{"repo":"nf-core/crisprseq","free":true,"listed":false,"github":"https://github.com/nf-core/crisprseq","clone":"git clone https://github.com/nf-core/crisprseq.git","description":"A pipeline for the analysis of CRISPR edited data. It allows the evaluation of the quality of gene editing experiments using targeted next generation sequencing (NGS) data (`targeted`) as well as the discovery of important genes from knock-out or activation CRISPR-Cas9 screens using CRISPR pooled DNA (`screening`).","language":"Nextflow","stars":63,"topics":["crispr","crispr-analysis","crispr-cas","nextflow","nf-core","ngs","pipeline","workflow"],"license":"MIT","category":"workflow-automation","readme_excerpt":"Introduction nf-core/crisprseq is a bioinformatics best-practice analysis pipeline for the analysis of CRISPR edited data. It allows the evaluation of the quality of gene editing experiments using targeted next generation sequencing (NGS) data ( targeted ) as well as the discovery of important genes from knock-out or activation CRISPR-Cas9 screens using CRISPR pooled DNA ( screening ). nf-core/crisprseq can be used to analyse: - CRISPR gene knockouts (KO) - CRISPR knock-ins (KI) - Base editing (BE) and prime editing (PE) experiments - CRISPR screening experiments (KO, CRISPRa (activation) or CRISPRi (interference)) The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community! On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to ","default_branch":null,"files":null,"tree":[],"storefront":"/r/nf-core","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/nf-core/crisprseq/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}