hAMRonization

This repo contains the hAMRonization module and CLI parser tools combine the outputs of 18 (as of 2022-09-25) disparate antimicrobial resistance gene detection tools into a single unified format.

This is an implementation of the hAMRonization AMR detection specification scheme which supports gene presence/absence resistance and mutational resistance (if supported by the underlying tool).

This supports a variety of summary options including an interactive summary.

Installation

This tool requires python>=3.7 and pandas and the latest release can be installed directly from pip, conda, docker, this repository, or from the galaxy toolshed:

pip install hAMRonization

Or

conda create --name hamronization --channel conda-forge --channel bioconda --channel defaults hamronization

Or to install using docker:

docker pull finlaymaguire/hamronization:latest

Or to install the latest development version:

git clone https://github.com/pha4ge/hAMRonization
pip install hAMRonization

Alternatively, hAMRonization can also be installed and used in galaxy via the galaxy toolshed.

Usage

NOTE: Only the output format used in the "last updated" version of the AMR prediction tool has been tested for accuracy. Older tool versions or updates which lead to a change in output format may not work. In theory, this should only be a problem with major version changes but not all tools follow semantic versioning. If you encounter any issues with newer tool versions then please create an issue in this repository.

usage: hamronize <tool> <options>

Convert AMR gene detection tool output(s) to hAMRonization specification format

options:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit

Tools with hAMRonizable reports:
  {abricate,amrfinderplus,amrplusplus,ariba,csstar,deeparg,fargene,groot,kmerresistance,resfams,resfinder,mykrobe,pointfinder,rgi,srax,srst2,staramr,tbprofiler,summarize}
    abricate            hAMRonize abricate's output report i.e., OUTPUT.tsv
    amrfinderplus       hAMRonize amrfinderplus's output report i.e., OUTPUT.tsv
    amrplusplus         hAMRonize amrplusplus's output report i.e., gene.tsv
    ariba               hAMRonize ariba's output report i.e., OUTDIR/OUTPUT.tsv
    csstar              hAMRonize csstar's output report i.e., OUTPUT.tsv
    deeparg             hAMRonize deeparg's output report i.e.,
                        OUTDIR/OUTPUT.mapping.ARG
    fargene             hAMRonize fargene's output report i.e., retrieved-
                        genes-*-hmmsearched.out
    groot               hAMRonize groot's output report i.e., OUTPUT.tsv (from `groot
                        report`)
    kmerresistance      hAMRonize kmerresistance's output report i.e., OUTPUT.res
    resfams             hAMRonize resfams's output report i.e., resfams.tblout
    resfinder           hAMRonize resfinder's output report i.e.,
                        ResFinder_results_tab.txt
    mykrobe             hAMRonize mykrobe's output report i.e., OUTPUT.json
    pointfinder         hAMRonize pointfinder's output report i.e.,
                        PointFinder_results.txt
    rgi                 hAMRonize rgi's output report i.e., OUTPUT.txt or
                        OUTPUT_bwtoutput.gene_mapping_data.txt
    srax                hAMRonize srax's output report i.e., sraX_detected_ARGs.tsv
    srst2               hAMRonize srst2's output report i.e., OUTPUT_srst2_report.tsv
    staramr             hAMRonize staramr's output report i.e., resfinder.tsv
    tbprofiler          hAMRonize tbprofiler's output report i.e., OUTPUT.results.json
    summarize           Provide a list of paths to the reports you wish to summarize

To look at a specific tool e.g. abricate:

>hamronize abricate -h 
usage: hamronize abricate <options>

Applies hAMRonization specification to output from abricate (OUTPUT.tsv)

positional arguments:
  report                Path to tool report

optional arguments:
  -h, --help            show this help message and exit
  --format FORMAT       Output format (tsv or json)
  --output OUTPUT       Output location
  --analysis_software_version ANALYSIS_SOFTWARE_VERSION
                        Input string containing the analysis_software_version for abricate
  --reference_database_version REFERENCE_DATABASE_VERSION
                        Input string containing the reference_database_version for abricate

Therefore, hAMRonizing abricates output:

hamronize abricate ../test/data/raw_outputs/abricate/report.tsv --reference_database_version 3.2.5 --analysis_software_version 1.0.0 --format json

