effector_predictor
ingenannot effector_predictor uses a combination of tools to predict potential fungal effector proteins.
usage
$ ingenannot -v 2 effector_predictor proteins.fasta
positional arguments:
fasta |
Fasta file of proteins |
optional arguments:
-h, –help |
show this help message and exit |
–signalp SIGNALP |
Path to signalp, default=/usr/local/bin/signalp (from system lookup) |
–tmhmm TMHMM |
Path to tmhmm, default=/usr/local/bin/tmhmm-2.0c/bin/tmhmm (from system lookup) |
–targetp TARGETP |
Path to targetp, default=/usr/local/bin/targetp (from system lookup) |
–effectorp EFFECTORP |
Path to signalp, default=None (from system lookup) |
–signalp_cpos SIGNALP_CPOS |
Maximal position of signal peptide cleavage site, default=25 |
–effectorp_score EFFECTORP_SCORE |
Minimal effectorp score, default=0.7 |
–max_len MAX_LEN |
Maximal length of protein, default=300 |
–min_len MIN_LEN |
Minimal length of protein, default=30 |
inputs
Fasta file of proteins
outputs
effectors.txt (export of results from pandas dataframe 2-level index)
,length,signalp,signalp,signalp,signalp,tmhmm,targetp,effectorp,effectorp
,,Cpos,Smax,SP,network,domains,Localization,prediction,probability
Seq,,,,,,,,,
cl_32_2,77,21,0.959,Y,SignalP-noTM,0,S,Effector,0.892
cl_35_4,66,18,0.958,Y,SignalP-noTM,0,S,Effector,0.747
cl_66_0,61,19,0.991,Y,SignalP-noTM,0,S,Effector,0.891
if you want to reuse this file as pandas.DataFrame():
>> import pandas as pd
>> df = pd.read_csv('effectors.txt', header=[0,1], index_col=0)
>> df
length signalp tmhmm targetp effectorp
Unnamed: 1_level_1 Cpos Smax SP network domains Localization prediction probability
Seq
cl_32_2 77 21 0.959 Y SignalP-noTM 0 S Effector 0.892
cl_35_4 66 18 0.958 Y SignalP-noTM 0 S Effector 0.747
cl_66_0 61 19 0.991 Y SignalP-noTM 0 S Effector 0.891