sigfit: flexible Bayesian inference of mutational signatures

Gori, Kevin; Baez‐Ortega, Adrian

doi:10.1101/372896

Cited by 96 publications

(94 citation statements)

References 23 publications

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“…The clustering results also confirm that most of the total observed point mutations are contributed by background and methylation signatures. In fact, we identify 8 clusters (clusters 1,5,9,11,16,18,21 and 24) where these two signatures alone explain more than 50% of mutations. We show in Supplementary Figure 13 the averaged observed counts for the patients of each of these clusters.…”

Section: Sparsesignatures Discovers Sparse Differentiated Signaturesmentioning

confidence: 80%

“…The original signature discovery method was based on Non-Negative Matrix Factorization (NMF) (Alexandrov et al 2013). While other approaches have been considered (Gehring et al 2015;Shiraishi et al 2015;Fischer et al 2013), NMF-based methods are by far the most widely used (Bolli et al 2014;Schulze et al 2015;Nik-Zainal et al 2016;Covington et al 2016;Goncearenco et al 2017;Gori et al 2018) and have resulted in a commonly used catalog of 30 signatures across human cancers (Alexandrov et al 2015), available in the COSMIC database. A recent study (Alexandrov et al 2018) using two NMF-based methods presented higher numbers (49 and 60) of putative signatures.…”

Section: Introductionmentioning

confidence: 99%

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De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures

Ramazzotti

Liu

et al. 2018

Preprint

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AbstractCancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or “mutational signatures”. Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates DNA replication error as a background, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using standard metrics. We then apply SparseSignatures to whole genome sequences of 147 tumors from pancreatic cancer, discovering 8 signatures in addition to the background.

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Section: Sparsesignatures Discovers Sparse Differentiated Signaturesmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures

Ramazzotti

Liu

et al. 2018

Preprint

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“…As a control, we performed the same task using another package called SigFit (Gori and Baez-Ortega, 2018), which runs a signature fitting method based on a Markov Chain Monte Carlo (MCMC) sampling to fit the input set of mutational signatures to a mutational catalog, using a Non-Negative Matrix Factorization (NMF) model to sample from. We applied the method with the parameters iter = 13000, warmup = 3000, and hpd_prob = 0.90 to get the exposures and 90% highest posterior density intervals.…”

Section: Mutational Signatures Fitting and Assignmentmentioning

confidence: 99%

Somatic and Germline Mutation Periodicity Follow the Orientation of the DNA Minor Groove around Nucleosomes

Pich

Muiños

Sabarinathan

et al. 2018

Cell

110

154

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Graphical AbstractHighlights d Somatic and germline mutation rates show a 10-bp periodicity in nucleosome-occupied DNA d This periodicity tracks DNA minor groove facing toward and away from the histones d The orientation of the periodicity depends on the mutational processes active in the tissue d This has contributed to the AT/CG 10-bp periodicity in eukaryotic genomes SUMMARYMutation rates along the genome are highly variable and influenced by several chromatin features. Here, we addressed how nucleosomes, the most pervasive chromatin structure in eukaryotes, affect the generation of mutations. We discovered that within nucleosomes, the somatic mutation rate across several tumor cohorts exhibits a strong 10 base pair (bp) periodicity. This periodic pattern tracks the alternation of the DNA minor groove facing toward and away from the histones. The strength and phase of the mutation rate periodicity are determined by the mutational processes active in tumors. We uncovered similar periodic patterns in the genetic variation among human and Arabidopsis populations, also detectable in their divergence from close species, indicating that the same principles underlie germline and somatic mutation rates. We propose that differential DNA damage and repair processes dependent on the minor groove orientation in nucleosomebound DNA contribute to the 10-bp periodicity in AT/CG content in eukaryotic genomes.