To parse multiple reports from the same tool at once just give a list of reports as the argument, and they will be concatenated appropriately (i.e. only one header for tsv)

hamronize rgi --input_file_name rgi_report --analysis_software_version 6.0.0 --reference_database_version 3.2.5 test/data/raw_outputs/rgi/rgi.txt test/data/raw_outputs/rgibwt/Kp11_bwtoutput.gene_mapping_data.txt

You can summarize hAMRonized reports regardless of format using the 'summarize' function:

> hamronize summarize -h
usage: hamronize summarize <options> <list of reports>

Concatenate and summarize AMR detection reports

positional arguments:
  hamronized_reports    list of hAMRonized reports

optional arguments:
  -h, --help            show this help message and exit
  -t {tsv,json,interactive}, --summary_type {tsv,json,interactive}
                        Which summary report format to generate
  -o OUTPUT, --output OUTPUT
                        Output file path for summary

This will take a list of report and create single sorted report in the specified format just containing the unique entries across input reports. This can handle mixed json and tsv hamronized report formats.

hamronize summarize -o combined_report.tsv -t tsv abricate.json ariba.tsv

The interactive summary option will produce an html file that can be opened within the browser for navigable data exploration (feature developed with @alexmanuele).

Using within scripts

Alternatively, hAMRonization can be used within scripts (the metadata must contain the mandatory metadata that is not included in that tool's output, this can be checked by looking at the CLI flags in hamronize <tool> --help):

import hAMRonization
metadata = {"analysis_software_version": "1.0.1", "reference_database_version": "2019-Jul-28"}
parsed_report = hAMRonization.parse("abricate_report.tsv", metadata, "abricate")

The parsed_report is then a generator that yields hAMRonized result objects from the parsed report:

for result in parsed_report:
      print(result)

Alternatively, you can use the .write attribute to export all results left in the generator to a file (if a filepath isn't provided, this will write to stdout).

parsed_report.write('hAMRonized_abricate_report.tsv')

You can also output a json formatted hAMRonized report:

parsed_report.write('all_hAMRonized_abricate_report.json', output_format='json')

If you want to write multiple reports to one file, this .write method can accept append_mode=True to append rather than overwrite the output file and not include the header (in tsv format).

parsed_report.write('all_hAMRonized_abricate_report.tsv', append_mode=True)

Implemented Parsers

Currently implemented parsers and the last tool version for which they have been validated:

1. abricate: last updated for v1.0.0
2. amrfinderplus: last updated for v3.10.40
3. amrplusplus: last updated for c6b097a
4. ariba: last updated for v2.14.6
5. csstar: last updated for v2.1.0
6. deeparg: last updated for v1.0.2
7. fargene: last updated for v0.1
8. groot: last updated for v1.1.2
9. kmerresistance: late updated for v2.2.0
10. mykrobe: last updated for v0.8.1
11. pointfinder: last updated for v4.1.0
12. resfams: last updated for hmmer v3.3.2
13. resfinder: last updated for v4.1.0
14. rgi (includes RGI-BWT) last updated for v5.2.0
15. srax: last updated for v1.5
16. srst2: last updated for v0.2.0
17. staramr: last updated for v0.8.0
18. tbprofilder: last updated for v3.0.8

Implementation Details

hAMRonizedResult Data Structure

The hAMRonization specification is implemented in the hAMRonizedResult dataclass.

This is a simple datastructure that uses positional and key-word args to distinguish mandatory from optional hAMRonization fields. It also uses type-hinting to validate the supplied values are of the correct type

Each parser follows a similar strategy, using a common interface. This has been designed to match the biopython SeqIO parse function

>>> import hAMRonization
>>> filename = "abricate_report.tsv"
>>> metadata = {"analysis_software_version": "1.0.1", "reference_database_version": "2019-Jul-28"}
>>> for result in hAMRonization.parse(filename, metadata, "abricate"):
...    print(result)

Where the final argument to the hAMRonization.parse command is whichever tool is being parsed.

hAMRonizedResultIterator

An abstract iterator is then implemented to ingest a given AMR tool's report (via the appropriate subclassed implementation), hAMRonize results i.e. translate the original inputs to the fields in the hAMRonization specification, and yield a stream of hAMRonizedResult dataclasses.

