argnorm 0.6.0

Normalize antibiotic resistance genes (ARGs) results by using the ARO ontology (developed by CARD).

argNorm: Normalize ARG annotations to the ARO

What is argNorm?

argNorm is a tool to normalize antibiotic resistance genes (ARGs) by mapping them to the antibiotic resistance ontology (ARO) created by the CARD database.

argNorm also enhances antibiotic resistance gene annotations by providing drug categorization of the drugs that antibiotic resistance genes confer resistance to.

Why argNorm?

Right now, many tools exist for annotation ARGs in genomes and metagenomes. However, each tools will have distinct output formats.

The hAMRonization package can normalize file formats, but each tool will use different names/identifiers (e.g., TetA or TETA or tet(A) or tet-A are all different ways to spell the same gene name).

For a small number of isolate genomes, a human user can manually evaluate the outputs. However, in metagnomics, especially for large-scale projects, this becomes infeasible. Thus argNorm normalizers the output vocabulary of ARG annotation tools by mapping them to the same ontology (ARO).

Besides performing normalization, argNorm also provides categorization of drugs that antibiotic resistance genes confer resistance to.

For example, the PBP2b (ARO:3003042) gene confers resistance to the drug class amoxicillin. amoxicillin is then categorized into a broader category of beta lactam antibiotic.

argNorm provides support for this, and adds the confers_resistance_to and resistance_to_drug_classes columns to ARG annotations.

The confers_resistance_to column will contain ARO numbers of all the drug classes that a gene provides resistance to (ARO:0000064 for amoxicillin in the previous example).

The resistance_to_drug_classes column will contain ARO numbers of the broader categories of the drug classes in the confers_resistance_to column (ARO:3000007 for beta lactam antibiotic in the previous example).

argNorm Overview

General overview of argNorm. (a) Genomes and metagenomes can be annotated using ARG annotation tools. argNorm accepts the outputs of these ARG annotation tools directly or after the outputs are processed by hAMRonization to perform ARO normalization and drug categorization. (b) The argNorm workflow includes: mapping gene names in the ARG annotation outputs to ARO accessions from ARO annotation tables constructed using RGI and manual curation; and mapping gene ARO accessions to drugs and drugs classes. (c) In categorizing drugs, agrNorm reports the immediate child of the ‘antibiotic molecule’ node in the ARO

argNorm workflow

Schematic illustration of argNorm workflow through a Resfinder output example: mapping gene names in the ARG annotation outputs to gene names from the ARO mapped ARG databases and adding corresponding drug categorization, namely “confers resistance to immediate drug class” and “overall category of drug class”, from the ARO ontology file.

Supported tools

• DeepARG (v1.0.2)
• ARGs-OAP (v3)
• ABRicate (v1.0.1) with NCBI (v3.6), ResFinder (v4.1.11), MEGARes (v2.0), ARG-ANNOT (v5), ResFinderFG (v2)
• ResFinder (v4.0)
• AMRFinderPlus (v3.10.30)
• GROOT (v1.1.2)

Installation

argNorm can be installed using pip:

pip install argnorm

argNorm can also be installed through conda:

conda install bioconda::argnorm

argNorm is also available as an nf-core module. If using an nf-core pipeline, argNorm can be installed using:

nf-core modules install argnorm

Tutorial video

Basic usage

The only positional argument required is tool which can be:

• deeparg
• argsoap
• abricate
• resfinder
• amrfinderplus
• groot

The available options are:

• -h or --help: shows available options and exits.
• --db: database used to perform ARG annotation. Supported databases are:
  • SARG (sarg)
  • NCBI (ncbi)
  • ResFinder (resfinder)
  • DeepARG (deeparg)
  • MEGARes (megares)
  • ARG-ANNOT (argannot)
  • groot-core-db, groot-db, groot-resfinder, groot-argannot, groot-card
• --hamronized: use this if the input is hamronized by hAMRonization
• -i or --input: path to the annotation result
• -o or --output: the file to save normalization results

Use argnorm -h or argnorm --help to see available options.

