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Related Experiment Video

Updated: Nov 9, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Moss enables high sensitivity single-nucleotide variant calling from multiple bulk DNA tumor samples.

Chuanyi Zhang1, Mohammed El-Kebir2, Idoia Ochoa3,4

  • 1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

Nature Communications
|April 14, 2021
PubMed
Summary

Moss identifies low-frequency somatic single-nucleotide variants (SNVs) across multiple tumor samples. This method enhances variant calling sensitivity and precision for cancer genomics research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Intra-tumor heterogeneity complicates somatic single-nucleotide variant (SNV) identification.
  • Low-frequency SNVs are difficult to distinguish from sequencing artifacts.
  • Existing multi-sample SNV callers lack high sensitivity.

Purpose of the Study:

  • To develop a sensitive method for identifying low-frequency SNVs in multiple tumor samples.
  • To enhance existing single-sample SNV callers for multi-sample analysis.
  • To improve the accuracy of variant calling in cancer genomics.

Main Methods:

  • Developed Moss, a method to identify recurrent low-frequency SNVs across multiple samples from the same tumor.
  • Integrated Moss with existing single-sample SNV callers.
  • Evaluated Moss using simulated datasets and multi-sample cancer datasets (hepatocellular carcinoma, acute myeloid leukemia, colorectal cancer).

Main Results:

  • Moss improved recall while maintaining high precision on simulated data.
  • Identified new low-frequency variants in real cancer datasets meeting manual review criteria.
  • Detected variants in more tumor samples than single-sample callers, consistent with mutational signatures.

Conclusions:

  • Moss enhances the sensitivity of SNV calling for low-frequency variants.
  • The method effectively leverages multi-sample tumor sequencing data.
  • Moss facilitates more detailed downstream analyses in cancer genomics.