# cAMRah: a scalable and portable workflow for harmonized antimicrobial resistance gene prediction from bacterial genomes

## TL;DR

cAMRah is a flexible workflow that combines multiple tools to accurately predict antimicrobial resistance genes in bacterial genomes.

## Contribution

cAMRah introduces a harmonized, consensus-based approach for AMR gene prediction using multiple tools and databases.

## Key findings

- cAMRah integrates six AMR-finder tools and databases for comprehensive AMR gene prediction.
- The workflow harmonizes results into consistent gene symbols and ontologies.
- cAMRah is portable and can run on any system with Docker.

## Abstract

cAMRah is a curated workflow designed to predict antimicrobial resistance (AMR) genes in microbial genomes, either in the cloud or on any personal computer running Docker containers. Numerous AMR gene-finding packages exist, each utilizing different algorithms and prediction methods. cAMRah adopts a consensus-based approach to AMR prediction, recognizing that no single tool can identify all AMR genes. It integrates and runs six AMR-finder tools and databases (with plans for future expansion), scores the AMR predictions, maps all results to CDS coordinates and harmonizes the annotation, resulting in consistent gene symbols and ontologies.

Source code, demo data and detailed documentation are freely available at https://github.com/JCVenterInstitute/CAMRA.