>argnorm -h
usage: argnorm [-h]
               [--db {sarg,ncbi,resfinder,deeparg,megares,argannot,resfinderfg,groot-argannot,groot-resfinder,groot-db,groot-core-db,groot-card}]
               [--hamronized] [-i INPUT] [-o OUTPUT]
               {argsoap,abricate,deeparg,resfinder,amrfinderplus,groot}

argNorm normalizes ARG annotation results from different tools and databases to the same ontology, namely ARO (Antibiotic Resistance Ontology).

positional arguments:
  {argsoap,abricate,deeparg,resfinder,amrfinderplus,groot}
                        The tool you used to do ARG annotation.

optional arguments:
  -h, --help            show this help message and exit
  --db {sarg,ncbi,resfinder,deeparg,megares,argannot,resfinderfg,groot-argannot,groot-resfinder,groot-db,groot-core-db,groot-card}
                        The database you used to do ARG annotation.
  --hamronized          Use this if the input is hamronized (processed using the hAMRonization tool)
  -i INPUT, --input INPUT
                        The annotation result you have
  -o OUTPUT, --output OUTPUT
                        The file to save normalization results

Here is a basic outline of calling argNorm.

argnorm [tool] -i [original_annotation.tsv] -o [annotation_result_with_aro.tsv]

Example 1: argNorm as a command line tool

Here is a quick demo of running argNorm on the command line.

Step 1: Install argNorm

Install argNorm and check installation

pip install argnorm
argnorm -h

argnorm -h or argnorm --help will display all the available options to run argNorm with.

> argnorm -h
usage: argnorm [-h]
               [--db {sarg,ncbi,resfinder,deeparg,megares,argannot,resfinderfg}]
               [--hamronized] [-i INPUT] [-o OUTPUT]
               {argsoap,abricate,deeparg,resfinder,amrfinderplus}

argNorm normalizes ARG annotation results from different tools and databases to the same ontology, namely ARO (Antibiotic Resistance Ontology).

positional arguments:
  {argsoap,abricate,deeparg,resfinder,amrfinderplus}
                        The tool you used to do ARG annotation.

optional arguments:
  -h, --help            show this help message and exit
  --db {sarg,ncbi,resfinder,deeparg,megares,argannot,resfinderfg}
                        The database you used to do ARG annotation.
  --hamronized          Use this if the input is hamronized (processed using
                        the hAMRonization tool)
  -i INPUT, --input INPUT
                        The annotation result you have
  -o OUTPUT, --output OUTPUT
                        The file to save normalization results

Step 2: Create working directory & download sample data

argNorm adds ARO mappings and drug categories as additional columns to the outputs of ARG annotation tools.

For this example, we will run argNorm on the ARG annotation output from the ResFinder tool (with the ResFinder database).

Create a folder called argNorm_tutorial and store the downloaded data file in it. Navigate into the argNorm_tutorial folder.

mkdir argNorm_tutorial
cd argNorm_tutorial

Click here to download the input data.

If you are on Linux:

wget https://raw.githubusercontent.com/BigDataBiology/argNorm/main/examples/raw/resfinder.resfinder.orfs.tsv

Step 3: Running argNorm

Here is a basic outline of most argNorm commands:

argnorm [tool] -i [original_annotation.tsv] -o [argnorm_result.tsv] [--hamronized]

Here, tool refers to the ARG annotation tool used (ResFinder in this case). original_annotation.tsv is the path to the input data and argnorm_result.tsv is the path to output file where the resulting table from argNorm will be stored. --hamronized is an option to indicate if the input data is a result of using the hAMRonization package. In our example, the input data is not a result of using the hAMRonization package, and so the --hamronized option can be omitted.

To run argNorm on the input data, use this command in your terminal:

argnorm resfinder -i ./resfinder.resfinder.orfs.tsv -o ./resfinder.resfinder.orfs.normed.tsv

The argNorm result will be stored in the file resfinder.resfinder.orf.normed.tsv.

