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Motivation: RNA-seq has made feasible the analysis of a whole set ofexpressed mRNAs. Mapping-based assembly of RNA-seq reads sometimes isinfeasible due to lack of high-quality references. However, de novo assembly isvery challenging due to uneven expression levels among transcripts and also theread coverage variation within a single transcript. Existing methods eitherapply de Bruijn graphs of single-sized k-mers to assemble the full set oftranscripts, or conduct multiple runs of assembly, but still apply graphs ofsingle-sized k-mers at each run. However, a single k-mer size is not suitablefor all the regions of the transcripts with varied coverage. Contribution: Thispaper presents a de novo assembler Bermuda with new insights for handlinguneven coverage. Opposed to existing methods that use a single k-mer size forall the transcripts in each run of assembly, Bermuda self-adaptively uses a fewk-mer sizes to assemble different regions of a single transcript according totheir local coverage. As such, Bermuda can deal with uneven expression levelsand coverage not only among transcripts, but also within a single transcript.Extensive tests show that Bermuda outperforms popular de novo assemblers inreconstructing unevenly-expressed transcripts with longer length, bettercontiguity and lower redundancy. Further, Bermuda is computationally efficientwith moderate memory consumption.

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