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anexVis: visual analytics framework for analysis of RNA expression
Author(s) -
Diem-Trang Tran,
Tian Zhang,
Ryan Stutsman,
Matthew Might,
Umesh R. Desai,
Balagurunathan Kuberan
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/bty122
Subject(s) - analytics , visual analytics , computer science , expression (computer science) , computational biology , data science , visualization , data mining , biology , programming language
Although RNA expression data are accumulating at a remarkable speed, gaining insights from them still requires laborious analyses, which hinder many biological and biomedical researchers. This report introduces a visual analytics framework that applies several well-known visualization techniques to leverage understanding of an RNA expression dataset. Our analyses on glycosaminoglycan-related genes have demonstrated the broad application of this tool, anexVis (analysis of RNA expression), to advance the understanding of tissue-specific glycosaminoglycan regulation and functions, and potentially other biological pathways.

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