bio-workflows-crispr-editing-pipeline

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.

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Repositorio: PKU-YuanGroup/OpenAI4S
Última actividad en el origen: 21 de agosto de 2026 a las 05:05
Idioma detectado de SKILL.md: inglés
Estrellas: 378
Forks: 45

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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.

CRISPR Editing Pipeline

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

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"}}

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"}}

Goal / edit	Modality	Route to
Gene knockout (any frameshift)	nuclease + NHEJ	grna-design (rank by frameshift fraction)
Knockout without a DSB / non-dividing / multiplex	base-editor premature stop or splice disruption	base-editing-design
CG->TA or AT->GC transition	base editing (CBE/ABE)	base-editing-design
C->G transversion	CGBE	base-editing-design
Other transversion, small indel, combined edit	prime editing	prime-editing-design
Small precise edit, no DSB tolerated	prime editing (PE)	prime-editing-design
Tag / reporter / allele replacement (cycling cells)	HDR knock-in	hdr-template-design
Large insertion / post-mitotic cells	HDR (AAV/HITI) or PE+integrase (PASTE/twinPE)	hdr-template-design / prime-editing-design

Error / symptom	Cause	Solution
Top guide has a near-perfect off-target	picked by on-target score alone	re-rank by specificity; on-target and specificity are separate axes
Efficient editing, no knockout phenotype	in-frame indels / late-exon / compensation	rank by frameshift fraction; target an early constitutive exon; verify protein
Base edit "80% efficient" but messy genotypes	bystanders in the window	report the spectrum; reposition or use a narrowed-window editor
HDR gives only indels	donor lacks a blocking mutation	add a codon-checked PAM/seed block; the edit was re-cut
Validation shows ~0% editing on a working edit	EDITED (or wrong) sequence supplied as `--amplicon_seq`, so edited reads match the reference / wrong guide window	give CRISPResso2 the UNEDITED reference as `--amplicon_seq` (+ `--expected_hdr_amplicon_seq` for HDR) and the actual `--guide_seq`
"No off-targets" claimed	LoD not stated / reference-only	state the LoD; variant-aware for therapeutics

bio-workflows-crispr-editing-pipeline

Opciones de instalación

Revisa los archivos de origen

Version Compatibility

CRISPR Editing Pipeline

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

Version Compatibility

CRISPR Editing Pipeline

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

Edit-Modality Decision Tree (the pivotal branch)

Workflow Overview

Stage 1 -- Guide Design (-> grna-design)

Stage 2 -- Off-Target Assessment (-> off-target-prediction)

Stage 3 -- Modality-Specific Design

Stage 4 -- Validation

Common Errors (integration level)

References

Related Skills

bio-workflows-crispr-editing-pipeline

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Más de este repositorio

Más de este repositorio

Version Compatibility

CRISPR Editing Pipeline

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

Version Compatibility

CRISPR Editing Pipeline

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

Edit-Modality Decision Tree (the pivotal branch)

Workflow Overview

Stage 1 -- Guide Design (-> grna-design)

Stage 2 -- Off-Target Assessment (-> off-target-prediction)

Stage 3 -- Modality-Specific Design

Stage 4 -- Validation

Common Errors (integration level)

References

Related Skills