Powerful differential expression analysis incorporating network topology for next-generation sequencing data | Zendy

Malathi S.I. Dona | Zendy; Luke A. Prendergast | Zendy; Suresh Mathivanan | Zendy; Shivakumar Keerthikumar | Zendy; Agus Salim | Zendy

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Powerful differential expression analysis incorporating network topology for next-generation sequencing data

Author(s) -

Malathi S.I. Dona,

Luke A. Prendergast,

Suresh Mathivanan,

Shivakumar Keerthikumar,

Agus Salim

Publication year - 2017

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/btw833

Subject(s) - computer science , differential (mechanical device) , computational biology , expression (computer science) , network topology , topology (electrical circuits) , data mining , computer network , biology , mathematics , programming language , engineering , combinatorics , aerospace engineering

RNA-seq has become the technology of choice for interrogating the transcriptome. However, most methods for RNA-seq differential expression (DE) analysis do not utilize prior knowledge of biological networks to detect DE genes. With the increased availability and quality of biological network databases, methods that can utilize this prior knowledge are needed and will offer biologists with a viable, more powerful alternative when analyzing RNA-seq data.

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