The functional annotation of genes based on sequence homology with genes from model species genomes is time-consuming because it is necessary to mine several unrelated databases. The aim of the present work was to develop a functional annotation database for common wheat Triticum aestivum (L.). The database, named dbWFA, is based on the reference NCBI UniGene set, an expressed gene catalogue built by expressed sequence tag clustering, and on full-length coding sequences retrieved from the TriFLDB database. Information from good-quality heterogeneous sources, including annotations for model plant species Arabidopsis thaliana (L.) Heynh. and Oryza sativa L., was gathered and linked to T. aestivum sequences through BLAST-based homology searches. Even though the complexity of the transcriptome cannot yet be fully appreciated, we developed a tool to easily and promptly obtain information from multiple functional annotation systems (Gene Ontology, MapMan bin codes, MIPS Functional Categories, PlantCyc pathway reactions and TAIR gene families). The use of dbWFA is illustrated here with several query examples. We were able to assign a putative function to 45% of the UniGenes and 81% of the full-length coding sequences from TriFLDB. Moreover, comparison of the annotation of the whole T. aestivum UniGene set along with curated annotations of the two model species assessed the accuracy of the annotation provided by dbWFA. To further illustrate the use of dbWFA, genes specifically expressed during the early cell division or late storage polymer accumulation phases of T. aestivum grain development were identified using a clustering analysis and then annotated using dbWFA. The annotation of these two sets of genes was consistent with previous analyses of T. aestivum grain transcriptomes and proteomes.Database URL: urgi.versailles.inra.fr/dbWFA/
DroPNet (Drosophila Protein Network) is a Drosophila-dedicated web portal for generating and analyzing protein–protein interaction (PPI) networks. This platform integrates users’ experimental data provided as one or two lists of genes with PPI data from Drosophila and other species. These experimental data can, for example, come from RNAi screens, for which this approach is known to be valuable. DroPNet, therefore, provides an essential basis for further biological analysis by linking functional and physical interactions and reinforcing the relevance of each. DroPNet focuses on the search of PPIs between genes of the entry list, and includes the possibility of searching for intermediate genes for which the corresponding protein indirectly links two entry data. It also offers multiple functions for editing the networks obtained, providing users with interactive possibilities to progressively improve and refine the results. This approach gives a global view of the studied process and makes it possible to highlight specific interactions that have so far been understudied. DroPNet is freely available at http://dropnet.isima.fr.
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