Research article Sequence and structural analysis of the Asp-box motif and Asp-box beta-propellers; a widespread propeller-type characteristic of the Vps10 domain family and several glycoside hydrolase families

XMPP for cloud computing in bioinformatics supporting discovery and invocation of asynchronous web services

Research article Molecular evolution of the hyperthermophilic archaea of the Pyrococcus genus: analysis of adaptation to different environmental conditions

Amphioxus (Branchiostoma floridae) has orthologs of

Research article Comparison of eukaryotic phylogenetic profiling approaches using species tree aware methods

In the late 1900s, advancements in X‐ray crystallography, Nuclear Magnetic Resonance (NMR), gene cloning, and protein expression enabled the creation of structural genomics initiatives worldwide.

Biogen base serves the maize (Zea mays L.) research community by making a wealth of genetics and genomics data available through an intuitive Web-based interface. We have developed an Open access database as a resource to enhance research with the unique data obtained from the genomics and proteomics lab of TNAU. Biogen Base is an interactive database in bringing out the different traits of the inbreds of Tamil Nadu Agricultural University (UMI- University Maize Inbreds‟s) and the SSR Markers. The database interface is developed in PHP and HTML as the front end and MySQL as the backend tools. The webpage was developed using Dreamweaver and the database in MySQL is connected with the web server. The Current version of this database has four major parts and functions; (1) Germplasm- contains 101 germplasm lines and its 28 corresponding traits with values, (2) Genotype Search – enables the search among 31 SSR markers along with the chromosome number, gel patterns, forward and reverse primer, and allele size, (3) Phenotype Search- which contains text descriptions of all the phenotypic terminologies and their corresponding abbreviations stored in the database and (4) Mutant Phenotype Search – contains 5 mutant phenotypes with its traits and values. In addition, it includes brief description about the terms, and links to other publicly available databases. Images of plants with novel characteristics are also available at the web site. The large and growing body of experimental data on maize germplasm and DNA markers is of enormous value in the Genomics and Proteomics laboratories. The database can be searched using a user friendly web interface. This database is publicly available at

The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein–RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein–RNA complexes are provided. The PRIDB database is freely available online at

Abstract—In this paper we have analyzed current terminological resources in order to extract available cross-references (i.e. mappings) between biomedical terminologies. These crossreferences have been applied within the CALBC challenge, an initiative which aims at automatically creating a large-scale and community-wide text corpus annotated with biomedical entities. We also show that cross-references are essential for a precise evaluation of the identifiers associated with the annotated entities by the CALBC systems. Keywords-cross-references; mappings; thesaurus; CALBC; normalization; alignment I.

CvManGO, a method for leveraging computational predictions to improve