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genomic-medicine-sweden / tomte

A nextflow pipeline for analysing expression and splicing in RNA seq data from rare disease patient
MIT License
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tomte tomte

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Nextflow run with conda run with docker run with singularity Launch on Seqera Platform

Introduction

genomic-medicine-sweden/tomte is a bioinformatics best-practice analysis pipeline to analyse RNAseq from raredisease patients.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

Pipeline summary

TrancriptOMe Total Evaluation (TOMTE):

  1. Trim reads (FASTP)
  2. Transcript quantification (Salmon)
  3. Align reads to the genome (STAR)
  4. Output junction tracks
  5. Output bigwig (UCSC wigToBigWig)
  6. Choice to subsample overrepresented regions (Samtools)
  7. Choice to downsample number of reads (Samtools)
  8. Detection of aberrant expression (DROP)
  9. Detection of aberrant splicing (DROP)
  10. Filter aberrant expression and aberrant splicing results
  11. Guided transcript assembly (StringTie)
  12. Filtering results of guided transcript assembly (GffCompare)
  13. To Call SNVs either path a or b can be followed. Path A will run by default a. Call SNVs
    1. (BCFtools Mpileups)
  14. b. Call SNVs
    1. Split cigar reads (SplitN Cigar Reads)
    2. Haplotype caller (Haplotype Caller)
    3. Variant filtration (Variant Filtration)
    4. BCFtools statistics (BCFtools stats)
  15. Allele Specific Read Counter (ASEReadCounter)
  16. Assess allelic imbalance (BootstrapAnn)
  17. Annotation (VEP)
  18. Alignment QC (Picard CollectRnaSeqMetrics)
  19. Present QCs (MultiQC)

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.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

case,sample,fastq_1,fastq_2,strandedness
case_id,sample_id,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,reverse

Each row represents a pair of fastq files (paired end).

-->

Now, you can run the pipeline using:

nextflow run genomic-medicine-sweden/tomte \
   -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

For more details about the output files and reports, please refer to the output documentation.

Credits

genomic-medicine-sweden/tomte was written by Clinical Genomics Stockholm, Sweden, with major contributions from Lucía Peña-Pérez, Anders Jemt, and Jesper Eisfeldt.

Additional contributors were Ramprasad Neethiraj, Felix Lenner, Jakob Willforss, Esmee ten Berk de Boer, Vadym Ivanchuk, and Mei Wu.

We thank the nf-core community 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 by opening an issue.

Citations

If you use genomic-medicine-sweden/tomte for your analysis, please cite it using the following doi: 10.5281/zenodo.10828946

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.