GenomeQAML 0.0.13

CFIA OLC Genome Quality Assessment with Machine Learning

GenomeQAML: Genome Quality Assessment with Machine Learning

The GenomeQAML is a script that uses a pre-computed ExtraTreesClassifier model in order to classify FASTA-formatted de novo assemblies as bad, good, or very good. It's easy to use, and has minimal dependencies.

External Dependencies

• Mash (v2.0 or greater)
• Prodigal (>=2.6.2)

Both of these need to be downloaded and included on your $PATH.

Installation

All you need to do is install with pip: pip install genomeqaml.

Usage of a virtualenv is highly recommended.

Usage

GenomeQAML takes a directory containing uncompressed fasta files as input - these will be classified and a report written to a CSV-formatted file for your inspection.

To run, type classify.py -t /path/to/fasta/folder

This will create a report, by default called QAMLreport.csv. You can change the name of the report with the -r argument.

usage: classify.py [-h] -t TEST_FOLDER [-r REPORT_FILE]

optional arguments:
  -h, --help            show this help message and exit
  -t TEST_FOLDER, --test_folder TEST_FOLDER
                        Path to folder containing FASTA files you want to
                        test.
  -r REPORT_FILE, --report_file REPORT_FILE
                        Name of output file. Default is QAMLreport.csv.