MetaCerberus 1.2

Versatile Functional Ontology Assignments for Metagenomes via Hidden Markov Model (HMM) searching with environmental focus of shotgun meta'omics data

Welcome to MetaCerberus

About

MetaCerberus transforms raw shotgun metaomics sequencing (i.e. metagenomics/metatranscriptomic) data into knowledge. It is a start to finish python code for versatile analysis of the Functional Ontology Assignments for Metagenomes (FOAM), KEGG, CAZy, VOG/pVOG, PHROG, and COG databases via Hidden Markov Models (HMM) for whole ecosystem metabolomic analysis. MetaCerberus also provides automatic differential statistics using DESeq2/EdgeR, pathway enrichments with GAGE, and pathway visualization with Pathview R.

Installing MetaCerberus

Option 1) Mamba

• Mamba install from bioconda with all dependencies:

Install mamba using conda

conda install mamba

• NOTE: Make sure you install mamba in your base conda environment

Install MetaCerberus with mamba

mamba create -n metacerberus -c bioconda -c conda-forge metacerberus
conda activate metacerberus
metacerberus.py --setup

• NOTE: Mamba is the fastest installer. Anaconda or miniconda can be slow. Also, install mamba from conda not from pip. The pip mamba doesn't work for install.

Option 2) Anaconda

• Anaconda install from bioconda with all dependencies:

conda create -n metacerberus -c conda-forge -c bioconda metacerberus -y
conda activate metacerberus
metacerberus.py --setup

Option 3) Manual Install

1. Clone github Repo

git clone https://github.com/raw-lab/metacerberus.git

2. Run Setup File

cd metacerberus
bash install_metacerberus.sh
conda activate metacerberus

This creates an anaconda environment called "metacerberus" with all dependencies installed.

Input formats

• From any NextGen sequencing technology (from Illumina, PacBio, Oxford Nanopore)
• type 1 raw reads (.fastq format)
• type 2 nucleotide fasta (.fasta, .fa, .fna, .ffn format), assembled raw reads into contigs
• type 3 protein fasta (.faa format), assembled contigs which genes are converted to amino acid sequence

Output Files

• If an output directory is given, that folder will be created where all files are stored.
• If no output directory is specified, the 'pipeline' subfolder will be created in the current directory.
• Gage/Pathview R analysis provided as separate scripts within R.

Visualization of Outputs

• We use Plotly to visualize the data
• Once the program is executed the html reports with the visuals will be saved to the last step of the pipeline.
• The HTML files require plotly.js to be present. One has been provided in the package and is saved to the report folder.

Quick start examples

Genome examples

All databases

conda activate metacerberus
metacerberus.py --prodigal lambda.fna --hmm "KOFam_all, COG, VOG, PHROG, CAZy" --dir_out lambda_dir

Only KEGG/FOAM all

conda activate metacerberus
metacerberus.py --prodigal lambda.fna --hmm "KOFam_all" --dir_out lambda_ko-only_dir

Only KEGG/FOAM prokaryotic centric

conda activate metacerberus
metacerberus.py --prodigal ecoli.fna --hmm "KOFam_prokaryote" --dir_out ecoli_ko-only_dir

Only KEGG/FOAM eukaryotic centric

conda activate metacerberus
metacerberus.py --fraggenescan human.fna --hmm "KOFam_eukaryote" --dir_out human_ko-only_dir

Only Viral/Phage databases

conda activate metacerberus
metacerberus.py --prodigal lambda.fna --hmm "VOG, PHROG" --dir_out lambda_vir-only_dir

• NOTE: You can pick any single database you want for your analysis including KOFam_all, COG, VOG, PHROG, CAZy or specific KO databases for eukaryotes and prokaryotes (KOFam_eukaryote or KOFam_prokaryote).

