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Welcome to the CRISPRAnalyzeR Github page!
CRISPRAnalyeR is a web-based, fully interactive suite for the analysis and documentation of pooled CRISPR Screens.
See CRISPRAnalyzeR in action! You can even go and analyse your own data right away!
Check out the Wiki Page or scroll down for more information and help
You can use the provided CRISPRAnalyzeR web-service or download the suite for installation within your lab/company.
CRISPRAnalyzeR has been specifically developed to provide a fully-interactive, hollistic and exploratory analysis of pooled CRISPR Screens especially for those people that perform the screens themselves. You can easily analyse your screen using 6 different hit calling and 1 essential gene calling methods as well as perform gene annotation enrichment, gene set analysis and get detailed information about your sgRNAs - all in a convenient web-browser interface.
All you need is your sequencing data and the pooled CRISPR screen library file (we provide you with the most common ones) - and CRISPRAnalyzeR will help you to go from rawdata to potential followup candidates!
CRISPRAnalyzeR uses a guided-analysis approach. This means you will be guided through the whole analysis, so that you can focus on the most important thing - your data.
In brief, CRISPAnalyzeR consists of four sections, all paired with a great user-friendly UI:
- Screen Quality Estimation
- Hit Calling using multiple published hit calling methods
- Hit Confirmation with extensive Gene information, sgRNA information, Gene Set Analysis and much more
- Download of the interactive report that provides a comprehensive documentation of your screen
Analysing CRISPR Screens has never been easier - and has never been so much fun!
CRISPRAnalyzeR is easy-to-use and assists you with the analysis of your screening data. It contains 4 different steps, each filled with nice visualizations, interactive tables and all the information you need.
CRISPRAnalyzeR combines the power of several CRISPR Analysis Workflows and incorporates them into a streamlined, straight-forward and convenient workflow.
You run one analysis and get the information of 8 different analysis workflows
We implemented SIX analysis workflows based on differentially expressed read counts:
- Wilcoxon Test
- DESeq2 (based on gene-level read counts)
- MAGeCK
- sgRSEA
- edgeR
Furthermore, we implmented TWO analysis workflows based on bayesion models that specifically identify gene essentiality/dropouts:
- BAGEL
- ScreenBEAM
Use various external ressources to enrich information about your favourite candidate - all within the app and included in your report!
