Accurate plant pathogen effector protein classification ab initio with deepredeff: an ensemble of convolutional neural networks.

ABSTRACT:

Background

Plant pathogens cause billions of dollars of crop loss every year and are a major threat to global food security. Effector proteins are the tools such pathogens use to infect the cell, predicting effectors de novo from sequence is difficult because of the heterogeneity of the sequences. We hypothesised that deep learning classifiers based on Convolutional Neural Networks would be able to identify effectors and deliver new insights.

Results

We created a training set of manually curated effector sequences from PHI-Base and used these to train a range of model architectures for classifying bacteria, fungal and oomycete sequences. The best performing classifiers had accuracies from 93 to 84%. The models were tested against popular effector detection software on our ow

SUBMITTER: Kristianingsih R 

PROVIDER: S-EPMC8285798 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature