Genomic decay is a common feature of intracellular bacteria that have entered into symbiosis with plant sap-feeding insects. This study of the whitefly Bemisia tabaci and two bacteria (Portiera aleyrodidarum and Hamiltonella defensa) cohoused in each host cell investigated whether the decay of Portiera metabolism genes is complemented by host and Hamiltonella genes, and compared the metabolic traits of the whitefly symbiosis with other sap-feeding insects (aphids, psyllids, and mealybugs). Parallel genomic and transcriptomic analysis revealed that the host genome contributes multiple metabolic reactions that complement or duplicate Portiera function, and that Hamiltonella may contribute multiple cofactors and one essential amino acid, lysine. Homologs of the Bemisia metabolism genes of insect origin have also been implicated in essential amino acid synthesis in other sap-feeding insect hosts, indicative of parallel coevolution of shared metabolic pathways across multiple symbioses. Further metabolism genes coded in the Bemisia genome are of bacterial origin, but phylogenetically distinct from Portiera, Hamiltonella and horizontally transferred genes identified in other sap-feeding insects. Overall, 75% of the metabolism genes of bacterial origin are functionally unique to one symbiosis, indicating that the evolutionary history of metabolic integration in these symbioses is strongly contingent on the pattern of horizontally acquired genes. Our analysis, further, shows that bacteria with genomic decay enable host acquisition of complex metabolic pathways by multiple independent horizontal gene transfers from exogenous bacteria. Specifically, each horizontally acquired gene can function with other genes in the pathway coded by the symbiont, while facilitating the decay of the symbiont gene coding the same reaction.

Accurate detection of viruses in plants and animals is critical for agriculture production and human health. Deep sequencing and assembly of virus-derived small interfering RNAs has proven to be a highly efficient approach for virus discovery. Here we present VirusDetect, a bioinformatics pipeline that can efficiently analyze large-scale small RNA (sRNA) datasets for both known and novel virus identification. VirusDetect performs both reference-guided assemblies through aligning sRNA sequences to a curated virus reference database and de novo assemblies of sRNA sequences with automated parameter optimization and the option of host sRNA subtraction. The assembled contigs are compared to a curated and classified reference virus database for known and novel virus identification, and evaluated for their sRNA size profiles to identify novel viruses. Extensive evaluations using plant and insect sRNA datasets suggest that VirusDetect is highly sensitive and efficient in identifying known and novel viruses. VirusDetect is freely available at http://bioinfo.bti.cornell.edu/tool/VirusDetect/.

The Cucurbitaceae family (cucurbit) includes several economically important crops, such as melon, cucumber, watermelon, pumpkin, squash and gourds. During the past several years, genomic and genetic data have been rapidly accumulated for cucurbits. To store, mine, analyze, integrate and disseminate these large-scale datasets and to provide a central portal for the cucurbit research and breeding community, we have developed the Cucurbit Genomics Database (CuGenDB; http://cucurbitgenomics.org) using the Tripal toolkit. The database currently contains all available genome and expressed sequence tag (EST) sequences, genetic maps, and transcriptome profiles for cucurbit species, as well as sequence annotations, biochemical pathways and comparative genomic analysis results such as synteny blocks and homologous gene pairs between different cucurbit species. A set of analysis and visualization tools and user-friendly query interfaces have been implemented in the database to facilitate the usage of these large-scale data by the community. In particular, two new tools have been developed in the database, a ‘SyntenyViewer’ to view genome synteny between different cucurbit species and an ‘RNA-Seq’ module to analyze and visualize gene expression profiles. Both tools have been packed as Tripal extension modules that can be adopted in other genomics databases developed using the Tripal system.