CROPSR - CRISPR Guide Design Pipeline

A computational pipeline for designing CRISPR guides targeting homeologous genes in polyploid organisms.

Overview

CROPSR identifies high-quality guide RNAs that target multiple homeologs simultaneously while minimizing off-target effects. The pipeline uses ortholog identification, sequence analysis, and scoring algorithms to prioritize guides for multiplex gene editing.

Prerequisites

Python 3.x
DIAMOND BLAST
MongoDB (running on mongodb://127.0.0.1:27017)
GFF database file (braker.gff.db)
Reference protein sequences

Workflow

Step 1: Obtain Protein Sequence

Start with the protein sequence from an Arabidopsis thaliana ortholog.

Example:

>AT_OST1
MDRPAVSGPMDLPIMHDSDRYELVKDIGSGNFGVARLMRDKQSNELVAVKYIERGEKIDENVKREIINHRSLRHPNIVRFKEVILTPTHLAIVMEYASGGELFERICNAGRFSEDEARFFFQQLISGVSYCHAMQVCHRDLKLENTLLDGSPAPRLKICDFGYSKSSVLHSQPKSTVGTPAYIAPEVLLKKEYDGKVADVWSCGVTLYVMLVGAYPFEDPEEPKNFRKTIHRILNVQYAIPDYVHISPECRHLISRIFVADPAKRISIPEIRNHEWFLKNLPADLMNDNTMTTQFDESDQPGQSIEEIMQIIAEATVPPAGTQNLNHYLTGSLDIDDDMEEDLESDLDDLDIDSSGEIVYAM

Step 2: Identify Orthologs with DIAMOND

Run DIAMOND BLAST to identify likely orthologs in the target organism's protein database.

Command:

diamond blastp -d A -q OST1_prot.fa -o OST1_vs_A.tsv \
  --very-sensitive --evalue 1e-5 --max-target-seqs 1000 \
  --query-cover 30 --subject-cover 30 \
  --outfmt 6 qseqid sseqid pident length evalue bitscore qcovhsp scovhsp \
  --threads 16

Expected Output:

AT_OST1    g3201.t1     89.6    364    3.16e-237    648    100    99.7
AT_OST1    g23419.t1    89.3    364    4.29e-235    643    100    99.7
AT_OST1    g37842.t1    86.0    364    2.47e-220    605    100    99.7
AT_OST1    g36701.t2    81.1    366    5.33e-213    587    100    99.7

Step 3: Design Guide RNAs

Use two_gene_guides.py to identify guides that target two homeologs simultaneously.

Command:

python3 two_gene_guides.py OST1 g3201.t1 g23419.t1

Available Options:

Option	Default	Description
`gene_name`	(required)	Name of the target gene
`gene_id_1`	(required)	First gene/transcript ID (e.g., `g15664` or `g15664.t2`)
`gene_id_2`	(required)	Second gene/transcript ID (e.g., `g36136` or `g36136.t1`)
`--gffdb`	`braker.gff.db`	Path to GFF database file
`--gff`	`None`	Path to GFF file (optional)
`--mongo-uri`	`mongodb://127.0.0.1:27017`	MongoDB connection URI
`--db`	`CROPSR`	MongoDB database name
`--guides-col`	`guides`	Collection name for guides
`--counts-col`	`guide_counts`	Collection name for guide counts
`--pam`	`NGG`	PAM sequence for Cas9
`--score-min`	`0.10`	Minimum on-target score threshold
`--write-bed`	`False`	Write output in BED format
`--out`	`None`	Custom output file path

Step 4: Validate Guides

Select the 2 guides with the highest on-target scores
Manually validate using BLAST or SnapGene to confirm specificity
Document validated guides in the appropriate validation file

Step 5: Order Oligos

After validation, order the guide oligos from IDT for Golden Gate cloning.

Output Files

The pipeline generates several output files:

{GENE}_common_keys_with_counts.csv - Guides with occurrence counts
{GENE}_common_keys.csv - List of common guide sequences
{GENE}_occ2_loci_debug.csv - Debug information for guides with 2 occurrences
{GENE}_target1_guides_raw.csv - Raw guides for target 1
{GENE}_target2_guides_raw.csv - Raw guides for target 2
{GENE}_validated_guides.csv - Final validated guide set

Examples

Example gene targets included in this repository:

TMM (Too Many Mouths)
SDD1 (Stomatal Density and Distribution 1)
BLUS1 (Blue Light Signaling 1)
GAD4 (Glutamate Decarboxylase 4)
MPK9 (Mitogen-Activated Protein Kinase 9)
EPF1 (Epidermal Patterning Factor 1)

Each gene folder contains the complete set of input files, intermediate results, and validated guides.

For questions or issues, please open an issue on GitHub.

