bio-workflows-crispr-editing-pipeline

name	bio-workflows-crispr-editing-pipeline
description	Orchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rather than a single step. Defers each step's mechanics to the genome-engineering skills.
workflow	true
depends_on	["genome-engineering/grna-design","genome-engineering/off-target-prediction","genome-engineering/base-editing-design","genome-engineering/prime-editing-design","genome-engineering/hdr-template-design"]
qc_checkpoints	[{"after_grna_design":"Context-valid on-target shortlist (CRISPOR: Rule Set 2 for U6/lentiviral, CRISPRscan for T7/embryo); reject TTTT and GC extremes; rank by predicted frameshift/out-of-frame fraction, not raw activity; carry 3-6 guides in an early constitutive NMD-competent exon"},{"after_offtarget":"Escalate predicted -> detected -> validated; reject guides with a low-mismatch high-CFD off-target in a gene; variant-aware (gnomAD) for therapeutic guides; high-fidelity nuclease in the delivery format used"},{"after_template":"Blocking (PAM/seed) mutation present AND codon-checked; edit within ~10 bp of the cut; donor format matches cell type (ssODN/lssDNA/dsDNA/AAV; HITI for post-mitotic); report edit:indel purity for base editing"}]
origin	openai4s
category	bioskills/workflows
metadata	{"tool_type":"mixed","primary_tool":"CRISPOR","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: BioPython 1.83+, pandas 2.2+, matplotlib 3.8+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

This workflow coordinates the five genome-engineering skills; it does not re-implement their scoring. Real on-target ranking comes from CRISPOR (context-valid model), off-target nomination from Cas-OFFinder/CRISPRme, base-editor outcomes from BE-Hive, and prime-editing ranking from PRIDICT/DeepPrime -- the embedded code is illustrative orchestration only.

CRISPR Editing Pipeline

"Design a complete CRISPR editing experiment for my target" -> Run guide design -> off-target assessment -> edit-modality selection -> template/donor design -> validation, applying a QC checkpoint at each handoff and routing every mechanic to the relevant genome-engineering skill.

• Python: orchestrate the stages; enumerate/filter candidate guides with Bio.Seq
• CLI/web: CRISPOR (on-target + off-target), Cas-OFFinder/CRISPRme (off-target), BE-Hive, PRIDICT

The Single Most Important Modern Insight -- the pipeline is a chain of handoffs, each with a checkpoint, and the pivotal decision is the edit modality

A CRISPR experiment fails most often not at one step but at a handoff where an unstated assumption carries through: a guide picked by on-target score that turns out non-specific, an "efficient" guide that never knocks out the protein, a base edit reported by efficiency that is a genotype soup, an HDR donor with no blocking mutation whose edit is silently re-cut. The workflow's job is to make each handoff explicit and gated. The pivotal branch is which edit modality: a transition (C->T/A->G) is usually a base-editing job; any other small precise edit is prime editing; a knockout is a plain nuclease; a large or non-transition insertion is HDR (or PE+integrase). Choosing the modality first reframes every downstream step. The cross-cutting traps the checkpoints exist to catch: on-target activity != specificity (two separate axes), efficient editing != knockout (frameshift fraction and NMD-competent exon biology decide it), base-editor efficiency != purity (bystanders), a donor without a blocking mutation self-destructs (re-cutting reads out as failed HDR), and predicted != detected != validated for off-targets.