Running GTNet¶
GTNet comes with multiple commands. The simplest way of running GTNet is to use the classify
command.
gtnet classify genome.fna > genome.tax.csv
This command generates one classification for the entire file, and should be used to get classification for metagenome bin.
Use the -s/--seqs
flag to get classifications for the individual sequences in genome.fna
Attention
The first time you run classify
and predict
(see below), the model file will be downloaded and stored in the same
directory that the gtnet package is installed in. Therefore, for the this to be successful, you must have write privileges
on the directory that gtnet is installed in.
gtnet classify --seqs genome.fna > genome.seqs.tax.csv
The classify
command can take multiple fasta files, and will produce line per file in the output. For example, the following
command will contain two lines:
gtnet classify bin1.fna bin2.fna > bins.tax.csv
GTNet steps¶
GTNet consists of two main steps: 1) get scored predictions of taxonoimc assignments and 2) filter scored predictions. The previous command combines these two commands into a single command with a default false-positive rate. The two steps have been separated into two commands for those who want to experiment with different false-positive rates.
Getting predictions¶
To get predictinos for all sequences in a Fasta file, use the predict
subcommand. This command also accepts multiple fasta files
and the -s/--seqs
argument for getting predictions for individual sequences.
gtnet predict genome.fna > genome.tax.raw.csv
Filtering predictions¶
After getting predicted and scored taxonomic classifications, you can filter the raw classifications to a desired false-positive rate.
gtnet filter --fpr 0.05 genome.tax.raw.csv > genome.tax.csv
The filter
command supports predictions for whole files and individual sequences.
GPU acceleration¶
If CUDA is available on your system, the classify
and predict
commands will have the option -g/--gpu
to enable
using the available GPU to accelerate neural network calculations.