Minimal genome-wide human CRISPR-Cas9 library

AbstractCRISPR guide RNA libraries have been iteratively improved to provide increasingly efficient reagents, although their large size is a barrier for many applications. We design an optimised minimal genome-wide human CRISPR-Cas9 library (MinLibCas9) by mining existing large-scale gene loss-of-function datasets, resulting in a greater than 42% reduction in size compared to other CRISPR-Cas9 libraries while preserving assay sensitivity and specificity. MinLibCas9 provides backward compatibility with existing datasets, increases the dynamic range of CRISPR-Cas9 screens and extends their application to complex models and assays.

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Minimal genome-wide human CRISPR-Cas9 library

10.1101/848895 ◽

2019 ◽

Cited By ~ 1

Author(s):

Emanuel Gonçalves ◽

Mark Thomas ◽

Fiona M Behan ◽

Gabriele Picco ◽

Clare Pacini ◽

...

Keyword(s):

Large Scale ◽

Gene Loss ◽

Dynamic Range ◽

Loss Of Function ◽

Assay Sensitivity ◽

Guide Rna ◽

Minimal Genome ◽

Large Size ◽

Genome Wide ◽

Complex Models

AbstractCRISPR guide-RNA libraries have been iteratively optimised to provide increasingly efficient reagents, although their large size is a barrier for many applications. We designed an optimised minimal genome-wide human CRISPR-Cas9 library (MinLibCas9), by mining existing large-scale gene loss-of-function datasets, resulting in a greater than 42% reduction in size compared to other libraries while preserving assay sensitivity and specificity. MinLibCas9 increases the dynamic range of CRISPR-Cas9 loss-of-function screens and extends their application to complex models and assays.

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Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier

10.1101/2020.05.30.125526 ◽

2020 ◽

Cited By ~ 2

Author(s):

Eiru Kim ◽

Traver Hart

Keyword(s):

Dynamic Range ◽

Quality Data ◽

Loss Of Function ◽

Genetic Screens ◽

Gene Essentiality ◽

Guide Rna ◽

Genome Wide ◽

Improved Model ◽

Target Effects

AbstractIdentifying essential genes in genome-wide loss of function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome wide genetic screens. Here, we introduce an updated version, BAGEL2, which employs an improved model that offers greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes, and a multi-target correction that reduces false positives from off-target CRISPR guide RNA. We also suggest a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower-quality data in substantially different ways. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github (https://github.com/hart-lab/bagel).

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Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier

Genome Medicine ◽

10.1186/s13073-020-00809-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Eiru Kim ◽

Traver Hart

Keyword(s):

Dynamic Range ◽

Quality Data ◽

Loss Of Function ◽

Genetic Screens ◽

Gene Essentiality ◽

Guide Rna ◽

Genome Wide ◽

And Performance ◽

Improved Model ◽

Sensitivity Specificity

Abstract Background Identifying essential genes in genome-wide loss-of-function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome-wide genetic screens. Results We introduce an updated version, BAGEL2, which employs an improved model that offers a greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes; a multi-target correction that reduces false positives from off-target CRISPR guide RNA; and the implementation of a cross-validation strategy that improves performance ~ 10× over the prior bootstrap resampling approach. We also describe a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower quality data in substantially different ways. Conclusions BAGEL2 substantially improves the sensitivity, specificity, and performance over BAGEL and establishes the new state of the art in the analysis of CRISPR knockout fitness screens. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github (https://github.com/hart-lab/bagel).

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GUIDES: sgRNA design for loss-of-function screens

10.1101/168849 ◽

2017 ◽

Author(s):

Joshua A. Meier ◽

Feng Zhang ◽

Neville E. Sanjana

Keyword(s):

Graphical User Interface ◽

Large Scale ◽

Protein Annotation ◽

Loss Of Function ◽

Sequencing Data ◽

Web Based ◽

Guide Rna ◽

Sgrna Design ◽

Dna Editing ◽

Human And Mouse

GUIDES (Graphical User Interface for DNA Editing Screens) is a web-based tool for the design of custom, large-scale CRISPR libraries for loss-of-function screens in human and mouse. GUIDES combines multi-tissue RNA-sequencing data to target expressed exons, protein annotation to target functional domains, sophisticated on-target and off-target guide RNA scoring and other optimizations to create CRISPR libraries directly from a list of genes without requiring any programming expertise.

