We have developed the database, TMBETA-GENOME, for annotated beta-barrel 
membrane proteins in genomic sequences using statistical methods and machine 
learning algorithms. The statistical methods are based on amino acid 
composition, reside pair preference and motifs. In machine learning techniques, 
the combination of amino acid and dipeptide compositions has been used as main 
attributes. In addition, annotations have been made using the criterion based on 
the identification of beta-barrel membrane proteins and exclusion of globular 
and transmembrane helical proteins. A web interface has been developed for 
identifying the annotated beta-barrel membrane proteins in all known genomes. 
The users have the feasibility of selecting the genome from the three kingdoms 
of life, archaea, bacteria and eukaryote, and five different methods. Further, 
the statistics for all genomes have been provided along with the links to 
different algorithms and related databases. It is freely available at 
http://tmbeta-genome.cbrc.jp/annotation/.