Sample Output:

Click to expand sample output

================================================================================
STEP 1: Resolve Gene/Transcript IDs
================================================================================
[GFF] Loading database: braker.gff.db
[Target 1] transcript:g3201.t1
  Gene: g3201 (Chr11_RagTag:74519010-74523234 -)
  Transcripts: g3201.t1
[Target 2] transcript:g23419.t1
  Gene: g23419 (Chr1_RagTag:7156515-7160934 -)
  Transcripts: g23419.t1

================================================================================
STEP 2: Extract CDS Intervals
================================================================================
  [ChromNorm] Chr11_RagTag -> chr11
  [ChromNorm] Chr11_RagTag -> chr11
  [ChromNorm] Chr11_RagTag -> chr11
  [ChromNorm] Chr11_RagTag -> chr11
  [ChromNorm] Chr11_RagTag -> chr11
  [ChromNorm] Chr11_RagTag -> chr11
  [ChromNorm] Chr11_RagTag -> chr11
  [ChromNorm] Chr11_RagTag -> chr11
  [ChromNorm] Chr11_RagTag -> chr11
  [ChromNorm] Chr1_RagTag -> chr1
  [ChromNorm] Chr1_RagTag -> chr1
  [ChromNorm] Chr1_RagTag -> chr1
  [ChromNorm] Chr1_RagTag -> chr1
  [ChromNorm] Chr1_RagTag -> chr1
  [ChromNorm] Chr1_RagTag -> chr1
  [ChromNorm] Chr1_RagTag -> chr1
  [ChromNorm] Chr1_RagTag -> chr1
  [ChromNorm] Chr1_RagTag -> chr1
[Target 1] 9 CDS intervals
[Target 2] 9 CDS intervals

================================================================================
STEP 3: Query Guides from MongoDB
================================================================================
[MongoDB] Connected to CROPSR.guides and CROPSR.guide_counts
[Target 1] Found 63 guides in CDS
[Target 2] Found 66 guides in CDS
[CSV] Wrote 63 rows to OST1_target1_guides_raw.csv
[CSV] Preview (first 2 rows):
  {'_id': ObjectId('68c9926fa2ac8ac671809a42'), 'sequence': 'GACCTTGATATTGATAGCAG', 'chromosome': 'chr11', 'start_pos': 74519035, 'end_pos': 74519055, 'cutsite': 74519038, 'PAM_sequence': 'CGG', 'strand': '-', 'on_site_score': 0.3333319556814113}
  {'_id': ObjectId('68c9923aa2ac8ac67125912f'), 'sequence': 'CTCCGCTGCTATCAATATCA', 'chromosome': 'chr11', 'start_pos': 74519030, 'end_pos': 74519050, 'cutsite': 74519047, 'PAM_sequence': 'AGG', 'strand': '+', 'on_site_score': 0.11248173285482777}
[CSV] Wrote 66 rows to OST1_target2_guides_raw.csv
[CSV] Preview (first 2 rows):
  {'_id': ObjectId('68c99183a2ac8ac671e5098d'), 'sequence': 'GACCTTGATATTGATAGCAG', 'chromosome': 'chr1', 'start_pos': 7156540, 'end_pos': 7156560, 'cutsite': 7156543, 'PAM_sequence': 'CGG', 'strand': '-', 'on_site_score': 0.3333319556814113}
  {'_id': ObjectId('68c99152a2ac8ac6718dfc38'), 'sequence': 'CTCCGCTGCTATCAATATCA', 'chromosome': 'chr1', 'start_pos': 7156535, 'end_pos': 7156555, 'cutsite': 7156552, 'PAM_sequence': 'AGG', 'strand': '+', 'on_site_score': 0.11248173285482777}

================================================================================
STEP 4: Identify Common Guides
================================================================================
[Common] 37 guides target both genes
[CSV] Wrote 37 rows to OST1_common_keys.csv
[CSV] Preview (first 5 rows):
  {'sequence': 'AAATCACAAATCTTTAGCCT', 'PAM_sequence': 'TGG'}
  {'sequence': 'AAATTTTCGCAAGACAATAC', 'PAM_sequence': 'AGG'}
  {'sequence': 'AAGGTAATGATTAAGGCTGT', 'PAM_sequence': 'TGG'}
  {'sequence': 'AATCATGCAGATAATAACTG', 'PAM_sequence': 'AGG'}
  {'sequence': 'AGATTGATGAAAATGTAAAG', 'PAM_sequence': 'AGG'}

