SMISS: A protein function prediction server by integrating multiple sources

TL;DR

SMISS is a web server that predicts protein functions by integrating multiple data sources and can automatically switch to ab initio prediction when homologs are absent, supporting multiple input sequences and platforms.

Contribution

It introduces a novel integrated web server for protein function prediction combining various data sources and ab initio methods, enhancing accuracy and usability.

Findings

Integrates sequence, interaction networks, and mined rules for prediction.

Supports multiple sequences without slowing down.

Operates across various platforms without plugins.

Abstract

SMISS is a novel web server for protein function prediction. Three different predictors can be selected for different usage. It integrates different sources to improve the protein function prediction accuracy, including the query protein sequence, protein-protein interaction network, gene-gene interaction network, and the rules mined from protein function associations. SMISS automatically switch to ab initio protein function prediction based on the query sequence when there is no homologs in the database. It takes fasta format sequences as input, and several sequences can submit together without influencing the computation speed too much. PHP and Perl are two primary programming language used in the server. The CodeIgniter MVC PHP web framework and Bootstrap front-end framework are used for building the server. It can be used in different platforms in standard web browser, such as Windows, Mac OS X, Linux, and iOS. No plugins are needed for our website. Availability: http://tulip.rnet.missouri.edu/profunc/.