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bioinformatics-env-setup

Guide users through installing and verifying bioinformatics software for NGS, RNA-seq, scRNA-seq, and metagenomics pipelines using conda, pip, or manual installation.

personAuthor: TashanworldhubOpenAPI
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Skill: Bioinformatics Environment Setup

Use When

  • User is starting a new analysis and wants to install the required bioinformatics tools.
  • A pipeline step fails because a tool is missing or the wrong version is installed.
  • User asks how to install a specific bioinformatics tool (e.g., STAR, Cell Ranger, Scanpy, Seurat).
  • User wants a ready-to-use conda environment for a specific pipeline.
  • User is setting up a new compute environment (HPC, cloud VM, local workstation).

Inputs

  • Required:
    • Target pipeline(s): one or more of ngs-shared, wgs, rnaseq, scrnaseq, metagenomics, or all
  • Optional:
    • Installation method: conda (default), docker, or manual
    • Operating system: linux (default), macos
    • Environment name prefix (default: bioinfo)
    • Conda channel priority: strict (default, reproducible) or flexible
    • Whether to verify existing installations before installing (default: true)

Workflow

  1. Detect the environment: Check whether conda/mamba is available (which conda), whether Docker is available (docker --version), and whether an HPC module system is present (module avail). Report findings to the user and recommend the best installation path.

  2. Check existing installations: For each tool in the requested pipeline group(s), run which <tool> && <tool> --version 2>&1 | head -1 to detect installed versions. Report a summary table: tool name, found/missing, installed version, required minimum version.

  3. Select the installation asset: Choose the appropriate conda environment YAML from assets/envs/:

    • ngs-shared.yaml — FastQC, MultiQC, fastp, Trimmomatic, samtools
    • wgs.yaml — BWA-MEM2, GATK, Picard, bcftools, VEP, SnpEff
    • rnaseq.yaml — STAR, HISAT2, featureCounts (subread), salmon, kallisto, DESeq2 (R), edgeR (R)
    • scrnaseq-python.yaml — STARsolo (STAR), Scanpy, scVI-tools, scVelo, Scrublet, CellBender, harmonypy
    • scrnaseq-r.yaml — Seurat v5, SingleR, scDblFinder, SoupX, Monocle3, DoubletFinder
    • metagenomics.yaml — Bowtie2, MetaPhlAn4, HUMAnN3, Kraken2, Bracken, metaSPAdes, MEGAHIT, Prokka

  4. Create the conda environment:

    mamba env create -f assets/envs/<pipeline>.yaml
# or, if mamba is unavailable:
conda env create -f assets/envs/<pipeline>.yaml

    Prefer mamba over conda for significantly faster dependency resolution.

  5. Handle tools that cannot be installed via conda — prompt the user with manual steps:

    • Cell Ranger: Download from the 10x Genomics website (requires registration). Add to PATH: export PATH=/path/to/cellranger:$PATH.
    • CellBender: Install via pip inside the conda environment: pip install cellbender. Requires CUDA for GPU acceleration.
    • DoubletFinder: Install from GitHub inside R: remotes::install_github("chris-mcginnis-ucsf/DoubletFinder").
    • Monocle3: Install from Bioconductor: BiocManager::install("monocle3"). Alternatively: remotes::install_github("cole-trapnell-lab/monocle3").
    • velocyto (for RNA velocity loom generation): pip install velocyto. Requires samtools and a reference GTF.

  6. Verify all installations: After installation, re-run the version check for each tool. For R packages, run Rscript -e 'packageVersion("<pkg>")'. Report a final verification table: tool, expected version, installed version, status (PASS/FAIL).

  7. Report any failures with the specific error message and the most likely fix (e.g., missing system library, incompatible CUDA version, wrong conda channel).

Output Contract

  • Environment detection summary (printed to console)
  • Pre-installation version check table (TSV or formatted table): tool, status, version
  • Installation commands executed (echo each command before running)
  • Post-installation verification table (TSV or formatted table): tool, expected_min_version, installed_version, pass/fail
  • List of any tools that require manual installation steps, with exact instructions

Limits

  • Cell Ranger requires registration and manual download from 10x Genomics; it cannot be distributed via conda or pip.
  • CellBender GPU acceleration requires CUDA ≥ 11.0 and a compatible NVIDIA GPU; CPU-only mode is available but slow (hours per sample).
  • Monocle3 has complex Bioconductor dependencies that frequently cause installation conflicts; allocate extra time and follow the official installation guide.
  • Conda environments can be large (2–5 GB each); ensure sufficient disk space before creating them.
  • On HPC clusters, use module load for pre-installed tools before attempting conda installation; conda may conflict with system modules.
  • macOS (Apple Silicon) requires CONDA_SUBDIR=osx-64 for some bioinformatics tools that lack native ARM builds.
  • Common failure cases:
    • conda solve timing out on complex environments; switch to mamba for faster resolution.
    • GATK or Picard failing due to wrong Java version; ensure Java 17+ is available (java --version).
    • R package installation failing due to missing system libraries (e.g., libgdal, libcurl); install the system library first with apt-get or yum.
    • STARsolo requiring a STAR version ≥ 2.7.9a for Velocyto output; check STAR version with STAR --version.