×

nf-core/tbanalyzer

An nf-core (meta) pipeline for analysis of different members of Mycobacterium tuberculosis complex.

mycobacterium-tuberculosis mycobacterium-tuberculosis-complex
Version: 0.0.0-7e2936d Git revision: 7e2936d3bf3b50253b9682494702d994c97b1266 Commit: Content hash:
Run:
Repository: nf-core/tbanalyzer
Workflow: main.nf
Website: https://nf-co.re/tbanalyzer

nf-core/tbanalyzer

Open in GitHub Codespaces GitHub Actions CI Status GitHub Actions Linting StatusAWS CICite with Zenodo nf-test

Nextflow nf-core template version run with conda run with docker run with singularity Launch on Seqera Platform

Get help on SlackFollow on BlueskyFollow on MastodonWatch on YouTube

Introduction

nf-core/tbanalyzer is a bioinformatics pipeline that ...

1. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))2. Present QC for raw reads ([`MultiQC`](http://multiqc.info/))

Usage

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.

Now, you can run the pipeline using:

nextflow run nf-core/tbanalyzer \
   -profile <docker/singularity/.../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.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/tbanalyzer was originally written by @abhi18av.

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #tbanalyzer channel (you can join with this invite).

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

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.

51 parameter(s) in 6 group(s)

MTBseq options#

MTBseq variant-calling and reference settings. These are exposed as parameters so they can be swept via a -params-file for parameter optimization.

--mtbseq_path#

Name/path of the MTBseq executable inside the container. Rarely needs changing.

type string
default MTBseq
--mtbseq_user#
type string
default root
--mtbseq_window#

Window size in which multiple variants within the same sample cause the positions to be excluded.

type integer
default 12
--mtbseq_mincovf#

Minimum forward read coverage threshold. Alleles with lower forward coverage are not considered.

type integer
default 4
--mtbseq_mincovr#

Minimum reverse read coverage threshold. Alleles with lower reverse coverage are not considered.

type integer
default 4
--mtbseq_minfreq#

Minimum allele frequency (percentage) for an allele to be called.

type integer
default 75
--mtbseq_project#

Project/analysis name used by MTBseq to label output files.

type string
default mtbseqnf
--mtbseq_unambig#

Minimum percentage of samples with unambiguous information for a position.

type integer
default 95
--mtbseq_distance#

SNP distance used to classify samples into groups via agglomerative clustering.

type integer
default 12