Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate skills.
Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate skills.
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.
Output depends on inputs more than tool versions: on-target scores are (a 0.7 Azimuth score is not a 0.7 CRISPRscan score), and the , not by preference. Record the nuclease, the delivery context (U6/lentiviral vs in-vitro T7/RNP), and the reference genome build used for any off-target step.
model-specific and not interchangeable
valid model is set by how the guide is delivered/transcribed
Guide RNA Design
"Design guide RNAs to knock out my gene" -> Establish the delivery context, scan the target for the nuclease's PAM on both strands, drop guides that fail hard filters, rank survivors with the context-valid on-target model, choose the cut site by exon/transcript biology, and prefer guides whose predicted indel spectrum is frameshift-rich.
Python: enumerate PAMs and apply hard filters with Bio.Seq + re; compute a Bae-style microhomology out-of-frame score
CLI/web: crispor.py <genome> in.fa out.tsv aggregates the context-appropriate on-target score + off-target nomination per genome
Web/code: inDelphi / FORECasT / Lindel for the full repair-outcome distribution
The Single Most Important Modern Insight -- a guide produces a reproducible indel distribution, not "a cut", and knockout success is a property of that distribution
Two facts that naive design ignores and that pass review constantly:
On-target efficiency scores are weak, context-locked predictors. Rule Set 2, CRISPRscan, and DeepCas9 scores correlate with measured cutting at only Spearman ~0.4 across realistic contexts (~0.7 is the ceiling even within one matched context; the same guides re-tested in another cell line correlate ~0.37-0.48). Each was trained on one assay -- U6-Pol-III lentiviral vs in-vitro T7 vs RNP -- and does not transfer across nuclease, delivery, promoter, cell type, or temperature (Haeussler 2016). Using CRISPRscan (T7/zebrafish-trained) to rank guides for a U6 lentiviral screen is a category error. Rank to shortlist, then design 3-6 guides and validate -- never trust the rank as truth.
Efficient editing is not knockout. A cut yields a characteristic, reproducible set of indels (Shen 2018; Allen 2019; Chen 2019); roughly 1/3 of indels are in-frame, so a 95%-efficient guide can still leave functional protein. Worse, even a confirmed frameshift may not eliminate protein -- translation reinitiation, exon skipping, NMD escape, and transcriptional adaptation rescue ~1/3 of verified knockouts (Smits 2019; Mou 2017; El-Brolosy 2019). So the modern question is "which guide, at which site, produces a high out-of-frame fraction in an NMD-competent, constitutive transcript region?" -- couple an outcome model to exon biology, not just an efficiency score. Verify the knockout at the protein level.
On-Target Score Taxonomy -- each model is valid for ONE context
Model
Citation
Trained on (valid for)
Notes
Rule Set 1
Doench 2014 Nat Biotechnol 32:1262
U6 mammalian
superseded; origin of GC/position rules
Rule Set 2 / Azimuth
Doench/Fusi 2016 Nat Biotechnol 34:184
U6/lentiviral mammalian KO -- the default for screens & cell lines
gradient-boosted; best U6 predictor (Haeussler 2016)
CRISPRscan
Moreno-Mateos 2015 Nat Methods 12:982
in-vitro T7 / embryo injection -- NOT U6
wrong tool for lentiviral screens
DeepSpCas9
Kim 2019 Sci Adv 5:eaax9249
SpCas9 mammalian; strong transfer
CNN
DeepHF
Wang 2019 Nat Commun 10:4284
conditions on the enzyme variant (WT, eSpCas9, HF1)
use when using a high-fidelity Cas9
DeepCpf1 / Seq-deepCpf1
Kim 2018 Nat Biotechnol 36:239
AsCas12a (Deep adds chromatin)
use for Cas12a, not Cas9
Treat any score as a rank-and-shortlist signal (Spearman ~0.4 across context), never an oracle.
Default to WT-SpCas9-NGG; escalate to NG/xCas9/SpRY only when no acceptable NGG sits in the required window, and expect to validate harder (the valid on-target score and the off-target burden both change).