Illumina data

Bacterial, Archaea and Bacteriophage metagenomes/metatranscriptomes

conda activate metacerberus
metacerberus.py --prodigal [input_folder] --illumina --meta --dir_out [out_folder] 

Eukaryotes and Viruses metagenomes/metatranscriptomes

conda activate metacerberus
metacerberus.py --fraggenescan [input_folder] --illumina --meta --dir_out [out_folder] 

Nanopore data

Bacterial, Archaea and Bacteriophage metagenomes/metatranscriptomes

conda activate metacerberus
metacerberus.py --prodigal [input_folder]  --nanopore --meta --dir_out [out_folder]

Eukaryotes and Viruses metagenomes/metatranscriptomes

conda activate metacerberus
metacerberus.py --fraggenescan [input_folder] --nanopore --meta --dir_out [out_folder] 

PacBio data

Microbial, Archaea and Bacteriophage metagenomes/metatranscriptomes

conda activate metacerberus
metacerberus.py --prodigal [input_folder]  --pacbio --meta --dir_out [out_folder]

Eukaryotes and Viruses metagenomes/metatranscriptomes

conda activate metacerberus
metacerberus.py --fraggenescan [input_folder]  --pacbio --meta --dir_out [out_folder]

SUPER (both methods)

conda activate metacerberus
metacerberus.py --super [input_folder]  --pacbio/--nanopore/--illumina --meta --dir_out [out_folder]

• Note: Fraggenescan will work for prokaryotes and viruses/bacteriophage but prodigal will not work well for eukaryotes.

Prerequisites and dependencies

• python >= 3.8

Available from Bioconda

• fastqc

• fastp

• porechop

• bbmap

• prodigal

• HMMER

• NOTE: The KEGG database contains KOs related to Human disease. It is possible that these will show up in the results, even when analyzing microbes.

MetaCerberus databases

All pre-formatted databases are present at OSF

• OSF

Sources for databases for MetaCerberus

• KEGG/KOfams

• COG

• dbCAN/CAZy

• VOG

• pVOG

• PHROG

• NOTE: pfam, eggNOG, MEROPS, GVDB, and FunGene databases are coming soon. If you want a custom HMM build please let us know by email or leaving an issue.

MetaCerberus Options

• If the metacerberus environment is not used, make sure the dependencies are in PATH or specified in the config file.
• Run metacerberus.py with the options required for your project.

usage: metacerberus.py [-c CONFIG] [--prodigal PRODIGAL] [--fraggenescan FRAGGENESCAN] [--super SUPER] [--protein PROTEIN] [--illumina | --nanopore | --pacbio] [--setup]
                       [--uninstall] [--dir_out DIR_OUT] [--meta] [--scaffolds] [--minscore MINSCORE] [--evalue EVALUE] [--cpus CPUS] [--chunker CHUNKER] [--replace]
                       [--keep] [--hmm HMM] [--class CLASS] [--tmpdir TMPDIR] [--version] [-h] [--adapters ADAPTERS] [--qc_seq QC_SEQ]

options:
  --illumina            Specifies that the given FASTQ files are from Illumina
  --nanopore            Specifies that the given FASTQ files are from Nanopore
  --pacbio              Specifies that the given FASTQ files are from PacBio

Required arguments
At least one sequence is required.
<accepted formats {.fastq .fasta .faa .fna .ffn .rollup}>
Example:
> metaerberus.py --prodigal file1.fasta
> metacerberus.py --config file.config
*Note: If a sequence is given in .fastq format, one of --nanopore, --illumina, or --pacbio is required.:
  -c CONFIG, --config CONFIG
                        Path to config file, command line takes priority
  --prodigal PRODIGAL   Prokaryote nucleotide sequence (includes microbes, bacteriophage)
  --fraggenescan FRAGGENESCAN
                        Eukaryote nucleotide sequence (includes other viruses, works all around for everything)
  --super SUPER         Run sequence in both --prodigal and --fraggenescan modes
  --protein PROTEIN, --amino PROTEIN
                        Protein Amino Acid sequence

optional arguments:
  --setup               Set this flag to ensure dependencies are setup [False]
  --uninstall           Set this flag to remove downloaded databases and FragGeneScan+ [False]
  --dir_out DIR_OUT     path to output directory, creates "pipeline" folder. Defaults to current directory. [./results-metacerberus]
  --meta                Metagenomic nucleotide sequences (for prodigal) [False]
  --scaffolds           Sequences are treated as scaffolds [False]
  --minscore MINSCORE   Score cutoff for parsing HMMER results [25]
  --evalue EVALUE       E-value cutoff for parsing HMMER results [1e-09]
  --cpus CPUS           Number of CPUs to use per task. System will try to detect available CPUs if not specified [Auto Detect]
  --chunker CHUNKER     Split files into smaller chunks, in Megabytes [Disabled by default]
  --replace             Flag to replace existing files. [False]
  --keep                Flag to keep temporary files. [False]
  --hmm HMM             Specify the database for HMMER. (KOFam_all, KOFam_eukaryote, KOFam_prokaryote, COG, CAZy, PHROG, COG) [KOFam_all]
  --class CLASS         path to a tsv file which has class information for the samples. If this file is included scripts will be included to run Pathview in R
  --tmpdir TMPDIR       temp directory for RAY [system tmp dir]
  --version, -v         show the version number and exit
  -h, --help            show this help message and exit

