ArticleBioinformatics (Oxford, England)2022
SCONCE: a method for profiling copy number alterations in cancer evolution using single-cell whole genome sequencing.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 22 citations in OpenAlex.
- Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing.Bioinformatics (Oxford, England) · 2024Article
- SCCNAInfer: a robust and accurate tool to infer the absolute copy number on scDNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- LoRA-TV: read depth profile-based clustering of tumor cells in single-cell sequencing.Briefings in bioinformatics · 2024Article
- CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data.Briefings in bioinformatics · 2024Article
- On the core segmentation algorithms of copy number variation detection tools.Briefings in bioinformatics · 2024Article
- Inferring single-cell copy number profiles through cross-cell segmentation of read counts.BMC genomics · 2024Article
- Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review.Global challenges (Hoboken, NJ) · 2024Review
- A zero-agnostic model for copy number evolution in cancer.PLoS computational biology · 2023Article
- CNAsim: improved simulation of single-cell copy number profiles and DNA-seq data from tumors.Bioinformatics (Oxford, England) · 2023Article
- CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples.Genome biology · 2023Article
- A zero-agnostic model for copy number evolution in cancer.bioRxiv : the preprint server for biology · 2023Article
- SCONCE2: jointly inferring single cell copy number profiles and tumor evolutionary distances.BMC bioinformatics · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
motivationCopy number alterations (CNAs) are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, CNAs are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content and do not account for the stochastic evolutionary process.
resultsWe present a theoretical framework to use tumor evolutionary history to accurately call CNAs in a principled manner. To model the tumor evolutionary process and account for technical noise from low coverage single-cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public data sets and simulations, we show SCONCE accurately decodes copy number profiles, and provides a useful tool for understanding tumor evolution. AVAILABILITYAND IMPLEMENTATION: SCONCE is implemented in C++11 and is freely available from https://github.com/NielsenBerkeleyLab/sconce. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.