  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.
