ArticleGenome medicine2019
Hidden Markov models lead to higher resolution maps of mutation signature activity in cancer.
Article in Genome medicine, 2019. 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.
- Combined inference of known and novel mutational signatures with ReDeNovo.bioRxiv : the preprint server for biology · 2026Article
- A mutation-level covariate model for mutational signatures.PLoS computational biology · 2023Article
- Influence network model uncovers relations between biological processes and mutational signatures.Genome medicine · 2023Article
- A Biterm Topic Model for Sparse Mutation Data.Cancers · 2023Article
- Regional mutational signature activities in cancer genomes.PLoS computational biology · 2022Article
- A mixture model for signature discovery from sparse mutation data.Genome medicine · 2021Article
- RepairSig: Deconvolution of DNA damage and repair contributions to the mutational landscape of cancer.Cell systems · 2021Article
- Mutational Signatures: From Methods to Mechanisms.Annual review of biomedical data science · 2021Article
- Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model.BMC bioinformatics · 2021Article
- Recognizing Pattern and Rule of Mutation Signatures Corresponding to Cancer Types.Frontiers in cell and developmental biology · 2021Article
- Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer.Genome medicine · 2020Article
- A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer.iScience · 2020Article
- DNA Repair Footprint Uncovers Contribution of DNA Repair Mechanism to Mutational Signatures.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to characterize the signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome, leading to statistical dependencies among neighboring mutations. To account for such dependencies, we develop the first sequence-dependent model, SigMa, for mutation signatures. We apply SigMa to characterize genomic and other factors that influence the activity of mutation signatures in breast cancer. We show that SigMa outperforms previous approaches, revealing novel insights on signature etiology. The source code for SigMa is publicly available at https://github.com/lrgr/sigma.
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Registered trials
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.