Identifying differentially expressed transcripts from RNA-seq data with biological variation

@article{Glaus2011IdentifyingDE,
  title={Identifying differentially expressed transcripts from RNA-seq data with biological variation},
  author={Peter Glaus and Antti Honkela and Magnus Rattray},
  journal={Bioinformatics},
  year={2011},
  volume={28},
  pages={1721 - 1728}
}
Motivation: High-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression (DE) estimation requires a probabilistic approach to properly account for ambiguity caused by shared exons and finite read sampling as well as the intrinsic biological variance of transcript expression. Results: We present Bayesian inference of transcripts from sequencing data (BitSeq), a Bayesian approach for… 

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