# Bioconductor Bumphunter

Tools for finding bumps in genomic data

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

## Description

Tools for finding bumps in genomic data

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

# bumphunter

## Dependencies & Environment

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

- **Version:** 1.54.0 · **Bioconductor:** 3.23 · **R:** ≥ 4.6
- **Depends:** S4Vectors, IRanges, Seqinfo, GenomicRanges, foreach, iterators, locfit
- **Imports:** matrixStats, limma, doRNG, BiocGenerics, GenomicFeatures, AnnotationDbi
- **Install:** `BiocManager::install("bumphunter")`

## When to Use
- Finding continuous, spatially clustered genomic regions ("bumps") that differ significantly between conditions using `bumphunter()`.
- Grouping genomic locations into distinct clusters based on maximum distance using `clusterMaker()`.
- Extracting positive, near-zero, and negative segments from a vector of test statistics using `getSegments()`.
- Packaging segmented regions into a table of bump characteristics using `regionFinder()`.

## When NOT to Use
- For basic linear modeling of independent, unclustered genomic features, use `limma` because `bumphunter` is specifically designed to share information between nearby clustered locations.
- For end-to-end analysis of Illumina 450k arrays without manual matrix setup, use `minfi` because it provides a tailored wrapper around the bumphunter engine.
- For whole-genome bisulfite sequencing (WGBS) data requiring specialized smoothing, use `bsseq` because it adapts the bump hunting methodology specifically for bisulfite data.

## Data Requirements
- **Signal Matrix**: A numeric matrix (`y`) of genomic signals where rows represent genomic locations and columns represent biological replicates.
- **Design Matrix**: A design matrix (`X`) representing the experimental covariates, created with standard R modeling functions.
- **Genomic Coordinates**: Vectors for chromosome (`chr`) and genomic positions (`pos`) corresponding to the rows of the signal matrix.

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