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 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

---

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	2.5.4	Alignment (preferred over BWA for ATAC-seq short fragments) (Langmead & Salzberg 2012)
MACS2	2.2.9.1	Peak calling (pipeline-atacseq calls it with -f BAMPE on Tn5-shifted reads) (Zhang et al. 2008)
IDR	2.0.4.2	Irreproducible Discovery Rate for replicate consistency (Li et al. 2011)
samtools	1.19	BAM manipulation, mitochondrial read filtering
Picard	3.1.1	Duplicate marking, insert size metrics
deeptools	3.5.5	alignmentSieve (Tn5 offset), bamCoverage (signal tracks), plotFingerprint
bedtools	2.31.0	Blacklist filtering, interval operations
FastQC	0.12.1	Raw read quality and adapter content assessment
Trim Galore	0.6.10	Adapter trimming (Nextera adapters for ATAC-seq)
MultiQC	1.21	Aggregate QC reporting

Key ATAC-seq parameters: Tn5 transposase introduces a +4/-5 bp offset that must be corrected. Fragment size distribution should show nucleosomal ladder (sub-nucleosomal, mono-, di-, tri-). TSS enrichment score should be >= 5 (GRCh38), >= 6 (hg19), or >= 10 (mm10) for high-quality data (ENCODE data standards).

Environment file: environments/atacseq-env.yml

---

RNA-seq Environment (encode-rnaseq)

For gene expression quantification following ENCODE RNA-seq standards (Conesa et al. 2016, ENCODE Consortium 2020).

Tool	Version	Purpose
STAR	2.7.11b	Splice-aware alignment with 2-pass mapping (Dobin et al. 2013)
RSEM	1.3.3	Gene/transcript quantification with expectation-maximization (Li & Dewey 2011)
Kallisto	0.50.1	Pseudoalignment-based transcript quantification (Bray et al. 2016)
Salmon	1.10.3	Quasi-mapping transcript quantification with GC bias correction (Patro et al. 2017)
featureCounts (subread)	2.0.6	Gene-level read counting for count-based DE methods (Liao et al. 2014)
samtools	1.19	BAM handling, flagstat, idxstats
FastQC	0.12.1	Read quality assessment
Trim Galore	0.6.10	Adapter and quality trimming
MultiQC	1.21	Aggregate QC report
RSeQC	5.0.3	RNA-seq-specific QC: gene body coverage, read distribution, inner distance (Wang et al. 2012)

Memory: STAR genome generation requires 32+ GB RAM for human genome. STAR alignment requires ~30 GB RAM. Kallisto and Salmon are memory-efficient alternatives (~4 GB).

Environment file: environments/rnaseq-env.yml

---

Hi-C Environment (encode-hic)

For chromatin conformation capture processing following ENCODE Hi-C standards (Yardimci et al. 2019, Rao et al. 2014).

Tool	Version	Purpose
BWA-MEM	0.7.18	Chimeric read alignment (each mate aligned independently)
pairtools	1.1.2	Parse, sort, deduplicate, filter contact pairs (Open2C)
cooler	0.9.3	Multi-resolution contact matrix storage and balancing (Abdennur & Mirny 2020)
openjdk	>=11	Java runtime for Juicer Tools (the jar itself is installed separately, see below)
samtools	1.19	BAM handling for chimeric alignment parsing
bedtools	2.31.0	Restriction fragment and TAD boundary operations
FastQC	0.12.1	Read quality assessment
Trim Galore	0.6.10	Adapter trimming
MultiQC	1.21	Aggregate QC reporting

Key Hi-C parameters: Cis/trans ratio > 60%, long-range cis contacts (> 20 kb) > 40%. Resolution depends on sequencing depth: ~1 billion valid pairs for 5 kb resolution on human.

Juicer Tools is not in this environment. The YAML installs only the Java runtime it needs. Download juicer_tools.2.20.00.jar from the aidenlab/Juicebox GitHub releases and invoke it with java -jar. The Hi-C pipeline image (pipeline-hic/scripts/Dockerfile) already contains it.

The environment also installs cooltools, hic-straw and pyGenomeTracks from PyPI (unpinned).

Environment file: environments/hic-env.yml

---

WGBS Environment (encode-wgbs)

For whole-genome bisulfite sequencing (DNA methylation) following ENCODE standards (Foox et al. 2021, Schultz et al. 2015).

