METHOD Open Access

摘要: 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 transcriptome assembly built on Bayesian model of the process. Under this model, samples posterior distribution over their abundance values are obtained using Gibbs sampling. By frequency at which observed during sampling to select final assembly, demonstrate marked improvements in sensitivity precision state-of-the-art assemblers both simulated real data. Bayesembler is available https://github.com/bioinformatics-centre/bayesembler. Background The massive throughput second-generation technologies rapidly changing our ability explore complex transcriptomic landscapes as it can reveal sample-specific variants abundances (i.e. expression levels). However, due combination alternative splicing short fragments characteristic these methods, often not possible determine directly exons linked splice longer sequence distances. Instead, variation between variants, read coverage along junctions be used infer most likely exon combinations.