Predicting double-strand DNA breaks using epigenome marks or DNA at kilobase resolution

Mourad, Raphaël; Cuvier, Olivier

doi:10.1101/149039

Cited by 8 publications

(14 citation statements)

References 53 publications

(92 reference statements)

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“…Consistent with our results, this binary classifier suggested prominent roles for DNase accessible regulatory sites and CTCF binding, and recapitulated many of the patterns reported by Lensing et al (2016). However, the model of Mourad et al (2018) omitted replication timing and does not provide quantitative predictions of DSB susceptibility across the genome.…”

Section: Discussionsupporting

confidence: 85%

“…Our models of genome-wide DSB susceptibility predict DSB frequencies for all 50kb loci, and reflect the established correlations between replication timing and DSB frequency (50) as well as tumour SV rates (9,10). A recent complementary study has shown that 84,946 high confidence peaks of NHEK DSBCapture signal (22), marking small (median: 391bp) sites of unusually high DSB susceptibility, can be accurately classified from control sites using underlying genomic features (34). Consistent with our results, this binary classifier suggested prominent roles for DNase accessible regulatory sites and CTCF binding, and recapitulated many of the patterns reported by Lensing et al (2016).…”

Section: Discussionmentioning

confidence: 99%

“…This is consistent with observations that structural variants disproportionately accumulate within the early replicating, relatively gene rich regions of the genome in cancer, and are relatively depleted in late replicating heterochromatin (9,10). DNase-seq open chromatin ranks second in three datasets and fourth in the MCF7 model and is also the most important feature for predicting DSB peaks in the study of Mourad et al (34) in which they do not include replication timing. The influence of G-quadruplex forming regions is notably variable, ranking as a relatively important feature in the NHEK datasets, but having little and no predictive value in the K562 and MCF7 datasets.…”

Section: Accurate Models Of Genome-wide Dsb Frequency Across Cell Typesmentioning

confidence: 99%

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Modelling double strand break susceptibility to interrogate structural variation in cancer

Ballinger

Bouwman

Mirzazadeh

et al. 2018

Preprint

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26Background: Structural variants (SVs) are known to play important roles in a 27 variety of cancers, but their origins and functional consequences are still poorly 28 understood. Many SVs are thought to emerge via errors in the repair processes 29 following DNA double strand breaks (DSBs) and previous studies have 30 experimentally measured DSB frequencies across the genome in cell lines. 31Results: Using these data we derive the first quantitative genome-wide models 32 of DSB susceptibility, based upon underlying chromatin and sequence features. 33These models are accurate and provide novel insights into the mutational 34 mechanisms generating DSBs. Models trained in one cell type can be successfully 35 applied to others, but a substantial proportion of DSBs appear to reflect cell type 36 specific processes. Using model predictions as a proxy for susceptibility to DSBs 37 in tumours, many SV enriched regions appear to be poorly explained by 38 selectively neutral mutational bias alone. A substantial number of these regions 39show unexpectedly high SV breakpoint frequencies given their predicted 40 susceptibility to mutation, and are therefore credible targets of positive selection 41 in tumours. These putatively positively selected SV hotspots are enriched for 42 3 genes previously shown to be oncogenic. In contrast, several hundred regions 43 across the genome show unexpectedly low levels of SVs, given their relatively 44 high susceptibility to mutation. These novel 'coldspot' regions appear to be 45 subject to purifying selection in tumours and are enriched for active promoters 46 and enhancers. 47 Conclusions:We conclude that models of DSB susceptibility offer a rigorous 48 approach to the inference of SVs putatively subject to selection in tumours. 49 50 Keywords: Double strand break, cancer, structural variaton, chromatin, 51 modelling 52 53 Background 54 55 Structural variation (SV) in tumour genomes is known to play important roles in 56 disease progression and may be critical in driving the development of certain 57cancer types (1-3). However, challenges remain not only in ascertaining accurate 58 SV calls, as evidenced by the compendium of SV calling algorithms used in many 59 projects (4-6), but also in predicting their functional impact. Some SVs have 60 apparently direct consequences; for example, amplification of oncogenes leading 61 to overexpression, deletion of tumor suppressors leading to dysfunction, and 62 translocations generating oncogenic fusion proteins (4). Reportedly indirect 63 consequences of SVs include changes in enhancer targeting, affecting the 64 expression of nearby genes, or "enhancer hijacking" (7). However, it remains 65 Project (30) and others, allowing well-matched models to be constructed for all 131 datasets. We demonstrate that these models provide accurate estimates for the 132 expected rate of DSBs in a given region and can be cross applied between DSB 133 datasets. In addition the models can be used to explore tumour SV breakpoint 134 data, to nominate novel regions p...