Tool	Version	Purpose
Bismark	0.24.2	Bisulfite-aware alignment and methylation extraction (Krueger & Andrews 2011)
MethylDackel	0.6.1	Fast methylation extraction from bisulfite BAMs (Ryan 2023)
samtools	1.19	BAM manipulation, merge, index
bedtools	2.31.0	Interval operations for DMR analysis
FastQC	0.12.1	Read quality assessment (note: bisulfite libraries have biased base composition)
Trim Galore	0.6.10	Adapter trimming with --rrbs or default mode
MultiQC	1.21	Aggregate QC reporting with Bismark module
htslib	1.19	Provides tabix and bgzip for indexed, block-gzipped methylation BED files
Bowtie2	2.5.4	Backend aligner required by Bismark

Key WGBS parameters: Bisulfite conversion rate ≥ 98% (check unmethylated spike-in lambda DNA). CpG coverage >= 10x for reliable DMR calling. M-bias plots should be checked for end-repair artifacts.

Environment file: environments/wgbs-env.yml

---

DNase-seq Environment (encode-dnaseseq)

For DNase I hypersensitive site mapping following ENCODE standards (Thurman et al. 2012, ENCODE Consortium 2020).

Tool	Version	Purpose
BWA-MEM	0.7.18	Read alignment to reference genome
Picard	3.1.1	Duplicate marking and library complexity metrics
BEDOPS	unpinned	sort-bed and unstarch for the Hotspot2 .starch archives (Neph et al. 2012)
HINT (RGT)	1.0.2	TF footprinting from DNase-seq data (Li et al. 2019); installed from PyPI
F-Seq2	2.0.3	Feature density estimation for peak calling (Boyle et al. 2008, Zhao et al. 2020); installed from PyPI
samtools	1.19	BAM handling and filtering
bedtools	2.31.0	Interval operations, blacklist filtering
FastQC	0.12.1	Read quality assessment
Trim Galore	0.6.10	Adapter trimming
MultiQC	1.21	Aggregate QC reporting
bedGraphToBigWig	unpinned	Convert bedGraph signal to bigWig

Hotspot2 2.1.2 and its modwt dependency are not in this environment — neither is packaged for conda. Build both from source (pipeline-dnaseseq/scripts/Dockerfile shows the exact steps) or run the pipeline through that image, which is what pipeline-dnaseseq does.

Environment file: environments/dnaseseq-env.yml

---

CUT&RUN / CUT&Tag Environment (encode-cutandrun)

For antibody-targeted chromatin profiling via CUT&RUN (Skene & Henikoff 2017) and CUT&Tag (Kaya-Okur et al. 2019).

Tool	Version	Purpose
Bowtie2	2.5.4	Alignment (recommended for shorter CUT&RUN/Tag fragments)
r-base	>=4.3	R runtime that the SEACR shell script calls (SEACR itself is installed separately, see below)
MACS2	2.2.9.1	Alternative peak calling with adjusted parameters
samtools	1.19	BAM handling, spike-in alignment filtering
Picard	3.1.1	Duplicate marking (low duplication expected for CUT&RUN/Tag)
deeptools	3.5.5	Signal tracks, heatmaps, spike-in normalization
bedtools	2.31.0	Interval operations, suspect list filtering
FastQC	0.12.1	Read quality assessment
Trim Galore	0.6.10	Adapter trimming
MultiQC	1.21	Aggregate QC reporting

SEACR 1.3 is not in this environment. It is a shell script plus an R script; download the v1.3 tarball from FredHutch/SEACR and put both SEACR_1.3.sh and SEACR_1.3.R on the PATH, alongside the r-base this environment installs. The CUT&RUN pipeline image (pipeline-cutandrun/scripts/Dockerfile) already contains it.

Key CUT&RUN/Tag notes: These assays have inherently lower background than ChIP-seq. Do NOT apply ChIP-seq quality thresholds — use CUT&RUN-specific metrics (Nordin et al. 2023). Apply the CUT&RUN suspect list instead of the standard ENCODE blacklist. Spike-in normalization (E. coli DNA for CUT&RUN, carry-over for CUT&Tag) is strongly recommended for quantitative comparisons.

Environment file: environments/cutandrun-env.yml

R/Bioconductor Packages

Install all R packages needed for ENCODE downstream analysis. The install script at scripts/install-r-packages.R handles BiocManager setup, version locking, and category-based installation.

