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CREATE / CRISPR-enabled trackable genome engineering
Links each guide RNA to homologous repair cassettes that both edit loci and function as barcodes to track genotype-phenotype relationships. CREATE combines automated design of CREATE cassettes (modular guide RNA-editing oligos), arraybased CREATE cassette synthesis, and sequencing in a streamlined workflow for genome engineering. CREATE was applied to site saturation mutagenesis for protein engineering, reconstruction of adaptive laboratory evolution experiments, and identification of stress tolerance and antibiotic resistance genes in bacteria.
MAGeCK / Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout
Identifies positively and negatively selected sgRNAs and genes in genome-scale CRISPR/Cas9 knockout experiments. MAGeCK consists of four steps: read count normalization, mean-variance modeling, sgRNA ranking and gene ranking. The software results are robust across different sequencing depths and numbers of sgRNAs per gene. It is also able to perform both positive and negative selection screens simultaneously, and identify biologically meaningful and cell type-specific essential genes and pathways.
Allows users to estimate the effects of gene knockouts (KO) in clustered regularly interspaced short palindromic repeats (CRISPR) screens. MAGeCK-VISPR consists of an algorithm named “MAGeCK-MLE” that contains the following functions: (1) define a set of quality control (QC) measurements; (2) extend the MAGeCK algorithm to call essential genes under multiple conditions while considering single guide RNA (sgRNA) knockout efficiency; and (3) provide a web-based visualization framework (VISPR) for interactive exploration of CRISPR screen QC and analysis results.
PinAPL-Py / Platform-INdependent Analysis of PooLed screens using PYthon
Allows users to proceed alignment, quality control, enrichment/depletion analysis and gene ranking. PinAPL-Py is optimized for transparency and user-friendly operation. It facilitates standardized, reproducible data analysis that can be carried out directly by the scientists conducting the experiments. The tool is based on well-established methods to run its analysis workflow. It provides adjustable configuration settings, such as alternative methods for read count normalization, gene ranking, or various technical parameters for individual steps of the workflow.
ENCoRE / Easy NGS-to-Gene CRISPR REsults
Allows users to distill CRISPR screening results candidate gene lists. ENCoRE enables bench scientists to keep pace with large-scale data generation in by processing next-generation sequencing (NGS) sequence files and generating graphical outputs with statistical representation. The software is able to distill large datasets into workable files and to give an overview of genes involved in a process. Users with java knowledge can implement their own modules for processing and reporting.
PBNPA / Permutation-Based Non-Parametric Analysis
Allows analysis of clustered regularly-interspaced short palindromic repeats (CRISPR) data. PBNPA is a method that mitigates the three major drawbacks of existing CRISPR methods. It avoids restrictive distributional assumptions by computing p-values at the gene level by permuting single guide RNA (sgRNA) labels. The software can also be applied to analyze genetic screens implemented with small interfering RNA (siRNAs) or short hairpin RNA (shRNAs) and drug screens.
SAVE / Screening Analysis Visual Explorer
Serves as a useful web-based analysis pipeline for reanalysis of pooled CRISPR screening datasets. SAVE serves a dual purpose of extracting, clustering and analyzing raw next generation sequencing (NGS) files derived from pooled screening experiments while at the same time presenting them in a user-friendly way on a secure web-based platform. This framework is expected to accelerate development of web-based bioinformatics tool for handling all studies which include NGS data.
Simulates the impact of screen parameters on the robustness of screen results. CRISPulator facilitates the design of pooled genetic screens by enabling the exploration of a large space of experimental parameters in silico, rather than through costly experimental trial and error. It is based on a strategy for phenotypic selection: fluorescence-activated cells sorting (FACS)-based screens. The power of the tool was illustrated by deriving non-obvious rules for optimal screen design.
sgRSEA / single-guide RNA Set Enrichment Analysis
Identifies essential genes from genetic screening data using CRISPR (clustered regularly interspaced short palindromic repeats) and Cas9 (CRISPR-associated nuclease 9) system. sgRSEA computes sgRNA and gene level statistics, given normalized sgRNA read counts under treatment and control. It calculates P-values of the gene scores using a permutation method to identify genes where some or all of the sgRNA read counts in treatment are significantly higher/lower compared to control, that is, genes with positive/negative treatment effect.
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