ArticleNature communications2021
Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Phylogenetic and phylodynamic approaches to understanding the evolution of non-small cell lung cancer using single-cell sequencing.Oncogene · 2026Article
- Review
- Geospatial genetic evolution and phenotypic plasticity in triple-negative breast cancer.Genome medicine · 2026Article
- Differential performance of strategies for single-cell whole-genome amplification.Cell reports methods · 2025Article
- Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints.bioRxiv : the preprint server for biology · 2025Article
- Application and research progress of single cell sequencing technology in leukemia.Frontiers in oncology · 2024Review
- SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data.Genome biology · 2022Article
- Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence.Bioinformatics (Oxford, England) · 2022Article
- SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing.Bioinformatics (Oxford, England) · 2022Article
- Scelestial: Fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm.PLoS computational biology · 2022Article
- CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data.Genome biology · 2022Article
- Somatic variant calling from single-cell DNA sequencing data.Computational and structural biotechnology journal · 2022Review
- doubletD: detecting doublets in single-cell DNA sequencing data.Bioinformatics (Oxford, England) · 2021Article
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Authors and funding
6 authors.
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Abstract
Accurate single cell mutational profiles can reveal genomic cell-to-cell heterogeneity. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. The resulting data violates assumptions of variant callers developed for bulk sequencing. Thus, only dedicated models accounting for amplification bias and errors can provide accurate calls. We present ProSolo for calling single nucleotide variants from multiple displacement amplified (MDA) single cell DNA sequencing data. ProSolo probabilistically models a single cell jointly with a bulk sequencing sample and integrates all relevant MDA biases in a site-specific and scalable-because computationally efficient-manner. This achieves a higher accuracy in calling and genotyping single nucleotide variants in single cells in comparison to state-of-the-art tools and supports imputation of insufficiently covered genotypes, when downstream tools cannot handle missing data. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly. ProSolo is implemented in an extendable framework, with code and usage at: https://github.com/prosolo/prosolo.
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