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Establishment of reference standards for multifaceted mosaic variant analysis.

Yoo-Jin Ha1, Myung Joon Oh2, Junhan Kim1

  • 1Department of Biomedical Systems Informatics, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.

Scientific Data
|February 4, 2022
PubMed
Summary

Researchers developed a new reference standard for detecting somatic mosaicism using ultra-deep sequencing. This dataset aids in improving variant detection accuracy and developing new algorithms for genome research.

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Area of Science:

  • Genomics
  • Molecular Biology

Background:

  • Detecting somatic mosaicism in non-proliferative cells is challenging due to the absence of a ground truth.
  • Current detection strategies for mosaic variants lack validated accuracy.

Purpose of the Study:

  • To create a reference standard dataset for evaluating somatic mosaicism detection.
  • To provide ultra-deep sequenced data for optimizing variant detection algorithms.

Main Methods:

  • Generated reference standards using mixtures of cell lines with known genotypes.
  • Performed ultra-deep whole-exome sequencing (WES).
  • Included a comprehensive set of mosaic single-nucleotide variants (SNVs), insertion-deletion mutations (INDELs), non-variant sites, and germline variants.

Main Results:

  • Cataloged 386,613 mosaic SNVs and INDELs with variant allele frequencies (VAFs) from 0.5% to 56%.
  • Included 35,113,417 non-variant sites and 19,936 germline variant sites as controls.
  • Identified 741 potential inter-sample relationships based on variant sharing and VAF asymmetry.

Conclusions:

  • The generated reference data is crucial for optimizing current mosaic variant detection methods.
  • This dataset will facilitate the development of improved algorithms for future somatic mosaicism studies.