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Related Experiment Video

Updated: Jan 14, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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CoDing Sequence Typer (CDST): a fast, simple, decentralized and interoperable solution for bacterial genomic typing

Menghan Li1,2, Séamus Fanning1,2, Zhemin Zhou3,4,5

  • 1National Health Commission Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing 100021, PR China.

Microbial Genomics
|October 23, 2025
PubMed
Summary
This summary is machine-generated.

We developed the CoDing Sequence Typer (CDST) pipeline for decentralized bacterial typing. This open-source tool efficiently computes genomic distances and identifies population structures, aiding in surveillance and outbreak investigations.

Keywords:
Salmonella entericabacteria genome typingcore-genome multilocus sequence typing (cgMLST)outbreak surveillance

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate bacterial typing is crucial for public health surveillance and outbreak investigations.
  • Existing methods like cgMLST and wgMLST can be computationally intensive and require central databases.
  • There is a need for efficient, privacy-preserving, and decentralized typing frameworks.

Purpose of the Study:

  • To introduce a novel, decentralized framework for bacterial typing using predicted coding sequences (CDSs).
  • To implement this framework in the open-source CoDing Sequence Typer (CDST) pipeline.
  • To evaluate CDST's performance against established genomic analysis methods.

Main Methods:

  • Developed a decentralized MD5 hash-based framework for indexing CDSs and computing genomic distances.
  • Implemented the framework in the CoDing Sequence Typer (CDST) pipeline.
  • Applied CDST to 1,961 *Salmonella enterica* genomes and benchmarked against cgMLST, wgMLST, SNP, Mash, and Split Kmer analyses.

Main Results:

  • CDST produced distance matrices highly concordant with established typing schemes (cgMLST, wgMLST, SNP, Mash, Split Kmer).
  • CDST demonstrated significant speed improvements (~8x faster) and reduced storage requirements (~4% of FASTA size) compared to cg/wgMLST.
  • Identified three optimal resolution levels (HC67, HC186, HC441) for bacterial population structure analysis, consistent across species (*S. enterica*, *L. monocytogenes*, *E. coli*).

Conclusions:

  • CDST offers a reproducible, privacy-preserving, and computationally efficient solution for bacterial typing.
  • The pipeline provides a scalable and interoperable framework for bacterial population structure analysis.
  • CDST is suitable for diverse applications, including epidemiological surveillance and outbreak investigations across laboratories.