Core Genomic Infrastructure

These packages provide the foundation for all genomic data manipulation in R:

Package	Purpose
GenomicRanges	Interval arithmetic on genomic coordinates (Lawrence et al. 2013)
GenomicFeatures	Gene model and transcript annotation handling
rtracklayer	Import/export BED, bigWig, GFF, narrowPeak, broadPeak
IRanges	Integer range operations (underlying GenomicRanges)
GenomeInfoDb	Chromosome naming conventions (UCSC vs Ensembl vs NCBI)
BiocGenerics	Common S4 generics across Bioconductor
S4Vectors	S4 class infrastructure for Bioconductor objects
AnnotationDbi	Unified interface to annotation databases
biomaRt	Ensembl BioMart query interface for gene annotation (Durinck et al. 2009)

Differential Analysis

Package	Purpose
DESeq2	Differential gene expression with shrinkage estimators (Love et al. 2014)
edgeR	Differential expression using empirical Bayes (Robinson et al. 2010)
limma	Linear models for microarray and RNA-seq data (Ritchie et al. 2015)
DiffBind	Differential binding analysis for ChIP-seq/ATAC-seq peaks (Stark & Brown 2011)
ChIPQC	ChIP-seq quality control in R (Carroll et al. 2014)
chromVAR	Chromatin accessibility variation across single cells (Schep et al. 2017)

Annotation and Pathway Analysis

Package	Purpose
ChIPseeker	Peak annotation and visualization (Yu et al. 2015)
annotatr	Annotate genomic regions with CpG islands, genes, enhancers (Cavalcante & Sartor 2017)
clusterProfiler	Gene ontology and KEGG pathway enrichment (Yu et al. 2012)
org.Hs.eg.db	Human gene annotation database
org.Mm.eg.db	Mouse gene annotation database
TxDb.Hsapiens.UCSC.hg38.knownGene	Human transcript models (GRCh38)
TxDb.Mmusculus.UCSC.mm10.knownGene	Mouse transcript models (mm10)

Single-Cell Analysis

Package	Purpose
Seurat	Comprehensive single-cell RNA-seq analysis (Hao et al. 2021)
Signac	Single-cell chromatin accessibility (ATAC-seq) analysis (Stuart et al. 2021)
SingleCellExperiment	Core Bioconductor container for single-cell data
scater	Single-cell QC, normalization, visualization (McCarthy et al. 2017)
scran	Single-cell normalization and feature selection (Lun et al. 2016)

Bulk-to-Single-Cell Deconvolution

Package	Purpose
BisqueRNA	Reference-based and marker-based deconvolution (Jew et al. 2020)
DWLS	Dampened Weighted Least Squares deconvolution (Tsoucas et al. 2019)
BayesPrism	Bayesian deconvolution with scRNA-seq reference (Chu et al. 2022). GitHub only — the script prints the devtools::install_github() line, it does not install it
InstaPrism	Fast approximation of BayesPrism for large datasets (Wang et al. 2024). GitHub only, same as BayesPrism

DNA Methylation Analysis

Package	Purpose
DMRcate	Differentially methylated region detection (Peters et al. 2021)
bsseq	Bisulfite sequencing data handling and smoothing (Hansen et al. 2012)
methylKit	Methylation analysis from bisulfite sequencing (Akalin et al. 2012)

Visualization

Package	Purpose
ComplexHeatmap	Publication-quality heatmaps with annotations (Gu et al. 2016)
EnhancedVolcano	Volcano plots for differential expression (Blighe et al. 2018)
Gviz	Genome browser-style track visualization (Hahne & Ivanek 2016)
ggplot2	Grammar of graphics for all custom plots (Wickham 2016)

Statistics and Batch Correction

Package	Purpose
sva (ComBat)	Surrogate variable analysis and batch correction (Leek et al. 2012)
WGCNA	Weighted Gene Co-expression Network Analysis (Langfelder & Horvath 2008)
ReactomePA	Reactome pathway analysis (Yu & He 2016)

