snakemake-bacterial-riboseq

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A Snakemake workflow for the analysis of bacterial riboseq data.

• snakemake-bacterial-riboseq
  • Usage
  • Workflow overview
  • Deployment options
  • Running the workflow
    • Input data
      • Reference genome
      • Read data
  • Authors
  • References

Usage

The usage of this workflow is described in the Snakemake Workflow Catalog.

Detailed information about input data and workflow configuration can be found in the config/README.md.

If you use this workflow in a paper, don't forget to give credits to the author(s) by citing the URL of this repository, the release, and its DOI if available.

Workflow overview

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This workflow is a best-practice workflow for the analysis of ribosome footprint sequencing (Ribo-Seq) data.

The workflow is built using snakemake and consists of the following steps:

1. Obtain genome database in fasta and gff format (python, NCBI Datasets)
   1. Using automatic download from NCBI with a RefSeq ID
   2. Using user-supplied files
2. Check quality of input sequencing data (FastQC)
3. Cut adapters and filter by length and/or sequencing quality score (cutadapt)
4. Deduplicate reads by unique molecular identifier (UMI, umi_tools)
5. Map reads to the reference genome (STAR aligner)
6. Sort and index for aligned seq data (samtools)
7. Filter reads by feature type (bedtools)
8. Generate summary report for all processing steps (MultiQC)
9. Shift ribo-seq reads according to the ribosome's P-site alignment (R, ORFik)
10. Calculate basic gene-wise statistics such as RPKM (R, ORFik)
11. Return report as HTML and PDF files (R markdown, weasyprint)

If you want to contribute, report issues, or suggest features, please get in touch on github.

Deployment options

To run the workflow from command line, change the working directory.

cd path/to/snakemake-bacterial-riboseq

Adjust options in the default config file config/config.yml. Before running the complete workflow, you can perform a dry run using:

snakemake --dry-run

To run the workflow with test files using conda:

snakemake --cores 2 --sdm conda --directory .test

To run the workflow with test files using apptainer:

snakemake --cores 2 --sdm conda apptainer --directory .test

Running the workflow

Input data

Reference genome

An NCBI Refseq ID, e.g. GCF_000006945.2. Find your genome assembly and corresponding ID on NCBI genomes. Alternatively use a custom pair of *.fasta file and *.gff file that describe the genome of choice.

Important requirements when using custom *.fasta and *.gff files:

• *.gff genome annotation must have the same chromosome/region name as the *.fasta file (example: NC_003197.2)
• *.gff genome annotation must have gene and CDS type annotation that is automatically parsed to extract transcripts
• all chromosomes/regions in the *.gff genome annotation must be present in the *.fasta sequence
• but not all sequences in the *.fasta file need to have annotated genes in the *.gff file

Read data

Ribosome footprint sequencing data in *.fastq.gz format. The currently supported input data are single-end, strand-specific reads. Input data files are supplied via a mandatory table, whose location is indicated in the config.yml file (default: samples.tsv). The sample sheet has the following layout:

sample	condition	replicate	fq1
RPF-RTP1	RPF-RTP	1	data/RPF-RTP1_R1_001.fastq.gz
RPF-RTP2	RPF-RTP	2	data/RPF-RTP2_R1_001.fastq.gz

Some configuration parameters of the pipeline may be specific for your data and library preparation protocol. The options should be adjusted in the config.yml file. For example:

• Minimum and maximum read length after adapter removal (see option cutadapt: default). Here, the test data has a minimum read length of 15 + 7 = 22 (2 nt on 5'end + 5 nt on 3'end), and a maximum of 45 + 7 = 52.
• Unique molecular identifiers (UMIs). For example, the protocol by McGlincy & Ingolia, 2017 creates a UMI that is located on both the 5'-end (2 nt) and the 3'-end (5 nt). These UMIs are extracted with umi_tools (see options umi_extraction: method and pattern).

Example configuration files for different sequencing protocols can be found in resources/protocols/.

Authors

• Dr. Rina Ahmed-Begrich
  • Affiliation: Max-Planck-Unit for the Science of Pathogens (MPUSP), Berlin, Germany
  • ORCID profile: https://orcid.org/0000-0002-0656-1795
  • github page: https://github.com/rabioinf
• Dr. Michael Jahn
  • Affiliation: Max-Planck-Unit for the Science of Pathogens (MPUSP), Berlin, Germany
  • ORCID profile: https://orcid.org/0000-0002-3913-153X
  • github page: https://github.com/m-jahn

Visit the MPUSP github page at https://github.com/MPUSP for more info on this workflow and other projects.

References

• Essential tools are linked in the top section of this document
• The sequencing library preparation is based on the publication:

McGlincy, N. J., & Ingolia, N. T. Transcriptome-wide measurement of translation by ribosome profiling. Methods, 126, 112–129, 2017. https://doi.org/10.1016/J.YMETH.2017.05.028.