Snakemake workflow: mt_quant

A Snakemake workflow for mt_quant:

• Preprocessing:
  • Trim reads with fastp
  • Remove rRNAs with ribodetector
  • Remove host RNA with STAR
  • Screen your reads with kraken2
• Quantification:
  • Map reads to a MAG catalogue with bowtie2
  • Get count tables with CoverM
• Report
  • Get a gazillion of reports with samtools, fastqc and multiqc

Usage

1. Make sure you have conda, mamba and snakemake installed.

   conda --version
   mamba --version
   snakemake --version

2. Clone this git repository and get it

   git clone https://github.com/3d-omics/mt_quant
   cd mt_quant

3. Test your installation by running the pipeline with test data. It will download all the necessary software through conda / mamba. It should take less than five minutes.

   ./run

4. Run it with your own data:

   1. Edit config/samples.tsv and add your sample names, a library identifier in case you have more than one file per sample, their paths and adapters used.

      sample_id	library_id	forward_filename	reverse_filename	forward_adapter	reverse_adapter
      sample1	1	resources/reads/GBRF1.1_1.fq.gz	resources/reads/GBRF1.1_2.fq.gz	AGATCGGAAGAGCACACGTCTGAACTCCAGTCA	AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
      sample2	1	resources/reads/GBRM1.1_1.fq.gz	resources/reads/GBRM1.1_2.fq.gz	AGATCGGAAGAGCACACGTCTGAACTCCAGTCA	AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT

   2. Edit config/features.yml with your reference hosts, mags and external databases. You can have multiple hosts and multiple catalogues. You can even have no host files in case you are analyzing environmental samples.

      hosts:  # Comment the next lines of no host
          human:
              genome: resources/reference/chrX_sub.fa.gz
              gtf: resources/reference/chrX_sub.gtf.gz
      
      mag_catalogues:
          mag1: resources/reference/mags_mock.fa.gz
          # mag2: resources/reference/mags_mock.fa.gz
      
      databases:
          kraken2:  # Comment the next lines if no database
              mock: resources/databases/kraken2/kraken_mock
              # mock2: resources/databases/kraken2/kraken_mock

   3. Edit config/params.yml with the execution parameters. The defaults are reasonable.

5. Run the pipeline

   ./run -j8  # locally with 8 cpus
   ./run_slurm  # on a cluster with slurm

References

• fastp
• ribodetector
• STAR
• bowtie2
• samtools
• CoverM
• FastQC
• multiqc