Aftekas (meta) -- a minimal metagenomic workflow

This Nextflow workflow was conceived to implement more efficient execution of the nf-core/mag workflow in slurm hpc cluster.

1. The main feature is the use of array jobs when executed in slurm cluster.
2. Many data munging steps are moved into the customised local modules.
3. The workflow is simple and devoid of forking paths:
   • uses only double-ended short reads,
   • uses fastp to trim raw reads,
   • always maps reads to host genome and phix
   • uses MEGAHIT for assembly
   • uses MAXBIN2, METABAT2, VAMB, CONCOCT for binning
   • uses BINETTE for bin refinement
   • assigns taxonomy to refined bins using GTDBTk.

By default, aftekas uses the "human-t2t-hla masked with 150mers for 985 FDA-ARGOS bacterial, 18,719 RefSeq viral, and 26,928 Millard Lab phage genomes" reference genome and bowtie2 index from the bede/hostile.

Usage

Set up databases

1. Download and setup CheckM2 database as instructed in https://github.com/chklovski/CheckM2.

2. Download and setup GTDBTk database (release 226, recommended) as instructed in https://ecogenomics.github.io/GTDBTk/installing/index.html.

Set up sample data

Sample data file must have three columns: sample, fastq_1, fastq_2 e.g.

sample,fastq_1,fastq_2
EV25,data/EV25_L1_resampled_1.fq.gz,data/EV25_L1_resampled_2.fq.gz
EV25,data/EV25_L3_resampled_1.fq.gz,data/EV25_L3_resampled_2.fq.gz

In the example above the sample EV25 was spread to two lines during sequencing and is merged by the workflow before trimming. Assigning them unique sample ids keeps them separate if desired.

Running

nextflow pull tpall/aftekas

Locally, this workflow can be run using docker, assuming that java, nextflow and docker are running:

nextflow run tpall/aftekas --input my_samples.csv --checkm2_db "<path to directory with>/CheckM2_database/uniref100.KO.1.dmnd" --gtdbtk_db "<path to directory with>/release226" --fastp_dedup --fastp_trim_polyg

In a slurm cluster: activate required software (java, nextflow, singularity) and run e.g.:

nextflow run tpall/aftekas -profile cluster --input my_samples.csv --array_size 4 --queue main -resume --checkm2_db "<path to directory with>/CheckM2_database/uniref100.KO.1.dmnd" --gtdbtk_db "<path to directory with>/release226" --fastp_dedup --fastp_trim_polyg 

Above, we have four unique sample ids in my_samples.csv, therefore --array_size 4.

Todo

Further analyses are open-ended. The original idea was to integrate DRAM for metabolic characterisation. Still, since its 2nd version is going to be a Nextflow workflow itself, it's probably easier to run it separately using the final bins as input. The same holds for the virus identification workflow and the AMR workflow (nf-core/funcscan).