Workflow Type: Nextflow
Stable

Nextflow run with docker run with singularity

mettannotator

Introduction

mettannotator is a bioinformatics pipeline that generates an exhaustive annotation of prokaryotic genomes using existing tools. The output is a GFF file that integrates the results of all pipeline components. Results of each individual tool are also provided.

Workflow and tools



The workflow uses the following tools and databases:

Tool/Database Version Purpose
Prokka 1.14.6 CDS calling and functional annotation (default)
Bakta 1.9.3 CDS calling and functional annotation (if --bakta flag is used)
Bakta db 2024-01-19 with AMRFinderPlus DB 2024-01-31.1 Bakta DB (when Bakta is used as the gene caller)
InterProScan 5.62-94.0 Protein annotation (InterPro, Pfam)
eggNOG-mapper 2.1.11 Protein annotation (eggNOG, KEGG, COG, GO-terms)
eggNOG DB 5.0.2 Database for eggNOG-mapper
UniFIRE 2023.4 Protein annotation
AMRFinderPlus 3.12.8 Antimicrobial resistance gene annotation; virulence factors, biocide, heat, acid, and metal resistance gene annotation
AMRFinderPlus DB 3.12 2024-01-31.1 Database for AMRFinderPlus
DefenseFinder 1.2.0 Annotation of anti-phage systems
DefenseFinder models 1.2.3 Database for DefenseFinder
GECCO 0.9.8 Biosynthetic gene cluster annotation
antiSMASH 7.1.0 Biosynthetic gene cluster annotation
SanntiS 0.9.3.4 Biosynthetic gene cluster annotation
run_dbCAN 4.1.2 PUL prediction
dbCAN DB V12 Database for run_dbCAN
CRISPRCasFinder 4.3.2 Annotation of CRISPR arrays
cmscan 1.1.5 ncRNA predictions
Rfam 14.9 Identification of SSU/LSU rRNA and other ncRNAs
tRNAscan-SE 2.0.9 tRNA predictions
pyCirclize 1.4.0 Visualise the merged GFF file
VIRify 2.0.0 Viral sequence annotation (runs separately)
Mobilome annotation pipeline 2.0 Mobilome annotation (runs separately)

Installation and dependencies

This workflow is built using Nextflow. It uses containers (Docker or Singularity) making installation simple and results highly reproducible.

Although it's possible to run the pipeline on a personal computer, due to the compute requirements, we encourage users to run it on HPC clusters. Any HPC scheduler supported by Nextflow is compatible; however, our team primarily uses Slurm and IBM LSF for the EBI HPC cluster, so those are the profiles we ship with the pipeline.

Reference databases

The pipeline needs reference databases in order to work, they take roughly 180G.

Path Size
amrfinder 217M
antismash 9.4G
bakta 71G
dbcan 7.5G
defense_finder 242M
eggnog 48G
interproscan 45G
interpro_entry_list 2.6M
rfam_models 637M
total 180G

mettannotator has an automated mechanism to download the databases using the --dbs flag. When this flag is provided, the pipeline inspects the folder to verify if the required databases are already present. If any of the databases are missing, the pipeline will automatically download them.

Users can also provide individual paths to each reference database and its version if needed. For detailed instructions, please refer to the Reference databases section in the --help of the pipeline.

It's important to note that users are not allowed to mix the --dbs flag with individual database paths and versions; they are mutually exclusive. We recommend users to run the pipeline with the --dbs flag for the first time in an appropriate path and to avoid downloading the individual databases separately.

Usage

Input file

First, prepare an input file in the CSV format that looks as follows:

assemblies_sheet.csv:

prefix,assembly,taxid
BU_ATCC8492VPI0062,/path/to/BU_ATCC8492VPI0062_NT5002.fa,820
EC_ASM584v2,/path/to/GCF_000005845.2.fna,562
...

Here, prefix is the prefix and the locus tag that will be assigned to output files and proteins during the annotation process; maximum length is 24 characters;

assembly is the path to where the assembly file in FASTA format is located;

taxid is the NCBI TaxId (if the species-level TaxId is not known, a TaxId for a higher taxonomic level can be used). If the taxonomy is known, look up the TaxID here.

