Collection of core classes to help with building structure- and chemistry-based feature datasets to train machine learning models to predict antimicrobial resistance.
NextFlow pipeline that encapsualtes
- a variant call file (vcf) file output from a genetics processing pipeline
- a resistance catalogue
- a reference genome, as a GenBank file
- a list of antibiotics and their predicted effects
- CSV files of the discerned mutations and genetic variants
Designed to be the basis of a TB analysis pipeline. Based on
A python3 package that contains a single class,
ResistanceCatalogue that is instantiated with a resistance catalogue described using the GARC grammar. The instance has a
predict method that returns a dictionary containing the appropriate predictions for the antibiotics.
A number of published tuberculosis resistance catalogues described using the GARC grammar can be found here.
This is a contraction of “Genetics with Numpy”. It is a series of classes, based primarily on a
Genome object that is instantiated by a GenBank file. Gumpy is pythonic with appropriately overloaded operators; for example subtracting one genome from another yields a
This is built off pyniverse, this adds some analysis specific to this project.
This is a generic Python package able to analyse the downloaded classifications from a Zooniverse project. It automatically produces a series of graphs describing (i) how many classifications have been made every day/week/month as well as (ii) how many new users have signed up and (iii) the resulting Gini curve.
For a more detailed description of my Automated Mycobacterial Growth Detection Algorithm, including how to download, please go to the AMyGDA page.
A python3 package that provides a simple class, NucleotideStretch, for systematically and programmatically determining how many minor variations of a specified sequence of nucleotides exist in the Sequence Read Archive using the BIGSI index. As an example, some Python is included that counts how many variants of OXA-1 have been deposited in the SRA.