BetAware Detection and topology predcition of outer-membrane β-barrels in prokaryotes. Welcome to the BetAware TMBB prediction server BetAware is a web server for TransMembrane β-Barrel (TMBB) detection and topology prediction. Both prediction steps are based on advanced machine-learning methods. For TMBB detection, BetAware exploits a new machine learning approach based on N-to-1 Extreme Learning Machines, while TMBB topology prediction is carried-out using a probabilistic model based on Grammatical-Restrained Hidden Conditional Random Fields, a discriminative framework introduced to address sequence labelling tasks in Bioinformatics.. For details see: Savojardo C., Fariselli P., Casadio R., "BETAWARE: a machine-learning tool to detect and predict trans-membrane β-barrel proteins in Prokaryotes", Bioinformatics (2013) 29(4): 504-505. Savojardo C., Fariselli P., Casadio R., "Improving the detection of transmembrane β-barrel chains with N-to-1 Extreme Learning Machines", Bioinformatics (2011) 27 (22): 3123-3128. Fariselli P., Savojardo C., Martelli P.L., Casadio R., "Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics Applications", Algorithms for Molecular Biology 2009, 4:13. Submit a sequence To start using BetAware you simply need to paste a FASTA sequence in the text area below and press the "Start prediciton" button. The server accepts a single FASTA sequence, at least 30 residue long. Fill with a test sequence Retrieve your job results Prediction results of previously executed jobs are available for a limited amount time (1 month) and can be retrieved by the user. Please, paste the job id you received at submission time (e.g. 154f0677-b6cf-45c9-b945-a7e25ff32958) in the field below and press "Search job" to retrieve the results. Information Home Method description Software download !!! WARNING !!! In this web site we make use of "Technical Cookies". The technical Cookies are needed to show our web page, make it work correctly. These technical cookies are necessary for our web page to function properly. For more information please see: UniBo policies or European Commission COPYRIGHT (C) 2013 BOLOGNA BIOCOMPUTING GROUP - UNIVERSITY OF BOLOGNA. ALL RIGHTS RESERVED.