nf-core pipeline · genomics, quality-control
nf-core/seqinspector
A dedicated quality control pipeline for sequencing data, producing sample and group MultiQC reports.
What nf-core/seqinspector is
nf-core/seqinspector is a bioinformatics pipeline designed specifically for quality control (QC) of high-throughput sequencing data. It evaluates raw sequencing reads in FASTQ format, providing sample-level subsampling, quality assessment, duplication profiling, complexity assessment, and detection of adapter content, technical artifacts, and common biological contaminants.
Originally written by developers at the Swedish National Genomics Infrastructure and Clinical Genomics Stockholm, the pipeline is maintained by Maxime U Garcia. It is targeted at sequencing core facilities and research teams operating sequencing instruments with high data volumes.
The workflow unifies a wide collection of sequencing quality tools and aggregates their metrics into customisable MultiQC reports. These reports can be configured to display project-wide summaries, individual sample outputs, or cohort groupings, and can optionally incorporate instrument run folder statistics directly from Illumina sequencers.
What the pipeline does
Input sequencing reads are defined through a CSV samplesheet specifying single- or paired-end FASTQ files, run directories, and grouping tags. The pipeline first validates and lints the reads using fq, and can optionally subsample them with Seqtk or trim adapter sequences using Chelae or Fastp. Deduplication and sequence complexity metrics can also be gathered using BBMap Clumpify.
Quality assessment is conducted across reads using FastQC, Sequali, SeqFu Stats, and FastqScreen. Where reference data are provided, reads can be mapped via BWA-MEM2 and SAMtools to collect alignment metrics through Picard CollectMultipleMetrics, Picard CollectHsMetrics, or Riker. Contaminant profiling is supported via Kraken2 with Krona visualisation, and sequencer run metrics are evaluated with checkQC. Finally, metrics are aggregated and visualised in per-tag or global reports via MultiQC and multiqcsav.
Key facts
- Pipeline type
- Sequencing quality control
- Input formats
- FASTQ (single-end or paired-end), Illumina run directory (rundir)
- Data compatibility
- DNA, RNA, and synthetic sequencing data
- Output formats
- MultiQC reports (HTML), QC metric files
- Alignment tools
- BWA-MEM2 (v2.3), SAMtools (v1.24)
- QC tools
- FastQC, Sequali, SeqFu Stats, FastqScreen, checkQC, Picard, Riker
- Reporting tools
- MultiQC (v1.35), Krona (v2.8.1)
- Current version
- 1.1.2
- Maintained by
- Maxime U Garcia (National Genomics Infrastructure)
What you can use it for
- Core facility sequencing QC
- Process raw FASTQ files across entire sequencing runs to monitor run statistics, contamination levels, and lane quality across multiple projects.
- Hybrid-selection target QC
- Assess target enrichment and alignment metrics for capture sequencing data by calculating hybrid-selection statistics with Picard CollectHsMetrics.
- Contamination screening and taxonomic classification
- Detect biological contaminants and profile taxonomic read fractions using FastqScreen and Kraken2 paired with Krona visualization.
- Tag-based cohort reporting
- Generate segregated MultiQC reports for distinct project subsets, lanes, or experimental groups using metadata tags supplied in the samplesheet.
Run it here
The research agent runs nf-core/seqinspector with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Integrates a large collection of read-level and alignment-level quality control tools within a single automated pipeline.
- Supports tailored reporting by grouping MultiQC outputs through metadata tags specified in the input samplesheet.
- Capable of directly ingesting Illumina run folder directories to incorporate instrument-level run statistics into the final report.
- Handles DNA, RNA, and synthetic sequence data for both short-read and long-read quality control workflows.
Watch out for
- Subsampling, trimming, and deduplication steps require specific optional parameters or tool arguments to be explicitly enabled.
- Alignment-based QC modules such as Picard metrics and Riker depend on the provision of external reference genomes, FASTA files, or target interval files.
- Certain QC tools, such as checkQC, require an Illumina run directory and are not functional on isolated FASTQ files alone.
Questions
Can I run nf-core/seqinspector without my own cluster?
The research agent runs nf-core/seqinspector with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/seqinspector under?
MIT, like every nf-core pipeline.
What input format does the pipeline require?
It requires a CSV samplesheet defining the sample name, paths to FASTQ files (fastq_1 and optional fastq_2), an optional Illumina run directory path, and colon-separated tags for grouped reporting.
Can the pipeline process both DNA and RNA data?
Yes. Most integrated tools, including FastQC, Sequali, FastqScreen, and BWA-MEM2, are compatible with both DNA and RNA sequence data, while certain tools like Riker are restricted to DNA.
How are MultiQC reports grouped?
You can provide colon-separated labels in the 'tags' column of the input samplesheet, allowing the pipeline to output grouped MultiQC summaries alongside global reports.
Does the pipeline align reads automatically?
Yes, default mapping with BWA-MEM2 and SAMtools is included to support alignment-level QC metrics such as Picard CollectMultipleMetrics when reference data are supplied.
How to cite it
10.5281/zenodo.18757486
Written from nf-core/seqinspector's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.