Sequana: a set of NGS pipelines

This page serves as an additional set of resources for the Sequana project. Sequana is comprised of a Python library, the documentation of which is accessible on Read The Docs. The source code for the Python library can be found on GitHub.

One of the primary objective of the Sequana project is to offer a collection of NGS pipelines. The pipelines currently available are located on PyPI and the Sequana GitHub organisation page, each having their own repository. For instance, the RNA-seq pipeline can be found at sequana/rnaseq.

This page is also a place for providing supplementary information to the main repositories.

Pipelines

Sequana provides a collection of NGS pipelines. Each pipeline lives in its own repository on the Sequana GitHub organisation. Below are some highlighted pipelines:

sequana_fastqc pipeline overview
sequana_fastqc
A quality-control pipeline that runs FastQC and MultiQC on raw FASTQ files to assess sequencing quality.
🔗 GitHub 📖 README GitHub stars 📄 Report
sequana_rnaseq pipeline overview
sequana_rnaseq
An RNA-seq pipeline that covers trimming, alignment, feature counting, and differential expression analysis.
🔗 GitHub 📖 README GitHub stars 📄 Report
sequana_variant_calling pipeline diagram placeholder
sequana_variant_calling
A variant-calling pipeline that performs read trimming, alignment, and SNP/indel detection using standard tools such as BWA and freebayes, with interactive HTML reports.
🔗 GitHub 📖 README GitHub stars 📄 Report
sequana_lora pipeline diagram placeholder
sequana_lora
A long-read assembly pipeline for Oxford Nanopore (and PacBio) data that performs basecalling, quality control, de-novo assembly, polishing, and assembly quality assessment with tools such as Flye, Medaka, and BUSCO.
🔗 GitHub 📖 README GitHub stars 📄 Report
sequana_multitax pipeline DAG
sequana_multitax
A taxonomic classification pipeline that runs multiple classifiers (Kraken2, Centrifuge, etc.) on sequencing reads, merges results, and produces comparative summary reports.
🔗 GitHub 📖 README GitHub stars
sequana_demultiplex pipeline diagram placeholder
sequana_demultiplex
A demultiplexing pipeline built around bcl2fastq / bcl-convert that converts Illumina BCL files into FASTQ files, validates sample sheets, and generates MultiQC summary reports.
🔗 GitHub 📖 README GitHub stars 📄 Report

Tools & Standalone Applications

Sequana ships published tools and a set of standalone sub-commands invocable directly from the command line (sequana <subcommand>), all distributed as part of the sequana Python package.

Sequana Coverage — genome depth-of-coverage analysis (GigaScience 2018 Fig. 1)
Sequana Coverage
Published in GigaScience — DOI: 10.1093/gigascience/giy110
Fast detection of genomic regions with abnormal depth of coverage. Models coverage with a mixture of distributions, highlights deletions, duplications, and low-complexity regions, and produces interactive HTML reports.
🔗 GitHub 📖 Docs
Sequanix — desktop GUI for Snakemake pipelines
Sequanix
Published in Bioinformatics — DOI: 10.1093/bioinformatics/bty034
Desktop GUI (PyQt5) for configuring and executing Snakemake pipelines without writing code. Dynamically renders widgets from config files and monitors pipeline progress in real time.
🔗 GitHub 📖 Docs
sequana taxonomy
Taxonomic classification of reads using Kraken2, with summary plots for metagenomics studies.
📖 Docs
sequana summary
Summary statistics (read counts, GC content, quality metrics) for FASTQ files without running a full pipeline.
📖 Docs
sequana enrichment
Gene Ontology and KEGG pathway enrichment analysis on differentially expressed genes, with interactive visualisations.
📖 Docs
sequana samplesheet
Validate and convert Illumina sample-sheet files, catching formatting errors before demultiplexing.
📖 Docs
sequana gtf_fixer
Fix common inconsistencies in GTF/GFF annotation files so they are compatible with downstream RNA-seq tools.
📖 Docs

Talks

Applications

Check your Illumina Sample Sheet

Report examples (click on the image)

The following was generated with the Sequana Coverage standalone application and keep here a it is reference in other places. This is the coverage along a virus genome where the black dot line represent the coverage, the blue is the median coverage, and red lines are the top and bottow thresholds for detecting event of interest.