Bioinformatics guidance page

MetaCerberus

Introduction

MetaCerberus transforms raw sequencing (i.e. genomic, transcriptomics, metagenomics, metatranscriptomic) data into knowledge (Figueroa III et al. 2024). It is a start to finish python code for versatile analysis of the Functional Ontology Assignments for Metagenomes (FOAM), KEGG, CAZy/dbCAN, VOG, pVOG, PHROG, COG, and a variety of other databases including user customized databases via Hidden Markov Models (HMM) for functional annotation for complete metabolic analysis across the tree of life (i.e., bacteria, archaea, phage, viruses, eukaryotes, and whole ecosystems). MetaCerberus also provides automatic differential statistics using DESeq2/EdgeR, pathway enrichments with GAGE, and pathway visualization with Pathview R.

For more information, please visit the software’s github page.

This software contains the following databases as of August 2024:

Database	Last Update	Version	Publication	MetaCerberus Update Version
KEGG/KOfams	2024-01-01	Jan24	Aramaki et al. 2020	beta
FOAM/KOfams	2017	1	Prestat et al. 2014	beta
COG	2020	2020	Galperin et al. 2020	beta
dbCAN/CAZy	2023-08-02	12	Yin et al., 2012	beta
VOG	2017-03-03	80	Website	beta
pVOG	2016	2016	Grazziotin et al. 2017	1.2
PHROG	2022-06-15	4	Terizan et al., 2021	1.2
PFAM	2023-09-12	36	Mistry et al. 2020	1.3
TIGRfams	2018-06-19	15	Haft et al. 2003	1.3
PGAPfams	2023-12-21	14	Tatusova et al. 2016	1.3
AMRFinder-fams	2024-02-05	2024-02-05	Feldgarden et al. 2021	1.3
NFixDB	2024-01-22	2	Bellanger et al. 2024	1.3
GVDB	2021	1	Aylward et al. 2021	1.3
Pads Arsenal	2019-09-09	1	Zhang et al. 2020	Coming soon
efam-XC	2021-05-21	1	Zayed et al. 2021	Coming soon
NMPFams	2021	1	Baltoumas et al. 2024	Coming soon
MEROPS	2017	1	Rawlings et al. 2018	Coming soon
FESNov	2024	1	Rodríguez del Río et al. 2024	Coming soon

Installation

Installed on crunchomics: Yes,

MetaCerberus v1.3.2 is installed as part of the bioinformatics share. If you have access to crunchomics and have not yet access to the bioinformatics you can send an email with your Uva netID to Nina Dombrowski, n.dombrowski@uva.nl.
Afterwards, you can add the bioinformatics share as follows (if you have already done this in the past, you don’t need to run this command):

conda config --add envs_dirs /zfs/omics/projects/bioinformatics/software/miniconda3/envs/

If you want to install it yourself, you can run:

mamba create -n metacerberus_1.3.2 -c conda-forge -c bioconda metacerberus

conda activate metacerberus_1.3.2 

metacerberus.py --setup #Setting up FragGeneScanRS
metacerberus.py --download # Downloading required databases

conda deactivate

Usage

General usage notes:

MetaCerberus can use three different input files:
1. raw read data from any sequencing platform (Illumina, PacBio, or Oxford Nanopore),
2. assembled contigs, as MAGs, vMAGs, isolate genomes, or a collection of contigs,
3. amino acid fasta (.faa), previously called pORFs.
In QC mode, raw reads are quality controlled via FastQC prior and post trim FastQC. Raw reads are then trimmed via data type; if the data is Illumina or PacBio, fastp is called, otherwise it assumes the data is Oxford Nanopore then Porechop is utilized PoreChop.
In the formatting and gene prediction stage, contigs and genomes are checked for N repeats. These N repeats are removed by default.
Contigs can be converted to pORFs using Prodigal, FragGeneScanRs, and Prodigal-gv as specified by user preference.

Example usage on a folder with two protein fasta files:

conda activate metacerberus_1.3.2 

mkdir metacerberus

# Run MetaCerberus on a folder with two protein files
metacerberus.py --protein faa/ --hmm ALL --dir_out metacerberus --cpus 20 

conda deactivate

When running this example, the results will be stored in a metacerberus folder. Inside this folder, the final folder will contain:

File Extension	Description Summary	MetaCerberus Update Version
.gff	General Feature Format	1.3
.gbk	GenBank Format	1.3
.fna	Nucleotide FASTA file of the input contig sequences.	1.3
.faa	Protein FASTA file of the translated CDS/ORFs sequences.	1.3
.ffn	FASTA Feature Nucleotide file, the Nucleotide sequence of translated CDS/ORFs.	1.3
.html	Summary statistics and/or visualizations, in step 10 folder	1.3
.txt	Statistics relating to the annotated features found.	1.3
level.tsv	Various levels of hierachical steps that is tab-separated file from various databases	1.3
rollup.tsv	All levels of hierachical steps that is tab-separated file from various databases	1.3
.tsv	Final Annotation summary, Tab-separated file of all features from various databases	1.3

Since final_annotation_summary.tsv only provides the best hit across all databases, we provide two small scripts that you can run if you want to generate a table that allows you to compare the annotations across all databases for each protein. You can run this as follows (only tested on protein files so far):