Decision Tree by Scenario
Scenario
Recommended
Why
Single-gene KO, NGG in an early constitutive exon
SpCas9 + Rule Set 2/Azimuth shortlist -> outcome model -> off-target
frameshift in an NMD-competent exon kills all isoforms
In-vitro-transcribed / embryo / RNP injection
score with CRISPRscan, apply T7 (not U6) filters
Rule Set 2 is invalid here; TTTT/5'G Pol-III rules do not apply
AT-rich target, no good NGG; or multiplex KO
Cas12a (TTTV) + DeepCpf1
PAM availability and crRNA-array multiplexing, not on-target score, are limiting
AAV in-vivo delivery
SaCas9 (NNGRRT)
packaging limit dictates the compact nuclease, which dictates the PAM set
Functional/negative-selection screen
tile sgRNAs across the conserved functional domain (Shi 2015)
domain indels are LoF even in-frame -> more true nulls than 5'-exon targeting
Have ranked candidates, need specificity
-> off-target-prediction
on-target score does not predict specificity
Scale to many genes
-> crispr-screens/library-design
pooled library construction
Single base change / no DSB tolerated
-> base-editing-design or prime-editing-design
scarless, DSB-free; KO-by-stop also avoids indels
Enumerate and Filter Candidate Guides
Goal: Return valid candidate spacers for a target, on both strands, dropping guides that cannot work in the chosen delivery context.
Approach: Scan both strands for the nuclease's PAM, extract the protospacer upstream (Cas9) or downstream (Cas12a) of each PAM, and apply hard filters -- reject TTTT (Pol-III terminator) for U6/H1 expression, flag a missing 5' G for U6 (prepend a G rather than replace the first base), and note GC outside ~40-70% as a soft penalty. Ranking comes from the context-valid model (route to CRISPOR), not from a hand-rolled score.
from Bio.Seq import Seq
import re
GC_MIN, GC_MAX = 0.40, 0.70# outside this band on-target activity falls off (Doench 2014); soft penaltydeffind_guides(sequence, pam='NGG', guide_length=20):
'''Enumerate SpCas9 (NGG) spacers on both strands; spacer is 5' of the PAM.'''
seq = sequence.upper()
guides = []
for m in re.finditer(r'(?=([ACGT]GG))', seq):
pos = m.start()
if pos >= guide_length:
guides.append({'spacer': seq[pos - guide_length:pos], 'pam': seq[pos:pos + 3],
'cut': pos - 3, 'strand': '+'}) # SpCas9 cuts ~3 bp 5' of the PAM
rc = str(Seq(seq).reverse_complement())
n = len(seq)
for m in re.finditer(r'(?=([ACGT]GG))', rc):
pos = m.start()
if pos >= guide_length:
guides.append({'spacer': rc[pos - guide_length:pos], 'pam': rc[pos:pos + 3],
'cut': n - (pos - 3), 'strand': '-'})
return guides
defpasses_u6_filters(spacer):
'''Hard filters for U6/H1 Pol-III expression (NOT applicable to in-vitro T7/RNP).'''
gc = sum(c in'GC'for c in spacer) / len(spacer)
return'TTTT'notin spacer and GC_MIN <= gc <= GC_MAX # TTTT terminates Pol III
Rank On-Target Activity in the Valid Context
Goal: Shortlist guides by predicted cutting using the model that matches the delivery context.
Approach: Do NOT hand-roll a scoring matrix. Route to CRISPOR, which selects the context-appropriate score (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo) per the Haeussler 2016 logic and also nominates off-targets against the chosen genome. Treat the returned score as a shortlist signal, then carry 3-6 candidates forward.
# CRISPOR: aggregates the context-valid on-target score + off-target nomination per genome
crispor.py hg38 target.fa guides.tsv --maxOcc 60000
# columns include the on-target score (context-selected) and off-target counts/specificity
Choose the Cut Site by Exon Biology (the under-used lever)
KO success is mostly won here, and pure efficiency ranking fails:
Target an early, constitutive coding exon (present in all protein-coding isoforms) -- but not the start-ATG region (downstream reinitiation can rescue an N-terminal truncation).
Avoid the last exon and the last ~50 nt of the penultimate exon -- PTCs there escape NMD, leaving a stable, possibly-functional truncated protein.
