/bioinformatics-installer

name: bioinformatics-installer
description: "Install bioinformatics tools for ENCODE data analysis. Covers CLI tools (BWA, STAR, samtools, MACS2), R/Bioconductor packages (DESeq2, Seurat, ChIPseeker), Python packages (Scanpy, deeptools), and Nextflow pipeline infrastructure. Generates conda environments, R install scripts, and Python requirements. Use when the user needs to set up a bioinformatics workstation, install tools for a specific assay, create reproducible environments, or troubleshoot dependency issues. Trigger on: install tools, set up environment, conda create, bioinformatics setup, install R packages, install Bioconductor, install pipeline tools."

Bioinformatics Installer for ENCODE Data Analysis

Install all bioinformatics tools needed for ENCODE data analysis, organized by assay type. This skill provides ready-to-use conda environment definitions, R/Bioconductor install scripts, Python package lists, and Nextflow pipeline infrastructure setup. Every primary tool is version-pinned for reproducibility; a few utility packages (`bedops`, `ucsc-bedgraphtobigwig`, `pigz`, `openjdk`, `r-base`) float so the solver can satisfy the pinned tools around them.

When to Use

• User wants to install bioinformatics tools needed for ENCODE data analysis
• User asks about "install tools", "conda environment", "setup bioinformatics", or "install HOMER/MACS2/deeptools"
• User needs pre-configured conda environments for specific assay pipelines (ChIP-seq, ATAC-seq, RNA-seq, etc.)
• User wants to install R/Bioconductor packages (DESeq2, Seurat, ChIPseeker) or Python packages (Scanpy, pysam)
• Example queries: "install tools for ChIP-seq analysis", "set up a conda environment for ATAC-seq", "install deeptools and bedtools"

Overview

ENCODE data analysis requires a broad ecosystem of tools spanning command-line aligners, peak callers, signal processors, statistical analysis frameworks in R, Python visualization and single-cell packages, and workflow engines. Setting up these tools correctly — with compatible versions, proper channel priorities, and no dependency conflicts — is a significant barrier for new users and a reproducibility concern for experienced analysts.

This skill solves that by providing:

• **7 assay-specific conda environments** with pinned tool versions matching ENCODE pipeline standards
• **R/Bioconductor install script** covering 47 packages across 8 categories
• **Python install script** for single-cell, Hi-C, and genomics packages, locked by `scripts/constraints.txt`
• **Nextflow install script + container checks** for pipeline execution on local, HPC, and cloud platforms

The environment files, `scripts/requirements.in` and `scripts/install-r-packages.R` are the authoritative package lists; the tables below summarise them.

All environments use the same channel priority (conda-forge > bioconda). Every file is dry-run solved for Linux x86_64 in CI, so the pinned versions exist and install together. Several tools have no macOS arm64 build on bioconda; on Apple Silicon use the pipeline Docker images instead.

For every tool that an environment and the matching `pipeline-*` Docker image both install, the two pin the same version, and CI fails if they drift. Some tools exist on only one side — for example `phantompeakqualtools`, `salmon`, `subread` and the Hi-C `bedtools` are conda-only, while juicer_tools, SEACR, Hotspot2 and `modwt` are image-only because they are not conda packages. Those are noted in the sections below.

Quick Start

Install a complete environment for any assay type with a single command:

# ChIP-seq (histone or TF)
conda env create -f skills/bioinformatics-installer/environments/chipseq-env.yml

# ATAC-seq
conda env create -f skills/bioinformatics-installer/environments/atacseq-env.yml

# RNA-seq
conda env create -f skills/bioinformatics-installer/environments/rnaseq-env.yml

# Hi-C
conda env create -f skills/bioinformatics-installer/environments/hic-env.yml

# Whole-Genome Bisulfite Sequencing (WGBS)
conda env create -f skills/bioinformatics-installer/environments/wgbs-env.yml

# DNase-seq
conda env create -f skills/bioinformatics-installer/environments/dnaseseq-env.yml

# CUT&RUN / CUT&Tag
conda env create -f skills/bioinformatics-installer/environments/cutandrun-env.yml

Using mamba for faster solves (recommended):

mamba env create -f skills/bioinformatics-installer/environments/chipseq-env.yml

Install R and Python packages:

# All R/Bioconductor packages
Rscript skills/bioinformatics-installer/scripts/install-r-packages.R --all

# All Python packages
bash skills/bioinformatics-installer/scripts/install-python-packages.sh --all

# Install the pinned Nextflow release and check for a Docker runtime
bash skills/bioinformatics-installer/scripts/install-nextflow.sh --docker

Per-Assay Environments

ChIP-seq Environment (`encode-chipseq`)

For histone modification and transcription factor ChIP-seq processing following ENCODE uniform pipeline standards (Landt et al. 2012, ENCODE Consortium 2020).

| Tool | Version | Purpose | |------|---------|---------| | BWA-MEM | 0.7.18 | Read alignment to reference genome (Li & Durbin 2009) | | samtools | 1.19 | BAM manipulation, sorting, indexing, flagstat (Li et al. 2009) | | MACS2 | 2.2.9.1 | Peak calling for narrow (TF) and broad (histone) marks (Zhang et al. 2008) | | Picard | 3.1.1 | Duplicate marking and library complexity metrics (Broad Institute) | | phantompeakqualtools | 1.2.2 | Strand cross-correlation (NSC/RSC) quality metrics (Kharchenko et al. 2008) | | IDR | 2.0.4.2 | Irreproducible Discovery Rate for replicate consistency (Li et al. 2011) | | deeptools | 3.5.5 | Signal normalization (bamCoverage), fingerprint, correlation (Ramirez et al. 2016) | | bedtools | 2.31.0 | Interval operations, blacklist filtering (Quinlan & Hall 2010) | | FastQC | 0.12.1 | Raw read quality assessment (Andrews 2010) | | Trim Galore | 0.6.10 | Adapter and quality tri