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“…The exposure of COSMIC mutational signatures (n=30) in each sample was computed using sigfit (v1.3.1) (https://github.com/kgori/sigfit) 22 . OncodriveFML (v2.2.0) 39 and dNdScv (v0.0.1) 35 were applied on the somatic variants to detect cancer driver genes with signals of positive selection.…”

Section: Methodsmentioning

confidence: 99%

Exome sequencing identifies ARID2 as a novel tumor suppressor in early-onset sporadic rectal cancer

Bala

Singh

Kavadipula

et al. 2020

Preprint

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24Early-onset sporadic rectal cancer (EOSRC) is a unique and predominant colorectal cancer 25 (CRC) subtype in India. In order to understand the tumorigenic process in EOSRC, we 26 performed whole exome sequencing of 47 microsatellite stable EOSRC samples. Signature 1 was 27 the predominant mutational signature in EOSRC, as previously shown in other CRC exome 28 studies. More importantly, we identified TP53, KRAS, APC, PIK3R1 and SMAD4 as significantly 29 mutated (q<0.1) and ARID1A and ARID2 as near-significantly mutated (restricted hypothesis 30 testing; q<0.1) candidate drivers. Unlike the other candidates, the tumorigenic potential of 31 ARID2, encoding a component of the SWI/SNF chromatin remodeling complex, is largely 32 unexplored in CRC. shRNA mediated ARID2 knockdown performed in two different CRC cell 33 lines resulted in significant alterations in transcript levels of cancer-related target genes. More 34 importantly, ARID2 knockdown promoted several tumorigenic features including cell viability, 35proliferation, ability to override contact inhibition of growth, and migration besides significantly 36 increasing tumor formation ability in nude mice. The observed gain in tumorigenic features were 37 rescued upon ectopic expression of ARID2. Analyses of the TCGA CRC dataset revealed poorer 38 survival in patients with ARID2 alterations. We therefore propose ARID2 as a novel tumor 39 suppressor in CRC. 40 41 Keywords: Rectal cancer; mutational signatures; tumor suppressor; cancer driver genes; ARID2 42 4 genes/pathways 28, 42 . We now report identification of ARID2 as a novel tumor suppressor for 66 CRC based on a whole exome sequencing analysis of EOSRC samples. 67 68Results 69 Whole Exome sequencing reveals known and novel characteristics in EOSRC 70To identify molecular alterations underlying rectal adenocarcinoma, we performed whole exome 71 sequencing analysis of 47 carefully selected well annotated microsatellite stable (MSS) rectal 72 tumor and matched normal sample pairs (EOSRC-IN). All samples were from patients aged 73 below 61 years (average 46 years; range 22-60; Table S1). Sequence data analyses and variant 74 calling (see Methods and Figure S1) identified 17,471 substitutions and 1,432 small insertions 75 and deletions (indels) ( Table S2A). The number of substitutions predominated over indels across 76 all samples with a mean rate (per megabase (MB)) of 6.4 (range 1-35 per sample) for 77 substitutions and 0.5 (range 0-2.35 per sample) for indels ( Figure 1a). Five samples (EOSRC-IN-78 1095, 2575, 2603, 2643 and 2669) showed a significantly higher mutation rate (>12/MB) and 79 can be considered to exhibit a 'Hypermutator-like condition' as described in The Cancer Genome 80Atlas (TCGA) study on CRC 6 . Given that all samples were MSS (status of the 'hypermutator-81 like' samples was re-confirmed using the same DNA sample that was used for exome 82 sequencing), it is surprising to find a high mutation rate. The mean rate for substitutions and 83 indels in non-hypermutated (excluding the 'hypermu...

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sigfit: flexible Bayesian inference of mutational signatures

Cited by 96 publications

References 23 publications

De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures

De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures

Somatic and Germline Mutation Periodicity Follow the Orientation of the DNA Minor Groove around Nucleosomes

Exome sequencing identifies ARID2 as a novel tumor suppressor in early-onset sporadic rectal cancer

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