SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data | Zendy

Xu Shi | Zendy; Xiao Wang | Zendy; TianLi Wang | Zendy; Leena HilakiviClarke | Zendy; Robert Clarke | Zendy; Jianhua Xuan | Zendy

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SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data

Author(s) -

Xu Shi,

Xiao Wang,

TianLi Wang,

Leena HilakiviClarke,

Robert Clarke,

Jianhua Xuan

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/btx557

Subject(s) - rna seq , overfitting , identification (biology) , computer science , gibbs sampling , computational biology , bayesian probability , alternative splicing , gene isoform , data mining , transcriptome , biology , machine learning , artificial intelligence , gene , gene expression , genetics , botany , artificial neural network

Recent advances in high-throughput RNA sequencing (RNA-seq) technologies have made it possible to reconstruct the full transcriptome of various types of cells. It is important to accurately assemble transcripts or identify isoforms for an improved understanding of molecular mechanisms in biological systems.

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