scholarly journals sigfit: flexible Bayesian inference of mutational signatures

2018 ◽  
Author(s):  
Kevin Gori ◽  
Adrian Baez-Ortega
Keyword(s):  
Bayesian Inference ◽  
Probabilistic Models ◽  
R Package ◽  
Signature Analysis ◽  
Mutational Signatures ◽  
Mutational Spectra ◽  
Mutational Signature ◽  
Linear Optimisation ◽  
Biological Patterns ◽  
User Friendly

Mutational signature analysis aims to infer the mutational spectra and relative exposures of processes that contribute mutations to genomes. Different models for signature analysis have been developed, mostly based on non-negative matrix factorisation or non-linear optimisation. Here we present sigfit, an R package for mutational signature analysis that applies Bayesian inference to perform fitting and extraction of signatures from mutation data. We compare the performance of sigfit to prominent existing software, and find that it compares favourably. Moreover, sigfit introduces novel probabilistic models that enable more robust, powerful and versatile fitting and extraction of mutational signatures and broader biological patterns. The package also provides user-friendly visualisation routines and is easily integrable with other bioinformatic packages.

scholarly journals mutSigMapper: an R package to map spectra to mutational signatures based on shot-noise modeling

2020 ◽  
Author(s):  
Julián Candia
Keyword(s):  
Shot Noise ◽  
Statistical Significance ◽  
R Package ◽  
Signature Analysis ◽  
Mutational Signatures ◽  
Noise Modeling ◽  
Mutational Signature ◽  
Link Type ◽  
Non Parametric

AbstractSummarymutSigMapper aims to resolve a critical shortcoming of existing software for mutational signature analysis, namely that of finding parsimonious and biologically plausible exposures. By implementing a shot-noise-based model to generate spectral ensembles, this package addresses this gap and provides a quantitative, non-parametric assessment of statistical significance for the association between mutational signatures and observed spectra.Availability and implementationThe mutSigMapper R package is available under GPLv3 license at https://github.com/juliancandia/mutSigMapper. Its documentation provides additional details and demonstrates applications to biological datasets.

scholarly journals MetaMutationalSigs: Comparison of mutational signature refitting results made easy

2021 ◽  
Author(s):  
Palash Pandey ◽  
Sanjeevani Arora ◽  
Gail Rosen
Keyword(s):  
Cancer Genetics ◽  
Research Question ◽  
Treatment Decision ◽  
Signature Analysis ◽  
Cancer Evolution ◽  
Mutational Signatures ◽  
Mutational Signature ◽  
Specific Research Question ◽  
Cancer Types ◽  
User Friendly

The analysis of mutational signatures is becoming increasingly common in cancer genetics, with emerging implications in cancer evolution, classification, treatment decision and prognosis. Recently, several packages have been developed for mutational signature analysis, with each using different methodology and yielding significantly different results. Because of the nontrivial differences in tools' refitting results, researchers may desire to survey and compare the available tools, in order to objectively evaluate the results for their specific research question, such as which mutational signatures are prevalent in different cancer types. There is a need for a software that can aggregate results from different refitting packages and present them in a user-friendly way to facilitate effective comparison of mutational signatures.

scholarly journals Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

2019 ◽  
Author(s):  
Harald Vöhringer ◽  
Arne van Hoeck ◽  
Edwin Cuppen ◽  
Moritz Gerstung
Keyword(s):  
Somatic Hypermutation ◽  
Translesion Synthesis ◽  
Strand Break ◽  
Signature Analysis ◽  
Transcription Start ◽  
Mutational Signatures ◽  
Transcription Start Sites ◽  
Mutational Signature ◽  
Cancer Genomes ◽  
Cancer Types