Finding TaxIds

If NCBI taxonomies of input genomes are not known, a tool such as CAT/BAT can be used. Follow the instructions for getting the tool and downloading the NCBI nr database for it.

If using CAT/BAT, here is the suggested process for making the mettannotator input file:

# Run BAT on each input genome, saving all results to the same folder
CAT bins -b ${genome_name}.fna -d ${path_to_CAT_database} -t ${path_to_CAT_tax_folder} -o BAT_results/${genome_name}

# Optional: to check what taxa were assigned, you can add names to them
CAT add_names -i BAT_results/${genome_name}.bin2classification.txt -o BAT_results/${genome_name}.name.txt -t ${path_to_CAT_tax_folder}

To generate an input file for mettannotator, use generate_input_file.py:

python3 preprocessing/generate_input_file.py -h
usage: generate_input_file.py [-h] -i INFILE -d INPUT_DIR -b BAT_DIR -o OUTFILE [--no-prefix]

The script takes a list of genomes and the taxonomy results generated by BAT and makes a
mettannotator input csv file. The user has the option to either use the genome file name
(minus the extension) as the prefix for mettannotator or leave the prefix off and fill it
out themselves after the script generates an input file with just the FASTA location and
the taxid. It is expected that for all genomes, BAT results are stored in the same folder
and are named as {fasta_base_name}.bin2classification.txt. The script will use the lowest-
level taxid without an asterisk as the taxid for the genome.

optional arguments:
  -h, --help    show this help message and exit
  -i INFILE     A file containing a list of genome files to include (file name only, with file
                extension, unzipped, one file per line).
  -d INPUT_DIR  Full path to the directory where the input FASTA files are located.
  -b BAT_DIR    Folder with BAT results. Results for all genomes should be in the same folder
                and should be named {fasta_base_name}.bin2classification.txt
  -o OUTFILE    Path to the file where the output will be saved to.
  --no-prefix   Skip prefix generation and leave the first column of the output file empty for
                the user to fill out. Default: False

For example:

python3 generate_input_file.py -i list_of_genome_fasta_files.txt -d /path/to/the/fasta/files/folder/ -b BAT_results/ -o mettannotator_input.csv

It is always best to check the outputs to ensure the results are as expected. Correct any wrongly detected taxa before starting mettannotator.

Note, that by default the script uses FASTA file names as prefixes and truncates them to 24 characters if they exceed the limit.

Running mettannotator

Running mettannotator with the --help option will pull the repository and display the help message:

nextflow run ebi-metagenomics/mettannotator/main.nf --help
N E X T F L O W  ~  version 23.04.3
Launching `mettannotator/main.nf` [disturbed_davinci] DSL2 - revision: f2a0e51af6


------------------------------------------------------
  ebi-metagenomics/mettannotator 
------------------------------------------------------
Typical pipeline command:

  nextflow run ebi-metagenomics/mettannotator --input assemblies_sheet.csv -profile docker

Input/output options
  --input                            [string]  Path to comma-separated file containing information about the assemblies with the prefix to be used.
  --outdir                           [string]  The output directory where the results will be saved. You have to use absolute paths to storage on Cloud
                                               infrastructure.
  --fast                             [boolean] Run the pipeline in fast mode. In this mode, InterProScan, UniFIRE, and SanntiS won't be executed, saving
                                               resources and speeding up the pipeline.
  --email                            [string]  Email address for completion summary.
  --multiqc_title                    [string]  MultiQC report title. Printed as page header, used for filename if not otherwise specified.

