TempSnap-Trace: A temporal snapshot-based framework for haplotype network tracing.

To address the challenge of tracking large-scale viral evolutionary history, this study introduces TempSnap-Trace, a novel computational framework designed for this purpose. The methodology processes genomic data to generate variant-featured haplotype strings. Subsequently, the minimum-cost arborescence network (McAN) algorithm is employed to infer phylogenetic relationships, from which weighted temporal snapshot networks are constructed. Core evolutionary nodes are then identified using community detection approach. This method integrates mutation sites, network topology, and directional information to reconstruct backbone evolutionary pathways based on inter-community similarity over time. Compared to community detection methods based on unweighted graphs, this approach increased modularity by 17.4 % and reduced code length by 30.1 %. This approach was validated on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data, where it successfully identified the French B.1 lineage as a key transmission hub and accurately traced the backbone evolutionary paths of major variants, including Beta (B.1.351) and Zeta (P.2). Its utility was further demonstrated by delineating the evolutionary trajectory of the Mpox IIb B.1 variant. Furthermore, through extensive parallelization and algorithmic optimizations, the framework exhibits exceptional computational efficiency and scalability: in benchmark tests against the viral genome evolutionary analysis system (VENAS) workflow, our end-to-end pipeline demonstrated a 27-fold speedup (55.1 s vs. 1,492.6 s) and successfully processed a massive dataset in 23.6 h that caused VENAS to fail due to memory limitations. These findings validate the utility of TempSnap-Trace for large-scale viral surveillance, highlighting its distinct advantages in cross-border transmission warning and variant origin tracing. The code of TempSnap-Trace is available at https://github.com/Jiajun0413/TempSnap-Trace.