SUMMARY: FeatureStack is a Perl module for the automatic generation of multi-gene images. FeatureStack takes BioPerl-compliant gene or transcript features as input and renders them side by side using a user-defined BioPerl glyph. Output images can be generated in SVG or PNG format. FeatureStack comes with a new BioPerl glyph, decorated_gene, which can highlight protein features on top of gene models. Used in combination, FeatureStack and decorated_gene enable rapid and automated generation of annotation-rich images of stacked gene models that greatly facilitate evolutionary studies of related gene structures and gene families. AVAILABILITY AND IMPLEMENTATION: Bio-Draw-FeatureStack and Bio-Graphics-glyph-decorated_gene are freely available at the Comprehensive Perl Archive Network (CPAN) and GitHub. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genes underlying important phenotypic differences between Plasmodium species, the causative agents of malaria, are frequently found in only a subset of species and cluster at dynamically evolving subtelomeric regions of chromosomes. We hypothesized that chromosome-internal regions of Plasmodium genomes harbour additional species subset-specific genes that underlie differences in human pathogenicity, human-to-human transmissibility, and human virulence. We combined sequence similarity searches with synteny block analyses to identify species subset-specific genes in chromosome-internal regions of six published Plasmodium genomes, including Plasmodium falciparum, Plasmodium vivax, Plasmodium knowlesi, Plasmodium yoelii, Plasmodium berghei, and Plasmodium chabaudi. To improve comparative analysis, we first revised incorrectly annotated gene models using homology-based gene finders and examined putative subset-specific genes within syntenic contexts. Confirmed subset-specific genes were then analyzed for their role in biological pathways and examined for molecular functions using publicly available databases. We identified 16 genes that are well conserved in the three primate parasites but not found in rodent parasites, including three key enzymes of the thiamine (vitamin B1) biosynthesis pathway. Thirteen genes were found to be present in both human parasites but absent in the monkey parasite P. knowlesi, including genes specifically upregulated in sporozoites or gametocytes that could be linked to parasite transmission success between humans. Furthermore, we propose 15 chromosome-internal P. falciparum-specific genes as new candidate genes underlying increased human virulence and detected a currently uncharacterized cluster of P. vivax-specific genes on chromosome 6 likely involved in erythrocyte invasion. In conclusion, Plasmodium species harbour many chromosome-internal differences in the form of protein-coding genes, some of which are potentially linked to human disease and thus promising leads for future laboratory research.

BACKGROUND: Protein-protein interaction (PPI) data sets generated by high-throughput experiments are contaminated by large numbers of erroneous PPIs. Therefore, computational methods for PPI validation are necessary to improve the quality of such data sets. Against the background of the theory that most extant PPIs arose as a consequence of gene duplication, the sensitive search for homologous PPIs, i.e. for PPIs descending from a common ancestral PPI, should be a successful strategy for PPI validation. RESULTS: To validate an experimentally observed PPI, we combine FASTA and PSI-BLAST to perform a sensitive sequence-based search for pairs of interacting homologous proteins within a large, integrated PPI database. A novel scoring scheme that incorporates both quality and quantity of all observed matches allows us (1) to consider also tentative paralogs and orthologs in this analysis and (2) to combine search results from more than one homology detection method. ROC curves illustrate the high efficacy of this approach and its improvement over other homology-based validation methods. CONCLUSION: New PPIs are primarily derived from preexisting PPIs and not invented de novo. Thus, the hallmark of true PPIs is the existence of homologous PPIs. The sensitive search for homologous PPIs within a large body of known PPIs is an efficient strategy to separate biologically relevant PPIs from the many spurious PPIs reported by high-throughput experiments.

BACKGROUND: The recent availability of an expanding collection of genome sequences driven by technological advances has facilitated comparative genomics and in particular the identification of synteny among multiple genomes. However, the development of effective and easy-to-use methods for identifying such conserved gene clusters among multiple genomes-synteny blocks-as well as databases, which host synteny blocks from various groups of species (especially eukaryotes) and also allow users to run synteny-identification programs, lags behind. DESCRIPTIONS: OrthoClusterDB is a new online platform for the identification and visualization of synteny blocks. OrthoClusterDB consists of two key web pages: Run OrthoCluster and View Synteny. The Run OrthoCluster page serves as web front-end to OrthoCluster, a recently developed program for synteny block detection. Run OrthoCluster offers full control over the functionalities of OrthoCluster, such as specifying synteny block size, considering order and strandedness of genes within synteny blocks, including or excluding nested synteny blocks, handling one-to-many orthologous relationships, and comparing multiple genomes. In contrast, the View Synteny page gives access to perfect and imperfect synteny blocks precomputed for a large number of genomes, without the need for users to retrieve and format input data. Additionally, genes are cross-linked with public databases for effective browsing. For both Run OrthoCluster and View Synteny, identified synteny blocks can be browsed at the whole genome, chromosome, and individual gene level. OrthoClusterDB is freely accessible. CONCLUSION: We have developed an online system for the identification and visualization of synteny blocks among multiple genomes. The system is freely available at (http://genome.sfu.ca/orthoclusterdb/).