Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer
Author(s) -
Bahman Afsari,
Theresa Guo,
Michael Considine,
Liliana Florea,
Luciane T. Kagohara,
Genevieve Stein-O’Brien,
Dylan Z. Kelley,
Emily Flam,
Kristina Diana A. Zambo,
Patrick K. Ha,
Donald Geman,
Michael F. Ochs,
Joseph A. Califano,
Daria A. Gaykalova,
Alexander V. Favorov,
Elana J. Fertig
Publication year - 2018
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/bty004
Subject(s) - alternative splicing , splice , gene isoform , biology , computational biology , gene , phenotype , genetic heterogeneity , rna splicing , genetics , bioconductor , rna
Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches.
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