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Characterizing Mutational Load and Clonal Composition of Human Blood
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Reconstructing phylogenetic trees from genome-wide somatic mutations in clonal samples.

Tim H H Coorens1,2, Michael Spencer Chapman3,4,5, Nicholas Williams6

  • 1Wellcome Sanger Institute, Hinxton, UK. tcoorens@broadinstitute.org.

Nature Protocols
|February 23, 2024
PubMed
Summary

Sequoia reconstructs cellular lineage trees using somatic mutations as barcodes. This computational pipeline aids in understanding cell development and tissue dynamics in health and disease.

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Area of Science:

  • Genomics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Phylogenetic trees visualize evolutionary history across various organisms and cell types.
  • Somatic mutations discovered through whole-genome sequencing act as natural barcodes for reconstructing cellular developmental history.

Purpose of the Study:

  • To introduce Sequoia, a computational pipeline for reconstructing lineage trees from normal cell clones.
  • To enable detailed analysis of cellular development and tissue dynamics.

Main Methods:

  • Candidate somatic mutations are identified and filtered against the human reference genome to remove germline and artifactual variants.
  • Phylogeny reconstruction is performed using a maximum parsimony framework.
  • A maximum likelihood framework is employed to map mutations onto the phylogenetic tree branches.

Main Results:

  • Sequoia generates phylogenetic trees representing cellular lineage history.
  • The pipeline is flexible, customizable, and can be applied to various clonal somatic mutation datasets, including single-cell DNA sequencing data.
  • Core script runtime is typically minutes to an hour for moderate datasets.

Conclusions:

  • Sequoia provides a robust method for reconstructing cellular lineage trees from somatic mutations.
  • The generated phylogenies support diverse downstream analyses, including embryonic development, tissue dynamics, and mutational signature studies.
  • Requires bioinformatic expertise in R and access to high-performance computing clusters for large datasets.