Deconvolving tumor purity and ploidy by integrating copy number alterations and loss of heterozygosity
Author(s) -
Yi Li,
Xiaohui Xie
Publication year - 2014
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btu174
Subject(s) - identifiability , loss of heterozygosity , computer science , python (programming language) , dna sequencing , genome , biology , computational biology , data mining , algorithm , genetics , machine learning , gene , allele , operating system
Next-generation sequencing (NGS) has revolutionized the study of cancer genomes. However, the reads obtained from NGS of tumor samples often consist of a mixture of normal and tumor cells, which themselves can be of multiple clonal types. A prominent problem in the analysis of cancer genome sequencing data is deconvolving the mixture to identify the reads associated with tumor cells or a particular subclone of tumor cells. Solving the problem is, however, challenging because of the so-called 'identifiability problem', where different combinations of tumor purity and ploidy often explain the sequencing data equally well.
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