BBMerge – Accurate paired shotgun read merging via overlap

TLDR

Merging paired‑end shotgun reads improves downstream bioinformatics tasks, but as sequencing data and CPU cores grow, speed, scalability, and accuracy of merging tools become increasingly critical. The authors designed BBMerge to maximize accuracy while minimizing processing time, enabling read merging on larger datasets and in error‑sensitive analyses. BBMerge was benchmarked against eight other merging tools, evaluating speed, accuracy, and scalability on synthetic and real‑world datasets. BBMerge achieved higher accuracy and faster performance than all tested tools and, by using k‑mer frequency to fill unsequenced gaps, it increased merge rates while maintaining or improving accuracy.

Abstract

Merging paired-end shotgun reads generated on high-throughput sequencing platforms can substantially improve various subsequent bioinformatics processes, including genome assembly, binning, mapping, annotation, and clustering for taxonomic analysis. With the inexorable growth of sequence data volume and CPU core counts, the speed and scalability of read-processing tools becomes ever-more important. The accuracy of shotgun read merging is crucial as well, as errors introduced by incorrect merging percolate through to reduce the quality of downstream analysis. Thus, we designed a new tool to maximize accuracy and minimize processing time, allowing the use of read merging on larger datasets, and in analyses highly sensitive to errors. We present BBMerge, a new merging tool for paired-end shotgun sequence data. We benchmark BBMerge by comparison with eight other widely used merging tools, assessing speed, accuracy and scalability. Evaluations of both synthetic and real-world datasets demonstrate that BBMerge produces merged shotgun reads with greater accuracy and at higher speed than any existing merging tool examined. BBMerge also provides the ability to merge non-overlapping shotgun read pairs by using k-mer frequency information to assemble the unsequenced gap between reads, achieving a significantly higher merge rate while maintaining or increasing accuracy.