Letting the Data Speak for Themselves: a Fully Bayesian Approach to 
Transcriptome Assembly

Schulz, Marcel H.

doi:10.1186/s13059-014-0498-8

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Letting the Data Speak for Themselves: a Fully Bayesian Approach to Transcriptome Assembly

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Schulz, Marcel H.
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Schulz, M. H. (2014). Letting the Data Speak for Themselves: a Fully Bayesian Approach to Transcriptome Assembly. Genome Biology, 15(10): 498. doi:10.1186/s13059-014-0498-8.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0024-934A-8

Abstract

A novel method for transcriptome assembly, Bayesembler, provides greater accuracy without sacrifice of computational speed, and particular advantages for alternative transcripts expressed at low levels.