# Bioconductor Decipher

A toolset for deciphering and managing biological sequences.

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

## Description

A toolset for deciphering and managing biological sequences.

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

# DECIPHER

## Dependencies & Environment

> Package-intrinsic requirements from the Bioconductor landing page, reproduce in any R environment.

- **Version:** 3.8.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6
- **Depends:** Biostrings
- **Imports:** DBI, S4Vectors, IRanges, XVector
- **Install:** `BiocManager::install("DECIPHER")`

## When to Use
- **Multiple Sequence Alignment (MSA)**: Aligning large sets of DNA, RNA, or amino acid sequences directly in R with high accuracy.
- **Taxonomic Classification**: Classifying marker gene sequences (e.g., 16S/18S rRNA, ITS) using the robust IDTAXA algorithm.
- **Large-Scale Sequence Clustering**: Grouping millions of sequences into operational taxonomic units (OTUs) or clusters using memory-efficient database-backed methods.
- **Primer and Probe Design**: Designing and validating group-specific PCR primers, microarrays, and FISH probes targeting specific taxonomic groups.
- **Gene Finding & Chimera Detection**: Identifying protein-coding genes, non-coding RNAs, or chimeric sequences in metagenomic and genomic assemblies.

## When NOT to Use
- For basic, small-scale alignments where simple command-line tools like ClustalW or MUSCLE are already integrated into your pipeline, use `msa` instead because DECIPHER has a steeper learning curve due to its SQLite database backend.
- For ultra-fast, heuristic clustering of millions of raw metagenomic reads without downstream R analysis, use external command-line tools like `MMseqs2` or `vsearch` because they are optimized for raw disk-based speed.
- For phylogenetic tree reconstruction using advanced evolutionary models (e.g., Maximum Likelihood or Bayesian), use `phangorn` or `ggtree` because DECIPHER's tree-building (`TreeLine`) is primarily distance-based.

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