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

TL;DR

Bermuda is a novel de novo transcript assembler that adaptively uses multiple k-mer sizes to effectively handle uneven coverage within and across transcripts, outperforming existing methods in accuracy and efficiency.

Contribution

This paper introduces Bermuda, a de novo assembler that self-adaptively employs multiple k-mer sizes for better handling of uneven transcript coverage, a significant improvement over single k-mer approaches.

Findings

Bermuda reconstructs longer, more contiguous transcripts.

It outperforms existing assemblers in accuracy and redundancy.

Bermuda is computationally efficient with moderate memory use.

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 high-quality 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 different 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.