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CFIA OLC Genome Quality Assessment with Machine Learning

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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

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.

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GenomeQAML-0.0.13.tar.gz (8.1 MB view hashes)

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