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Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

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Published on: May 6, 2010

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

Jiajun Liu^1,2, Decheng Li^1,2, Yixue Li^1,2,3,4,5

¹Department of Artificial Intelligence and Digital Health, CAS Engineering Laboratory for Nutrition, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

Biosafety and Health

|January 16, 2026

Summary

This summary is machine-generated.

TempSnap-Trace, a new computational framework, tracks viral evolution by analyzing genomic data and inferring phylogenetic networks. It offers significant speed and scalability improvements for large-scale viral surveillance and variant tracing.

Keywords:

Community detection Evolutionary path Haplotype network Mpox Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

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Area of Science:

Virology
Computational Biology
Genomics

Background:

Tracking large-scale viral evolutionary history presents significant computational challenges.
Existing methods may struggle with the scale and complexity of viral genomic data.

Purpose of the Study:

Introduce TempSnap-Trace, a novel computational framework for tracking large-scale viral evolutionary history.
Enhance viral surveillance and variant origin tracing capabilities.

Main Methods:

Generate variant-featured haplotype strings from genomic data.
Employ minimum-cost arborescence network (McAN) algorithm for phylogenetic inference.
Construct weighted temporal snapshot networks and identify core evolutionary nodes via community detection.

Main Results:

The framework integrates mutation sites, network topology, and directional information to reconstruct evolutionary pathways.
Achieved 17.4% increased modularity and 30.1% reduced code length compared to unweighted graph methods.
Successfully traced SARS-CoV-2 and Mpox variants, identified transmission hubs, and demonstrated a 27-fold speedup over VENAS, processing data that VENAS failed on due to memory limits.

Conclusions:

TempSnap-Trace is computationally efficient and scalable for large-scale viral surveillance.
The framework provides distinct advantages for cross-border transmission warning and variant origin tracing.
Validated utility on SARS-CoV-2 and Mpox, demonstrating robust evolutionary path reconstruction.