Reference databases
  --dbs                              [string]  Folder for the tools' reference databases used by the pipeline for downloading.
  --interproscan_db                  [string]  The InterProScan reference database, ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/
  --interproscan_db_version          [string]  The InterProScan reference database version. [default: 5.62-94.0]
  --interpro_entry_list              [string]  TSV file listing basic InterPro entry information - the accessions, types and names,
                                               ftp://ftp.ebi.ac.uk/pub/databases/interpro/releases/94.0/entry.list
  --interpro_entry_list_version      [string]  InterPro entry list version [default: 94]
  --eggnog_db                        [string]  The EggNOG reference database folder,
                                               https://github.com/eggnogdb/eggnog-mapper/wiki/eggNOG-mapper-v2.1.5-to-v2.1.12#requirements
  --eggnog_db_version                [string]  The EggNOG reference database version. [default: 5.0.2]
  --rfam_ncrna_models                [string]  Rfam ncRNA models, ftp://ftp.ebi.ac.uk/pub/databases/metagenomics/genomes-pipeline/ncrna/
  --rfam_ncrna_models_rfam_version   [string]  Rfam release version where the models come from. [default: 14.9]
  --amrfinder_plus_db                [string]  AMRFinderPlus reference database,
                                               https://ftp.ncbi.nlm.nih.gov/pathogen/Antimicrobial_resistance/AMRFinderPlus/database/. Go to the following
                                               documentation for the db setup https://github.com/ncbi/amr/wiki/Upgrading#database-updates.
  --amrfinder_plus_db_version        [string]  The AMRFinderPlus reference database version. [default: 2023-02-23.1]
  --defense_finder_db                [string]  Defense Finder reference models, https://github.com/mdmparis/defense-finder#updating-defensefinder. The
                                               Microbiome Informatics team provides a pre-indexed version of the models for version 1.2.3 on this ftp location:
                                               ftp://ftp.ebi.ac.uk/pub/databases/metagenomics/pipelines/tool-dbs/defense-finder/defense-finder-models_1.2.3.tar.gz.
  --defense_finder_db_version        [string]  The Defense Finder models version. [default: 1.2.3]
  --antismash_db                     [string]  antiSMASH reference database, go to this documentation to do the database setup
                                               https://docs.antismash.secondarymetabolites.org/install/#installing-the-latest-antismash-release.
  --antismash_db_version             [string]  The antiSMASH reference database version. [default: 7.1.0]
  --dbcan_db                         [string]  dbCAN indexed reference database, please go to the documentation for the setup
                                               https://dbcan.readthedocs.io/en/latest/. The Microbiome Informatics team provides a pre-indexed version of the
                                               database for version 4.0 on this ftp location:
                                               ftp://ftp.ebi.ac.uk/pub/databases/metagenomics/pipelines/tool-dbs/dbcan/dbcan_4.0.tar.gz
  --dbcan_db_version                 [string]  The dbCAN reference database version. [default: 4.1.3_V12]

Generic options
  --multiqc_methods_description      [string]  Custom MultiQC yaml file containing HTML including a methods description.

Other parameters
  --bakta                            [boolean] Use Bakta instead of Prokka for CDS annotation. Prokka will still be used for archaeal genomes.

 !! Hiding 17 params, use --validationShowHiddenParams to show them !!
------------------------------------------------------
If you use ebi-metagenomics/mettannotator for your analysis please cite:

* The nf-core framework
  https://doi.org/10.1038/s41587-020-0439-x

* Software dependencies
  https://github.com/ebi-metagenomics/mettannotator/blob/master/CITATIONS.md
------------------------------------------------------

Now, you can run the pipeline using:

nextflow run ebi-metagenomics/mettannotator \
   -profile  \
   --input assemblies_sheet.csv \
   --outdir  \
   --dbs 

Warning: Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

Gene caller choice

By default, mettannotator uses Prokka to identify protein-coding genes. Users can choose to use Bakta instead by running mettannotator with the --bakta flag. mettannotator runs Bakta without ncRNA and CRISPR annotation as these are produced by separate tools in the pipeline. Archaeal genomes will continue to be annotated using Prokka as Bakta is only intended for annotation of bacterial genomes.

Fast mode

To reduce the compute time and the amount of resources used, the pipeline can be executed with the --fast flag. When run in the fast mode, mettannotator will skip InterProScan, UniFIRE and SanntiS. This could be a suitable option for a first-pass of annotation or if computational resources are limited, however, we recommend running the full version of the pipeline whenever possible.

When generating an input file for a fast mode run, it is sufficient to indicate the taxid of the superkingdom (2 for bacteria and 2157 for Archaea) in the "taxid" column rather than the taxid of the lowest known taxon.

