Deriving Ranges of Optimal Estimated Transcript Expression due to Nonidentifiability.

ABSTRACT: Current expression quantification methods suffer from a fundamental but undercharacterized type of error: the most likely estimates for transcript abundances are not unique. This means multiple estimates of transcript abundances generate the observed RNA-seq reads with equal likelihood, and the underlying true expression cannot be determined. This is called nonidentifiability in probabilistic modeling. It is further exacerbated by incomplete reference transcriptomes where reads may be sequenced from unannotated transcripts. Graph quantification is a generalization to transcript quantification, accounting for the reference incompleteness by allowing exponentially many unannotated transcripts to express reads. We propose methods to calculate a "confidence range of expression" for each transc

SUBMITTER: Zheng H 

PROVIDER: S-EPMC8892959 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature