ReviewPloS one2019
Computational tools to detect signatures of mutational processes in DNA from tumours: A review and empirical comparison of performance.
Review in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
39 citing papers in PubMed.
- Integrating liquid biopsy and mutational signatures to advance precision oncology.NPJ precision oncology · 2026Review
- BASCULE: bayesian inference and clustering of mutational signatures leveraging biological priors.Genome biology · 2025Article
- StarSignDNA: mutation-to-signature attribution for accurate representation of mutational processes in low-mutation contexts.NAR genomics and bioinformatics · 2025Article
- A signature-agnostic test for differences between tumor mutation spectra reveals carcinogen and ancestry effects.bioRxiv : the preprint server for biology · 2025Article
- Advancing precision oncology with AI-powered genomic analysis.Frontiers in pharmacology · 2025Review
- A review on trends in development and translation of omics signatures in cancer.Computational and structural biotechnology journal · 2024Review
- Benchmarking 13 tools for mutational signature attribution, including a new and improved algorithm.Briefings in bioinformatics · 2024Article
- Unravelling mutational signatures with plasma circulating tumour DNA.Nature communications · 2024Article
- A comprehensive comparison of tools for fitting mutational signatures.Nature communications · 2024Article
- Topography of mutational signatures in non-small cell lung cancer: emerging concepts, clinical applications, and limitations.The oncologist · 2024Review
- Somatic CpG hypermutation is associated with mismatch repair deficiency in cancer.Molecular systems biology · 2024Article
- Mutational signatures of colorectal cancers according to distinct computational workflows.Briefings in bioinformatics · 2024Article
- Improved identification of cancer mutational processes.Nature genetics · 2024Article
- Accurate and sensitive mutational signature analysis with MuSiCal.Nature genetics · 2024Article
- Mutational signature assignment heterogeneity is widespread and can be addressed by ensemble approaches.Briefings in bioinformatics · 2023Article
- Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization.BMC bioinformatics · 2023Article
- Single duplex DNA sequencing with CODEC detects mutations with high sensitivity.Nature genetics · 2023Article
- Dissection of Cancer Mutational Signatures with Individual Components of Cigarette Smoking.Chemical research in toxicology · 2023Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- mSigHdp: hierarchical Dirichlet process mixture modeling for mutational signature discovery.NAR genomics and bioinformatics · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutational signatures refer to patterns in the occurrence of somatic mutations that might be uniquely ascribed to particular mutational process. Tumour mutation catalogues can reveal mutational signatures but are often consistent with the mutation spectra produced by a variety of mutagens. To date, after the analysis of tens of thousands of exomes and genomes from about 40 different cancer types, tens of mutational signatures characterized by a unique probability profile across the 96 trinucleotide-based mutation types have been identified, validated and catalogued. At the same time, several concurrent methods have been developed for either the quantification of the contribution of catalogued signatures in a given cancer sequence or the identification of new signatures from a sample of cancer sequences. A review of existing computational tools has been recently published to guide researchers and practitioners through their mutational signature analyses, but other tools have been introduced since its publication and, a systematic evaluation and comparison of the performance of such tools is still lacking. In order to fill this gap, we have carried out an empirical evaluation of the main packages available to date, using both real and simulated data. Among other results, our empirical study shows that the identification of signatures is more difficult for cancers characterized by multiple signatures each having a small contribution. This work suggests that detection methods based on probabilistic models, especially EMu and bayesNMF, have in general better performance than NMF-based methods.
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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.