Test

To run the pipeline using a test dataset, execute the following command:

wget https://raw.githubusercontent.com/EBI-Metagenomics/mettannotator/master/tests/test.csv

nextflow run ebi-metagenomics/mettannotator \
   -profile  \
   --input test.csv \
   --outdir  \
   --dbs 

Outputs

The output folder structure will look as follows:

└─
   ├─antimicrobial_resistance
   │  └─amrfinder_plus
   ├─antiphage_defense
   │  └─defense_finder
   ├─biosynthetic_gene_clusters
   │  ├─antismash
   │  ├─gecco
   │  └─sanntis
   ├─functional_annotation
   │  ├─dbcan
   │  ├─eggnog_mapper
   │  ├─interproscan
   │  ├─merged_gff
   │  ├─prokka
   │  └─unifire
   ├─mobilome
   │  └─crisprcas_finder
   ├─quast
   │  └─
   │      ├─basic_stats
   │      └─icarus_viewers
   ├─rnas
   │  ├─ncrna
   │  └─trna
   ├─multiqc
   │  ├─multiqc_data
   │  └─multiqc_plots
   │      ├─pdf
   │      ├─png
   │      └─svg
   ├─pipeline_info
   │  ├─software_versions.yml
   │  ├─execution_report_.txt
   │  ├─execution_report_.html
   │  ├─execution_timeline_.txt
   │  ├─execution_timeline_.html
   │  ├─execution_trace_.txt
   │  ├─execution_trace_.html
   │  └─pipeline_dag_.html

Merged GFF

The two main output files for each genome are located in //functional_annotation/merged_gff/:

  • _annotations.gff: annotations produced by all tools merged into a single file

  • _annotations_with_descriptions.gff: a version of the GFF file above that includes descriptions of all InterPro terms to make the annotations human-readable. Not generated if --fast flag was used.

Both files include the genome sequence in the FASTA format at the bottom of the file.

Additionally, for genomes with no more than 50 annotated contigs, a Circos plot of the _annotations.gff file is generated and included in the same folder. An example of such plot is shown below:

Data sources

Below is an explanation of how each field in column 3 and 9 of the final GFF file is populated. In most cases, information is taken as is from the reporting tool's output.

Feature (column 3) Attribute Name (column 9) Reporting Tool Description
ncRNA all* cmscan + Rfam ncRNA annotation (excluding tRNA)
tRNA all* tRNAscan-SE tRNA annotation
LeftFLANK, RightFLANK all* CRISPRCasFinder CRISPR array flanking sequence
CRISPRdr all* CRISPRCasFinder Direct repeat region of a CRISPR array
CRISPRspacer all* CRISPRCasFinder CRISPR spacer
CDS ID, eC_number, Name, Dbxref, gene, inference, locus_tag Prokka/Bakta Protein annotation
CDS product mettannotator Product assigned as described in Determining the product
CDS product_source mettannotator Tool that reported the product chosen by mettannotator
CDS eggNOG eggNOG-mapper Seed ortholog from eggNOG
CDS cog eggNOG-mapper COG category
CDS kegg eggNOG-mapper KEGG orthology term
CDS Ontology_term eggNOG-mapper GO associations
CDS pfam InterProScan Pfam accessions
CDS interpro InterProScan InterPro accessions. In _annotations_with_descriptions.gff each accession is followed by its description and entry type: Domain [D], Family [F], Homologous Superfamily [H], Repeat [R], Site [S]
CDS nearest_MiBIG SanntiS MiBIG accession of the nearest BGC to the cluster in the MIBIG space
CDS nearest_MiBIG_class SanntiS BGC class of nearest_MiBIG
CDS gecco_bgc_type GECCO BGC type
CDS antismash_bgc_function antiSMASH BGC function
CDS amrfinderplus_gene_symbol AMRFinderPlus Gene symbol according to AMRFinderPlus
CDS amrfinderplus_sequence_name AMRFinderPlus Product description
CDS amrfinderplus_scope AMRFinderPlus AMRFinderPlus database (core or plus)
CDS element_type, element_subtype AMRFinderPlus Functional category
CDS drug_class, drug_subclass AMRFinderPlus Class and subclass of drugs that this gene is known to contribute to resistance of
CDS dbcan_prot_type run_dbCAN Predicted protein function: transporter (TC), transcription factor (TF), signal transduction protein (STP), CAZyme
CDS dbcan_prot_family run_dbCAN Predicted protein family
CDS substrate_dbcan-pul run_dbCAN Substrate predicted by dbCAN-PUL search
CDS substrate_dbcan-sub run_dbCAN Substrate predicted by dbCAN-subfam
CDS defense_finder_type, defense_finder_subtype DefenseFinder Type and subtype of the anti-phage system found
CDS uf_prot_rec_fullname, uf_prot_rec_shortname, uf_prot_rec_ecnumber UniFIRE Protein recommended full name, short name and EC number according to UniFIRE
CDS uf_prot_alt_fullname, uf_prot_alt_shortname, uf_prot_alt_ecnumber UniFIRE Protein alternative full name, short name and EC number according to UniFIRE
CDS uf_chebi UniFIRE ChEBI identifiers
CDS uf_ontology_term UniFIRE GO associations
CDS uf_keyword UniFIRE UniFIRE keywords
CDS uf_gene_name, uf_gene_name_synonym UniFIRE Gene name and gene name synonym according to UniFIRE
CDS uf_pirsr_cofactor UniFIRE Cofactor names from PIRSR

