations in cancer sequencing data.

Entities:  

Mesh:

Year:  2014        PMID: 24870132     DOI: 10.1007/978-1-4939-0847-9_6

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  6 in total

1.  Inference of mutability landscapes of tumors from single cell sequencing data.

Authors:  Viachaslau Tsyvina; Alex Zelikovsky; Sagi Snir; Pavel Skums
Journal:  PLoS Comput Biol       Date:  2020-11-30       Impact factor: 4.475

2.  Next Generation Sequencing: From Research Area to Clinical Practice.

Authors:  Chiara Di Resta; Maurizio Ferrari
Journal:  EJIFCC       Date:  2018-11-07

3.  Inference of clonal selection in cancer populations using single-cell sequencing data.

Authors:  Pavel Skums; Viachaslau Tsyvina; Alex Zelikovsky
Journal:  Bioinformatics       Date:  2019-07-15       Impact factor: 6.937

4.  COPB2: A Novel Prognostic Biomarker That Affects Progression of HCC.

Authors:  Jiayao Zhang; Xiaoyu Wang; Guangbing Li; Jingyi He; Ziwen Lu; Yang Yang; Yong Jiang; Liyong Jiang; Feiyu Li; Jun Liu
Journal:  Biomed Res Int       Date:  2021-03-20       Impact factor: 3.411

5.  Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology.

Authors:  Javier Arsuaga; Tyler Borrman; Raymond Cavalcante; Georgina Gonzalez; Catherine Park
Journal:  Microarrays (Basel)       Date:  2015-08-12

6.  High expression of COPB2 predicts adverse outcomes: A potential therapeutic target for glioma.

Authors:  Yan Zhou; Xuan Wang; Xing Huang; Xu-Dong Li; Kai Cheng; Hao Yu; Yu-Jie Zhou; Peng Lv; Xiao-Bing Jiang
Journal:  CNS Neurosci Ther       Date:  2019-11-11       Impact factor: 5.243

  6 in total

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