catg-umag/nanotax is a bioinformatics pipeline for the analysis of 16S rRNA gene sequencing data obtained by Nanopore sequencing. It takes a samplesheet with POD5 or FastQ files and barcodes (optional) and groups (optional) as input and performs basecalling and demultiplexing, quality control (QC), taxonomic assignment with databases, functional prediction and alpha diversity metrics and produces tables and plots with all the results. All the process are optional except taxonomic assignment.
The pipeline then:
- Basecalling and demultiplexing with (
Dorado). - Read QC (
FastQC) and (NanoQ). - Quality and length filter with (
NanoQ). - Sampling by quality with (
Filtlong) - Present QC for raw reads (
MultiQC) - Assigns taxonomy to reads using (
MMSeqs2) with (Genbank) or (SILVA) database. - (optionally) alpha diversity metrics with (
Vegan) (R). - (optionally) functional prediction with (
PICRUSt2) . - (optionally) differential expression for functional prediction with(
LEfSe).
All plots and tables are generated using Python, through either (pandas) or (polars).
Note
If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.
The columns in the sample file will vary depending on the analyses to be performed. Each row represents a sample, specifying either its associated FASTQ file or the barcode used during sequencing.
By default, the pipeline performs quality control and taxonomic assignment. For these tasks, the sample file must include the columns "samples" and "fastq".
sample,fastq
sample_1,sample_1.fastq.gz
sample_2,sample_2.fastq.gz
If you wish to start from the basecalling step, the fastq column should be replaced with barcode , and the directory containing the POD5 files must be specified using the --basecalling.pod5_dir parameter.
sample,barcode
sample_1,barcode01
sample_2,barcode02
To perform diversity analyses or differential expression of metabolic pathways, the sample file must include a groups column.
sample,fastq,group
sample_1,sample_1.fastq.gz,G1
sample_2,sample_2.fastq.gz,G2
Now, you can run the pipeline using:
nextflow run catg-umag/nanotax \
-profile <docker/apptainer/.../institute> \
--input samplesheet.csv \
--outdir <OUTDIR>Warning
Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.
The following parameters can be modified to enable or disable specific modules:
| Parameter | Type | Description | Default |
|---|---|---|---|
| basecalling.run | boolean | Enable run basecalling and demultiplexing | false |
| qc.run | boolean | Enable run quality check | true |
| diversity.run | boolean | Enable run diversity analysis module | true if samplesheet has groups |
| functional_pred.run | boolean | Enable run functional prediction | true if samplesheet has groups |
| exclude | list | samples that are not included in the analyses, but their quality and quantity of readings information will be reported | [] |
Each module has specific parameters that can be configured when enabled.
| Parameter | Type | Description | Default |
|---|---|---|---|
| basecalling.pod5_dir | string | directory containing POD5 files | input/pod5 |
| basecalling.gpus | integer | Number of GPUs to use | 1 |
| basecalling.dorado_basecalling_model | string | Basecalling model to use (fast, hac, sup) | sup |
| basecalling.qscore_filter | integer | Q-score threshold for passing and failing reads | 10 |
| basecalling.barcoding_kit | string | Barcoding kit used for multiplexing | SQK-16S114-24 |
| basecalling.save_reads | boolean | Save reads after basecalling and demultiplexing in the results directory | false |
| Parameter | Type | Description | Default |
|---|---|---|---|
| qc.subsampling | integer | Number of reads to sampling | 100000 |
| qc.min_length | integer | Minimum required length for a read | 1000 |
| qc.max_length | integer | Maximum allowed length for a read | 2000 |
| qc.min_qscore | integer | Minimum q-score | 15 |
| qc.save_reads | boolean | Save reads after quality control in the results directory | false |
| Parameter | Type | Description | Default |
|---|---|---|---|
| taxonomic_assignment.min_aln | integer | Minimum alignment length to retain an alignment | 1000 |
| taxonomic_assignment.min_identity | integer | Minimum sequence identity between the read and database hit (range: 0–1) | 0.95 |
| taxonomic_assignment.download_db | boolean | Download the database from the internet | true |
| taxonomic_assignment.db_name | string | Database name to use (genbank or silva) | genbank |
| taxonomic_assignment.db_dir | string | Directory containing the database (required if download_db is false) | `` (empty) |
The diversity and functional prediction modules do not have specific parameters associated with them.
catg-umag/nanotax was originally written by JacquelineAldridge.
If you would like to contribute to this pipeline, please see the contributing guidelines.
An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.
This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.
The nf-core framework for community-curated bioinformatics pipelines.
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.