Year: 2014
Type: article
Abstract: RNA sequencing allows for simultaneous transcript discovery and quantification, but reconstructing complete transcripts from such data remains difficult. Here, we introduce Bayesembler, a novel probabilistic method for transcriptome assembly built on a Bayesian model of the RNA sequencing process. Under this model, samples from the posterior distribution over transcripts and their abundance values are obtained using Gibbs sampling. By using the frequency at which transcripts are observed during sampling to select the final assembly, we demonstrate marked improvements in sensitivity and precision over state-of-the-art assemblers on both simulated and real data. Bayesembler is available at https://github.com/bioinformatics-centre/bayesembler.
Source: Genome biology
Authors: Lasse Maretty, Jonas Sibbesen, Anders Krogh
Institution: University of Copenhagen
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,490
Funders: Novo Nordisk, Novo Nordisk Fonden
OpenAlex ID: W2011565693