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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

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Benchmarking 13 tools for mutational signature attribution, including a new and improved algorithm.

Nanhai Jiang1,2, Yang Wu1,2, Steven G Rozen1,2,3

  • 1Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore.

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|February 6, 2025
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Summary

Benchmarking mutational signature attribution methods reveals challenges in identifying cancer mutation patterns. The Presence Attribute Signature Activity (PASA) approach shows promise, but optimal method selection varies by cancer type.

Keywords:
mSigActmutational signature activitymutational signature analysismutational signature attributionmutational signature exposuresoftware benchmarking

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Mutational signatures provide insights into endogenous and exogenous factors driving cancer development.
  • Accurate identification and quantification of mutational signatures (signature attribution) are crucial for understanding cancer etiology.
  • Limited benchmarking exists for signature attribution methods, hindering reliable analysis.

Purpose of the Study:

  • To benchmark existing and novel approaches for mutational signature attribution.
  • To evaluate method performance across different mutation types (single-base substitutions, doublet-base substitutions, indels) and cancer types.
  • To provide guidance on selecting appropriate signature attribution tools for specific research objectives.

Main Methods:

  • Developed large synthetic datasets (2700 samples) simulating mutational repertoires of nine cancer types.
  • Benchmarked 13 signature attribution approaches, including a new method, Presence Attribute Signature Activity (PASA).
  • Assessed performance for single-base substitutions, doublet-base substitutions, and small insertions/deletions.

Main Results:

  • PASA and MuSiCal demonstrated superior performance for single-base substitutions across all cancer types.
  • Method rankings varied significantly by cancer type for all mutation categories.
  • PASA generally outperformed other methods for doublet-base substitutions and indels, though performance varied by cancer type.

Conclusions:

  • Signature attribution is inherently challenging due to multiple plausible solutions and combinatorial complexity.
  • No single method excels across all cancer types and mutation types, necessitating careful tool selection.
  • The study provides data-driven guidance for choosing optimal mutational signature attribution approaches based on cancer type and study goals.