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