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Accurate Models Of Genome-wide Dsb Frequency Across Cell Typesmentioning

confidence: 99%

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Modelling double strand break susceptibility to interrogate structural variation in cancer

Ballinger

Bouwman

Mirzazadeh

et al. 2018

Preprint

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“…H3K36me3 may negatively impede DSBs by restricting chromatin accessibility through nucleosome positioning or, more directly, by favoring the repair of DSBs [73,74]. Disruption of H3K36me3 coupled DSBs repair inhibits the immunoglobulin V(D)J rearrangement in B cells [75] and promotes tumorigenesis of aggressive cancers, such as clear cell renal cell carcinoma (ccRCC) [76], acute myeloid leukemia (AML) [77], and diffuse intrinsic pontine glioma (DIPG) [78].…”

Section: H3k36me3 Associated Ddr In Tumorigenesismentioning

confidence: 99%

H3K36me3, message from chromatin to DNA damage repair

et al. 2020

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Histone marks control many cellular processes including DNA damage repair. This review will focus primarily on the active histone mark H3K36me3 in the regulation of DNA damage repair and the maintenance of genomic stability after DNA damage. There are diverse clues showing H3K36me3 participates in DNA damage response by directly recruiting DNA repair machinery to set the chromatin at a "ready" status, leading to a quick response upon damage. Reduced H3K36me3 is associated with low DNA repair efficiency. This review will also place a main emphasis on the H3K36me3-mediated DNA damage repair in the tumorigenesis of the newly found oncohistone mutant tumors. Gaining an understanding of different aspects of H3K36me3 in DNA damage repair, especially in cancers, would share the knowledge of chromatin and DNA repair to serve to the drug discovery and patient care.

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“…Subsequently, the function generates a set of genomic locations with sequences of nucleotide composition and length similar to those in the positive test set. This approach was successfully used previously for the prediction of double strand breaks at CTCF and accessible chromatin sites (17). The train and test set were obtained using the function sample.split in the R package caTools version 1.17.…”

Section: Prediction Of Variable Regions In K562 and Mescs Using Genommentioning

confidence: 99%

Computational identification of cell-specific variable regions in ChIP-seq data

Andreani

Albrecht

Fontaine

et al. 2019

Preprint

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Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used to identify genome-wide DNA regions bound by proteins. Several sources of variation can affect the reproducibility of a particular ChIP-seq assay, which can lead to a misinterpretation of where the protein under investigation binds to the genome in a particular cell type. Given one ChIP-seq experiment with replicates, binding sites not observed in all the replicates will usually be interpreted as noise and discarded. However, the recent discovery of high-occupancy target (HOT) regions suggests that there are regions where binding of multiple transcription factors can be identified. To investigate these regions, we developed a reproducibility score and a method that identifies cell-specific variable regions in ChIP-seq data by integrating replicated ChIP-seq experiments for multiple protein targets on a particular cell type. Using our method, we found variable regions in human cell lines K562, GM12878, HepG2, MCF-7, and in mouse embryonic stem cells, defined as protein binding regions with non-reproducible results across replicated experiments. These variable-occupancy target (VOT) regions are CG dinucleotide rich, and show enrichment at promoters and R-loops. They overlap significantly with HOT regions, but are not blacklisted regions producing non-specific binding ChIPseq peaks. Interestingly, among various genomic features, DNA accessibility is a better predictor of VOTs than CpG islands or epigenetic marks. Our method can be useful to point to such regions along the genome in a given cell type of interest, to improve the downstream interpretative analysis before follow up experiments.

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Predicting double-strand DNA breaks using epigenome marks or DNA at kilobase resolution

Cited by 8 publications

References 53 publications

Modelling double strand break susceptibility to interrogate structural variation in cancer

Modelling double strand break susceptibility to interrogate structural variation in cancer

H3K36me3, message from chromatin to DNA damage repair

Computational identification of cell-specific variable regions in ChIP-seq data

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