Keep the cut away from splice donor/acceptor sites unless splice disruption is the goal -- indels there cause exon skipping that can restore frame (Mou 2017).
Confirm the exon is constitutive in the cell type of interest (an exon spliced out of the dominant isoform is a silent failure), and screen for SNPs under the protospacer/PAM in the actual background (mismatch/PAM loss -> allele dropout).
For ruthless KO / screens: tile the conserved functional domain (Shi 2015), not the gene start.
Predict the Editing Outcome (frameshift fraction decides KO)
Goal: Prefer guides whose predicted indel spectrum is frameshift-rich (and, for a single-genotype line, dominated by one outcome).
Approach: Cas9 repair outcomes are predictable from the ~30 bp of local sequence flanking the cut. The cheap, no-ML signal is the Bae 2014 microhomology out-of-frame score: enumerate microhomology pairs flanking the cut, weight each predicted MMEJ deletion, and report the fraction whose length is not a multiple of 3. For a full genotype distribution use inDelphi (Shen 2018), FORECasT (Allen 2019), or Lindel (Chen 2019). Rank by (editing efficiency) x (out-of-frame fraction) -- a 70%-efficient guide with frameshift fraction 0.9 beats a 90%-efficient guide at 0.5. (See examples/grna_design.py for a runnable Bae-style out-of-frame implementation.)
Per-Method Failure Modes
"We used the top-ranked guide" with no validation
Trigger: sorting by on-target score and taking #1. Mechanism: scores are Spearman ~0.4 across context. Symptom: confident ranking, poor empirical hit rate. Fix: design 3-6 guides per gene and validate; treat the score as triage.
Score used out of its training context
Trigger: CRISPRscan for a lentiviral screen, or Rule Set 2 for embryo RNP. Mechanism: each model is an assay artifact (Haeussler 2016). Symptom: "principled" but wrong ranking. Fix: pick the score from the delivery context before reading any number.
Efficient cut, no knockout phenotype
Trigger: ranking by editing efficiency. Mechanism: ~1/3 in-frame indels + reinitiation/exon-skipping/NMD-escape/compensation. Symptom: high indel %, residual protein, milder-than-knockdown phenotype. Fix: rank by frameshift fraction (Bae/inDelphi), target early constitutive NMD-competent exons, verify at protein level.
Last-exon / splice-site guide
Trigger: "early exon" applied naively. Mechanism: late PTC escapes NMD; splice-site indel skips the exon. Symptom: stable truncated/reframed protein. Fix: retarget an early constitutive exon away from junctions.
Poly-T or missing 5' G in a U6 construct
Trigger: spacer with TTTT or non-G 5' end expressed from U6/H1. Mechanism: Pol-III termination / poor initiation. Symptom: little or no sgRNA. Fix: reject TTTT; prepend (do not replace) a 5' G. (Irrelevant for in-vitro T7/RNP.)
Allele dropout in a non-reference background
Trigger: designing against GRCh38 for a patient/hybrid/cancer line. Mechanism: a SNP in the seed or PAM blocks one allele. Symptom: heterozygous "knockout" with a retained functional allele. Fix: design against the actual genotype.
Quantitative Thresholds
Parameter
Value
Source / rationale
On-target score use
rank/shortlist only; ~0.4 Spearman across context
Haeussler 2016
GC content
~40-70% (soft penalty)
Doench 2014
Pol-III terminator
reject TTTT (U6/H1 only)
Pol-III termination
5' G (U6)
prepend a G if absent
Pol-III initiation preference
SpCas9 cut
~3 bp 5' of NGG (blunt)
Jinek 2012
Bae out-of-frame score
prefer >66
Bae 2014 frameshift-reliability recommendation
KO ranking
efficiency x out-of-frame fraction
frameshift fraction, not cutting, drives KO
Guides per gene
3-6, validate empirically
scores are weak; redundancy buys back error
Exon target
early, constitutive, NMD-competent (not last exon / last ~50 nt of penult.)
