Pipeline user guide#

This guide walks through running a Sequana pipeline end-to-end: install, configure, run, inspect. The same skeleton applies to every pipeline.

Install a pipeline#

Each pipeline is an independent PyPI package. Install it inside a Python 3.10+ environment alongside Sequana:

pip install sequana_fastqc --upgrade

Verify:

sequana_fastqc --help

Initialise the working directory#

Pipelines come with a config file, a snakefile, and a runner script. The initialisation command copies them into a working directory of your choice (default: the pipeline name):

sequana_fastqc --input-directory my_data --working-directory test1
cd test1

Inside test1 you will typically find:

  • config.yaml — pipeline parameters (input/output, tools, resources).

  • <name>.rules — the Snakefile.

  • <name>.sh — convenience launcher (snakemake with sensible defaults).

  • apptainers.yaml — container URIs used when --use-apptainer is set.

Common CLI options#

Every Sequana pipeline understands these options (with sensible defaults):

--input-directory

Where to look for input FASTQ files. Default: ..

--input-pattern

Glob to select files. Default: *fastq.gz. Use */*fastq.gz if samples sit in sub-directories.

--input-readtag

Pattern used to detect paired-end reads. Default: _R[12]_. Not all pipelines support this option; check sequana_<name> --help for pipeline-specific flags.

--working-directory

Where the pipeline files get copied. Use --force to overwrite.

--profile {local,slurm}

Run locally or generate a SLURM-aware launcher. Auto-detected when sbatch is on the path.

--apptainer-prefix (or --apptainer-args)

Use Apptainer containers for tools. See the pipeline's apptainers.yaml for available containers.

--deps

Print external dependencies and check whether they are installed.

Use sequana_<name> --help to discover pipeline-specific flags.

Edit the configuration#

config.yaml is plain YAML. The most common fields are at the top:

input_directory: /abs/path/to/data
input_readtag: _R[12]_
input_pattern: '*fastq.gz'

Every tool used by the pipeline has its own section (cutadapt:, bwa:, coverage: …). The defaults are tuned for typical datasets; tweak them for unusual cases (short genomes, very deep coverage, single-end data, …).

Run the pipeline#

The simplest way: run directly from the initialization command with --execute:

sequana_<name> --input-directory my_data --execute

This skips creating a working directory and runs the pipeline in-place.

Alternatively, run from the working directory:

sh <pipeline>.sh

or via snakemake directly:

snakemake -s <pipeline>.rules -j 4 -p

-j N sets the number of parallel jobs, -p prints shell commands. On a SLURM cluster, the generated <pipeline>.sh already includes the cluster profile.

When the run is complete, the HTML report is at:

./summary.html

Clean up#

To remove temporary files but keep the report, use snakemake's cleanup rule:

snakemake -s <pipeline>.rules --cleanup

Tips#

  • Always run --deps once after installing a new pipeline.

  • Re-run with --force to overwrite an existing working directory.

  • For long runs on a cluster, prefer --apptainer-prefix — it pins the tool versions and eliminates conda-env clashes.

  • See Pipelines for the full pipeline catalogue and Tutorial for end-to-end examples.