AbstractMutational signature analysis is an essential part of the cancer genome analysis toolkit. Conventionally, mutational signature analysis extracts patterns of different mutation types across many cancer genomes. Here we present TensorSignatures, an algorithm to learn mutational signatures jointly across all variant categories and their genomic context. The analysis of 2,778 primary and 3,824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that practically all signatures operate dynamically in response to various genomic and epigenomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis, which is detected in 7 different cancer types. The analysis also unmasks replication- and double strand break repair-driven APOBEC mutagenesis, which manifests with differential numbers and length of mutation clusters indicating a differential processivity of the two triggers. As a fourth example, TensorSignatures detects a signature of somatic hypermutation generating highly clustered variants around the transcription start sites of active genes in lymphoid leukaemia, distinct from a more general and less clustered signature of Polη-driven translesion synthesis found in a broad range of cancer types.Key findingsSimultaneous inference of mutational signatures across mutation types and genomic features refines signature spectra and defines their genomic determinants.Analysis of 6,602 cancer genomes reveals pervasive intra-genomic variation of mutational processes.Distinct mutational signatures found in quiescent and active regions of the genome reveal differential repair and mutagenicity of UV- and tobacco-induced DNA damage.APOBEC mutagenesis produces two signatures reflecting highly clustered, double strand break repair-initiated and lowly clustered replication-driven mutagenesis, respectively.Somatic hypermutation in lymphoid cancers produces a strongly clustered mutational signature localised to transcription start sites, which is distinct from a weakly clustered translesion synthesis signature found in multiple tumour types.

scholarly journals iMutSig: a web application to identify the most similar mutational signature using shiny

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 586
Author(s):  
Zhi Yang ◽  
Priyatama Pandey ◽  
Paul Marjoram ◽  
Kimberly D. Siegmund
Keyword(s):  
Web Application ◽  
Human Cancer ◽  
Similarity Measures ◽  
Model Specification ◽  
Mutational Signatures ◽  
Mutational Signature ◽  
Link Type ◽  
Cosine Similarity Measures ◽  
User Friendly ◽  
Intuitive Interface

There are two frameworks for characterizing mutational signatures which are commonly used to describe the nucleotide patterns that arise from mutational processes. Estimated mutational signatures from fitting these two methods in human cancer can be found online, in the Catalogue Of Somatic Mutations In Cancer (COSMIC) website or a GitHub repository. The two frameworks make differing assumptions regarding independence of base pairs and for that reason may produce different results. Consequently, there is a need to compare and contrast the results of the two methods, but no such tool currently exists. In this paper, we provide a simple and intuitive interface that allows such comparisons to be easily performed. When using our software, the user may download published mutational signatures of either type. Mutational signatures from the pmsignature data source are expanded to probabilistic vectors of 96-possible mutation types, the same model specification used by COSMIC, and then compared to COSMIC signatures. Cosine similarity measures the extent of signature similarity. iMutSig provides a simple and user-friendly web application allowing researchers to compare signatures from COSMIC to those from pmsignature, and vice versa. Furthermore, iMutSig allows users to input a self-defined mutational signature and examine its similarity to published signatures from both data sources. iMutSig is accessible online and source code is available for download on GitHub.

Bayesian inference from the conditional genetic stock identification model

2019 ◽  
Vol 76 (4) ◽  
pp. 551-560 ◽  
Author(s):  
Benjamin M. Moran ◽  
Eric C. Anderson
Keyword(s):  
Bayesian Inference ◽  
Cross Validation ◽  
Parametric Bootstrap ◽  
R Package ◽  
Stock Identification ◽  
Genetic Stock ◽  
Genetic Stock Identification ◽  
Bootstrap Approach ◽  
User Friendly ◽  
The Impact

Genetic stock identification (GSI) estimates stock proportions and individual assignments through comparison of genetic markers with reference populations. It is used widely in anadromous fisheries to estimate the impact of oceanic harvest on riverine populations. Here, we provide a formal, explicit description of Bayesian inference in the conditional GSI model, documenting an approach that has been widely used in the last 5 years, but not formally described until now. Subsequently, we describe a novel cross-validation method that permits accurate prediction of GSI accuracy when making Bayesian inference from the conditional GSI model. We use cross-validation and simulation of genetic data to confirm the occurrence of a bias in reporting-unit proportions recently reported in Hasselman et al. (2016) . Then, we introduce a novel parametric bootstrap approach to reduce this bias, and we demonstrate the efficacy of our correction. Our methods have been implemented as a user-friendly R package, rubias, which makes use of Rcpp for computational efficiency. We predict rubias will be widely useful for GSI of fish populations.

