DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/625578
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AbstractMotivation: Reconstructing the full- length expressed transcripts (a. k. a. the transcript assembly problem) from the short sequencing reads produced by RNA-seq protocol plays a central role in identifying novel genes and transcripts as well as in studying gene expressions and gene functions. A crucial step in transcript assembly is to accurately determine the splicing junctions and boundaries of the expressed transcripts from the reads alignment. In contrast to the splicing junctions that can be efficiently detected from spliced reads, the problem of identifying boundaries remains open and challenging, due to the fact that the signal related to boundaries is noisy and weak.
Results: We present DeepBound, an effective approach to identify boundaries of expressed transcripts from RNA-seq reads alignment. In its core DeepBound employs deep convolutional neural fields to learn the hidden distributions and patterns of boundaries. To accurately model the transition probabilities and to solve the label- imbalance problem, we novelly incorporate the AUC (area under the curve) score into the optimizing objective function. To address the issue that deep probabilistic graphical models requires large number of labeled training samples, we propose to use simulated RNA- seq datasets to train our model. Through extensive experimental studies on both simulation datasets of two species and biological datasets, we show that DeepBound consistently and significantly outperforms the two existing methods.
CitationShao M, Ma J, Wang S (2017) DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields. Bioinformatics 33: i267–i273. Available: http://dx.doi.org/10.1093/bioinformatics/btx267.
SponsorsM.S. was supported in part by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative Grant GBMF4554, the US National Science Foundation Grants CCF-1256087 and CCF-1319998, and the US National Institutes of Health Grant R01HG007104 to Carl Kinsford. S.W. was supported in part by the US National Institutes of Health Grant R01GM089753 and the US National Science Foundation Grant DBI-1564955 to Jinbo Xu.
PublisherOxford University Press (OUP)