Aryana-bs: context-aware alignment of bisulfite-sequencing reads.

Background: DNA methylation is essential in various biological processes, including imprinting, development, inflammation, and numerous disorders, such as cancer. Bisulfite sequencing (BS) serves as the gold standard for measuring DNA methylation at single-base resolution by converting unmethylated cytosines to thymines while leaving methylated cytosines intact. However, this C-to-T conversion presents a well-known challenge in conventional short-read aligners, which treat these conversions as substitutions. Many aligners that require seed sequences fail when frequent C-to-T conversions occur over short distances, resulting in reduced alignment accuracy. To address this challenge, two alignment methods have been well established: three-letter alignment and wildcard alignment. Three-letter alignment faces the significant issue of data loss by converting all thymines to cytosines, which obscures meaningful information. On the other hand, wildcard alignment introduces a biased alignment, failing to treat reads from unmethylated and methylated regions equally, leading to artifacts in methylation level estimation and inaccuracies in quantifying DNA methylation. This work introduces ARYANA-BS, a novel BS aligner that diverges from conventional DNA aligners by directly integrating BS-specific base alterations within its alignment engine. Leveraging known DNA methylation patterns across different genomic contexts, ARYANA-BS constructs five indexes from the reference genome, aligns each read to all indexes, and selects the alignment with the minimum penalty. To further refine alignment accuracy, an optional Expectation-Maximization (EM) step is incorporated, which integrates methylation probability information into the decision-making process for choosing the optimal index for each read. This approach aims to enhance BS read alignment accuracy by accommodating the complexities of DNA methylation patterns across diverse genomic contexts.

Results: Experimental evaluations on both simulated and real data reveal that ARYANA-BS achieves state-of-the-art accuracy, maintaining competitive speed and memory efficiency.

Conclusions: ARYANA-BS significantly improves alignment accuracy for bisulfite sequencing data by effectively integrating DNA methylation-specific alterations and genomic context. It outperforms existing methods, such as BSMAP, bwa-meth, Bismark, BSBolt, and abismal, particularly in robustness against genomic biases and alignment of longer, higher-error reads, demonstrating suitability for cancer research and cell-free DNA studies. While the Expectation-Maximization (EM) algorithm provides only modest initial improvements, it establishes a valuable framework for future refinement and potential enhancements in sensitive applications.