================================================================================
STEP 5: Filter by Occurrence Count
================================================================================
[Filtered] 35 guides have exactly 2 occurrences
[CSV] Wrote 37 rows to OST1_common_keys_with_counts.csv
[CSV] Preview (first 5 rows):
  {'sequence': 'AAATCACAAATCTTTAGCCT', 'PAM_sequence': 'TGG', 'occurrence_count': 3}
  {'sequence': 'AAATTTTCGCAAGACAATAC', 'PAM_sequence': 'AGG', 'occurrence_count': 2}
  {'sequence': 'AAGGTAATGATTAAGGCTGT', 'PAM_sequence': 'TGG', 'occurrence_count': 2}
  {'sequence': 'AATCATGCAGATAATAACTG', 'PAM_sequence': 'AGG', 'occurrence_count': 2}
  {'sequence': 'AGATTGATGAAAATGTAAAG', 'PAM_sequence': 'AGG', 'occurrence_count': 2}

================================================================================
STEP 6: Validate Loci Mapping
================================================================================
[Validated] 35 guides pass all filters
[CSV] Wrote 70 rows to OST1_occ2_loci_debug.csv
[CSV] Preview (first 5 rows):
  {'sequence': 'AAATTTTCGCAAGACAATAC', 'PAM_sequence': 'AGG', 'chromosome': 'chr1', 'cutsite': 7157413, 'strand': '-', 'on_site_score': 0.1395602425677323}
  {'sequence': 'AAATTTTCGCAAGACAATAC', 'PAM_sequence': 'AGG', 'chromosome': 'chr11', 'cutsite': 74519880, 'strand': '-', 'on_site_score': 0.1395602425677323}
  {'sequence': 'AAGGTAATGATTAAGGCTGT', 'PAM_sequence': 'TGG', 'chromosome': 'chr1', 'cutsite': 7156631, 'strand': '+', 'on_site_score': 0.1308576704344441}
  {'sequence': 'AAGGTAATGATTAAGGCTGT', 'PAM_sequence': 'TGG', 'chromosome': 'chr11', 'cutsite': 74519126, 'strand': '+', 'on_site_score': 0.1308576704344441}
  {'sequence': 'AATCATGCAGATAATAACTG', 'PAM_sequence': 'AGG', 'chromosome': 'chr1', 'cutsite': 7156664, 'strand': '-', 'on_site_score': 0.18188528187536224}

