chAMReDb 0.1

Package to find the equivalent antibiotic resistance genes (ARGs) in other databases based on ARG(s) from one AMR determinant database

CharmeDb

(pronounced 'charmed' /tʃɑː(r)md/)

Previously known as Project

(Abandoned for obvious trademark issues and the fact that the joke may be lost on non-Brits)

Introduction

This project originated from the dilema a scientist faces when choosing a database that stores antimicrobial resistance determinants. Multiple databases exist with comparative strengths and weaknesses. This project builds on the concepts of the haAMRonization project aiming to aggeregate and combine the information contained within the metadata associated with each project. The problem is exacerbated by the fact that the equivalent antimicrobial resistance genes (ARGs) can be named differently in each database.

The hypothesis for the project is as follows:

• given a match in one database
• find the matches in other databases
• aggregate the combined desriptive information pertaining to antimicrobial resistance contained in the union of the metadata
• report this to user for them to make intelligent informed choices

Methodology

• Download sequences and associated metadata of ARGs from 3 databases
  • CARD (Manuscript)
  • NCBI AMR Reference Gene Catalog
  • Resfinder 4 (Manuscipt)
    Details can be found in the appendices
• Parse the data to
  • extract the protein sequences and write into fasta format with the gene identifiers as the record ids.
  • extract the associated metadata and convert to a consistent JSON format
    Details can be found in the appendices
• Find best matches of each gene from one source database against the other two target databases
  • Where a reciprocal best hit (RBH) exists, report this.
    Details can be found in the appendices.
    A summary of the results can be found here
  • If a RBH does not exist, report the best match as long as thresholds for coverage and indentity are met.
    A summary of the results can be found here

For this purpose the MMseqs2 search tool was used that in its most sensitive mode is 100x faster than blastp and almost as sensitive. In a comparative manuscript demonstrated that even in the worst cases mmseqs would not miss more than 10% of the RBH produced by blastp. MMseqs2 also contains a convenient wrapper to perform the all-by-all search necessary to find RBHs.

• From the outputs of the MMseqs2 searches the RBHs or best matches of each gene from one database against the other two databases can be parsed to produce a Directed Graph. This netowrk was constructed using the networkx python package.
  Details of the method can be found here
  In this graph
  • the nodes represent a protein from one database
    • Node attributes contain the phenotype from the JSON metadata
  • the edges link nodes and represent the matches and attributes include
    • type either RBH or OWH (one way hit)
    • coverage (alignment length/query length)
    • identity (percent identity of match)
      See the image below for a pictoral example using made up data
      

Querying the graph

The graph can be queried in one of 3 ways

1. Querying an individual gene by specifying the identifier -i and database -d

   chamredb query -d ncbi -i WP_012695489.1 
   

   Alternatively the gene name can be used

   chamredb query -d ncbi -i qnrB2
   

   The output reports the matches and metadata from the other databases
   

   Another example where the matches are one way hits not RBHs

   chamredb query -d resfinder -i "aac(3)-IIIb"
   

   In these outputs ↔ means a RBH, and ➡ a search hit

2. Providing a list of identifiers from a single database -d in a text file -f and specifying a path for the tsv output file

   chamredb query -d card -f docs/data/card_ids.txt  -o docs/data/card_ids.tsv
   

   This will produce a TSV file containing the matches and associated metadata with one row per id in the text file

3. Use the hAMRonization softare to convert the outputs from an antimicrobial resistance gene detection tools into a unified format. Concatenate and summarize AMR detection reports into a single summary JSON file using the hamronize summarize command from this package. The JSON output from this step can be used to query ChamreDb.
   Please Note only outputs using data derived from AMR detection tools that have searched either the CARD, NCBI or Resfinder 4 databases can be used.

   chamredb query -j docs/data/hamronize_summary.json -o docs/data/hamronize_summary.tsv
   

   This will produce a TSV file containing the matches and associated metadata with one row per id in the text file