This iterator also implements a write function to enable outputting the contents to a output stream or filehandle in either tsv or json format.

Tool-specific Iterators

Each tool has a specific subclass of this abstract hAMRonizedResultIterator e.g. AbricateIO.AbricateIterator.

These include an attribute containing the mapping of the tools original output report fields to the hAMRonized specification fields (self.field_mapping), as well as handling specifying any additional required metadata.

The parse method of these subclasses then implements the tool-specific parsing logic required. This is typically a simple csv.DictReader but can be more complex such as the json parsing of resfinder output, or the modification of output fields required to better fit some tools into the hAMRonization specification.

Contributing

We welcome contributions for users in any form (from github issues flagging problems/requests) to pull requests of bug fixes or adding new parsers.

Setting up a Development Environment

First fork this repository and set up a development environment (replacing YOURUSERNAME with your github username:

git clone https://github.com/YOURUSERNAME/hAMRonization
conda create -n hAMRonization 
conda activate hAMRonization
cd hAMRonization
pip install pytest flake8
pip install -e .

Testing and Linting

On every commit github actions automatically runs tests and linting to check the code. You can manually run these in your development environment as well.

To run a full set of integration tests:

pushd test
bash run_integration_test.sh
popd

To run unit tests that verify parsing validity for each tool as well as generation of valid summaries you can use pytest:

pip install pytest
pushd test
pytest
popd

Finally to run linting and check whether your code matches the project code style:

pushd hAMRonization
flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
flake8 . --count --exit-zero --max-complexity=20 --max-line-length=127 --statistics
popd

Adding a new parser

If you wish to add a parser for a new tool here are the main steps required:

1. Add an entry into _RequiredToolMetadata and _FormatToIterator in hAMRonziation/__init__.py which points to the appropriate ToolNameIO.py containing the tool's Iterator subclass

2. In ToolNameIO.py add a required_metadata list containing any mandatory fields not implemented by the tool

3. Then add a class ToolNameIterator(hAMRonizedResultIterator) and implement the __init__ methods with the approriate mapping (self.field_mapping), and metadata (self.metadata).

4. To this class, add a parse method which reads an opened file stream into a dictionary per line/result (matching the keys of self.field_mapping) and yields the output of self.hAMRonize being applied to that dictionary.

5. To add a CLI parser for the tool, create a python file in the parsers directory:

   from hAMRonization import Interfaces
   if __name__ == '__main__': 
       Interfaces.cli_parser('toolname')
   

Alternatively, the hAMRonized_parser.py can be used as a common script interface to all implemented parsers.

6. Finally, following the template in test/test_parsing_validity.py, please generate a unit test that ensures the parser is working as you intend it to!

If you have any questions about any of this or need any help, please file an issue.

FAQ

• What's the difference between an Antimicrobial Resistance 'Result' and 'Report'?
  • For the purposes of this project, a 'Report' is an output file (or collection of files) from an AMR analysis tool. A 'Result' is a single entry in a report. For example, a single line in an abricate report file is a single Antimicrobial Resistance 'Result'.

Known Issues

Here are some known issues that we would welcome input on trying to solve!

Limitations of specification

• mandatory fields: gene_symbol and gene_name are confusing and not usually both present (only consistently used in AFP). Means tools either need 1:2 mapping i.e. single output field maps to both gene_symbol and gene_name OR have fragile text splitting of single field that won't be robust to databases changes. Current solution is 1:2 mapping e.g. staramr

• inconsistent nomenclature of terms being used in specification fields: target, query, subject, reference. Need to stick to one name for sequence with which the database is being searched, and one the hit that results from that search.

• sequence_identity: is sequence type specific %id amino acids != %id nucleotide but does this matter?

• coverage_depth seems to include both tool fields that are average depth of read and just plain overall read-count,

• contig_id isn't general enough when some tools this ID naturally corresponds to a read_name (deepARG), individual ORF (resfams), or protein sequence (AFP with protein input): change to query_id_name or similar?

Citation

If you use hAMRonization please cite the following publication:

I Mendes et al. 2024. "hAMRonization: Enhancing antimicrobial resistance prediction using the PHA4GE AMR detection specification and tooling". bioRxiv. https://doi.org/10.1101/2024.03.07.583950