Install script: scripts/install-r-packages.R

# Install all categories
Rscript scripts/install-r-packages.R --all

# Install only specific categories
Rscript scripts/install-r-packages.R --chipseq      # DiffBind, ChIPQC, ChIPseeker
Rscript scripts/install-r-packages.R --rnaseq       # DESeq2, edgeR, limma
Rscript scripts/install-r-packages.R --singlecell   # Seurat, Signac, scater, scran
Rscript scripts/install-r-packages.R --methylation   # DMRcate, bsseq, methylKit
Rscript scripts/install-r-packages.R --deconvolution # BisqueRNA, DWLS (+ GitHub lines for BayesPrism, InstaPrism)
Rscript scripts/install-r-packages.R --visualization # ComplexHeatmap, EnhancedVolcano, Gviz, ggplot2
Rscript scripts/install-r-packages.R --stats         # sva, WGCNA, ReactomePA

The tables above list the main packages per category. scripts/install-r-packages.R holds the complete, authoritative lists (47 packages across 8 categories), including supporting packages such as TFBSTools, motifmatchr, tximport, tximeta, celda, pheatmap, RColorBrewer and viridis.

Python Packages

Install Python packages for single-cell analysis, Hi-C processing, signal visualization, and genomic data manipulation.

Core Single-Cell Stack

Package	Purpose
scanpy	Single-cell RNA-seq analysis framework (Wolf et al. 2018)
anndata	Annotated data matrix for single-cell (Virshup et al. 2021)
scvi-tools	Deep generative models for single-cell (Gayoso et al. 2022)
numpy	Numerical computing
pandas	Data manipulation and tabular operations
scipy	Scientific computing (sparse matrices, statistics)
matplotlib	Plotting foundation
seaborn	Statistical visualization

Genomics and Signal Processing

Package	Purpose
deeptools	Signal tracks, heatmaps, correlation (also CLI; Ramirez et al. 2016)
pyBigWig	Read/write bigWig signal files (Ryan 2023)
pysam	Python interface to samtools/htslib (Li et al. 2009)
pybedtools	Python interface to bedtools (Dale et al. 2011)

Hi-C Analysis

Package	Purpose
cooler	Multi-resolution contact matrices (Abdennur & Mirny 2020)
cooltools	Analysis toolkit for cooler data: TADs, compartments, insulation
hic-straw	Read .hic files from Juicer/Juicebox (Durand et al. 2016)
pyGenomeTracks	Genome browser visualization including Hi-C tracks

Single-Cell QC and Integration

Package	Purpose
scrublet	Doublet detection for scRNA-seq (Wolock et al. 2019)
harmony-pytorch	Batch integration via Harmony in PyTorch (Korsunsky et al. 2019)
scanorama	Panoramic stitching of scRNA-seq datasets (Hie et al. 2019)
bbknn	Batch-balanced KNN graph construction (Polanski et al. 2020)

CellBender (ambient RNA removal, Fleming et al. 2023) is not installed by the script — it is GPU-oriented and is left to a manual pip install cellbender, which the script prints as a note.

Install script: scripts/install-python-packages.sh

# Install all Python packages
bash scripts/install-python-packages.sh --all

# Install only specific categories
bash scripts/install-python-packages.sh --genomics     # numpy, pandas, scipy, matplotlib, seaborn
bash scripts/install-python-packages.sh --singlecell   # scanpy, scvi-tools, harmony-pytorch
bash scripts/install-python-packages.sh --hic          # cooler, cooltools, hic-straw, bioframe
bash scripts/install-python-packages.sh --deeptools    # deeptools, pyBigWig, pysam, pybedtools

Every category except --genomics also installs the core genomics packages first. The complete, authoritative list of direct dependencies is scripts/requirements.in; exact versions for the whole dependency tree are locked in scripts/constraints.txt, which every install is constrained by.

Nextflow and Container Setup

ENCODE pipeline execution requires Nextflow DSL2 and a container runtime (Docker or Singularity).

Nextflow Installation

# Install the pinned Nextflow release (requires Java 17+) and check for Docker.
# Use --singularity for HPC, or --both.
bash scripts/install-nextflow.sh --docker

# Verify
nextflow -version

What the script does:

• Downloads the pinned, self-contained Nextflow release the pipelines are validated against (the version and its SHA-256 are at the top of scripts/install-nextflow.sh), verifies the checksum, and only then installs it to /usr/local/bin or ~/.local/bin.
• An existing Nextflow is accepted only if it is exactly that pinned release. Any other version is left untouched; the pinned release is installed next to it and the script tells you to put its directory first on PATH.
• Docker and Singularity are checked, not installed: the script reports what it finds and prints the install commands for your platform. Run those yourself.

