SeqBench

CRISPR Guide RNA Designer & PAM Finder

Scan a sequence for SpCas9, SaCas9 or Cas12a guide candidates with PAMs and scoring.

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Predicted, not measured
How good is it?
No held-out skill statistic is claimed. Both are pre-2016 models, superseded by Rule Set 3 (DeWeirdt et al., Nat Commun 2022) and by DeepHF. Rule Set 3 IS now shipped here, as crispr_ontarget, and unlike these two it carries a held-out calibration: on an independent tiling library, 87.7% / 74.9% / 82.0% of its lowest-scoring guides landed in the bottom two activity quintiles. Prefer it for SpCas9 with an NGG PAM; DeepHF is still not shipped. Treat these two as a ranking aid, not an efficiency prediction.
Only valid for:
SpCas9 with an NGG PAM and a 20 nt spacer, and only when enough genomic flanking context is present to build the model's 30-mer / 35-mer window — both scores are null rather than padded otherwise. Nothing is predicted for SaCas9, Cas12a or SpCas9-NG.
Fitted on:
Two published regressions over measured cutting: Doench et al. 2014 Rule Set 1 (human and mouse cell assays) and CRISPRscan (Moreno-Mateos et al. 2015, zebrafish embryo assays). SeqBench's own composite score on top of them is a transparent rule-of-thumb, not a fitted model.

Find protospacer and PAM candidates in a target DNA sequence. Paste a sequence, choose a nuclease (SpCas9 NGG, SpCas9-NG, SaCas9 NNGRRT or Cas12a TTTV), and the tool lists candidate guides on both strands with position, spacer sequence, PAM and GC content. Warnings for poly-T terminators and homopolymer runs, plus a transparent heuristic score, help you shortlist candidates for downstream off-target checking.

0 bp

Paste a target sequence above to list every guide with a valid PAM, on both strands.

Paste a target sequence to find guide candidates.

On-target efficiency models. Doench ’14(Rule Set 1) and CRISPRscan are published regression models, shown side-by-side with the heuristic Score. Both apply only to SpCas9 with an NGG PAM and a 20 nt spacer, and both need the guide’s flanking genomic context (Doench: a 30-mer = 4 nt 5′ + spacer + PAM + 3 nt 3′; CRISPRscan: a 35-mer = 6 nt 5′ + spacer + PAM + 6 nt 3′). Guides too close to the ends of the pasted sequence, or any non-SpCas9 nuclease, show “—” rather than a padded (and silently wrong) value. Doench is a logistic score in 0–1 (shown as %); CRISPRscan is CRISPOR’s integer ~0–100. Expand a Doench cell to see its GC term and top position contributions. These are legacy/complementary heuristics (Spearman ~0.3–0.5 vs. measured activity) — Doench RS1 is superseded by Rule Set 2/Azimuth, and CRISPRscan is tuned for T7-transcribed sgRNA in zebrafish. Refs: Doench et al. 2014,Nat Biotechnol (PMID 25184501); Moreno-Mateos et al. 2015,Nat Methods (PMID 26322839). Weights via the open-source CRISPOR.
Scope: the score is a transparent rule-of-thumb (GC content, poly-T terminators, homopolymer runs) to help you triage candidates — it is not a validated on-target efficiency prediction like Doench 2016. Off-target risk is not assessed: that requires aligning each guide against your target genome. Always verify shortlisted guides with a genome-aware specificity tool before ordering.

Score the shortlist with Rule Set 3

The composite above is a triage heuristic, and the two published scores beside it are from 2014 and 2015. Rule Set 3 (DeWeirdt et al., Nat Commun 2022) is the current successor, and unlike those two it carries a held-out calibration. It is deliberately reported alongside them rather than blended in: a composite of three models is a fourth model nobody validated, and they are allowed to disagree.

0 bases. A guide needs 4 bases before its spacer and 3 after its PAM to be scored — guides without that flank are skipped rather than scored on a padded window.

Not cosmetic — Rule Set 3 models the scaffold as a feature, and the paper measures its accuracy dropping when the wrong one is given.

Paste a target region and score it to rank the guides in it.

Now check the guide you picked for off-targets

Every score above is about the intended site, and the warning under the table says so: none of them assesses off-target risk, because that needs a genome rather than a spacer. This screens a protospacer against curated lab reference genomes for matches with a valid PAM on either strand.

What actually gets searched

A small curated set of common lab reference genomes — bacterial chromosomes plus the human mitochondrion, roughly 9.7 Mbp in total. Not the human, mouse or rat nuclear genome. For mammalian guide design this is a sanity check on the host and vector side and nothing more; the genome-wide search still has to happen somewhere else. Every run lists the exact genomes it covered, and anything absent from that list was not looked at.

0 nt after cleaning. SpCas9 uses a 20 nt spacer.

Still needed: a protospacer.

Scans ~9.7 Mbp on both strands — a second or so, and rate limited.

Design, edit, and verify complete constructsSeqStudio combines sequence editing and annotation, plasmid maps, primer/cloning/CRISPR design, Sanger verification, and GenBank/SnapGene files in one workspace.

How to use the CRISPR gRNA Designer tool

  1. 1Paste the target DNA region (raw or FASTA).
  2. 2Pick the nuclease / PAM and whether to search both strands.
  3. 3Review the ranked guide candidates, their PAMs, GC and warning flags, then shortlist for off-target checking.

Frequently asked questions

Which nucleases and PAMs are supported?

SpCas9 (NGG, 20 nt spacer), SpCas9-NG (NG), SaCas9 (NNGRRT, 21 nt) and Cas12a/Cpf1 (TTTV, 23 nt, 5′ PAM). Both strands are scanned and coordinates are reported on the forward strand.

What does the score mean?

It is a transparent rule-of-thumb (0–100) that penalizes GC content outside the usable range, poly-T tracts that terminate Pol III transcription, and long homopolymer runs. It is a triage aid, not a validated on-target efficiency model like Doench 2016.

Does it check off-target sites?

No. Off-target assessment requires aligning each guide against your target genome, which an in-browser tool can't do. Use this to shortlist candidates, then verify them in a genome-aware specificity tool before ordering.

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