atavide is a simple, yet complete workflow for metagenomics data analysis, including QC/QA, optional host removal, assembly and cross-assembly, and individual read based annotations. We have also built in some advanced analytics including tools to assign annotations from reads to contigs, and to generate metagenome-assembled genomes in several different ways, giving you the power to explore your data!

atavide is 100% snakemake and conda, so you only need to install the snakemake workflow, and then everything else will be installed with conda.

It is definitely a work in progress, but you can run it with the following command

snakemake --configfile config/atavide.yaml -s workflow/atavide.snakefile --profile slurm

But you will need a slurm profile to make this work!

1. Clone this repository from GitHub: git clone https://github.com/linsalrob/atavide.git
2. Set the location of the repository: export ATAVIDE_DIR=$PWD/atavide/
3. Install a few python dependencies. You probably already have most of these, but the one that trips up is pysam. We're working on getting conda configured properly to do this automatically. pip install -r $ATAVIDE_DIR/requirements.txt
4. Install the appropriate super-focus database [hint: probably version 2] and set the SUPERFOCUS_DB directory to point to the location of those files.
5. Copy the NCBI taxonomy (You really just need the taxdump.tar.gz file), and set the NCBI_TAXONOMY environment variable to point to the location of those files.
6. Have a directory of fastq files with both _R1_ and _R2_ files in a data directory: $DATA_DIR/fastq
7. Run atavide: cd $DATA_DIR && snakemake --configfile $ATAVIDE_DIR/atavide.yaml -s $ATAVIDE_DIR/workflow/atavide.snakefile --profile slurm

1. QC/QA with prinseq
2. optional host removal using bowtie2 and samtools, as described previously. To enable this, you need to provide a path to the host db and a host db.

1. pairwise assembly of each sample using megahit
2. extraction of all reads that do not assemble using samtools flags
3. assembly of all unassembled reads using megahit
4. compilation of all contigs into a single unified set using Flye
5. comparison of reads -> contigs to generate coverage

1. metabat
2. concoct
3. Pairwise comparisons using turbocor followed by clustering

1. Kraken2
2. singlem
3. SUPER-focus
4. FOCUS

Want something else added to the suite? File an issue on GitHub and we'll add it ASAP!