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Predicting Type III Effector Proteins Using the Effectidor Web Server
Year:
2022
Source of publication :
Methods in Molecular Biology
Authors :
Teper, Doron
;
.
Volume :
Co-Authors:

Naama Wagner
Doron Teper
Tal Pupko  

Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
Abstract:

Various Gram-negative bacteria use secretion systems to secrete effector proteins that manipulate host biochemical pathways to their benefit. We and others have previously developed machine-learning algorithms to predict novel effectors. Specifically, given a set of known effectors and a set of known non-effectors, the machine-learning algorithm extracts features that distinguish these two protein groups. In the training phase, the machine learning learns how to best combine the features to separate the two groups. The trained machine learning is then applied to open reading frames (ORFs) with unknown functions, resulting in a score for each ORF, which is its likelihood to be an effector. We developed Effectidor, a web server for predicting type III effectors. In this book chapter, we provide a step-by-step introduction to the application of Effectidor, from selecting input data to analyzing the obtained predictions.

Note:
Related Files :
Bacterial pathogenicity
Effectidor
Effector proteins
Machine learning
pathogenicity
Secretion system
Type III effectors
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Related Content
More details
DOI :
10.1007/978-1-0716-1971-1_3
Article number:
0
Affiliations:
Database:
PubMed
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
59090
Last updated date:
08/06/2022 14:44
Creation date:
29/05/2022 17:48
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Scientific Publication
Predicting Type III Effector Proteins Using the Effectidor Web Server

Naama Wagner
Doron Teper
Tal Pupko  

Predicting Type III Effector Proteins Using the Effectidor Web Server

Various Gram-negative bacteria use secretion systems to secrete effector proteins that manipulate host biochemical pathways to their benefit. We and others have previously developed machine-learning algorithms to predict novel effectors. Specifically, given a set of known effectors and a set of known non-effectors, the machine-learning algorithm extracts features that distinguish these two protein groups. In the training phase, the machine learning learns how to best combine the features to separate the two groups. The trained machine learning is then applied to open reading frames (ORFs) with unknown functions, resulting in a score for each ORF, which is its likelihood to be an effector. We developed Effectidor, a web server for predicting type III effectors. In this book chapter, we provide a step-by-step introduction to the application of Effectidor, from selecting input data to analyzing the obtained predictions.

Scientific Publication
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