================================================================================
STEP 7: Write Final Results
================================================================================
[CSV] Wrote 35 rows to OST1_validated_guides.csv
[CSV] Preview (first 10 rows):
  {'gene_name': 'OST1', 'gene_id_1': 'g3201.t1', 'gene_id_2': 'g23419.t1', 'sequence': 'AAATTTTCGCAAGACAATAC', 'PAM_sequence': 'AGG', 'occurrence_count': 2, 'g1_chr': 'chr11', 'g1_cutsite': 74519880, 'g1_strand': '-', 'g1_on_site_score': 0.1395602425677323, 'g2_chr': 'chr1', 'g2_cutsite': 7157413, 'g2_strand': '-', 'g2_on_site_score': 0.1395602425677323}
  {'gene_name': 'OST1', 'gene_id_1': 'g3201.t1', 'gene_id_2': 'g23419.t1', 'sequence': 'AAGGTAATGATTAAGGCTGT', 'PAM_sequence': 'TGG', 'occurrence_count': 2, 'g1_chr': 'chr11', 'g1_cutsite': 74519126, 'g1_strand': '+', 'g1_on_site_score': 0.1308576704344441, 'g2_chr': 'chr1', 'g2_cutsite': 7156631, 'g2_strand': '+', 'g2_on_site_score': 0.1308576704344441}
  {'gene_name': 'OST1', 'gene_id_1': 'g3201.t1', 'gene_id_2': 'g23419.t1', 'sequence': 'AATCATGCAGATAATAACTG', 'PAM_sequence': 'AGG', 'occurrence_count': 2, 'g1_chr': 'chr11', 'g1_cutsite': 74519159, 'g1_strand': '-', 'g1_on_site_score': 0.18188528187536224, 'g2_chr': 'chr1', 'g2_cutsite': 7156664, 'g2_strand': '-', 'g2_on_site_score': 0.18188528187536224}
  {'gene_name': 'OST1', 'gene_id_1': 'g3201.t1', 'gene_id_2': 'g23419.t1', 'sequence': 'AGATTGATGAAAATGTAAAG', 'PAM_sequence': 'AGG', 'occurrence_count': 2, 'g1_chr': 'chr11', 'g1_cutsite': 74522403, 'g1_strand': '-', 'g1_on_site_score': 0.510388157411842, 'g2_chr': 'chr1', 'g2_cutsite': 7160091, 'g2_strand': '-', 'g2_on_site_score': 0.510388157411842}
  {'gene_name': 'OST1', 'gene_id_1': 'g3201.t1', 'gene_id_2': 'g23419.t1', 'sequence': 'AGCCTTAATCATTACCTTAC', 'PAM_sequence': 'TGG', 'occurrence_count': 2, 'g1_chr': 'chr11', 'g1_cutsite': 74519110, 'g1_strand': '-', 'g1_on_site_score': 0.17795101645534164, 'g2_chr': 'chr1', 'g2_cutsite': 7156615, 'g2_strand': '-', 'g2_on_site_score': 0.17795101645534164}
  {'gene_name': 'OST1', 'gene_id_1': 'g3201.t1', 'gene_id_2': 'g23419.t1', 'sequence': 'AGGCTGTTGGTCCCAGCAGC', 'PAM_sequence': 'AGG', 'occurrence_count': 2, 'g1_chr': 'chr11', 'g1_cutsite': 74519139, 'g1_strand': '+', 'g1_on_site_score': 0.13429787061233003, 'g2_chr': 'chr1', 'g2_cutsite': 7156644, 'g2_strand': '+', 'g2_on_site_score': 0.13429787061233003}
  {'gene_name': 'OST1', 'gene_id_1': 'g3201.t1', 'gene_id_2': 'g23419.t1', 'sequence': 'ATACCTCATCCTCGCTGAAA', 'PAM_sequence': 'CGG', 'occurrence_count': 2, 'g1_chr': 'chr11', 'g1_cutsite': 74522156, 'g1_strand': '+', 'g1_on_site_score': 0.19684923615862226, 'g2_chr': 'chr1', 'g2_cutsite': 7159836, 'g2_strand': '+', 'g2_on_site_score': 0.19684923615862226}
  {'gene_name': 'OST1', 'gene_id_1': 'g3201.t1', 'gene_id_2': 'g23419.t1', 'sequence': 'CAATGTCCAAGCTACCAGTA', 'PAM_sequence': 'AGG', 'occurrence_count': 2, 'g1_chr': 'chr11', 'g1_cutsite': 74519107, 'g1_strand': '+', 'g1_on_site_score': 0.6352101442820706, 'g2_chr': 'chr1', 'g2_cutsite': 7156612, 'g2_strand': '+', 'g2_on_site_score': 0.6352101442820706}
  {'gene_name': 'OST1', 'gene_id_1': 'g3201.t1', 'gene_id_2': 'g23419.t1', 'sequence': 'CATATTGACACCAACTCATT', 'PAM_sequence': 'TGG', 'occurrence_count': 2, 'g1_chr': 'chr11', 'g1_cutsite': 74522226, 'g1_strand': '-', 'g1_on_site_score': 0.35796329769848734, 'g2_chr': 'chr1', 'g2_cutsite': 7159906, 'g2_strand': '-', 'g2_on_site_score': 0.35796329769848734}
  {'gene_name': 'OST1', 'gene_id_1': 'g3201.t1', 'gene_id_2': 'g23419.t1', 'sequence': 'CATCCATGAGATCTGCAGGA', 'PAM_sequence': 'AGG', 'occurrence_count': 2, 'g1_chr': 'chr11', 'g1_cutsite': 74519251, 'g1_strand': '+', 'g1_on_site_score': 0.3811753046164073, 'g2_chr': 'chr1', 'g2_cutsite': 7156756, 'g2_strand': '+', 'g2_on_site_score': 0.3811753046164073}

✓ Success! Found 35 validated guides
```csv
gene_name,gene_id_1,gene_id_2,sequence,PAM_sequence,occurrence_count,g1_chr,g1_cutsite,g1_strand,g1_on_site_score,g2_chr,g2_cutsite,g2_strand,g2_on_site_score
TMM,g11664.t1,g33559.t1,AAAGACTGGAAAATCCAACT,GGG,2,chr15,23058534,-,0.4551,chr5,68145613,+,0.4551
TMM,g11664.t1,g33559.t1,ACTTGGTATGCTCCAAAAAC,TGG,2,chr15,23058203,+,0.2809,chr5,68145944,-,0.2809
TMM,g11664.t1,g33559.t1,CAATCAACAGTCAACAAACA,TGG,2,chr15,23057582,+,0.1537,chr5,68146565,-,0.1537

Note: The pipeline outputs guides with an occurrence count of 2, meaning they target both homeologs without additional off-target hits.

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
.github/workflows		.github/workflows
README.md		README.md
two_gene_guides_v2.py		two_gene_guides_v2.py

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CROPSR - CRISPR Guide Design Pipeline

Overview

Table of Contents

Prerequisites

Workflow

Step 1: Obtain Protein Sequence

Step 2: Identify Orthologs with DIAMOND

Step 3: Design Guide RNAs

Step 4: Validate Guides

Step 5: Order Oligos

Output Files

Examples

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CROPSR - CRISPR Guide Design Pipeline

Overview

Table of Contents

Prerequisites

Workflow

Step 1: Obtain Protein Sequence

Step 2: Identify Orthologs with DIAMOND

Step 3: Design Guide RNAs

Step 4: Validate Guides

Step 5: Order Oligos

Output Files

Examples

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