Shi, XuWang, XiaoNeuwald, Andrew F.Hilakivi-Clarke, LeenaClarke, RobertXuan, Jianhua2022-03-302022-03-302021-09-032045-232217663http://hdl.handle.net/10919/109501De novo transcriptome assembly from billions of RNA-seq reads is very challenging due to alternative splicing and various levels of expression, which often leads to incorrect, mis-assembled transcripts. BayesDenovo addresses this problem by using both a read-guided strategy to accurately reconstruct splicing graphs from the RNA-seq data and a Bayesian strategy to estimate, from these graphs, the probability of transcript expression without penalizing poorly expressed transcripts. Simulation and cell line benchmark studies demonstrate that BayesDenovo is very effective in reducing false positives and achieves much higher accuracy than other assemblers, especially for alternatively spliced genes and for highly or poorly expressed transcripts. Moreover, BayesDenovo is more robust on multiple replicates by assembling a larger portion of common transcripts. When applied to breast cancer data, BayesDenovo identifies phenotype-specific transcripts associated with breast cancer recurrence.application/pdfenCreative Commons Attribution 4.0 InternationalArticle - Refereedhttps://doi.org/10.1038/s41598-021-97015-x11134480063