  --adapters ADAPTERS   FASTA File containing adapter sequences for trimming
  --qc_seq QC_SEQ       FASTA File containing control sequences for decontamination

Args that start with '--' (eg. --prodigal) can also be set in a config file (specified via -c). Config file syntax allows: key=value, flag=true, stuff=[a,b,c] (for
details, see syntax at https://goo.gl/R74nmi). If an arg is specified in more than one place, then commandline values override config file values which override defaults.

GAGE / PathView

After processing the HMM files MetaCerberus calculates a KO (KEGG Orthology) counts table from KEGG/FOAM for processing through GAGE and PathView. GAGE is recommended for pathway enrichment followed by PathView for visualize the metabolic pathways. A "class" file is required through the --class option to run this analysis. The output is saved under the step_10-visualizeData/combined/pathview folder. Also, at least 4 samples need to be used for this type of analysis.

GAGE and PathView also require internet access to be able to download information from a database. MetaCerberus will save a bash script 'run_pathview.sh' in the step_10-visualizeData/combined/pathview directory along with the KO Counts tsv files and the class file for running manualy in case MetaCerberus was run on a cluster without access to the internet.

Multiprocessing / Multi-Computing with RAY

MetaCerberus uses Ray for distributed processing. This is compatible with both multiprocessing on a single node (computer) or multiple nodes in a cluster.
MetaCerberus has been tested on a cluster using Slurm https://github.com/SchedMD/slurm.

A script has been included to facilitate running MetaCerberus on Slurm. To use MetaCerberus on a Slurm cluster, setup your slurm script and run it using sbatch.

sbatch example_script.sh

example script:

#!/usr/bin/env bash

#SBATCH --job-name=test-job
#SBATCH --nodes=3
#SBATCH --tasks-per-node=1
#SBATCH --cpus-per-task=16
#SBATCH --mem=128MB
#SBATCH -e slurm-%j.err
#SBATCH -o slurm-%j.out
#SBATCH --mail-type=END,FAIL,REQUEUE

echo "====================================================="
echo "Start Time  : $(date)"
echo "Submit Dir  : $SLURM_SUBMIT_DIR"
echo "Job ID/Name : $SLURM_JOBID / $SLURM_JOB_NAME"
echo "Node List   : $SLURM_JOB_NODELIST"
echo "Num Tasks   : $SLURM_NTASKS total [$SLURM_NNODES nodes @ $SLURM_CPUS_ON_NODE CPUs/node]"
echo "======================================================"
echo ""

# Load any modules or resources here
conda activate metacerberus
# source the slurm script to initialize the Ray worker nodes
source ray-slurm-metacerberus.sh
# run MetaCerberus
metacerberus.py --prodigal [input_folder] --illumina --dir_out [out_folder]

echo ""
echo "======================================================"
echo "End Time   : $(date)"
echo "======================================================"
echo ""

Contributing to MetaCerberus and Fungene

MetaCerberus as a community resource as recently acquired FunGene, we welcome contributions of other experts expanding annotation of all domains of life (viruses, bacteria, archaea, eukaryotes). Please send us an issue on our MetaCerberus GitHub open an issue; or email us we will fully annotate your genome, add suggested pathways/metabolisms of interest, make custom HMMs to be added to MetaCerberus and FunGene.

Citing MetaCerberus

If you are publishing results obtained using MetaCerberus, please cite:
Figueroa JL, Dhungel E, Brouwer CR, White III RA. 2023.
MetaCerberus: distributed highly parallelized HMM-based processing for robust functional annotation across the tree of life. bioRxiv

CONTACT

The informatics point-of-contact for this project is Dr. Richard Allen White III.
If you have any questions or feedback, please feel free to get in touch by email.
Dr. Richard Allen White III
Jose Luis Figueroa
Or open an issue.