PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features
Journal article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Sangphukieo A., Laomettachit T., Ruengjitchatchawalya M.
Publisher: Public Library of Science
Publication year: 2021
Journal: PLoS ONE (1932-6203)
Volume number: 16
Issue number: 3
Start page: e0248682
ISSN: 1932-6203
eISSN: 1932-6203
Languages: English-Great Britain (EN-GB)
View in Web of Science | View on publisher site | View citing articles in Web of Science
Abstract
A new web server called PhotoModPlus is presented as a platform for predicting photosynthetic proteins via genome neighborhood networks (GNN) and genome neighborhood-based machine learning. GNN enables users to visualize the overview of the conserved neighboring genes from multiple photosynthetic prokaryotic genomes and provides functional guidance on the query input. In the platform, we also present a new machine learning model utilizing genome neighborhood features for predicting photosynthesis-specific functions based on 24 prokaryotic photosynthesis-related GO terms, namely PhotoModGO. The new model performed better than the sequence-based approaches with an F1 measure of 0.872, based on nested five-fold cross-validation. Finally, we demonstrated the applications of the webserver and the new model in the identification of novel photosynthetic proteins. The server is user-friendly, compatible with all devices, and available at bicep.kmutt.ac.th/ photomod. Copyright: © 2021 Sangphukieo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords
No matching items found.
Contact Information