ArticleNature communications2020
Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Mutational signature refitting on sparse pan-cancer data.Algorithms for molecular biology : AMB · 2026Article
- Combined inference of known and novel mutational signatures with ReDeNovo.bioRxiv : the preprint server for biology · 2026Article
- Evolutionary dynamics of early and late mutational signatures in metastatic cancer.Frontiers in bioinformatics · 2026Article
- StarSignDNA: mutation-to-signature attribution for accurate representation of mutational processes in low-mutation contexts.NAR genomics and bioinformatics · 2025Article
- Nonhypermutator Cancers Access Driver Mutations Through Reversals in Germline Mutational Bias.Molecular biology and evolution · 2025Article
- Bayesian multi-study non-negative matrix factorization for mutational signatures.Genome biology · 2025Article
- Benchmarking 13 tools for mutational signature attribution, including a new and improved algorithm.Briefings in bioinformatics · 2024Article
- A comprehensive comparison of tools for fitting mutational signatures.Nature communications · 2024Article
- Accurate and sensitive mutational signature analysis with MuSiCal.Nature genetics · 2024Article
- Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment.Bioinformatics (Oxford, England) · 2023Article
- Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment.bioRxiv : the preprint server for biology · 2023Article
- Cancer driver mutations: predictions and reality.Trends in molecular medicine · 2023Review
- BRCA2 Germline Mutations Identify Gastric Cancers Responsive to PARP Inhibitors.Cancer research · 2023Article
- A phylogenetic approach to study the evolution of somatic mutational processes in cancer.Communications biology · 2022Article
- Computational analysis of cancer genome sequencing data.Nature reviews. Genetics · 2022Review
- Comprehensive profiling of 1015 patients' exomes reveals genomic-clinical associations in colorectal cancer.Nature communications · 2022Article
- Clone decomposition based on mutation signatures provides novel insights into mutational processes.NAR genomics and bioinformatics · 2021Article
- MSA: reproducible mutational signature attribution with confidence based on simulations.BMC bioinformatics · 2021Article
- 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
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Multiple mutational processes drive carcinogenesis, leaving characteristic signatures in tumor genomes. Determining the active signatures from a full repertoire of potential ones helps elucidate mechanisms of cancer development. This involves optimally decomposing the counts of cancer mutations, tabulated according to their trinucleotide context, into a linear combination of known signatures. Here, we develop sigLASSO (a software tool at github.com/gersteinlab/siglasso) to carry out this optimization efficiently. sigLASSO has four key aspects: (1) It jointly optimizes the likelihood of sampling and signature fitting, by explicitly factoring multinomial sampling into the objective function. This is particularly important when mutation counts are low and sampling variance is high (e.g., in exome sequencing). (2) sigLASSO uses L1 regularization to parsimoniously assign signatures, leading to sparse and interpretable solutions. (3) It fine-tunes model complexity, informed by data scale and biological priors. (4) Consequently, sigLASSO can assess model uncertainty and abstain from making assignments in low-confidence contexts.
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