CRISPR-GA documentation

CRISPR-GA is a platform to assess the quality of gene editing using next gen sequencing data. It provides an easy, sensitive, and comprehensive analysis of gene editing results.
  1. PCR amplification of the target locus
  2. Illumina Miseq Analysis
  3. CRISPR-GA 3-click analysis
  4. Example
  5. Reference
  6. Contact

1. PCR amplification of the target locus

A 2-step PCR is carried out from a population of cells. The region containing the locus edited is amplified. It is recommended to generate a PCR amplicon that covers the edited area. Primers should be designed so that the Illumina Miseq read covers the area edited.

First PCR (primers with specific sequences targeting the edited locus with overhangs matching NEB universal primers):

primer-fw: 5' ACACTCTTTCCCTACACGACGCTCTTCCGATCT - (specific forward primer) 3'
primer-rv: 5' GACTGGAGTTCAGACGTGTGCTCTTCCGATCT - (specific reverse primer) 3'

Second PCR (general primers encoding barcodes, NEB P/N E7335S):


2. Illumina Miseq Sequencing

Most the standard v2, and v3 Miseq kits would produce optimal results. We recommend to design the experiment following these guidelines:

We usually use Illumina based technologies but any next generation sequencing platform providing reads with the corresponding quality (FASTQ) could be used.

3. CRISPR-GA 3-click analysis

The CRISPR-GA pipeline consists of five different steps: Miseq reads quality control, mapping, indel calling; HR and NHEJ estimation, and graphical representation. First, the sequencing reads are uploaded, the 3' end is trimmed of nucleotides with a Phred score lower than 20, and any reads shorter than 80bp are discarded. Second, the reads are mapped to the reference sequence provided by the user using BLAT, which has a good support for indels. If the user inputs paired end reads, the first two steps will be done independently, and the results intersected. Most users will supply paired end reads, as all new Illumina kits only support paired ends. However, CRISPR-GA single end compatibility will be maintained to support all possible experimental setups. In the third step, R statistical language is used to process the mapped BLAT results, to call the insertions and deletions. Forth, pattern matching is used to compute the number of reads matching the expected sequence, generating othervariants. Fifth, R statistical language is used to produce a report that integrates plots for indels, and HR.


4. Example

This is an example of an editing experiment carried on the AAVS human locus. We provide the reference sequence, and the oligo targeting encoding the genetic change.

In this experiment, we measured 0.5% HR, and 0.25% NHEJ. We detected single base indels centered on the targeting site. See CRISPR-GA produced figures below:

5. Reference

Guell M, Yang L, Church G (2014) Genome Editing Assessment using CRISPR Genome Analyzer (CRISPR-GA) Bioinformatics.


Marc Güell: mguell # genetics.med.harvard.edu