Example 2: argNorm as a Python library

Code

Save this piece of Python code to a file called argnorm_tutorial.py

# Import from argNorm
from argnorm.lib import map_to_aro
from argnorm.drug_categorization import confers_resistance_to, drugs_to_drug_classes

# Creating a list of input genes to be mapped to the ARO
list_of_input_genes = ['sul1_2_U12338', 'sul1_3_EU855787', 'sul2_1_AF542061']

# The database from which the `list_of_input_genes` was created is the ResFinder database
database = 'resfinder'

# Looping through `list_of_input` genes and mapping each gene to the ARO
# Storing each ARO mapping in the `list_of_aros` list
list_of_aros = []
for gene in list_of_input_genes:
    # Using `id` attribute to get ARO number
    # `name` attribute can be used to get name of gene in ARO
    list_of_aros.append(map_to_aro(gene, database).id)
print(list_of_aros)

# Looping through `list_of_aros` and finding the drugs to which the each ARO confers resistance to
# Storing each drug in the `list_of_drugs` list
list_of_drugs = []
for aro in list_of_aros:
    list_of_drugs.append(confers_resistance_to(aro))
print(list_of_drugs)

# Looping through `list_of_drugs` and finding the superclass/drug class of each drug
# Storing each superclass/drug class in the `list_of_drug_classes` list
list_of_drug_classes = []
for drug in list_of_drugs:
    list_of_drug_classes.append(drugs_to_drug_classes(drug))
print(list_of_drug_classes)

Explanation

To use argNorm as a library, we must first import it in our Python file:

from argnorm.lib import map_to_aro
from argnorm.drug_categorization import confers_resistance_to, drugs_to_drug_classes

The lib module contains the function map_to_aro which will return the ARO number of a particular antibiotic resistance gene. The drug_categorization module contains the functions confers_resistance_to and drugs_to_drug_classes. The confers_resistance_to function returns the drugs to which a gene confers resistance to. The drugs_to_drug_classes function returns the drug class to which a specific drug belongs.

The map_to_aro function takes two arguments: gene and database. gene is the name of an antibiotic resistance gene. database is the database from which gene is taken from.

In this example, a list of genes from the ResFinder database is used, and map_to_aro maps each gene in the list to an ARO term.

The ARO numbers from the ARO terms are also stored in the list_of_aros list. The ARO numbers can be accessed using the id attribute of the ARO terms. The names of the gene of the ARO terms an be accessed using the name attribute.

database = 'resfinder'

list_of_aros = []
for gene in list_of_input_genes:
    list_of_aros.append(map_to_aro(gene, database).id)
print(list_of_aros)

Once a list of ARO numbers is created for each gene, the confers_resistance_to function can be used on each ARO number to create a list of drugs to which each gene/ARO confers resistance to:

list_of_drugs = []
for aro in list_of_aros:
    list_of_drugs.append(confers_resistance_to(aro))
print(list_of_drugs)

Now, each drug in the list_of_drugs can be categorized into a broader drug category using the drugs_to_drug_classes function:

list_of_drug_classes = []
for drug in list_of_drugs:
    list_of_drug_classes.append(drugs_to_drug_classes(drug))
print(list_of_drug_classes)

Output

List of ARO numbers
['ARO:3000410', 'ARO:3000410', 'ARO:3000412']

Confers resistance to
[['ARO:3000324', 'ARO:3000325', 'ARO:3000327', 'ARO:3000329', 'ARO:3000330', 'ARO:3000683', 'ARO:3000684', 'ARO:3000698', 'ARO:3000699'], ['ARO:3000324', 'ARO:3000325', 'ARO:3000327', 'ARO:3000329', 'ARO:3000330', 'ARO:3000683', 'ARO:3000684', 'ARO:3000698', 'ARO:3000699'], ['ARO:3000324', 'ARO:3000325', 'ARO:3000327', 'ARO:3000329', 'ARO:3000330', 'ARO:3000683', 'ARO:3000684', 'ARO:3000698', 'ARO:3000699']]

Resistance to drug classes
[['ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282'], ['ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282'], ['ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282', 'ARO:3000282']]

Authors

• Vedanth Ramji [e-mail]*
• Hui Chong [e-mail]
• Svetlana Ugarcina Perovic [e-mail]
• Finlay Maguire [e-mail]
• Luis Pedro Coelho [e-mail]

*: current maintainer