scholarly journals A framework for mutational signature analysis based on DNA shape parameters

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262495
Author(s):  
Aleksandra Karolak ◽  
Jurica Levatić ◽  
Fran Supek
Keyword(s):  
Dna Repair ◽  
Base Pair ◽  
Mutation Frequency ◽  
Structural Features ◽  
Sequence Motif ◽  
Signature Analysis ◽  
Mutational Signatures ◽  
Radiation Chemical ◽  
Mutational Signature ◽  
Dna Shape

The mutation risk of a DNA locus depends on its oligonucleotide context. In turn, mutability of oligonucleotides varies across individuals, due to exposure to mutagenic agents or due to variable efficiency and/or accuracy of DNA repair. Such variability is captured by mutational signatures, a mathematical construct obtained by a deconvolution of mutation frequency spectra across individuals. There is a need to enhance methods for inferring mutational signatures to make better use of sparse mutation data (e.g., resulting from exome sequencing of cancers), to facilitate insight into underlying biological mechanisms, and to provide more accurate mutation rate baselines for inferring positive and negative selection. We propose a conceptualization of mutational signatures that represents oligonucleotides via descriptors of DNA conformation: base pair, base pair step, and minor groove width parameters. We demonstrate how such DNA structural parameters can accurately predict mutation occurrence due to DNA repair failures or due to exposure to diverse mutagens such as radiation, chemical exposure, and the APOBEC cytosine deaminase enzymes. Furthermore, the mutation frequency of DNA oligomers classed by structural features can accurately capture systematic variability in mutagenesis of >1,000 tumors originating from diverse human tissues. A nonnegative matrix factorization was applied to mutation spectra stratified by DNA structural features, thereby extracting novel mutational signatures. Moreover, many of the known trinucleotide signatures were associated with an additional spectrum in the DNA structural descriptor space, which may aid interpretation and provide mechanistic insight. Overall, we suggest that the power of DNA sequence motif-based mutational signature analysis can be enhanced by drawing on DNA shape features.

scholarly journals Performance Characteristics of Mutational Signature Analysis in Targeted Panel Sequencing

2021 ◽  
Author(s):  
Lauren Lawrence ◽  
Christian A. Kunder ◽  
Eula Fung ◽  
Henning Stehr ◽  
James Zehnder
Keyword(s):  
Ultraviolet Radiation ◽  
Tobacco Smoking ◽  
High Specificity ◽  
Primary Site ◽  
Clinical Samples ◽  
Signature Analysis ◽  
Mutational Signatures ◽  
Targeted Next Generation Sequencing ◽  
Mutational Signature ◽  
Poorly Differentiated

Context.— Mutational signatures have been described in the literature and a few centers have implemented pipelines for clinical reporting. Objective.— To describe the performance of a mutational signature caller with clinical samples sequenced on a targeted next-generation sequencing panel with a small genomic footprint. Design.— One thousand six hundred eighty-two (n = 1682) clinical samples were analyzed for the presence of mutational signatures using deconstructSigs on variant calls with at least 20 variant reads. Results.— Signature 10 (associated with POLe mutation) achieved separation of cases and controls in hypermutated samples. Signatures 4 (associated with tobacco smoking) and 7 (associated with ultraviolet radiation) as an indicator of pulmonary or cutaneous primary sites showed moderate sensitivity and high specificity at optimal cutpoints. Mutational signatures in malignancies with unknown primaries were somewhat consistent with the clinically suspected primary site, with an apparent dose-response relationship between the number of variants analyzed and the ability of mutational signature analysis to correctly suggest a primary site. Conclusions.— Mutational signatures represent an opportunity for orthogonal testing of primary site, which may be particularly useful in supporting cutaneous or pulmonary sites in poorly differentiated neoplasms. Tobacco smoking, ultraviolet radiation, and POLe mutational signatures are the most appropriate signatures for implementation. Even relatively small numbers of variants appear capable of supporting a clinically suspected primary.

scholarly journals Identification of mutational signatures active in individual tumors

