qDSB-Seq is a general method for genome-wide quantification of DNA double-strand breaks using sequencing

Zhu, Yingjie; Biernacka, Anna; Pardo, Benjamín; Dojer, Norbert; Forey, Romain; Skrzypczak, Magdalena; Fongang, Bernard; Nde, Jules; Yousefi, Razie; Pasero, Philippe; Ginalski, Krzysztof; Rowicka, Maga

doi:10.1038/s41467-019-10332-8

Cited by 47 publications

(57 citation statements)

References 39 publications

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“…However, when DSBs are evenly distributed, the hypergeometric test used in enrichment analysis is not capable of identifying these DSBs. For example, we characterized Zeocininduced DSBs by DSB sequencing 13 , even though based on our quantification results, Zeocininduced DSBs distributed along the whole genome, we did not observe significant difference between Zeocin and control data when the sequencing reads were normalized to the total number of reads or used for enrichment analysis. Therefore, DSB quantification is required in the samples with evenly distributed DSBs.…”

Section: Advantages and Limitationsmentioning

confidence: 87%

“…Restriction enzymes induce DSBs on their recognition sequences, where their length varies from 4 bp to 50 bp based on The Restriction Enzyme Database 18 . However, DNA replication-related DSBs may be located on a wider area from a couple of kilo base pairs 13 to mega base pairs 4 . Because enrichment analysis in iSeq is on the basis of statistical test, it is not limited by the shape of DSB patterns.…”

Section: Advantages and Limitationsmentioning

confidence: 99%

“…Our approach is also unique in that it provides multiscale analysis, starting from base pairs resolution and gradually progressing up to mega base resolution, and automatically generates reports about trends across scales. In addition, we introduce the calculation of DSBs per cell and across-samples comparison based on qDSB-Seq data and software 13 . The software of iSeq and qDSB-Seq can be find on the links of http://breakome.utmb.edu/software.html.…”

Section: Introductionmentioning

confidence: 99%

“…(Attention: The coordinates of cutting sites should be downstream of cutting sites on Watson and Crick strands. )13. Generate random genome coordinates (1 min).…”

mentioning

confidence: 99%

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Analyzing and interpreting DNA double-strand break sequencing data

Mitra

Dojer

Fongang

et al. 2020

Preprint

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DNA double-strand breaks (DSBs), are a major threat to genomic stability and may lead to cancer. Several technologies to accurately detect DSBs genome-wide have been developed recently, but still lacking publicly available tools for analysis of the resulting data. Here, we present a step-by-step iSeq package (http://breakome.utmb.edu/software.html), custom designed for analysis and interpretation of DSB-sequencing data. iSeq performs barcode trimming and read counting, and identifies DSB-enriched regions by statistical test and annotate them to the desired genomic features. Applying this package, users can identify and annotate DSB-enriched regions from base pair (eg. Cas9 cleavage sites) up to megabase (eg. DNA replication stress-induced) resolution, and if possible quantify DSB frequencies per cell genome-wide by combining with qDSB-Seq. iSeq can be used for any sequencing-based DSB detection techniques. The analysis for Steps 1-19 can be performed within ~4 hours.Recently, we presented a general method, qDSB-Seq 13 , to quantify DSBs genome-wide, which provides both DSB frequencies per cell and their precise genomic coordinates. qDSB-Seq has been applied to BLESS 4 and i-BLESS 5 , it is also available for END-Seq 6 , Break-Seq 7 , DSBCapture 8 , BLISS 15 , and so on. In this work, we induced spike-in DSBs by a site-specific endonuclease and used these induced DSBs to quantify studied DSBs. qDSB-Seq method has

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Section: Advantages and Limitationsmentioning

confidence: 87%

Section: Advantages and Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…(Attention: The coordinates of cutting sites should be downstream of cutting sites on Watson and Crick strands. )13. Generate random genome coordinates (1 min).…”

mentioning

confidence: 99%

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Analyzing and interpreting DNA double-strand break sequencing data

Mitra

Dojer

Fongang

et al. 2020

Preprint

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“…Another application of our simulations can be studying replication stress and DNA double-strand breaks (DSBs) originating from broken replication forks. Currently, the numbers of DSBs per cell can be precisely measured genome-wide using qDSB-Seq [44]. However, since replication stress typically substantially changes the replication program, increased numbers of breaks per cell do not have to mean that forks break more often.…”

