# Bioconductor Crisprbase

Provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base...

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-crisprbase -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-crisprbase -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-crisprbase -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-crisprbase -g -a gemini-cli -y`

## Description

Provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base editors.Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# crisprBase

## Workflows

### Standard Workflow

Define and manipulate S4 classes representing restriction enzymes, CRISPR nucleases, base editors, and nickases.

```r
library(crisprBase)

# 1. Define SpCas9 Nuclease (3' PAM)
SpCas9 <- CrisprNuclease(
  "SpCas9", 
  targetType = "DNA",
  pams = c("(3/3)NGG", "(3/3)NAG", "(3/3)NGA"),
  weights = c(1, 0.2593, 0.0694),
  pam_side = "3prime", 
  spacer_length = 20
)

# 2. Define AsCas12a Nuclease (5' PAM)
AsCas12a <- CrisprNuclease(
  "AsCas12a", 
  targetType = "DNA",
  pams = "TTTV(18/23)",
  pam_side = "5prime", 
  spacer_length = 23
)

# 3. Define a Cytosine Base Editor (BE4max) with custom editing weights
weightsFile <- system.file("be/b4max.csv", package = "crisprBase", mustWork = TRUE)
ws <- t(read.csv(weightsFile))
ws <- as.data.frame(ws)
colnames(ws) <- ws["Position", ]
ws <- ws[-c(match("Position", rownames(ws))), , drop = FALSE]
ws <- as.matrix(ws)

BE4max <- BaseEditor(
  SpCas9, 
  baseEditorName = "BE4max",
  editingStrand = "original", 
  editingWeights = ws, 
  scale = TRUE
)

# 4. Define CRISPR Nickases (Cas9D10A)
Cas9D10A <- CrisprNickase(
  "Cas9D10A", 
  nickingStrand = "opposite", 
  pams = c("(3)NGG", "(3)NAG", "(3)NGA"),
  weights = c(1, 0.2593, 0.0694),
  pam_side = "3prime", 
  spacer_length = 20
)
```
*Note on inputs/outputs:* Input is nuclease specifications and weight files; output is S4 objects representing custom CRISPR nucleases, base editors, and nickases.

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/transcriptomics/crisprbase/SKILL.md