SECLAF: A Webserver and Deep Neural Network Design Tool for Biological Sequence Classification

TL;DR

SECLAF is a neural network framework using TensorFlow for biological sequence classification, achieving state-of-the-art accuracy in protein and gene ontology tasks, and is adaptable for various sequence classification problems.

Contribution

Introduces SECLAF, a flexible neural network framework for biological sequence classification, demonstrating high accuracy and applicability across different biological datasets.

Findings

Achieved 99.99% AUC for UniProt protein classification

Achieved 99.45% AUC for Gene Ontology classification

Framework is available as open-source software

Abstract

Artificial intelligence (AI) tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught easily by using the existing enormous biological databases, and the resulting models can be used for the high-quality classification of novel, un-categorized data in numerous areas, including biological sequence analysis. Here we introduce SECLAF, an artificial neural-net based biological sequence classifier framework, which uses the Tensorflow library of Google, Inc. By applying SECLAF for residue-sequences, we have reported (Methods (2017), https://doi.org/10.1016/j.ymeth.2017.06.034) the most accurate multi-label protein classifier to date (UniProt --into 698 classes-- AUC 99.99\%; Gene Ontology --into 983 classes-- AUC 99.45\%). Our framework SECLAF can be applied for other sequence classification tasks, as we describe in the present contribution. Availability and implementation: The program SECLAF is implemented in Python, and is available for download, with example datasets at the website https://pitgroup.org/seclaf/. For Gene Ontology and UniProt based classifications a webserver is also available at the address above.