LAMPA, LArge Multidomain Protein Annotator, and its application to RNA virus polyproteins.

MOTIVATION: To facilitate accurate estimation of statistical significance of sequence similarity in profile-profile searches, queries should ideally correspond to protein domains. For multidomain proteins, using domains as queries depends on delineation of domain borders, which may be unknown. Thus, proteins are commonly used as queries that complicate establishing homology for similarities close to cutoff levels of statistical significance. RESULTS: In this article, we describe an iterative approach, called LAMPA, LArge Multidomain Protein Annotator, that resolves the above conundrum by gradual expansion of hit coverage of multidomain proteins through re-evaluating statistical significance of hit similarity using ever smaller queries defined at each iteration. LAMPA employs TMHMM and HHsearch for recognition of transmembrane regions and homology, respectively. We used Pfam database for annotating 2985 multidomain proteins (polyproteins) composed of >1000 amino acid residues, which dominate proteomes of RNA viruses. Under strict cutoffs, LAMPA outperformed HHsearch-mediated runs using intact polyproteins as queries by three measures: number of and coverage by identified homologous regions, and number of hit Pfam profiles. Compared to HHsearch, LAMPA identified 507 extra homologous regions in 14.4% of polyproteins. This Pfam-based annotation of RNA virus polyproteins by LAMPA was also superior to RefSeq expert annotation by two measures, region number and annotated length, for 69.3% of RNA virus polyprotein entries. We rationalized the obtained results based on dependencies of HHsearch hit statistical significance for local alignment similarity score from lengths and diversities of query-target pairs in computational experiments. AVAILABILITY AND IMPLEMENTATION: LAMPA 1.0.0 R package is placed at github (https://github.com/Gorbalenya-Lab/LAMPA). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome rearrangements and phylogeny reconstruction in Yersinia pestis.

Genome rearrangements have played an important role in the evolution of Yersinia pestis from its progenitor Yersinia pseudotuberculosis. Traditional phylogenetic trees for Y. pestis based on sequence comparison have short internal branches and low bootstrap supports as only a small number of nucleotide substitutions have occurred. On the other hand, even a small number of genome rearrangements may resolve topological ambiguities in a phylogenetic tree. We reconstructed phylogenetic trees based on genome rearrangements using several popular approaches such as Maximum likelihood for Gene Order and the Bayesian model of genome rearrangements by inversions. We also reconciled phylogenetic trees for each of the three CRISPR loci to obtain an integrated scenario of the CRISPR cassette evolution. Analysis of contradictions between the obtained evolutionary trees yielded numerous parallel inversions and gain/loss events. Our data indicate that an integrated analysis of sequence-based and inversion-based trees enhances the resolution of phylogenetic reconstruction. In contrast, reconstructions of strain relationships based on solely CRISPR loci may not be reliable, as the history is obscured by large deletions, obliterating the order of spacer gains. Similarly, numerous parallel gene losses preclude reconstruction of phylogeny based on gene content.