metaT: The Metatranscriptome Workflow

Summary

This workflow is designed to analyze metatranscriptomes.

All parts of this workflow are housed in their own repositories and imported via WDL v1.0 https importing. The following repositories are used in this workflow:

Version

0.0.6

Third party tools and packages

To run this workflow you will need a Docker (Docker ≥ v2.1.0.3) instance and cromwell. All the third party tools are pulled from Dockerhub.

cromwell ≥ 54
bbtools ≥ v38.94
Python ≥ v3.7.12
pandas ≥ v1.0.5 (python package)
gffutils ≥ v0.10.1 (python package)

Databases

metaT uses the same database uses for metagenome annotation. See README here for required databases. For QC databases see here

Running workflow

In a server with shifter

The submit script will request a node and launch the Cromwell. The Cromwell manages the workflow by using Shifter to run applications.

java -Dconfig.file=wdls/shifter.conf -jar /full/path/to/cromwell-XX.jar run -i input.json /full/path/to/wdls/metaT.wdl

Docker images

microbiomedata/meta_t:0.0.5
bryce911/bbtools:38.86

Inputs

{
    "metaT.input_files": ["./test_data/small_test/test_small_interleave.fastq.gz"],
    "metaT.project_id":"nmdc:xxxxxxx",
    "metaT.strand_type": "aRNA"
}

Input option descriptions:

project_id: A unique name for your project or sample.
input_files: Full path to the fastq file. The file must be intereleaved paired end fastq.
strand_type: (optional) RNA strandedness, either left blank, aRNA, or non_stranded_RNA

Outputs

All outputs can be found in the outdir folder. There are following subfolders:

outdir/annotation: contains gff files from annotation run.
outdir/assembly: contains FASTA files from assembly and BAM files where reads were mapped back to the contigs.
outdir/readMapping: JSON files for sense and antisense that have records for feature, their annotations, read counts, ans associated statistics.
outdir/readsQC: contains cleaned reads and a file with associated statistics.

Output JSON

The output file is a JSON formatted file called out.json with JSON records that contains reads and information from annotation. An example JSON record:

        {
        "featuretype": "CDS",
        "seqid": "nmdc:xxxxxxx_001",
        "id": "nmdc:xxxxxxx_001_1_588",
        "source": "Prodigal v2.6.3_patched",
        "start": 1,
        "end": 588,
        "length": 588,
        "strand": "+",
        "frame": "0",
        "product": "hypothetical protein",
        "product_source": "Hypo-rule applied",
        "sense_read_count": 25,
        "mean": 5.0,
        "median": 3.0,
        "stdev": 6.1,
        "antisense_read_count": 28,
        "meanA": 7.14,
        "medianA": 7,
        "stdevA": 5.7
    }

Test

To test the workflow, we have provided a small test dataset and a step by step guidance. See test_data folder.

microbiomedata/metaT