---
title: CRISPR FAQ
description: >-
  Frequently asked questions about CRISPR guide design, off-target analysis, and
  screen analysis tools on the platform.
tags:
  - crispr
  - faq
---

## CRISPR FAQ

These answers cover [CRISPR](/docs/crispr) guide design — supported Cas nucleases, on-target scoring reliability, GC content, and non-human organisms — along with off-target mismatch settings and MAGeCK screen analysis, including replicate requirements and CRISPRi/CRISPRa support.

## What Cas nucleases are supported for guide design?

The guide design tool supports any Cas nuclease by specifying its PAM sequence. Built-in presets include SpCas9 (NGG), SaCas9 (NNGRRT), and AsCas12a/Cpf1 (TTTV). You can also provide custom PAM sequences using IUPAC nucleotide codes. The on-target scoring model is optimized for SpCas9 but provides reasonable predictions for other nucleases.

## How reliable is the on-target score?

The on-target score is a sequence-composition heuristic (GC-content band, homopolymer-run penalty, seed-region G/T bias) rather than the trained Rule Set 2 model (Doench et al., 2016) — it is not benchmarked against experimental cutting-efficiency data, so no correlation figure applies to it. Treat the ranking as a rough sequence-quality filter, not a validated activity prediction, and always confirm with experimental validation for critical experiments. Factors like chromatin accessibility, which sequence-based heuristics can't capture either way, also affect cutting efficiency in cells.

## How many mismatches should I allow in off-target analysis?

The default of 3 mismatches provides a good balance between comprehensiveness and specificity. At 3 mismatches, Cas9 cutting is generally negligible, but reporting these sites helps identify potential risks. Increasing to 4 or 5 mismatches dramatically increases the number of reported sites (often thousands) and is usually unnecessary for standard experiments. For therapeutic applications where safety is critical, consider allowing 4 mismatches.

## Can I design guides for non-human organisms?

Yes, in the sense that guide design and off-target scanning both operate directly on whatever `target_sequence` you submit — there's no species-specific reference lookup involved, so any organism's sequence works. The `organism` parameter itself has no effect on the result; it isn't read by the backend. Since scoring is a generic sequence-composition heuristic rather than a species-trained model, there's no human-specific bias to correct for.

## What is the optimal GC content for guide RNAs?

Guides with 40-60% GC content generally perform best. Very low GC content (<30%) can reduce guide stability and binding efficiency, while very high GC content (>70%) can cause secondary structures that interfere with Cas9 loading. The guide design tool reports GC content for each candidate and can filter by minimum and maximum GC thresholds.

## Does this tool run MAGeCK?

No. `crispr_screen_analyze` runs a simpler method the backend describes as "inspired by MAGeCK": per-guide log2 fold change, aggregated to a per-gene median with a z-test, and Benjamini-Hochberg FDR correction. It does not run MAGeCK's Robust Rank Aggregation (RRA) algorithm, and the `method` parameter (`mageck`/`bagel2`) has no effect on which method runs. If you need MAGeCK's actual RRA statistics, run MAGeCK yourself on the exported count table.

## What is the minimum number of replicates needed for screen analysis?

MAGeCK can run with a single replicate per condition, but results are more reliable with 2-3 biological replicates. With fewer replicates, the tool relies more heavily on the median ratio normalization and may have higher false positive rates. For publication-quality results, 3 replicates per condition is recommended.

## Can I analyze CRISPRi or CRISPRa screens?

Yes. The screen analysis tool works with any pooled CRISPR library screen where read counts are measured before and after selection. CRISPRi (interference/knockdown) and CRISPRa (activation/overexpression) screens produce the same type of count data as knockout screens. The statistical analysis is identical -- only the biological interpretation of hits differs.

## How do I select guides with minimal off-target risk?

The recommended workflow is:

1. Design guides with `crispr_design_guides` (aim for on-target score > 0.5)
2. Run `crispr_offtarget` on the top candidates
3. Prefer guides with zero 1-mismatch off-target sites and fewer than 5 coding-region sites at 2 mismatches
4. Among guides meeting these criteria, choose the one with the highest on-target score

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