ATG_CRISPResso2_To_RIMA2

Background

Non-Homologous End Joining (NHEJ) and Microhomology-Mediated End Joining (MMEJ) are two highly conserved DNA repair pathways in eukaryotic cells. The NHEJ repair pathway is error-prone; it can repair DNA double-strand breaks (DSBs) either precisely or by introducing small insertions or deletions (indels). The MMEJ repair pathway, however, typically repairs DSBs by introducing deletions. Notably, a variation of MMEJ is thought to introduce de novo insertions. Classical MMEJ (c-MMEJ) repair events refer to deletions associated with microhomologies.

Studying repair events at Cas9 target sites in human cells provides insights into the cell's DNA repair capacity and can be used as an assay to screen compounds that alter DNA repair pathways. One method of studying repair events at Cas9 cleavage sites is through amplicon sequencing and bioinformatics analysis. Several data analysis packages are available for such studies, with CRISPResso being the most widely used software. CRISPResso can delineate modified reads into Homology-Directed Repair (HDR) and non-HDR events. However, it does not distinguish between NHEJ and MMEJ events, and all erroneous repairs are collectively categorized as NHEJ (non-HDR).

RIMA (Rational Indel Meta-Analysis), an Excel-based tool, was developed to specifically identify c-MMEJ deletions.

This repository contains:

• A Python script ATG_CRISPResso2_to_RIMA2.py, which reads CRISPResso output (specifically the Allele_Frequency_Table.zip file) and generates variant tables that can be used as RIMA input.
• The latest version of RIMA Excel workbook RIMA2_v20240420.xlsm, which analyzes variant tables, produces statistics on repair events, and provides an informative graphical representation of the alleles.

RIMA use-cases

• Restrictly where the a single guide-RNA and a wild-type Cas9 (or its variations like PEn) is used (especifically, SpyCas9 or Type-IIB Cas9s).
• HDR templates can be used only if the HDR repair outcome is insertion of 3 nucleotides or more.
• Up to 12 different repair template can be used at the same time, only if the repair outcome is the insrtion of 3 nucleotides or more. One can imagine a scenario in which templated-assisted repair events via HDR, NHEJ, MMEJ, and their variations result in insertions at the cut-site.
• Except extended insertions that could be classified as knock-ins, other repair events are classified into c-MMEJ and Other-EJ.
• Single nucleotide variations are considered sequencing errors and are eliminated from the analysis. Therefore, HDR experiments that exchange bases must not be analyses using RIMA. Likewise, RIMA shall not be used for the analysis of Base Edited and Prime Eidted samples.
• RIMA can also be used for the analysis of deep-sequenced off-target sites. RIMA offers a higher sensitivity than CRISPResso thanks to the adjustable aparameters in RIMA like min allele frequency, min No. of reads per allele, and its inherent algorithm to eliminate variants outside of the cutsite.

Requirements

crispresso2=2.2.14

Step-by-step guideline (Mac and Linux)

Install Miniconda

Conda is an open-source package and environment management system that allows user to conviniently create new environments and install packages. Follow the official Conda installation guideline here: https://docs.anaconda.com/free/miniconda/miniconda-install/

Download this repository

git clone https://github.com/ghahfarokhi/atg_crispresso2_to_rima2
cd atg_crispresso2_to_rima2

Create atg_crispresso2_to_rima2 environment

conda env create -f CRISPResso2_to_RIMA2_env.yml
conda activate atg_crispresso2_to_rima2

Test that the installation of CRISPResso2 has been successful:

• CRISPResso -h prints the help page for CRISPResso2.

For detailed description of CRISPResso functionalities and alternative ways of installation refer to its official github page: https://github.com/pinellolab/CRISPResso2

Run CRISPResso and ATG_CRISPResso2_to_RIMA2.py script

Run CRISPResso with a desired set of paramaters (-a and -g are required parameters for RIMA analysis). For this guideline, the following codes uses the test fastq data that are provided withinn this repository with the following amplicon ref and guide sequences:

cd test_data

CRISPResso -r1 01_fastq_examples/example_DMSO.fastq -a CGAGTCTAGAGGGCCCGTTTAAACCCGCTGGGCCATGGGCTATGAGTACAGGTCATGTACGGCCTCATAGTGGTACAGTAGTGACTCAAGACGATAGTTACCGGATAAGGCGCAGCGGTCGGGCTGAACGGGGGGTTCGTGCACACAGCCCAGCTTGGAGCGAACGACCTACACCGAACTGAGATACCTACAGCGTGAGCTA -g CTATGAGTACAGGTCATGTA -o 02_crispresso_examples/

CRISPResso -r1 01_fastq_examples/example_NHEJi.fastq -a CGAGTCTAGAGGGCCCGTTTAAACCCGCTGGGCCATGGGCTATGAGTACAGGTCATGTACGGCCTCATAGTGGTACAGTAGTGACTCAAGACGATAGTTACCGGATAAGGCGCAGCGGTCGGGCTGAACGGGGGGTTCGTGCACACAGCCCAGCTTGGAGCGAACGACCTACACCGAACTGAGATACCTACAGCGTGAGCTA -g CTATGAGTACAGGTCATGTA -o 02_crispresso_examples/

cd ..

python ATG_CRISPResso2_to_RIMA2.py  --out ./test_data/04_RIMA/

The test fastq files provided in test_data/01_fastq_examples/ are simulated for two samples transfected with SpyCas9 and a sgRNA, and treated with either DMSO or an NHEJ-inhibitory compound (NHEJi).