*all attributes in column 9 are populated by the tool

Determining the product

The following logic is used by mettannotator to fill out the product field in the 9th column of the GFF:

If the pipeline is executed with the --fast flag, only the output of eggNOG-mapper is used to determine the product of proteins that were labeled as hypothetical by the gene caller.

Contents of the tool output folders

The output folders of each individual tool contain select output files of the third-party tools used by mettannotator. For file descriptions, please refer to the tool documentation. For some tools that don't output a GFF, mettannotator converts the output into a GFF.

Note: if the pipeline completed without errors but some of the tool-specific output folders are empty, those particular tools did not generate any annotations to output.

Mobilome annotation

The mobilome annotation workflow is not currently integrated into mettannotator. However, the outputs produced by mettannotator can be used to run VIRify and the mobilome annotation pipeline and the outputs of these tools can be integrated back into the GFF file produced by mettannotator.

After installing both tools, follow these steps to add the mobilome annotation:

  1. Run the viral annotation pipeline:
nextflow run \
    emg-viral-pipeline/virify.nf \
    -profile  \
    --fasta  \
    --output 
  1. Run the mobilome annotation pipeline:
nextflow run mobilome-annotation-pipeline/main.nf \
    --assembly  \
    --user_genes true \
    --prot_gff /functional_annotation/merged_gff/_annotations.gff \
    --virify true # only if the next two VIRify files exist, otherwise skip this line \
    --vir_gff Virify_output_folder/08-final/gff/_virify.gff # only if file exists, otherwise skip this line \
    --vir_checkv Virify_output_folder/07-checkv/\*quality_summary.tsv # only if the GFF file above exists, otherwise skip this line \
    --outdir  \
    --skip_crispr true \
    --skip_amr true \
    -profile "
  1. Integrate the output into the mettannotator GFF
# Add mobilome to the merged GFF produced by mettannotator
python3 postprocessing/add_mobilome_to_gff.py \
    -m /gff_output_files/mobilome_nogenes.gff \
    -i //functional_annotation/merged_gff/_annotations.gff \
    -o _annotations_with_mobilome.gff

# Add mobilome to the GFF with descriptions produced by mettannotator
python3 postprocessing/add_mobilome_to_gff.py \
    -m /gff_output_files/mobilome_nogenes.gff \
    -i //functional_annotation/merged_gff/_annotations_with_descriptions.gff \
    -o _annotations_with_descriptions_with_mobilome.gff
  1. Optional: regenerate the Circos plot with the mobilome track added
pip install pycirclize
pip install matplotlib

python3 bin/circos_plot.py \
    -i _annotations_with_mobilome.gff \
    -o plot.png \
    -p  \
    --mobilome

Credits

ebi-metagenomics/mettannotator was originally written by the Microbiome Informatics Team at EMBL-EBI

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

This pipeline uses code developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

Version History

v1.2 (latest) Created 2nd Jul 2024 at 16:21 by Martin Beracochea

Merge pull request #36 from EBI-Metagenomics/dev

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v1.1 (earliest) Created 1st Jul 2024 at 17:34 by Martin Beracochea

Merge pull request #33 from EBI-Metagenomics/dev

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