DCSMAP: A Hybrid Linear-Pangenome Framework for Accurate and Efficient Read Alignment

Background: Linear-reference read alignment is computationally efficient but is susceptible to reference bias, whereas pangenome graph alignment better represents genetic diversity at substantially greater computational cost. Findings: We developed DCSMAP, a hybrid workflow that combines linear alignment with machine-learning-based selection of reads for remapping to a personalized pangenome graph, followed by local indel realignment. An XGBoost classifier trained on linear-alignment features achieved an area under the receiver operating characteristic curve of 0.9831 on held-out test data. Across 17 runs covering seven Genome in a Bottle samples, two sequencing platforms, and two read lengths, it selected a mean of 14.80% of read pairs for graph remapping. The optimized hybrid workflows reduced variant-calling errors by 46.66% relative to BWA-MEM2 and 8.39% relative to vg personalized-pangenome alignment. Heterozygous allelic balance indicated that the hybrid workflows substantially reduced reference bias relative to BWA-MEM2 linear alignment, yielding reference bias comparable to that of the pangenome alignment. On HG003 DNBSEQ PE150 data, DCSMAP achieved a 2 to 2.5 times speedup over the personalized-pangenome workflow; both workflows used 32 threads. Efficiency gains arose from both the hybrid architecture and tool-level optimizations, which accelerated personalized-graph construction by 2.5 times, graph alignment by 1.2 times, and indel realignment by 2.5 times. Conclusions: Selectively applying graph alignment to reads with potentially unreliable linear placements retains the accuracy benefits of personalized pangenomes while substantially reducing computation. DCSMAP provides an accurate and resource-efficient approach to short-read mapping on a standard CPU server.