DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields

Mingfu Shao, Jianzhu Ma, Sheng Wang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Motivation: 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.
Original languageEnglish (US)
Pages (from-to)i267-i273
Number of pages1
JournalBioinformatics
Volume33
Issue number14
DOIs
StatePublished - Apr 20 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: M.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.

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