MutSpot: detection of non-coding mutation hotspots in cancer genomes

Guo, Yu; Chang, Mei Mei; Skanderup, Anders Jacobsen

doi:10.1038/s41525-020-0133-4

Cited by 16 publications

(13 citation statements)

References 29 publications

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“…For further comparison, we ran four existing and available methods [DriverPower ( 27 ), Larva ( 28 ), MutSpot ( 29 ), and OncodriveFML ( 5 )] on the entire WGS dataset. This revealed that our genome-wide approach identified nearly all the noncoding events detected by these four methods in the genomic territory included in our analysis (figs.…”

Section: Resultsmentioning

confidence: 99%

“…In addition to the biological function of genomic positions, other factors, including nucleotide contexts, coverage fluctuation, read mappability, and kataegis events, affect positional clustering. Concepts similar to those of test 3 have been used in other methods for analyzing coding and noncoding regions ( 9 , 29 ). Therefore, test 3 examines whether mutations occur in different positions than expected by chance, but it does not analyze whether the total number of mutations deviates from the expectation and thus does not require calibration against regional fluctuations of the background mutation rates.…”

Section: Materials and Methods Summarymentioning

confidence: 99%

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Genome-wide analysis of somatic noncoding mutation patterns in cancer

Dietlein

Wang

Fagre

et al. 2022

Science

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We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6 , FGFR2 , RAD51B , SMC6 , TERT , and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene’s promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.

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Section: Resultsmentioning

confidence: 99%

Section: Materials and Methods Summarymentioning

confidence: 99%

Genome-wide analysis of somatic noncoding mutation patterns in cancer

Dietlein

Wang

Fagre

et al. 2022

Science

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“…Application of maxATAC to the growing number of genetic studies with population-level and single-cell ATAC-seq will improve the power of these studies to accurately predict TF mediators of allelic chromatin accessibility and gene expression. Furthermore, many genetic tools use overlap with TFBS to nominate potentially causal risk variants [58][59][60], where TFBS are predicted based on suboptimal TF motif scanning or ChIP-seq in a potentially suboptimal cell type or condition, due to lack of data in the in vivo disease context. Integration of maxATAC TFBS predictions into genetic analysis pipelines will be the focus of future work.…”

Section: Maxatac Performance Extends To Primary Cellsmentioning

confidence: 99%

maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

Cazares

Rizvi

Iyer

et al. 2022

Preprint

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Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the- art methods. Yet, while Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq datasets grow exponentially, suboptimal motif scanning is commonly used for TFBS prediction from ATAC-seq. Here, we present "maxATAC", a suite of user-friendly, deep neural network models for genome- wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of state-of-the-art TFBS models to date. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling state-of-the-art TFBS prediction in vivo. We demonstrate maxATAC's capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.

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“…For instance, highly recurrent mutations in the promoter region of TERT gene have been found in more than 50 tumor types, prompting efforts to identify additional non-coding driver events [134,135]. However, there are at present few computational tools specifically tailored for detecting drivers in non-coding regions (e.g., [82,89,136]). Identifying signals of positive selection in non-coding DNA is even more challenging because the non-coding region is about 50 times larger than the coding exome, and the number of known cancer genes with noncoding mutations is much more limited [134,137].…”

Section: Non-coding Driversmentioning

confidence: 99%

Machine learning methods for prediction of cancer driver genes: a survey paper

Andrades,

Recamonde-Mendoza

2021

Preprint

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Identifying the genes and mutations that drive the emergence of tumors is a major step to improve understanding of cancer and identify new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in identifying genomic patterns associated with cancer drivers and developing models to predict driver events. Machine learning (ML) has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

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MutSpot: detection of non-coding mutation hotspots in cancer genomes

Cited by 16 publications

References 29 publications

Genome-wide analysis of somatic noncoding mutation patterns in cancer

Genome-wide analysis of somatic noncoding mutation patterns in cancer

maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

Machine learning methods for prediction of cancer driver genes: a survey paper

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