VOGDB-Database of Virus Orthologous Groups.

Computational models of homologous protein groups are essential in sequence bioinformatics. Due to the diversity and rapid evolution of viruses, the grouping of protein sequences from virus genomes is particularly challenging. The low sequence similarities of homologous genes in viruses require specific approaches for sequence- and structure-based clustering.

Living the Sweet Life: How TMW 1.1822 Changes Its Behavior in the Presence of Sucrose in Comparison to Glucose.

() (formerly ) is one of the dominating lactic acid bacteria within the water kefir consortium, being highly adapted to survive in this environment, while producing high molecular weight dextrans from sucrose. In this work, we extensively studied the physiological response of TMW 1.1822 to sucrose compared to glucose, applying label-free, quantitative proteomics of cell lysates and exoproteomes.

Conserved Secondary Structures in Viral mRNAs.

RNA secondary structure in untranslated and protein coding regions has been shown to play an important role in regulatory processes and the viral replication cycle. While structures in non-coding regions have been investigated extensively, a thorough overview of the structural repertoire of protein coding mRNAs, especially for viruses, is lacking. Secondary structure prediction of large molecules, such as long mRNAs remains a challenging task, as the contingent of structures a sequence can theoretically fold into grows exponentially with sequence length.

Conserved RNA structures in the intergenic regions of ambisense viruses.

Ambisense viruses are negative-sense single-stranded RNA viruses that use a unique expression strategy. Their genome contains at least one ambisense RNA segment that carries two oppositely oriented reading frames separated by an intergenic region. It is believed that a structural RNA element within the intergenic region is involved in transcription termination.

Homology-based inference sets the bar high for protein function prediction.

Background: Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference.

Methods: Here, we describe a few methods that predict protein function exclusively through homology.

A large-scale evaluation of computational protein function prediction.

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high.