boulardlab/3t-seq

A Snakemake workflow for single copy gene, transposable elements and tRNA expression analysis

Overview

Topics: bioinformatics-pipeline rna-seq-analysis snakemake transposable-elements trna

Latest release: v1.1.0, Last update: 2024-11-28

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Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.

When using Mamba, run

mamba create -c conda-forge -c bioconda --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/boulardlab/3t-seq . --tag v1.1.0

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

To run the workflow using a combination of conda and apptainer/singularity for software deployment, use

snakemake --cores all --sdm conda apptainer

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Configuration instructions

See below for an example config file with explanation of each option and description of common use-cases.

A complete example

# config/config.yaml

# A list of datasets 
# Every dataset is defined by a name, a path to a sample sheet, trimmomatic, star and bamCoverage options.
# All these options are mandatory.
sequencing_libraries:


    

  • name: GSE13073
sample_sheet: sample-sheet.csv
trimmomatic: >-
      “ILLUMINACLIP:TruSeq3-PE.fa:1:0:15:2
       SLIDINGWINDOW:20:22
       MAXINFO:20:0.6
       LEADING:22
       TRAILING:20
       MINLEN:75”
    star: >-
      “–seedSearchStartLmax 30
       –outFilterMismatchNoverReadLmax 0.04
       –winAnchorMultimapNmax 40”
    bamCoverage: ”–binSize 50 –normalizeUsing None”
    • 



#   - name: …
#     sample_sheet: …
#     trimmomatic: …
#     star: …
#     bamCoverage: …


# Disable all functionalities related to TE analysis
disable_TE_analysis: false


# Disable tRNA analysis
disable_tRNA_analysis: false


globals:
# path to reads folder 
# NB: ./GSE13073 is expected to exist
reads_folder: .


# path to results folder
results_folder: results/


# path to qc
qc_folder: results/qc


# path to log
log_folder: results/log


# path to references
references_folder: results/references


# temp folder
tmp_folder: /tmp


# path to analysis
analysis_folder: results/analysis


# genome informations
genome:
# genome label
label: mm10


# annotation type
# can be ensembl, mgi, gencode
annotation_type: ensembl


# URL or path to genome sequence
fasta_url: <Genome fasta URL>


# URL or path to genome annotation file
gtf_url: <Genome annotation URL>


# URL to gtRNAdb zip file
gtrnadb_url: <GtRNADb bundle URL>


# Differential expression analysis parameters
deseq2:
# wd
working_directory: ../../..  


# DESeq2 test name, can be Wald or LRT
test: Wald


# name of the column from sample sheet with experimental variable
variable: genotype


# base level from variable column
reference_level: wt

How 3t-seq resolves reads paths

The pipeline resolves reads paths starting from two bits of information:

  1. reads_folder in the globals sections
  2. The name of a library in sequencing_libraries list of objects

In the example configuration above, reads_folder: . and sequencing_libraries[0].name: GSE13073. These resolve to ./GSE13073. It is crucial that this folder exists before starting the pipeline. This is because in this folder, the pipeline will look for input files.

Another example:

sequencing_libraries:
  - name: first-batch
    sample_sheet: sample-sheet-first-batch.csv
    # [...]

    

  • name: second-batch
sample_sheet: sample-sheet-second-batch.csv
# […]
    • 



globals:
reads_folder: reads
# […]

In this scenario, 3t-seq will look for the reads folder and inside of it will look for two folders names first-batch and second-batch: reads/first-batch and reads/second-batch. If any of the two is not detected, the pipeline will fail.

Naming convetion

Reads files need to have one of the following extensions: fq, fq.gz, fastq, fastq.gz. For a given sequencing library, the pipeline expects files to have the same extension.

For paired-end reads, the two mates should have one of the following idenfiers before the extension: (_1, _2), (_R1, _R2), (_1_sequence, _2_sequence).

How to use local reference files

The references_folder can be outside of results_folder. For instance:

globals:
  # [...]
  # path to results folder
  results_folder: results/

# path to references
references_folder: /path/to/references

This allows users to host their own reference files locally and set genome informations accordingly

genome:
  # [...]
  # This will evaluate to /path/to/references/custom-mm10.fa.gz
  fasta_url: custom-mm10.fa.gz

# This will evaluate to /path/to/references/custom-mm10.gtf.gz
gtf_url: custom-mm10.gtf.gz

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