1. What is beta-score?

Beta-score is a measurement of gene selections similar to the term of log fold change in differential expression analysis. A positive beta-core means a gene is positively selected in a condition, vice versa. Detailed explanation of beta-score please refer to MAGeCK-VISPR paper.

2. How to use CRISP-view?

Please refer to the tutorial page.

3. Data analysis procedure.

In order to keep data consistence, For each dataset, we download raw sequence data (if provided) or raw count table and process them with the workflow of MAGeCK-VISPR. Details please refer to MAGeCK-VISPR paper and MAGeCK-Flute protocol.

4. Quality control metrics.

We adopt some of the quality control measurements defined by MAGeCK-VISPR in CRISP-view. The quality control metrics can be divided into two categories: read count level and gene level. Firstly, MAGeCK-VISPR maps raw sequencing reads (if provided) to sgRNAs library with no mismatches tolerated. The number of mapped reads, sgRNAs with zero read count and the Gini index of read count distribution are adopted as read count level quality control metrics. We use enrichment score of negative selection on ribosomal genes from GSEA to measure data quality in gene level, because the knocking out of ribosomal genes will lead to a strong negative selection phenotype. A good negative selection experiment should have a big enrichment score. The thresholds of quality control characteristics of read count level are based on the definition in MAGeCK-VISPR. The thresholds of gene level quality control metrics are established using our collection of all samples, which makes 2/3 of samples pass the quality control test.

5. Problem caused by browser cache.

Brower usually caches the JavaScript, CSS and other data to improve speed and user experience. When CRISP-view is updated, user needs to empty the previous cache in the browser. For Safari, press Opt+Cmd+E to empty cache.

6. How to cite CRISP-view?

The CRISP-view paper is published in Nucleic Acids Research (NAR).

7. Contributors

We would like to thank all the contributors above for their hard work. We would also like to thank those authors for sharing of the orignal screening data.