AutoClassWeb: a simple web interface for Bayesian clustering

TL;DR

AutoClassWeb offers an accessible online platform for Bayesian clustering of large omics datasets, simplifying data input and result interpretation for genomics and proteomics research.

Contribution

It provides a user-friendly web interface for AutoClass Bayesian clustering, making advanced clustering accessible without command-line expertise.

Findings

Easy data upload via TSV files

Results compatible with common analysis tools

Open-source implementation in Python

Abstract

Objective: Data clustering is a common exploration step in the omics era, notably in genomics and proteomics where many genes or proteins can bequantified from one or more experiments. Bayesian clustering is a powerful algorithm that can classify several thousands of genes or proteins. AutoClass C, its original implementation, handles missing data, automatically determines the best number of clusters but is not user-friendly.Results: We developed an online tool called AutoClassWeb, which provides an easy-to-use web interface for Bayesian clustering with AutoClass. Input data are entered as TSV files. Results are provided in formats that ease further analyses with spreadsheet programs or with programming languages, such as Python or R. AutoClassWeb is implemented in Python and is published under the 3-Clauses BSD license. The source code is available athttps://github.com/pierrepo/autoclassweb along with a detailed documentation.