PTC must trigger NMD across all isoforms
Residual protein after frameshift
expect ~1/3 retain protein
Smits 2019
Common Errors
Error / symptom
Cause
Solution
No guides found
no PAM in window / wrong PAM for nuclease
try Cas12a (TTTV) for AT-rich; widen window; SpCas9-NG/SpRY as last resort
Guide cuts but no KO phenotype
last exon / 3'UTR / in-frame indels / compensation
retarget early constitutive exon; rank by frameshift; verify protein
Score looks low for a clearly good guide
score used outside its training context
use the context-valid model
Heterozygous result in a non-reference line
SNP under guide/PAM
design against the actual genotype
References
Jinek M, et al. (2012). A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337(6096):816-821.
Doench JG, et al. (2014). Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat Biotechnol 32(12):1262-1267.
Doench JG, Fusi N, Sullender M, et al. (2016). Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol 34(2):184-191.
Moreno-Mateos MA, et al. (2015). CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods 12(10):982-988.
Haeussler M, et al. (2016). Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol 17:148.
Kim HK, et al. (2019). SpCas9 activity prediction by DeepSpCas9. Sci Adv 5(11):eaax9249.
Wang D, et al. (2019). Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning (DeepHF). Nat Commun 10:4284.
Kim HK, et al. (2018). Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity (DeepCpf1). Nat Biotechnol 36(3):239-241.
Bae S, Kweon J, Kim HS, Kim JS (2014). Microhomology-based choice of Cas9 nuclease target sites. Nat Methods 11(7):705-706.
Shen MW, et al. (2018). Predictable and precise template-free CRISPR editing of pathogenic variants (inDelphi). Nature 563(7733):646-651.
Allen F, et al. (2019). Predicting the mutations generated by repair of Cas9-induced double-strand breaks (FORECasT). Nat Biotechnol 37(1):64-72.
Chen W, et al. (2019). Massively parallel profiling and predictive modeling of the outcomes of CRISPR-Cas9 double-strand break repair (Lindel). Nucleic Acids Res 47(15):7989-8003.
Shi J, et al. (2015). Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains. Nat Biotechnol 33(6):661-667.
Smits AH, et al. (2019). Biological plasticity rescues target activity in CRISPR knock outs. Nat Methods 16(11):1087-1093.
Mou H, et al. (2017). CRISPR/Cas9-mediated genome editing induces exon skipping by alternative splicing or exon deletion. Genome Biol 18(1):108.
El-Brolosy MA, et al. (2019). Genetic compensation triggered by mutant mRNA degradation. Nature 568(7751):193-197.
Ran FA, et al. (2015). In vivo genome editing using Staphylococcus aureus Cas9. Nature 520(7546):186-191.
Nishimasu H, et al. (2018). Engineered CRISPR-Cas9 nuclease with expanded targeting space (SpCas9-NG). Science 361(6408):1259-1262.
Hu JH, et al. (2018). Evolved Cas9 variants with broad PAM compatibility and high DNA specificity (xCas9). Nature 556(7699):57-63.
Walton RT, et al. (2020). Unconstrained genome targeting with near-PAMless engineered CRISPR-Cas9 variants (SpRY). Science 368(6488):290-296.
Kleinstiver BP, et al. (2019). Engineered CRISPR-Cas12a variants with increased activities and improved targeting ranges (enAsCas12a). Nat Biotechnol 37(3):276-282.
Concordet JP, Haeussler M (2018). CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res 46(W1):W242-W245.
Related Skills
off-target-prediction - Check genome-wide specificity after on-target design (a separate axis from activity)
base-editing-design - DSB-free knockout via premature stop / splice disruption when indels are unwanted
prime-editing-design - Scarless small edits without a double-strand break
hdr-template-design - Design the donor when the goal is a precise knock-in, not a knockout
crispr-screens/library-design - Pool guides into a screening library (domain tiling, Rule Set 2 logic)
crispr-screens/crispresso-editing - Quantify indel/editing outcomes from amplicon sequencing
primer-design/primer-basics - Design validation/genotyping primers around the cut
primer-design/primer-specificity - Confirm genotyping primers are unique near paralogs/off-targets
genome-intervals/gtf-gff-handling - Get exon coordinates to restrict guide placement