Docker (recommended for local/cloud)

# macOS
brew install --cask docker

# Linux (Ubuntu/Debian)
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io

# Add current user to docker group (Linux)
sudo usermod -aG docker $USER

Singularity (for HPC clusters)

# Most HPC clusters have Singularity pre-installed
# Check with: module load singularity && singularity version

# If not available, install via conda:
conda install -c conda-forge singularity

Nextflow Configuration Profiles

The pipeline skills (pipeline-chipseq, pipeline-atacseq, etc.) include nextflow.config files with profiles for local, SLURM, GCP, and AWS execution. Select the appropriate profile:

# Local with Docker
nextflow run main.nf -profile local

# HPC with Singularity
nextflow run main.nf -profile slurm

# Google Cloud
nextflow run main.nf -profile gcp

# AWS Batch
nextflow run main.nf -profile aws

Install script: scripts/install-nextflow.sh

Motif Analysis Tools

For transcription factor binding motif discovery and scanning.

Tool	Version	Type	Purpose
HOMER	4.11	CLI	De novo and known motif discovery, annotation (Heinz et al. 2010)
MEME Suite	5.5.5	CLI	MEME, DREME, STREME de novo discovery; FIMO scanning; AME enrichment (Bailey et al. 2015)
FIMO	5.5.5	CLI (part of MEME Suite)	Motif occurrence scanning across sequences
TFBSTools	R	R/Bioconductor	JASPAR motif handling, PFM/PWM conversion, motif scanning in R (Tan & Lenhard 2016)

HOMER Installation

# Download and configure HOMER
mkdir -p ~/software/homer
cd ~/software/homer
wget http://homer.ucsd.edu/homer/configureHomer.pl
perl configureHomer.pl -install homer
perl configureHomer.pl -install hg38   # Human genome
perl configureHomer.pl -install mm10   # Mouse genome

# Add to PATH
export PATH=$PATH:~/software/homer/bin

MEME Suite Installation

# Via conda (recommended)
conda install -c bioconda meme

# Or from source
wget https://meme-suite.org/meme/meme-software/5.5.5/meme-5.5.5.tar.gz
tar xzf meme-5.5.5.tar.gz
cd meme-5.5.5
./configure --prefix=$HOME/software/meme --enable-build-libxml2 --enable-build-libxslt
make && make install

Walkthrough: Setting Up a Complete ENCODE Analysis Environment

Goal: Install all bioinformatics tools needed to process ENCODE data, from raw FASTQ files through peak calling, annotation, and visualization, using Conda environments. Context: ENCODE analysis requires dozens of specialized tools. This skill automates installation with pre-configured Conda environments for each pipeline stage.

Step 1: Determine required tools by experiment type

encode_get_experiment(accession="ENCSR000AKA")

Expected output:

{
  "accession": "ENCSR000AKA",
  "assay_title": "Histone ChIP-seq",
  "target": "H3K27ac"
}

Interpretation: Histone ChIP-seq requires: BWA-MEM (alignment), SAMtools (BAM processing), MACS2 (peak calling), IDR (reproducibility), bedtools (interval operations), deepTools (signal visualization).

Step 2: Install the ChIP-seq Conda environment

# Using the pre-configured environment YAML
conda env create -f skills/bioinformatics-installer/environments/chipseq-env.yml
conda activate encode-chipseq

environments/chipseq-env.yml is the authoritative list of what that environment installs — read it rather than retyping the versions. It covers alignment (BWA), BAM processing (samtools, Picard), peak calling (MACS2), replicate consistency (IDR), cross-correlation metrics (phantompeakqualtools), signal processing (deeptools), interval operations (bedtools), and QC/trimming (FastQC, Trim Galore, MultiQC). See the ChIP-seq table above for the pinned versions.