Section: Applications and Future Directionsmentioning

confidence: 99%

Stochasticity of replication fork speed plays a key role in the dynamics of DNA replication

Yousefi

Rowicka

2019

Preprint

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Eukaryotic DNA replication is elaborately orchestrated to duplicate the genome timely and faithfully. Replication initiates at multiple origins from which replication forks emanate and travel bi-directionally. The complex spatio-temporal regulation of DNA replication remains incompletely understood. To study it, computational models of DNA replication have been developed in S. cerevisiae. However, in spite of the experimental evidence of replication speed stochasticity, all models assumed that replication fork speed is constant or varies only with genomic coordinates. Here, we present the first model of DNA replication assuming stochastic speed of the replication fork. Utilizing data from both wild-type and hydroxyurea-treated yeast cells, we show that our model is more accurate than models assuming constant fork speed and reconstructs dynamics of DNA replication faithfully starting both from population-wide data and data reflecting fork movement in individual cells. Completion of replication in a timely manner is a challenge due to its stochasticity; we propose an empirically derived modification to replication speed based on the distance to the approaching fork, which promotes timely completion of replication. In summary, our work discovers a key role that stochasticity of the fork speed plays in the dynamics of DNA replication. We show that without including stochasticity of fork speed it is not possible to accurately reconstruct movement of individual replication forks, measured by DNA combing. Author summaryDNA replication in eukaryotes starts from multiple sites termed replication origins. Replication timing at individual sites is stochastic, but reproducible population-wide. Complex and not yet completely understood mechanisms ensure that genome is replicated exactly once and that replication is finished in time. This complex spatio-temporal organization of DNA replication makes computational modeling a useful tool to study replication mechanisms. For simplicity, all previous models assumed constant replication fork speed. Here, we show that such models are incapable of accurately reconstructing distances travelled by individual replication forks. Therefore, we propose a model with a stochastic replication fork speed. We show that such model reproduces faithfully distances travelled by individual replication forks. Moreover, our model is simpler than previous model and thus avoids over-learning (fitting noise). We also discover how replication speed may be attuned to timely complete replication. We propose that fork speed exponentially increases with diminishing distance to the approaching fork, which we show promotes timely completion of replication. Such speed up can be e.g. explained by a synergy effect of chromatin unwinding by both forks. Our 019 1/18 model can be used to simulate phenomena beyond replication, e.g. DNA double-strand breaks resulting from broken replication forks.

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Cas9 Cuts and Consequences; Detecting, Predicting, and Mitigating CRISPR/Cas9 On‐ and Off‐Target Damage

2020

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Large deletions and genomic re‐arrangements are increasingly recognized as common products of double‐strand break repair at Clustered Regularly Interspaced, Short Palindromic Repeats ‐ CRISPR associated protein 9 (CRISPR/Cas9) on‐target sites. Together with well‐known off‐target editing products from Cas9 target misrecognition, these are important limitations, that need to be addressed. Rigorous assessment of Cas9‐editing is necessary to ensure validity of observed phenotypes in Cas9‐edited cell‐lines and model organisms. Here the mechanisms of Cas9 specificity, and strategies to assess and mitigate unwanted effects of Cas9 editing are reviewed; covering guide‐RNA design, RNA modifications, Cas9 modifications, control of Cas9 activity; computational prediction for off‐targets, and experimental methods for detecting Cas9 cleavage. Although recognition of the prevalence of on‐ and off‐target effects of Cas9 editing has increased in recent years, broader uptake across the gene editing community will be important in determining the specificity of Cas9 across diverse applications and organisms.

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qDSB-Seq is a general method for genome-wide quantification of DNA double-strand breaks using sequencing

Cited by 47 publications

References 39 publications

Analyzing and interpreting DNA double-strand break sequencing data

Analyzing and interpreting DNA double-strand break sequencing data

Stochasticity of replication fork speed plays a key role in the dynamics of DNA replication

Cas9 Cuts and Consequences; Detecting, Predicting, and Mitigating CRISPR/Cas9 On‐ and Off‐Target Damage

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