ArticleBioinformatics (Oxford, England)2021
Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed, 52 citations in OpenAlex.
- Statistical Inconsistency of Error-correction Objectives for Perfect Phylogenies.bioRxiv : the preprint server for biology · 2026Article
- Long-read sequencing of single cell-derived melanoma sublines reveals divergent and parallel genomic and epigenomic evolutionary trajectories.Nature communications · 2026Article
- Long-read sequencing of single cell-derived melanoma subclones reveals divergent and parallel genomic and epigenomic evolutionary trajectories.bioRxiv : the preprint server for biology · 2025Article
- Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints.bioRxiv : the preprint server for biology · 2025Article
- ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model.Genome biology · 2023Article
- Assessing the performance of methods for cell clustering from single-cell DNA sequencing data.PLoS computational biology · 2023Article
- Complex Analysis of Single-Cell RNA Sequencing Data.Biochemistry. Biokhimiia · 2023Review
- ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model.bioRxiv : the preprint server for biology · 2023Article
- bmVAE: a variational autoencoder method for clustering single-cell mutation data.Bioinformatics (Oxford, England) · 2023Article
- A phylogenetic approach to inferring the order in which mutations arise during cancer progression.PLoS computational biology · 2022Article
- Scelestial: Fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm.PLoS computational biology · 2022Article
- Recent advances in targeted drug delivery systems for resistant colorectal cancer.Cancer cell international · 2022Review
- Reconstructing Complex Cancer Evolutionary Histories from Multiple Bulk DNA Samples Using Pairtree.Blood cancer discovery · 2022Article
- CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data.Genome biology · 2022Article
- SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data.Frontiers in genetics · 2022Article
- Scalable Reconstruction of SARS-CoV-2 Phylogeny with Recurrent Mutations.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Via Clustering.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Studying the History of Tumor Evolution from Single-Cell Sequencing Data by Exploring the Space of Binary Matrices.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- The Bourque distances for mutation trees of cancers.Algorithms for molecular biology : AMB · 2021Article
- Triplet-based similarity score for fully multilabeled trees with poly-occurring labels.Bioinformatics (Oxford, England) · 2021Article
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Authors and funding
8 authors at 4 institutions in 2 countries.
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Abstract
motivationIn recent years, the well-known Infinite Sites Assumption has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions. However, recent studies leveraging single-cell sequencing (SCS) techniques have shown evidence of the widespread recurrence and, especially, loss of mutations in several tumor samples. While there exist established computational methods that infer phylogenies with mutation losses, there remain some advancements to be made.
resultsWe present Simulated Annealing Single-Cell inference (SASC): a new and robust approach based on simulated annealing for the inference of cancer progression from SCS datasets. In particular, we introduce an extension of the model of evolution where mutations are only accumulated, by allowing also a limited amount of mutation loss in the evolutionary history of the tumor: the Dollo-k model. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real datasets and in comparison with some other available methods. AVAILABILITY AND IMPLEMENTATION: The SASC tool is open source and available at https://github.com/sciccolella/sasc. 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.