Step 3: Install additional tools for downstream analysis

For peak annotation and motif analysis:

conda create -n encode-annotation -c conda-forge -c bioconda \
  homer bedtools bioconductor-chipseeker bioconductor-clusterprofiler bioconductor-rgreat
conda activate encode-annotation
# Includes: HOMER, bedtools, R/Bioconductor (ChIPseeker, clusterProfiler, rGREAT for GREAT queries)

Step 4: Verify installation

# Quick verification of key tools
bwa 2>&1 | head -3
samtools --version | head -1
macs2 --version
bedtools --version

Step 5: Download reference data for ENCODE analysis

encode_download_files(file_accessions=["ENCFF001ABC"], download_dir="/data/references")

Reference files needed:

• GRCh38 genome FASTA
• ENCODE blacklist v2 (Amemiya et al. 2019)
• Gene annotation GTF (GENCODE v36)

Integration with downstream skills

• Installed tools are used by → pipeline-chipseq through pipeline-cutandrun for processing
• Reference data feeds into → download-encode for FASTQ retrieval
• Environment setup enables → quality-assessment tool execution
• Installed annotation tools support → peak-annotation and motif-analysis

Code Examples

1. Find experiments to identify required tools

encode_search_experiments(
  assay_title="ATAC-seq",
  organ="pancreas"
)

Expected output:

{
  "results": [
    {
      "accession": "ENCSR799GHJ",
      "assay_title": "ATAC-seq",
      "biosample_summary": "pancreatic islet tissue male adult (44 years)",
      "organ": "pancreas",
      "status": "released"
    }
  ],
  "total": 8,
  "limit": 25,
  "offset": 0,
  "has_more": false,
  "next_offset": null
}

Install decision: ATAC-seq requires the atacseq-env.yml conda environment (Bowtie2 + MACS2 + deeptools + samtools + bedtools).

2. Get file info to understand format requirements

encode_get_file_info(accession="ENCFF001ABC")

Expected output:

{
  "accession": "ENCFF001ABC",
  "file_format": "fastq",
  "file_type": "fastq",
  "output_type": "reads",
  "file_size_human": "4.4 GB",
  "experiment_assay": "ATAC-seq",
  "biological_replicates": [1],
  "status": "released"
}

Install decision: raw ATAC-seq reads need Bowtie2 (not BWA), Picard for duplicate marking, and samtools for BAM processing — the atacseq-env.yml environment. Whether the FASTQ is one mate of a pair is on the file's page on encodeproject.org (paired_end, paired_with), not in this response.

Pitfalls & Edge Cases

• Conda solver conflicts: Large conda environments with many packages can take hours to solve. Use mamba instead of conda for faster dependency resolution, or install in smaller focused environments.
• R/Bioconductor version mismatch: R packages from CRAN and Bioconductor must match the R version. Installing Bioconductor 3.18 packages with R 4.4 will fail silently or produce errors. Use BiocManager::install() to ensure version compatibility.
• Python 2 vs Python 3: Some legacy bioinformatics tools (MACS 1.x, old HOMER) require Python 2. Never install Python 2 tools in the same environment as Python 3 tools — use separate conda environments.
• ARM Mac (M1/M2/M3) compatibility: Many bioinformatics tools lack native ARM builds. Use CONDA_SUBDIR=osx-64 or Rosetta 2 emulation for x86_64 packages. Some tools (samtools, BWA) have ARM-native builds.
• Nextflow requires Java 17+: check java -version before running pipelines. Install Nextflow with scripts/install-nextflow.sh, which pins the release the pipelines are validated against and verifies its checksum; avoid piping a remote installer straight into a shell.
• Docker vs Singularity on HPC: Most HPC clusters do not allow Docker (requires root). Use Singularity instead. The pipeline skills express this through the execution profile, not a runtime profile: -profile local enables Docker, -profile slurm enables Singularity. There is no docker or singularity profile. With -profile slurm, convert the image once and pass the file: singularity build pipeline-chipseq.sif docker-daemon://encode-toolkit/pipeline-chipseq:1.0.0, then --container /path/to/pipeline-chipseq.sif.