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Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits

10.1101/2021.11.03.466843 ◽

2021 ◽

Author(s):

Brian C Zhang ◽

Arjun Biddanda ◽

Pier Francesco Palamara

Keyword(s):

Complex Traits ◽

Large Scale ◽

Mixed Model ◽

Large Population ◽

Complex Trait ◽

Genotype Imputation ◽

Loss Of Function ◽

Genome Wide ◽

Wide Range ◽

Ancestral Recombination Graphs

Accurate inference of gene genealogies from genetic data has the potential to facilitate a wide range of analyses. We introduce a method for accurately inferring biobank-scale genome-wide genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies within linear mixed models to perform association and other complex trait analyses. We use these new methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and to detect associations in 7 complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 133, frequency range 0.0004% - 0.1%) than genotype imputation from ~65,000 sequenced haplotypes (N = 65). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants, which are enriched for missense (2.3×) and loss-of-function (4.5×) variation. Inferred genealogies also capture additional association signals in higher frequency variants. These results demonstrate that large-scale inference of gene genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.

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Identifying Lethal Dependencies with HUGE Predictive Power from Large-Scale Functional Genomic Screens

10.1101/2021.10.29.466419 ◽

2021 ◽

Author(s):

Fernando Carazo ◽

Edurne San Jose Eneriz ◽

Marian Gimeno ◽

Leire Garate ◽

Estibaliz Miranda ◽

...

Keyword(s):

Statistical Power ◽

Large Scale ◽

Synthetic Lethality ◽

Functional Genomic ◽

Loss Of Function ◽

Mutational Status ◽

Genome Wide ◽

Targeted Treatments ◽

Functional Screens

Recent functional genomic screens -such as CRISPR-Cas9 or RNAi screening- have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem related to a large number of gene knockouts limits the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the <HUb effect in Genetic Essentiality> (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens -Project Score, CERES score, and DEMETER score- identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. HUGE shows an increased enrichment in a recent harmonized knowledgebase of clinical interpretations of somatic genomic variants in cancer (with an AUROC up to 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer.

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GO-CRISPR: a highly controlled workflow to discover gene essentiality in loss-of-function screens

10.1101/2020.06.04.134841 ◽

2020 ◽

Author(s):

Pirunthan Perampalam ◽

James I. McDonald ◽

Frederick A. Dick

Keyword(s):

Software Package ◽

Dynamic Range ◽

Superior Performance ◽

Screening Methods ◽

High Grade ◽

Serous Ovarian Cancer ◽

Loss Of Function ◽

Cellular Mechanisms ◽

Sequence Detection ◽

Genome Wide

SUMMARYGenome-wide CRISPR screens are an effective discovery tool for genes that underlie diverse cellular mechanisms that can be scored through cell fitness. Loss-of-function screens are particularly challenging compared to gain-of-function because of the limited dynamic range of decreased sgRNA sequence detection. Here we describe Guide-Only control CRISPR (GO-CRISPR), an improved loss-of-function screening workflow, and its companion software package, Toolset for the Ranked Analysis of GO-CRISPR Screens (TRACS). We demonstrate a typical GO-CRISPR workflow in a non-proliferative 3D spheroid model of dormant high grade serous ovarian cancer and demonstrate superior performance to standard screening methods. The unique integration of the pooled sgRNA library quality and guide-only controls allows TRACS to identify novel molecular pathways that were previously unidentified in tumor dormancy. Together, GO-CRISPR and TRACS can robustly improve the discovery of essential genes in challenging biological scenarios.

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Genome-wide CRISPR Screens in Primary Human T Cells Reveal Key Regulators of Immune Function

10.1101/384776 ◽

2018 ◽

Cited By ~ 1

Author(s):

Eric Shifrut ◽

Julia Carnevale ◽

Victoria Tobin ◽

Theodore L. Roth ◽

Jonathan M. Woo ◽

...

