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SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data.

ABSTRACT: We present SIEVE, a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from single-cell DNA sequencing. SIEVE leverages raw read counts for all nucleotides and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods in phylogenetic reconstruction and variant calling accuracy, especially in the inference of homozygous variants. Applying SIEVE to three datasets, one for triple-negative breast (TNBC), and two for colorectal cancer (CRC), we find that double mutant genotypes are rare in CRC but unexpectedly frequent in the TNBC samples.

SUBMITTER: Kang S

PROVIDER: S-EPMC9714196 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data.

Kang Senbai S Borgsmüller Nico N Valecha Monica M Kuipers Jack J Alves Joao M JM Prado-López Sonia S Chantada Débora D Beerenwinkel Niko N Posada David D Szczurek Ewa E

Genome biology 20221130 1

We present SIEVE, a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from single-cell DNA sequencing. SIEVE leverages raw read counts for all nucleotides and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods in phylogenetic reconstruction and variant calling accuracy, especially in the inference of homozygous variants. Applying SIEVE to three datasets, one for triple-negative bre ...[more]

PMID: 36451239

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