CRISPR on the platform

Design and analyze genome editing experiments — score guide RNAs for a target gene, check candidate guides for off-target risk against the reference genome, and call hits from pooled CRISPR screens. Reach for these tools when you are doing functional target validation or knockout experiment design, upstream of picking a guide for the bench. CRISPR sits in the Target Identification stage of discovery, between gene-level genomics and experimental validation. See the Targets & Modalities overview for where this fits.

CRISPR overview

The CRISPR toolkit provides three complementary tools for genome editing experiment design and analysis. Guide design and off-target prediction use direct computation, returning results in seconds. Screen analysis uses Modal GPU compute for statistical hit calling on large-scale datasets.

Architecture

User Query (gene symbol, guide sequence, screen data)
  --> Intent Detection
       |- Guide design --> crispr_design_guides (5-15s)
       |- Off-target check --> crispr_offtarget (5-15s)
       +- Screen analysis --> crispr_screen_analyze (30-90s, Modal)
  --> Structured Results with Scores and Rankings

Tool inventory

Capabilities

Guide RNA design

Given a target sequence, crispr_design_guides identifies all valid PAM sites on both strands, extracts 20-nt guide sequences, and scores each with a sequence-composition heuristic (GC-content band, homopolymer-run penalty, seed-region G/T bias) rather than a trained on-target model. Results include guide sequence, PAM, genomic position, strand, GC content, and on-target score. Guides are ranked by that heuristic score.

Off-target prediction

crispr_offtarget takes a target_sequence, extracts the first valid guide it finds in it, and scans that same sequence on both strands for near-matches with up to 4 mismatches, scoring each with a simplified CFD (Cutting Frequency Determination)-style model that penalizes seed-region mismatches more heavily. It reports mismatch positions, the off-target sequence, position, strand, and a CFD score.

CRISPR screen analysis

crispr_screen_analyze accepts a read-count table from a pooled CRISPR library screen (uploaded to S3), computes a per-guide log2 fold change, aggregates it to a per-gene median with a z-test, and applies Benjamini-Hochberg FDR correction — a method the backend describes as "inspired by MAGeCK," not MAGeCK's actual RRA algorithm. It treats the first numeric count column as control and the remaining numeric columns as treatment.

When to use each tool

When to use other tools instead

Pipeline position

The CRISPR toolkit sits in the Target Identification stage of the drug discovery pipeline:

Target Identification
  |- Genomics (gene information)
  |- CRISPR (functional validation) <--
  |- Epigenomics (regulatory context)
  +- Literature (evidence gathering)

Guide design is typically preceded by target gene identification (via genomics or literature tools) and followed by experimental validation. Screen analysis results feed back into target prioritization.

Citations

Doench JG, et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016;34(2):184-191. doi: 10.1038/nbt.3437

Li W, et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 2014;15(12):554. doi: 10.1186/s13059-014-0554-4

Hsu PD, et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013;31(9):827-832. doi: 10.1038/nbt.2647

Chat usage

Example prompts

• "Design CRISPR guides for KRAS exon 2"
• "Check off-target sites for GACTTCGATCGATCGATCGA with 4 mismatches allowed"
• "Analyze my CRISPR screen to find genes essential for cell survival"
• "Design SaCas9 guides targeting MYC"
• "What are the top guides for knocking out CDK4 with minimal off-target risk?"

Workflow chaining

• Feeds into: Genomics (gene validation), Target Passport (druggability), Literature (essentiality evidence), Protein structure tools (target characterization)
• Accepts from: Genomics (gene symbols, coordinates), Literature (candidate gene lists), Epigenomics (chromatin state for guide accessibility), OpenTargets (target prioritization)

FAQ

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