FBB: a fast Bayesian-bound tool to calibrate RNA-seq aligners
Author(s) -
Irene Rodríguez-Luján,
Jeff Hasty,
Ramón Huerta
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/btw608
Subject(s) - bayesian probability , rna seq , computer science , artificial intelligence , biology , genetics , transcriptome , gene , gene expression
Despite RNA-seq reads provide quality scores that represent the probability of calling a correct base, these values are not probabilistically integrated in most alignment algorithms. Based on the quality scores of the reads, we propose to calculate a lower bound of the probability of alignment of any fast alignment algorithm that generates SAM files. This bound is called Fast Bayesian Bound (FBB) and serves as a canonical reference to compare alignment results across different algorithms. This Bayesian Bound intends to provide additional support to the current state-of-the-art aligners, not to replace them.
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