2017 ◽  
Author(s):  
Sandra Krüger ◽  
Rosario M Piro
Keyword(s):  
Somatic Mutations ◽  
R Package ◽  
Mutational Signatures ◽  
The Individual ◽  
Tumor Types ◽  
User Friendly ◽  
Mutational Processes ◽  
Tumor Class

The mutational processes responsible for the somatic mutations observed in tumor samples can significantly vary not only between tumor types but also among the individual cancers within a tumor class. Mutational processes can be represented by so called “mutational signatures” which reflect the occurrences of base changes within their sequence contexts (i.e., in dependence on their flanking bases). We present a user-friendly R package, called decompTumor2Sig, that can be used to evaluate the contribution of Shiraishi signatures to the somatic mutations found in an individual tumor.

scholarly journals A framework for mutational signature analysis based on DNA shape parameters

2020 ◽  
Author(s):  
Aleksandra Karolak ◽  
Fran Supek
Keyword(s):  
Dna Repair ◽  
Base Pair ◽  
Mutation Frequency ◽  
Chemical Exposure ◽  
Signature Analysis ◽  
Mutational Signatures ◽  
Frequency Spectra ◽  
Radiation Chemical ◽  
Mutational Signature ◽  
Dna Shape

AbstractThe propensity to acquire mutations depends on the oligonucleotide context of a DNA locus. In turn, this differential mutability of oligonucleotides varies across individuals due to exposure to mutagenic agents or due to variable efficiency of DNA repair pathways. Such variability is captured by mutational signatures, mathematical constructs resulting from a deconvolution of mutation frequency spectra across individuals. There is a need to enhance methods for inferring mutational signatures to make better use of sparse mutation frequency data that results from genome sequencing, and additionally to facilitate insight into underlying biological mechanisms. In cancer genomics, novel approaches to analyze somatic mutation patterns may help explain the etiology of various tumor types, as well as provide a more accurate baseline to infer positive and negative selection on somatic changes that drive tumor evolution. We propose a conceptualization of mutational signatures that represents oligonucleotides via descriptors of DNA conformation: base pair, base pair step, and minor groove width parameters. We demonstrate how such DNA structural parameters can accurately predict mutation occurrence due to DNA repair failures or due to exposure to diverse mutagens, including radiation, chemical exposure and the APOBEC cytosine deaminase enzymes. Furthermore, the mutation frequency of DNA oligomers classed by structural features can accurately capture systematic variability in mutational spectra of >1,000 tumors originating from diverse human tissues. Overall, we suggest that the power of DNA sequence-based mutational signature analysis can be enhanced by drawing on DNA shape features.

scholarly journals iMutSig: a web application to identify the most similar mutational signature using shiny

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 586
Author(s):  
Zhi Yang ◽  
Priyatama Pandey ◽  
Paul Marjoram ◽  
Kimberly D. Siegmund
Keyword(s):  
Web Application ◽  
Human Cancer ◽  
Similarity Measures ◽  
Base Pairs ◽  
Mutational Signatures ◽  
Mutational Signature ◽  
Link Type ◽  
Cosine Similarity Measures ◽  
User Friendly ◽  
Intuitive Interface

There are two frameworks for characterizing mutational signatures which are commonly used to describe the nucleotide patterns that arise from mutational processes. Estimated mutational signatures from fitting these two methods in human cancer can be found online, in the Catalogue Of Somatic Mutations In Cancer (COSMIC) website or a GitHub repository. The two frameworks make differing assumptions regarding independence of base pairs and for that reason may produce different results. Consequently, there is a need to compare and contrast the results of the two methods, but no such tool currently exists. In this paper, we provide a simple and intuitive interface that allows comparisons of pairs of mutational signatures to be easily performed. Cosine similarity measures the extent of signature similarity. To compare mutational signatures of different formats, one signature type (COSMIC or pmsignature) is converted to the format of the other before the signatures are compared. iMutSig provides a simple and user-friendly web application allowing researchers to download published mutational signatures of either type and to compare signatures from COSMIC to those from pmsignature, and vice versa. Furthermore, iMutSig allows users to input a self-defined mutational signature and examine its similarity to published signatures from both data sources. iMutSig is accessible online and source code is available for download from GitHub.

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