游离DNA中遗传和表观遗传特征的整合式碱基分辨率分析

Background: Epigenetic and genetic alterations synergistically drive cancer initiation and progression, yet one blood draw rarely yields enough cell-free DNA (cfDNA) to profile both modalities. Current co-detection strategies either demand high tumor burden or custom chemistry and cannot be retro-applied to existing datasets. We present MMcall, a computational tool that reconstructs the original four-base genome from conventional bisulfite-sequencing reads and simultaneously detects mutations and methylation variants without new benchwork, delivering fully integrated genomic/epigenomic signatures from one single library. Methods: MMcall reconstructs the original four-base genome from standard bisulfite-converted reads by jointly modeling complementary top- and bottom-strand base counts. Because the G nucleotide on the strand opposing a C is unaffected by bisulfite conversion, the strand-specificity principle is used to restore pre-conversion sequence. A machine-learning error-suppression module is then applied to suppress the high technical noise inherent to bisulfite treatment. The algorithm simultaneously outputs methylation-variant allele frequency (MVAF) and somatic-variant allele frequency (SVAF) from the same library, enabling epigenetic and genetic profiling without additional wet-lab steps. Results: Benchmarked against 0 -1% tumor-fraction serial dilutions of Seraseq® ctDNA Reference Material and an in-house standard (OverC Monitor panel; 1000X methylation depth, 20000X mutation depth), MMcall demonstrated near-perfect concordance with expected methylation levels (R²>0.99) and detected mutations down to 0.25%. At 0.5-1% VAF, SVAF measurements by MMcall closely matched those obtained by ultra-deep sequencing (HS-UMI, 35000X), yielding > 99.7% NPA (95% CI: 99.3-99.9%) and > 86.9 % PPA (95 % CI: 77.8-93.3%). No false-positive calls were observed across predefined hotspot loci in any negative control, confirming robust suppression of technical noise. Conclusion: MMcall jointly calls mutations and methylation variants at single-base resolution without additional bench steps. The method offers a cost-effective route to richer molecular information for early cancer detection and minimal residual disease monitoring.