See CRISPResso output for DMSO and NHEJi samples:

• CRISPResso_on_example_DMSO.html
• CRISPResso_on_example_NHEJi.html

The following three files should have been generated in the output folder upon successful completion of the python script run:

ls test_data/04_RIMA/

Output:

RIMA_example_DMSO-variants.tsv
RIMA_example_NHEJi-variants.tsv
experiment_sheet.tsv

column -t test_data/04_RIMA/RIMA_example_DMSO-variants.tsv

Output:

VariantNo  Position  Type       Length  Ref           Alt  Count
2          57        Insertion  1       -             T    35
3          50        Deletion   11      AGGTCATGTAC   -    25
4          54        Deletion   5       CATGT         -    16
5          57        Deletion   12      GTACGGCCTCAT  -    10
6          53        Deletion   10      TCATGTACGG    -    9
7          55        Deletion   2       AT            -    8
8          57        Deletion   1       G             -    6
9          56        Deletion   1       T             -    3
10         56        Deletion   4       TGTA          -    2

column -t test_data/04_RIMA/experiment_sheet.tsv

Output:

file_address                                 file_name                        amplicon_seq                                                                                                                                                                                                guide_seq             mapped_reads  wt_count  total_variants  cut_pos  mmej_reads  del_01_bp  del_02_05_bp  del_06_10_bp  del_above_10_bp  ins_01_bp  ins_02_05_bp  ins_06_10_bp  ins_above_10_bp  indel_combos
C:\RIMA\Raw\RIMA_example_DMSO-variants.tsv   RIMA_example_DMSO-variants.tsv   CGAGTCTAGAGGGCCCGTTTAAACCCGCTGGGCCATGGGCTATGAGTACAGGTCATGTACGGCCTCATAGTGGTACAGTAGTGACTCAAGACGATAGTTACCGGATAAGGCGCAGCGGTCGGGCTGAACGGGGGGTTCGTGCACACAGCCCAGCTTGGAGCGAACGACCTACACCGAACTGAGATACCTACAGCGTGAGCTA  CTATGAGTACAGGTCATGTA  180           66        9               56       62.0        9.0        92.0          9.0           35.0             35.0       0.0           0.0           0.0              0.0
C:\RIMA\Raw\RIMA_example_NHEJi-variants.tsv  RIMA_example_NHEJi-variants.tsv  CGAGTCTAGAGGGCCCGTTTAAACCCGCTGGGCCATGGGCTATGAGTACAGGTCATGTACGGCCTCATAGTGGTACAGTAGTGACTCAAGACGATAGTTACCGGATAAGGCGCAGCGGTCGGGCTGAACGGGGGGTTCGTGCACACAGCCCAGCTTGGAGCGAACGACCTACACCGAACTGAGATACCTACAGCGTGAGCTA  CTATGAGTACAGGTCATGTA  180           77        6               56       90.0        0.0        101.0         15.0          59.0             5.0        0.0           0.0           0.0              0.0

Transfer RIMA files to a Win or Mac computer

• Copy and paste experiment_sheet.tsv table to Experiment worksheet in RIMA2_v20240420.xlsm file.
• an already completed RIMA workbook for the test samples is provided in the test_data folder.
• Transfer RIMA variant tables to "C:\RIMA\Raw" folder or any other desited location except cloud-based folders like One Drive.
• In nessesary adjust the file addresses in the Experiment worksheet accordign to your local disk address.
• IMPORTANT NOTE: file addresses should be alphanumerical without spaces.
• Correct the Total Files in the Experiment worksheet cell "C13".
• Proceed to the "Single" worksheet, adjust the required user inputs in cells D11:D13 and K11:K18. If insertions at the cut-site are expected, then provide the expected insertion sequences in cells W6:W17 with their corresponding names in U6:U17.
• Press the Batch buttom and follow the instructions.
• IMPORTANT NOTE: close all other Excel workbooks before running RIMA. Also, don't use your computer during the run.
• Once the run is complete, consult the Log worksheet to check if any of the samples had errors during the run.

RIMA vs CRISPResso

How to cite RIMA

RIMA v1:

Taheri-Ghahfarokhi A, Taylor BJM, Nitsch R, Lundin A, Cavallo AL, Madeyski-Bengtson K, Karlsson F, Clausen M, Hicks R, Mayr LM, Bohlooly-Y M, Maresca M. Decoding non-random mutational signatures at Cas9 targeted sites. Nucleic Acids Res. 2018 Sep 19;46(16):8417-8434. doi: 10.1093/nar/gky653. PMID: 30032200; PMCID: PMC6144780.

RIMA v2:

Wimberger S, Akrap N, Firth M, Brengdahl J, Engberg S, Schwinn MK, Slater MR, Lundin A, Hsieh PP, Li S, Cerboni S, Sumner J, Bestas B, Schiffthaler B, Magnusson B, Di Castro S, Iyer P, Bohlooly-Y M, Machleidt T, Rees S, Engkvist O, Norris T, Cadogan EB, Forment JV, Šviković S, Akcakaya P, Taheri-Ghahfarokhi A, Maresca M. Simultaneous inhibition of DNA-PK and Polϴ improves integration efficiency and precision of genome editing. Nat Commun. 2023 Aug 14;14(1):4761. doi: 10.1038/s41467-023-40344-4. PMID: 37580318; PMCID: PMC10425386.