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d9a04ad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | import argparse
import textwrap
#import argnet_lsaa as lsaa
#import argnet_lsnt as lsnt
#import argnet_ssaa as ssaa
#import argnet_ssnt as ssnt
import sys
parser = argparse.ArgumentParser(
prog='ARGNet',
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent("""\
ARGNet: a deep nueral network for robust identification and annotation of antibiotic resistance genes.
--------------------------------------------------------------------------------------------------------
The standlone program is at https:...
The online service is at https:...
The input can be long amino acid sequences(full length/contigs), long nucleotide sequences,
short amino acid reads (30-50aa), short nucleotide reads (100-150nt) in fasta format.
If your input is short reads you should assign 'argnet-s' model, or if your input is full-length/contigs
you should assign 'argnet-l' to make the predict.
USAGE:
for full-length or contigs
python argnet.py --input input_path_data --type aa/nt --model argnet-l --outname output_file_name
for short reads
python argnet.py --input input_path_data --type aa/nt --model argnet-s --outname output_file_name
general options:
--input/-i the test file as input
--type/-t molecular type of your test data (aa for amino acid, nt for nucleotide)
--model/-m the model you assign to make the prediction (argnet-l for long sequences, argnet-s for short reads)
--outname/-on the output file name
"""
),
epilog='Hope you enjoy ARGNet journey, any problem please contact scpeiyao@gmail.com')
parser.print_help()
#parser.parse_args()
parser.add_argument('-i', '--input', required=True, help='the test data as input')
parser.add_argument('-t', '--type', required=True, choices=['aa', 'nt'], help='molecular type of your input file')
parser.add_argument('-m', '--model', required=True, choices=['argnet-s', 'argnet-l'], help='the model to make the prediction')
parser.add_argument('-on', '--outname', required=True, help='the name of results output')
args = parser.parse_args()
## for AESS_aa -> classifier
if args.type == 'aa' and args.model == 'argnet-s':
import argnet_ssaa_chunk as ssaa
ssaa.argnet_ssaa(args.input, args.outname)
# for AESS_nt -> classifier
if args.type == 'nt' and args.model == 'argnet-s':
import argnet_ssnt_new_chunk as ssnt
ssnt.argnet_ssnt(args.input, args.outname)
# for AELS_aa -> classifier
if args.type == 'aa' and args.model == 'argnet-l':
import argnet_lsaa_speed_sgpu as lsaa
lsaa.argnet_lsaa(args.input, args.outname)
# for AELS_nt -> classifier
if args.type == 'nt' and args.model == 'argnet-l':
import argnet_lsnt as lsnt
lsnt.argnet_lsnt(args.input, args.outname)
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