Literature Foundation

#	Reference	Key Contribution
1	Li & Durbin 2009, Bioinformatics, DOI:10.1093/bioinformatics/btp324 (~30,000 cit)	BWA aligner
2	Langmead & Salzberg 2012, Nat Methods, DOI:10.1038/nmeth.1923 (~25,000 cit)	Bowtie2 aligner
3	Li et al. 2009, Bioinformatics, DOI:10.1093/bioinformatics/btp352 (~20,000 cit)	SAMtools/BAM format
4	Zhang et al. 2008, Genome Biol, DOI:10.1186/gb-2008-9-9-r137 (~7,000 cit)	MACS2 peak caller
5	Dobin et al. 2013, Bioinformatics, DOI:10.1093/bioinformatics/bts635 (~15,000 cit)	STAR RNA-seq aligner
6	Love et al. 2014, Genome Biol, DOI:10.1186/s13059-014-0550-8 (~30,000 cit)	DESeq2
7	Ramirez et al. 2016, Nucleic Acids Res, DOI:10.1093/nar/gkw257 (~3,000 cit)	deeptools
8	Wolf et al. 2018, Genome Biol, DOI:10.1186/s13059-017-1382-0 (~5,000 cit)	Scanpy
9	Hao et al. 2021, Cell, DOI:10.1016/j.cell.2021.04.048 (~8,000 cit)	Seurat v4
10	Quinlan & Hall 2010, Bioinformatics, DOI:10.1093/bioinformatics/btq033 (~10,000 cit)	bedtools
11	Ewels et al. 2016, Bioinformatics, DOI:10.1093/bioinformatics/btw354 (~3,000 cit)	MultiQC
12	Krueger & Andrews 2011, Bioinformatics, DOI:10.1093/bioinformatics/btr167 (~5,000 cit)	Bismark
13	Heinz et al. 2010, Molecular Cell, DOI:10.1016/j.molcel.2010.05.004 (~7,000 cit)	HOMER motif analysis
14	Bailey et al. 2015, Nucleic Acids Res, DOI:10.1093/nar/gkv416 (~3,000 cit)	MEME Suite
15	Meers et al. 2019, Epigenetics Chromatin, DOI:10.1186/s13072-019-0287-4 (~800 cit)	SEACR for CUT&RUN
16	Di Tommaso et al. 2017, Nat Biotechnol, DOI:10.1038/nbt.3820 (~2,500 cit)	Nextflow
17	Landt et al. 2012, Genome Res, DOI:10.1101/gr.136184.111 (~4,000 cit)	ENCODE ChIP-seq standards
18	ENCODE Consortium 2020, Nature, DOI:10.1038/s41586-020-2493-4 (~1,656 cit)	ENCODE Phase 3
19	Amemiya et al. 2019, Sci Rep, DOI:10.1038/s41598-019-45839-z (~1,372 cit)	ENCODE Blacklist v2

Integration

This skill produces...	Feed into...	Purpose
Conda environments	pipeline-chipseq through pipeline-cutandrun	Provide tool dependencies for all pipeline stages
Installed reference data	download-encode	Reference genomes and annotations for alignment
Tool version inventory	data-provenance	Record exact tool versions for reproducibility
QC tool installations	quality-assessment	Enable FastQC, MultiQC, and ENCODE QC metric tools
Annotation tool setup	peak-annotation	HOMER, ChIPseeker for peak-to-gene assignment
Motif scanning tools	jaspar-motifs	MEME Suite for motif scanning against JASPAR
Visualization tools	visualization-workflow	deepTools, IGV, R/ggplot2 for data visualization
Liftover utilities	liftover-coordinates	UCSC liftOver binary for assembly conversion

Related Skills

• pipeline-guide: Parent skill for all pipeline execution; provides overview of available pipelines and tool selection guidance
• pipeline-chipseq: Uses the ChIP-seq conda environment tools for FASTQ-to-peaks processing
• pipeline-atacseq: Uses the ATAC-seq conda environment tools for accessibility analysis
• pipeline-rnaseq: Uses the RNA-seq conda environment for expression quantification
• pipeline-wgbs: Uses the WGBS conda environment for methylation analysis
• pipeline-hic: Uses the Hi-C conda environment for contact matrix generation
• pipeline-dnaseseq: Uses the DNase-seq conda environment for hotspot detection
• pipeline-cutandrun: Uses the CUT&RUN conda environment for CUT&RUN/CUT&Tag processing
• quality-assessment: Quality metrics require properly installed tools to compute
• setup: Initial ENCODE Toolkit server setup (MCP connection, not bioinformatics tools)
• motif-analysis: Requires HOMER and MEME Suite from this installer
• visualization-workflow: Uses deeptools, pyGenomeTracks, and R visualization packages from this installer
• single-cell-encode: Uses Seurat, Signac, Scanpy from this installer
• publication-trust: Assess scientific integrity of publications before relying on their methods or findings

Presenting Results

• Present installed tools as a checklist table: tool | version | status (installed/failed/skipped). Group by assay environment. Suggest: "Would you like to verify the installation by running a quick test on sample ENCODE data?"
• If any installation fails, provide the exact error and a targeted fix. Common fixes: update conda, set channel priority, install system dependencies.

For the request: "$ARGUMENTS"