/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 environment is version-pinned for reproducibility and tested against ENCODE uniform processing standards.

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 50+ packages across 8 categories
• **Python install script** for single-cell, Hi-C, and genomics packages
• **Nextflow + container setup** for pipeline execution on local, HPC, and cloud platforms

All environments use the same channel priority (conda-forge > bioconda > defaults) and are tested for cross-platform compatibility on Linux x86_64 and macOS (Intel + Apple Silicon where possible).

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

# Nextflow + Docker
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 | 0.7.17 | 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 | 2.0.3 | 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 trimming via Cutadapt (Krueger 2012) | | MultiQC | 1.21 | Aggregate QC report across all pipeline stages (Ewels et al. 2016) | | bedGraphToBigWig | — | Convert bedGraph signal to bigWig for genome browser viewing (Kent et al. 2010) |

**Memory**: BWA index for GRCh38 requires ~5.5 GB RAM. Peak calling with MACS2 typically requires 4-8 GB. phantompeakqualtools loads full BAM into memory.

**Environment file**: `environments/chipseq-env.yml`

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ATAC-seq Environment (`encode-atacseq`)

For chromatin accessibility profiling via ATAC-seq following ENCODE standards (Buenrostro et al. 2013, Corces et al. 2017).

| Tool | Version | Purpose | |------|---------|---------| | Bowtie2 | 2.5.3 | Alignment (preferred over BWA for ATAC-seq short fragments) (Langmead & Salzberg 2012) | | MACS2 | 2.2.9.1 | Peak calling with --nomodel --shift -100 --extsize 200 for ATA