Bermuda: De novo assembly of transcripts with new insights for handling uneven coverage

Qingming Tang, Sheng Wang, Jian Peng, Jianzhu Ma, Jinbo Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Motivation: RNA-seq has made feasible the analysis of a whole set of expressed mRNAs. Mapping-based assembly of RNA-seq reads sometimes is infeasible due to lack of highquality references. However, de novo assembly is very challenging due to uneven expression levels among transcripts and also the read coverage variation within a single transcript. Existing methods either apply de Bruijn graphs of single-sized k-mers to assemble the full set of transcripts, or conduct multiple runs of assembly, but still apply graphs of single-sized k-mers at each run. However, a single k-mer size is not suitable for all the regions of the transcripts with varied coverage. Contribution: This paper presents a de novo assembler Bermuda with new insights for handling uneven coverage. Opposed to existing methods that use a single k-mer size for all the transcripts in each run of assembly, Bermuda self-adaptively uses a few k-mer sizes to assemble difierent regions of a single transcript according to their local coverage. As such, Bermuda can deal with uneven expression levels and coverage not only among transcripts, but also within a single transcript. Extensive tests show that Bermuda outperforms popular de novo assemblers in reconstructing unevenly-expressed transcripts with longer length, better contiguity and lower redundancy. Further, Bermuda is computationally efficient with moderate memory consumption. Availability: Supplementary materials are available through http://ttic.uchicago.edu/~qmtang/.

Original languageEnglish (US)
Title of host publicationBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages166-175
Number of pages10
ISBN (Electronic)9781450338530
DOIs
StatePublished - Sep 9 2015
Event6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015 - Atlanta, United States
Duration: Sep 9 2015Sep 12 2015

Publication series

NameBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Other

Other6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015
CountryUnited States
CityAtlanta
Period9/9/159/12/15

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Keywords

  • De novo assembly
  • Multiple k-mer
  • RNA-Seq
  • Uneven coverage

ASJC Scopus subject areas

  • Software
  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

Cite this

Tang, Q., Wang, S., Peng, J., Ma, J., & Xu, J. (2015). Bermuda: De novo assembly of transcripts with new insights for handling uneven coverage. In BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 166-175). (BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808719.2808736