Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning

Xiang, Xi; Corsi, Giulia I.; Anthon, Christian; Qu, Kunli; Pan, Xiaoguang; Liang, Xue; Han, Peng; Dong, Zhanying; Liu, Lijun; Zhong, Jiayan; Ma, Tao; Wang, Jinbao; Zhang, Xiuqing; Jiang, Hui; Xu, Fengping; Liu, Xin; Xu, Xun; Wang, Jian; Yang, Huanming; Bolund, Lars; Church, George M.; Lin, Lin; Gorodkin, Jan; Luo, Yonglun

doi:10.1038/s41467-021-23576-0

Cited by 102 publications

(154 citation statements)

References 43 publications

(98 reference statements)

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“…We began by compiling data as discussed in the Methods Section, and illustrated in Fig. 2a 3 – 11 , 16 , 23 – 25 , 27 – 36 . Activity is reported as it was in the original dataset but we have inverted the sign on several datasets to ensure that in our comparisons more positive numbers correlate with more active gRNA.…”

Section: Resultsmentioning

confidence: 99%

“…We compiled 44 datasets from 23 papers (an overview is provided in Supplementary Data 1 , while datasets grouped by species are provided in Supplementary Data 2 - 6 ) 3 – 11 , 16 , 23 – 25 , 27 – 36 . We first filtered the data, only including results for gRNA where (1) we could find a matching target site in the target genome (if targeting an endogenous site) and (2) gRNA were targeting NGG PAM sites.…”

Section: Methodsmentioning

confidence: 99%

“…The “balanced” datasets were provided by the authors to better help differentiate features that drive differences in efficient and inefficient gRNA as the majority of the larger ~1.2 million gRNA library would be deemed efficient. Finally, from Xiang et al 2021 36 we included both the dataset with dox added and the dataset without. For each dataset, we averaged activity for day 8 and day 10, similar to what the authors did.…”

Section: Methodsmentioning

confidence: 99%

“…In the present study, we sought to better understand the mechanism by which gRNA sequence impacts gRNA activity. Toward this goal, we report a retrospective analysis of 44 gRNA library datasets from different species, with several Cas9 variants, using both endogenous and exogenous target sites, various activity outputs, and different experimental systems 3 – 11 , 16 , 23 – 25 , 27 – 36 . In this analysis, we confirm species dependent differences in how sequence influences gRNA activity and find strong evidence that gRNA sequence influences the time it takes for a given Cas9/gRNA complex to find the target site.…”

Section: Introductionmentioning

confidence: 99%

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Genome dependent Cas9/gRNA search time underlies sequence dependent gRNA activity

Moreb

Lynch

2021

Nat Commun

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CRISPR-Cas9 is a powerful DNA editing tool. A gRNA directs Cas9 to cleave any DNA sequence with a PAM. However, some gRNA sequences mediate cleavage at higher efficiencies than others. To understand this, numerous studies have screened large gRNA libraries and developed algorithms to predict gRNA sequence dependent activity. These algorithms do not predict other datasets as well as their training dataset and do not predict well between species. Here, to better understand these discrepancies, we retrospectively examine sequence features that impact gRNA activity in 44 published data sets. We find strong evidence that gRNA sequence dependent activity is largely influenced by the ability of the Cas9/gRNA complex to find the target site rather than activity at the target site and that this drives sequence dependent differences in gRNA activity between different species. This understanding will help guide future work to understand Cas9 activity as well as efforts to identify optimal gRNAs and improve Cas9 variants.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genome dependent Cas9/gRNA search time underlies sequence dependent gRNA activity

Moreb

Lynch

2021

Nat Commun

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“…Even if the gRNA architecture is well understood, the selection of the actual target-specific spacer sequence may still substantially affect the functioning of the detection system. Several bioinformatics tools aim to help select spacer sequences for Cas9 systems targeting DNA [ 65 ]. Tools for non-Cas9 systems have also been developed and often consider the secondary structure of both the gRNA and target RNA [ 66 , 67 , 68 ].…”

Section: Step-by-step Selection and Design Of Rna-targeting Crispr-ca...mentioning

confidence: 99%

How to Find the Right RNA-Sensing CRISPR-Cas System for an In Vitro Application

2022

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CRISPR-Cas systems have a great and still largely untapped potential for in vitro applications, in particular, for RNA biosensing. However, there is currently no systematic guide on selecting the most appropriate RNA-targeting CRISPR-Cas system for a given application among thousands of potential candidates. We provide an overview of the currently described Cas effector systems and review existing Cas-based RNA detection methods. We then propose a set of systematic selection criteria for selecting CRISPR-Cas candidates for new applications. Using this approach, we identify four candidates for in vitro RNA.

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A zebrafish model to elucidate the impact of host genes on the microbiota

Thormar,

Rasmussen,

Mathiessen

et al. 2024

Environmental DNA

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Every host species and organism provides a unique environmental niche contributing to the overall diversity of microbial ecosystems from the intestine of an animal to the oceans and forests of our planet. The study of host–microbiota interactions has long focused on the well‐established effects the microbiota has on its host. In contrast, little focus has been allocated to the role of the host in these intricate interactions. However, understanding the role of the host may well be an essential key to understanding the complexity of the relationship between the host and its microbiota. In this study, we present a model in which the effects of host genes on the microbiota can be elucidated and how such genetic effects may shape host‐associated microbiota. We demonstrate a hologenomic approach implementing the CRISPR/Cas system in the zebrafish model to combine the effects of a host gene with 16S metabarcoding and metabolomics data. We show that knocking out the gene coding for the rate‐limiting enzyme in melanogenesis, tyrosinase (tyr), correlates with changes in the intestinal microbiota of zebrafish and differences in the abundance of specific metabolites illustrating the value of our model for studying the impact of host genes on the composition and function of the intestinal microbiota.

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Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning

Cited by 102 publications

References 43 publications

Genome dependent Cas9/gRNA search time underlies sequence dependent gRNA activity

Genome dependent Cas9/gRNA search time underlies sequence dependent gRNA activity

How to Find the Right RNA-Sensing CRISPR-Cas System for an In Vitro Application

A zebrafish model to elucidate the impact of host genes on the microbiota

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