scientific-crispr-design

CRISPR gRNA design pipelines enumerate guide candidates, evaluate off-target risks using CFD/MIT scores, and predict activity. This integrates PAM matching, scoring, and prediction to reduce manual candidate selection.

Calculating CFD and MIT off-target scores involves matching gRNA sequences against the genome to identify potential mismatches. The pipeline processes these matches to output quantified off-target risk evaluations for each guide.

PAM sequence searching identifies specific pattern matches in target DNA to enumerate valid gRNA candidates. The pipeline filters these candidates based on appropriate GC content for CRISPR-Cas9 and Cas12a designs.

CRISPRscan and Rule Set 2 are supported for gRNA activity prediction within the pipeline. These algorithms analyze sequence features to estimate guide efficiency before library construction.

Constructing an sgRNA library by gene involves designing and assembling guides into targeted sets. The pipeline supports this construction using built-in Python-based TU tools to generate organized libraries.

The gRNA design pipeline supports both CRISPR-Cas9 and Cas12a designs. It accommodates different PAM pattern requirements for each nuclease during the candidate enumeration and scoring stages.