CRISPRcloud: a secure cloud-based pipeline for CRISPR pooled screen deconvolution

Jeong, Hyun-Hwan; Kim, Seon‐Young; Rousseaux, Maxime W.C.; Zoghbi, Huda Y.; Liu, Zhandong

doi:10.1093/bioinformatics/btx335

Cited by 26 publications

(21 citation statements)

References 21 publications

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“…Recently published tools for CRISPR pooled screen analysis, including CRISPRcloud (CC1) 27 , MAGeCK 25 , CarRpools 29 , CRISPRAnalyzeR 26 , and PinAPL-Py 28 , provide different methods for estimating the abundance of sgRNAs in each sample from pooled libraries. In most cases, input data consist of raw FASTQ-format sequencing result files.…”

Section: Algorithm For Quantifying Sgrna Abundance Previous Methods Amentioning

confidence: 99%

“…However, those analysis tools tend to be script-based because they were developed for bioinformaticians or scientists who are very computationally savvy. Of the newly-developed tools, the most user-friendly are CRISPRAnalyzeR 26 , CRISPRcloud 27 , and PinAPLPy 28 , as they have web-based interfaces and represents as a first-step toward enabling scientists who are actually generating the CRISPR/Cas9 screen data to analyze these large dataset. However, each of these tools have various of rate-limiting steps such as requiring intricate tuning of parameters for trimming and mapping data, long transfer times and file copying errors in the transfer of a large amount of sequence data over the internet, lack of fast and powerful statistical tools etc.…”

Section: Introductionmentioning

confidence: 99%

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CRISPRCloud2: A cloud-based platform for deconvolving CRISPR screen data

Jeong

Kim

Rousseaux

et al. 2018

Preprint

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The simplicity and cost-effectiveness of CRISPR technology have made high-throughput pooled screening approaches available to many. However, the large amount of sequencing data derived from these studies yields often unwieldy datasets requiring considerable bioinformatic resources to deconvolute data; a feature which is simply not accessible to many wet labs. To address these needs, we have developed a cloud-based webtool CRISPRCloud2 that provides a state-of-the-art accuracy in mapping short reads to CRISPR library, a powerful statistical test that aggregates information across multiple sgRNAs targeting the same gene, a user-friendly data visualization and query interface, as well as easy linking to other CRISPR tools and bioinformatics resources for target prioritization. CRISPRCloud2 is a one-stop shop for labs analyzing CRISPR screen data.

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Section: Algorithm For Quantifying Sgrna Abundance Previous Methods Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CRISPRCloud2: A cloud-based platform for deconvolving CRISPR screen data

Jeong

Kim

Rousseaux

et al. 2018

Preprint

Self Cite

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“…We had previously developed a web-based application called CRISPRcloud that could run any statistical testing and mapping algorithm through the cloud-based infrastructure provided by Amazon Web Service (AWS) (Jeong et al 2017). We implemented CB 2 in the platform and added new features to increase speed and data security (Supplemental Fig.…”

Section: Cb 2 Provides More Accurate Alignment Without Parameter Tuningmentioning

confidence: 99%

Beta-binomial modeling of CRISPR pooled screen data identifies target genes with greater sensitivity and fewer false negatives

et al. 2019

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The simplicity and cost-effectiveness of CRISPR technology have made high-throughput pooled screening approaches accessible to virtually any laboratory. Analyzing the large sequencing data derived from these studies, however, still demands considerable bioinformatics expertise. Various methods have been developed to lessen this requirement, but there are still three tasks for accurate CRISPR screen analysis that involve bioinformatic know-how, if not prowess: designing a proper statistical hypothesis test for robust target identification, developing an accurate mapping algorithm to quantify sgRNA levels, and minimizing the parameters that need to be fine-tuned. To make CRISPR screen analysis more reliable as well as more readily accessible, we have developed a new algorithm, called CRISPRBetaBinomial or CB 2. Based on the beta-binomial distribution, which is better suited to sgRNA data, CB 2 outperforms the eight most commonly used methods (HiTSelect, MAGeCK, PBNPA, PinAPL-Py, RIGER, RSA, ScreenBEAM, and sgRSEA) in both accurately quantifying sgRNAs and identifying target genes, with greater sensitivity and a much lower false discovery rate. It also accommodates staggered sgRNA sequences. In conjunction with CRISPRcloud, CB 2 brings CRISPR screen analysis within reach for a wider community of researchers.

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“…Although most pooled CRISPR screens have compared gRNA representation between two samples, it is also possible to have a multi-dimensional phenotypic readout, such as FACS-based sorting of multiple populations to determine relevant effect size or single-cell full transcriptome readout (Adamson et al, 2016; Datlinger et al, 2017; Dixit et al, 2016; Jaitin et al, 2016; Xie et al, 2017). Many different tools have been developed for analysis of gene-targeted screens and offer such features as automated determination of positively/negatively selected genes, pathway analysis, quality control analysis, and data visualization (Jeong et al, 2017; Li et al, 2014, 2015; List et al, 2016; Winter et al, 2016). …”

Section: Analysis Of Pooled Crispr Screensmentioning

confidence: 99%

High-Throughput Approaches to Pinpoint Function within the Noncoding Genome

2017

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The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas nuclease system is a powerful tool for genome editing, and its simple programmability has enabled high-throughput genetic and epigenetic studies. These high-throughput approaches offer investigators a toolkit for functional interrogation of not only protein-coding genes but also noncoding DNA. Historically, noncoding DNA has lacked the detailed characterization that has been applied to protein-coding genes in large part because there has not been a robust set of methodologies for perturbing these regions. Although the majority of high-throughput CRISPR screens have focused on the coding genome to date, an increasing number of CRISPR screens targeting noncoding genomic regions continue to emerge. Here, we review high-throughput CRISPR-based approaches to uncover and understand functional elements within the noncoding genome and discuss practical aspects of noncoding library design and screen analysis.

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CRISPRcloud: a secure cloud-based pipeline for CRISPR pooled screen deconvolution

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 26 publications

References 21 publications

CRISPRCloud2: A cloud-based platform for deconvolving CRISPR screen data

CRISPRCloud2: A cloud-based platform for deconvolving CRISPR screen data

Beta-binomial modeling of CRISPR pooled screen data identifies target genes with greater sensitivity and fewer false negatives

High-Throughput Approaches to Pinpoint Function within the Noncoding Genome

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