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An empirical Bayes change-point model for identifying 3′ and 5′ alternative splicing by next-generation RNA sequencing
Author(s) -
Jie Zhang,
Zhi Wei
Publication year - 2016
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/btw060
Subject(s) - computer science , bayes' theorem , annotation , software , java , data mining , machine learning , computational biology , bayesian probability , artificial intelligence , biology , programming language
Next-generation RNA sequencing (RNA-seq) has been widely used to investigate alternative isoform regulations. Among them, alternative 3 ': splice site (SS) and 5 ': SS account for more than 30% of all alternative splicing (AS) events in higher eukaryotes. Recent studies have revealed that they play important roles in building complex organisms and have a critical impact on biological functions which could cause disease. Quite a few analytical methods have been developed to facilitate alternative 3 ': SS and 5 ': SS studies using RNA-seq data. However, these methods have various limitations and their performances may be further improved.

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