ArticleBMC bioinformatics2020
gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints.bioRxiv : the preprint server for biology · 2025Article
- Dollo-CDP: a polynomial-time algorithm for the clade-constrained large Dollo parsimony problem.Algorithms for molecular biology : AMB · 2024Article
- Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing.Cell systems · 2023Article
- ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model.Genome biology · 2023Article
- ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model.bioRxiv : the preprint server for biology · 2023Article
- 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
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6 authors.
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Abstract
backgroundCancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates. Moreover, there has been some recent evidence of back mutations in cancer: this phenomenon is currently widely ignored.
resultsWe present a new tool, gpps, that reconstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at most a fixed number of times. The General Parsimony Phylogeny from Single cell (gpps) tool is open source and available at https://github.com/AlgoLab/gpps .
conclusionsgpps provides new insights to the analysis of intra-tumor heterogeneity by proposing a new progression model to the field of cancer phylogeny reconstruction on Single Cell data.
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