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RSpred, a set of Hidden Markov Models to detect and classify the RIFIN and STEVOR proteins of Plasmodium falciparum by Nicolas Joannin; Yvonne Kallberg; Mats Wahlgren; Bengt Persson is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is RSpred, a set of Hidden Markov Models to detect and classify the RIFIN and STEVOR proteins of Plasmodium falciparum about?

## Background Many parasites use multicopy protein families to avoid their host's immune system through a strategy called antigenic variation. RIFIN and STEVOR proteins are variable surface antigens uniquely found in the malaria parasites __Plasmodium falciparum__ and __P. reichenowi__. Although these two protein families are different, they have more similarity to each other than to any other proteins described to date. As a result, they have been grouped together in one Pfam domain. However, a recent study has described the sub-division of the RIFIN protein family into several functionally distinct groups. These sub-groups require phylogenetic analysis to sort out, which is not practical for large-scale projects, such as the sequencing of patient isolates and meta-genomic analysis. ## Results We have manually curated the __rif__ and __stevor__ gene repertoires of two __Plasmodium falciparum__ genomes, isolates DD2 and HB3. We have identified 25% of mis-annotated and ~30 missing __rif__ and __stevor__ genes. Using these data sets, as well as sequences from the well curated reference genome (isolate 3D7) and field isolate data from Uniprot, we have developed a tool named RSpred. Th

Who reads RSpred, a set of Hidden Markov Models to detect and classify the RIFIN and STEVOR proteins of Plasmodium falciparum?

Author

Nicolas Joannin; Yvonne Kallberg; Mats Wahlgren; Bengt Persson

Publisher

BioMed Central; Springer (Biomed Central Ltd.); London: BioMed Central, 2000-; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1471-2164)

Published

2011

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)