HEG-DB: a database of predicted highly expressed genes in prokaryotic complete genomes under translational selection

Abstract

The highly expressed genes database (HEG-DB) is a genomic database that includes the prediction of which genes are highly expressed in prokaryotic complete genomes under strong translational selection. The current version of the database contains general features for almost 200 genomes under translational selection, including the correspondence analysis of the relative synonymous codon usage for all genes, and the analysis of their highly expressed genes. For each genome, the database contains functional and positional information about the predicted group of highly expressed genes. This information can also be accessed using a search engine. Among other statistical parameters, the database also provides the Codon Adaptation Index (CAI) for all of the genes using the codon usage of the highly expressed genes as a reference set. The 'Pathway Tools Omics Viewer' from the BioCyc database enables the metabolic capabilities of each genome to be explored, particularly those related to the group of highly expressed genes. The HEG-DB is freely available at http://genomes.urv.cat/HEG-DB.

Figure 1.

Outputs provided from the HEG-DB: (a) ‘Genomes consult’ shows the list of all the genomes available in the database. In this section, users can select one or more genomes to see the statistical parameters (including the codon usage correspondence analysis plot used to predict translational selection) of the selected genomes. (b) The statistical and functional information available in each gene is accessible by a global consult of a specific genome or by a search engine. This section includes the CAI value of each gene. (c) List of predicted highly expressed genes in each genome. This section includes functional and positional information about each predicted gene. (d) The metabolic pathways, which involve highly expressed genes can be viewed through the ‘pathway tools overview expression viewer’ from the BioCyc database. In addition, this tool can be used to mark all genes according to their CAI on the pathway maps.