This is our Hack Med project, developed by: Gemma Rate, Matt Parker, Harry Cooper and Amber Barton.
ARAlert is a tool for diagnosing Antibiotic Resistance (AR) rapidly using nanopore long read technology and the Comprehensive Antibiotic Resistance Database (CARD).
ONT reads are aligned to known AR genes in CARD using Burrows Wheeler Alignment (BWA) and the corresponding antibiotics are identified from the Antibiotic Resistance Ontology (ARO).
Discovering antibiotic resistance is a time sensitive matter. To fully utilise the speed of ONT sequencing, and to speed up time from sample to diagnosis, ARAlert includes automated text messaging and email reports using Nexmo API, to alert doctors as soon as results are available.
- data from this paper
- downloaded data from ENA
- Aligned using BWA-MEM to CARD database
conda config --append channels bioconda
conda create -n hackmed python=3.5 bwa click pandas
source activate hackmed
pip install nexmo
pip install pronto
- BWA MEM
- Click
- pandas
- Nexmo Python API
- Pronto OBO parser
- CARD database
-i, --fastq TEXT ONT reads to map [required]
-j, --aro-dir TEXT Path to directory containing aro.json, etc
[required]
-o, --output TEXT Path to output csv [required]
-t, --to-number TEXT Number to send to [required]
-f, --from-number TEXT Nexmo number to send from [required]
-k, --key TEXT Nexmo API key [required]
-s, --secret TEXT Nexmo API secret [required]
-e, --email-from TEXT email address to send from [required]
-p, --email-password TEXT email address password [required]
-r, --email-to TEXT email address to send to [required]
--help Show this message and exit.
