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A pipeline implementing TB-Profiler for batch detection and reporting of anti-microbial resistance in TB for public health and clinical use.

Project description

Python 3.7

Troika

Detection of resistance mechanisms in Mycobacterium tuberculosis is dependent upon identification of SNPs that may confer decreased susceptibility to anti-mycobacterial drugs. Troika is a pipeline, which calls SNPs for both phylogenetic analysis and determination of AST. Troika leverages high quality tools, including Snippy and TB-profiler and its related database to detect resistance conferring mutations from Illumina read data and filters these results for reporting for public health and clinical use in Australia.

Motivation

There are many tools and databases available, however, for the purposes of reporting genomic AST for M. tuberculosis in the context of public health and clinical use in Australia customisation is required. Rather than reinventing the wheel, Troika leverages a high quality database and a detection tool which is highly customisable.

Pipeline

Troika is designed for batch reporting of AST in M. tuberculosis isolates generated from Illumina reads and phylogenetic analysis and clustering to identify potentially related isolates. This pipeline is in use at MDU Victoria Australia for Tuberculosis surveillance and AST reporting.

Installation

TO COME

Conda (Recomended)

PyPi

Running Troika

TO COME

Project details


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Files for Troika-TB, version 0.0.3
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Filename, size Troika_TB-0.0.3.tar.gz (19.5 kB) File type Source Python version None Upload date Hashes View

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