Keyword(s):

T Cells ◽

Large Scale ◽

Cell Receptor ◽

Loss Of Function ◽

Functional Studies ◽

Genome Wide ◽

Human T Cells ◽

T Cell Receptor Signaling ◽

Genetic Perturbations ◽

Signaling Components

SUMMARYHuman T cells are central effectors of immunity and cancer immunotherapy. CRISPR-based functional studies in T cells could prioritize novel targets for drug development and improve the design of genetically reprogrammed cell-based therapies. However, large-scale CRISPR screens have been challenging in primary human cells. We developed a new method, sgRNA lentiviral infection with Cas9 protein electroporation (SLICE), to identify regulators of stimulation responses in primary human T cells. Genome-wide loss-of-function screens identified essential T cell receptor signaling components and genes that negatively tune proliferation following stimulation. Targeted ablation of individual candidate genes validated hits and identified perturbations that enhanced cancer cell killing. SLICE coupled with single-cell RNA-Seq revealed signature stimulation-response gene programs altered by key genetic perturbations. SLICE genome-wide screening was also adaptable to identify mediators of immunosuppression, revealing genes controlling response to adenosine signaling. The SLICE platform enables unbiased discovery and characterization of functional gene targets in primary cells.

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Engineering digitizer circuits for chemical and genetic screens in human cells

Nature Communications ◽

10.1038/s41467-021-26359-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Nicole M. Wong ◽

Elizabeth Frias ◽

Frederic D. Sigoillot ◽

Justin H. Letendre ◽

Marc Hild ◽

...

Keyword(s):

Large Scale ◽

Dynamic Range ◽

Genetic Screens ◽

Genetic Circuits ◽

Transient Responses ◽

Reporter Activity ◽

Genome Wide ◽

Pathway Activation ◽

Crispr Screen ◽

Or Genes

AbstractCell-based transcriptional reporters are invaluable in high-throughput compound and CRISPR screens for identifying compounds or genes that can impact a pathway of interest. However, many transcriptional reporters have weak activities and transient responses. This can result in overlooking therapeutic targets and compounds that are difficult to detect, necessitating the resource-consuming process of running multiple screens at various timepoints. Here, we present RADAR, a digitizer circuit for amplifying reporter activity and retaining memory of pathway activation. Reporting on the AP-1 pathway, our circuit identifies compounds with known activity against PKC-related pathways and shows an enhanced dynamic range with improved sensitivity compared to a classical reporter in compound screens. In the first genome-wide pooled CRISPR screen for the AP-1 pathway, RADAR identifies canonical genes from the MAPK and PKC pathways, as well as non-canonical regulators. Thus, our scalable system highlights the benefit and versatility of using genetic circuits in large-scale cell-based screening.

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Optimized strategy for in vivo Cas9-activation in Drosophila

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1707635114 ◽

2017 ◽

Vol 114 (35) ◽

pp. 9409-9414 ◽

Cited By ~ 39

Author(s):

Ben Ewen-Campen ◽

Donghui Yang-Zhou ◽

Vitória R. Fernandes ◽

Delfina P. González ◽

Lu-Ping Liu ◽

...

Keyword(s):

Large Scale ◽

Target Gene ◽

In Vivo Studies ◽

Transcriptional Activators ◽

Loss Of Function ◽

Guide Rnas ◽

Genome Wide ◽

A Genome ◽

Endogenous Loci

While several large-scale resources are available for in vivo loss-of-function studies in Drosophila, an analogous resource for overexpressing genes from their endogenous loci does not exist. We describe a strategy for generating such a resource using Cas9 transcriptional activators (CRISPRa). First, we compare a panel of CRISPRa approaches and demonstrate that, for in vivo studies, dCas9-VPR is the most optimal activator. Next, we demonstrate that this approach is scalable and has a high success rate, as >75% of the lines tested activate their target gene. We show that CRISPRa leads to physiologically relevant levels of target gene expression capable of generating strong gain-of-function (GOF) phenotypes in multiple tissues and thus serves as a useful platform for genetic screening. Based on the success of this CRISRPa approach, we are generating a genome-wide collection of flies expressing single-guide RNAs (sgRNAs) for CRISPRa. We also present a collection of more than 30 Gal4 > UAS:dCas9-VPR lines to aid in using these sgRNA lines for GOF studies in vivo.

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