# Combine results from individual database folders for each protein file
for i in metacerberus/final/Protein_*_protein; do
    python /zfs/omics/projects/bioinformatics/scripts/merge_metacerberus_individual_dbs.py -i ${i} -o merged_annotations.tsv
done

# Concatenate the merged results into one document
python /zfs/omics/projects/bioinformatics/scripts/combine_metacerberus_annotations.py -b metacerberus/final/ -o combined_annotations.tsv

Options:

usage: metacerberus.py [--setup] [--update] [--list-db] [--download [DOWNLOAD ...]] [--uninstall] [-c CONFIG] [--prodigal PRODIGAL [PRODIGAL ...]]
                       [--fraggenescan FRAGGENESCAN [FRAGGENESCAN ...]] [--super SUPER [SUPER ...]] [--prodigalgv PRODIGALGV [PRODIGALGV ...]]
                       [--phanotate PHANOTATE [PHANOTATE ...]] [--protein PROTEIN [PROTEIN ...]] [--hmmer-tsv HMMER_TSV [HMMER_TSV ...]] [--class CLASS]
                       [--illumina | --nanopore | --pacbio] [--dir-out DIR_OUT] [--replace] [--keep] [--tmpdir TMPDIR] [--hmm HMM [HMM ...]] [--db-path DB_PATH] [--meta]
                       [--scaffolds] [--minscore MINSCORE] [--evalue EVALUE] [--skip-decon] [--skip-pca] [--cpus CPUS] [--chunker CHUNKER] [--grouped] [--version] [-h]
                       [--adapters ADAPTERS] [--qc_seq QC_SEQ]

Setup arguments:
  --setup               Setup additional dependencies [False]
  --update              Update downloaded databases [False]
  --list-db             List available and downloaded databases [False]
  --download [DOWNLOAD ...]
                        Downloads selected HMMs. Use the option --list-db for a list of available databases, default is to download all available databases
  --uninstall           Remove downloaded databases and FragGeneScan+ [False]

Input files
At least one sequence is required.
    accepted formats: [.fastq, .fq, .fasta, .fa, .fna, .ffn, .faa]
Example:
> metacerberus.py --prodigal file1.fasta
> metacerberus.py --config file.config
*Note: If a sequence is given in [.fastq, .fq] format, one of --nanopore, --illumina, or --pacbio is required.:
  -c CONFIG, --config CONFIG
                        Path to config file, command line takes priority
  --prodigal PRODIGAL [PRODIGAL ...]
                        Prokaryote nucleotide sequence (includes microbes, bacteriophage)
  --fraggenescan FRAGGENESCAN [FRAGGENESCAN ...]
                        Eukaryote nucleotide sequence (includes other viruses, works all around for everything)
  --super SUPER [SUPER ...]
                        Run sequence in both --prodigal and --fraggenescan modes
  --prodigalgv PRODIGALGV [PRODIGALGV ...]
                        Giant virus nucleotide sequence
  --phanotate PHANOTATE [PHANOTATE ...]
                        Phage sequence
  --protein PROTEIN [PROTEIN ...], --amino PROTEIN [PROTEIN ...]
                        Protein Amino Acid sequence
  --hmmer-tsv HMMER_TSV [HMMER_TSV ...]
                        Annotations tsv file from HMMER (experimental)
  --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
  --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

Output options:
  --dir-out DIR_OUT     path to output directory, defaults to "results-metacerberus" in current directory. [./results-metacerberus]
  --replace             Flag to replace existing files. [False]
  --keep                Flag to keep temporary files. [False]
  --tmpdir TMPDIR       temp directory for RAY (experimental) [system tmp dir]

Database options:
  --hmm HMM [HMM ...]   A list of databases for HMMER. Use the option --list-db for a list of available databases [KOFam_all]
  --db-path DB_PATH     Path to folder of databases [Default: under the library path of MetaCerberus]

optional arguments:
  --meta                Metagenomic nucleotide sequences (for prodigal) [False]
  --scaffolds           Sequences are treated as scaffolds [False]
  --minscore MINSCORE   Score cutoff for parsing HMMER results [60]
  --evalue EVALUE       E-value cutoff for parsing HMMER results [1e-09]
  --remove-n-repeats    Remove N repeats, splitting contigs [False]
  --skip-decon          Skip decontamination step. [False]
  --skip-pca            Skip PCA. [False]
  --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]
  --grouped             Group multiple fasta files into a single file before processing. When used with chunker can improve speed
  --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 '--' 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). In general, command-line values override config file values which override defaults.

Common Issues and Solutions

Issue 1: : The files providing counts have an issue an only provide counts of 1 with v1.3.2
- Solution 1: For now do not use these files, the authors of the software are aware

References

Figueroa III, Jose L, Eliza Dhungel, Madeline Bellanger, Cory R Brouwer, and Richard Allen White III. 2024. “MetaCerberus: Distributed Highly Parallelized HMM-Based Processing for Robust Functional Annotation Across the Tree of Life.” Edited by Inanc Birol. Bioinformatics 40 (3). https://doi.org/10.1093/bioinformatics/btae119.