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Related Concept Videos

Mutations01:39

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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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Linked-read analysis identifies mutations in single-cell DNA-sequencing data.

Craig L Bohrson1,2, Alison R Barton1,2, Michael A Lodato3,4,5

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Nature Genetics
|March 20, 2019
PubMed
Summary
This summary is machine-generated.

Whole-genome sequencing of single cells can reveal mutations, but distinguishing artifacts from real ones is hard. Linked-read analysis (LiRA) accurately identifies somatic single-nucleotide variants (sSNVs) using read-level phasing, enabling mutation signature and rate analysis.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Cancer Research

Background:

  • Whole-genome sequencing (WGS) of single cells offers insights into tissue mutational heterogeneity.
  • Distinguishing amplification artifacts from true somatic mutations in single-cell WGS data is a significant challenge.

Purpose of the Study:

  • To develop and present a novel method for accurate identification of somatic single-nucleotide variants (sSNVs) in single-cell whole-genome sequencing data.
  • To enable robust characterization of mutational signatures and estimation of somatic mutation rates at the single-cell level.

Main Methods:

  • Introduced linked-read analysis (LiRA), a method utilizing read-level phasing.
  • Employed nearby germline heterozygous polymorphisms to phase reads and distinguish true variants from artifacts.

Main Results:

  • LiRA accurately identifies somatic single-nucleotide variants (sSNVs) in single-cell WGS.
  • The method facilitates the characterization of mutational signatures within individual cells.
  • Enables reliable estimation of somatic mutation rates in single cells.

Conclusions:

  • Linked-read analysis (LiRA) provides a robust solution for accurate sSNV detection in single-cell genomics.
  • This method advances the study of mutational processes in normal and diseased tissues at the cellular level.