PerSeq is an annotation workflow implemented in Snakemake and is designed to be copied to your analysis directory. Dependencies are defined in envs/required.yaml and are installed at runtime via Bioconda (snakemake --use-conda) or Biocontainers (snakemake --use-singularity).

To report a bug or suggest changes, please use the GitHub repository.

conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda
conda install python3 snakemake

Create your experimental working directory and clone the workflow:

mkdir soil-metag && cd $_
git clone https://github.com/PNNL-CompBio/perseq
cd perseq

Download the HMM reference libraries from Zenodo:

https://zenodo.org/record/1476722

Locally, you should have the files:

dbCAN-HMMdb-V7-ACC.hmm
HAMAP-ACC.hmm
TIGRFAMs_15_0_ACC.hmm

Their full paths will be specified in the configuration file.

It is recommended that all file paths within the configuration have absolute paths, that is, they all start with a slash. It is assumed conda is available in your $PATH and that you want to use the environment defined in the perseq workflow.

Edit the configuration file with your references then run snakemake:

snakemake --use-conda --jobs 8 --configfile config/config.yml

Results write into the current working directory, though an alternate may be specified:

snakemake --directory results \
    --use-conda --jobs 8 --configfile config/config.yml

snakemake --use-conda --jobs 999 --configfile config/config.yml \
    --cluster-config config/cluster.yml \
    --cluster "sbatch --account {cluster.account} --nodes {cluster.nodes} --time {cluster.time} --job-name {cluster.name}"

Sequences are merged (step 01) using bbmerge.sh of BBTools. Merging allows for read extensions up to 300 bp and will quality trim the sequences after successfully merging R1 and R2. Merged sequences are then deduplicated (step 02) using clumpify.sh of BBTools. Unique, merged sequences are then mapped against contaminant libraries (step 03) in the form of key:value pairs in the configuration. To filter against rRNA and PhiX, the specification looks like:

contaminant_references:
    PhiX: resources/phix.fa
    rRNA: resources/rrna.fa.gz

Additional references are added to the list like:

contaminant_references:
    PhiX: resources/phix.fa
    rRNA: resources/rrna.fa.gz
    Ecoli: /local/path/ecoli.fa

Hits to contaminant references are detailed in quality_control/<sample>_03_<contaminant key>.fasta.gz.

Quicker QC stats can be achieved by subsampling the FASTQ files using seqtk sample. To activate, add "subsample: value" to your config.yml file where value is the subsampled total. This step occurs prior to merging, and impacts all downstream analysis.

If the subsample value provided is negative, then subsampling will not occur.

Counts for each stage and initial sequence quality are summarized in the HTML report:

Kmer-based taxonomic classification is performed on the merged reads using Kaiju [5] in greedy mode (-a greedy -E 0.05) with the user supplied reference index. To create a Kaiju reference of NCBI's nr database containing reference sequences for archaea, bacteria, viruses, fungi, and microbial eukaryotes execute makeDB.sh -e of Kaiju's executable library.

mkdir kaiju_db
cd kaiju_db
makeDB.sh -e -t {threads}

In the config, set kaiju_db as the absolute path to this directory:

kaijudb: /full/path/kaiju_db

Expected files after building the database are:

• names.dmp
• nodes.dmp
• kaiju_db.fmi

To get a quick overview of the Kaiju assignments, perseq compiles absolute and relative abundance plots in the report:

Functions are assigned to the best hit per sequence to an HMM within dbCAN2, HAMAP, and TIGRFAMs. The initial e-value cutoff is 0.05 and is reported for the best hit to facilitate appropriate downstream filtering.

The references are available at:

https://zenodo.org/record/1476722