PATRI, a Genomics Data Integration Tool for Biomarker Discovery.

The availability of genomic datasets in association with clinical, phenotypic, and drug sensitivity information represents an invaluable source for potential therapeutic applications, supporting the identification of new drug sensitivity biomarkers and pharmacological targets. Drug discovery and precision oncology can largely benefit from the integration of treatment molecular discriminants obtained from cell line models and clinical tumor samples; however this task demands comprehensive analysis approaches for the discovery of underlying data connections. Here we introduce PATRI (Platform for the Analysis of TRanslational Integrated data), a standalone tool accessible through a user-friendly graphical interface, conceived for the identification of treatment sensitivity biomarkers from user-provided genomics data, associated with information on sample characteristics. PATRI streamlines a translational analysis workflow: first, baseline genomics signatures are statistically identified, differentiating treatment sensitive from resistant preclinical models; then, these signatures are used for the prediction of treatment sensitivity in clinical samples, via random forest categorization of clinical genomics datasets and statistical evaluation of the relative phenotypic features. The same workflow can also be applied across distinct clinical datasets. The ease of use of the PATRI tool is illustrated with validation analysis examples, performed with sensitivity data for drug treatments with known molecular discriminants.

Selection of Apis mellifera workers by the parasitic mite Varroa destructor using host cuticular hydrocarbons.

The parasitic mite, Varroa destructor, is the most important threat for apiculture in most bee-keeping areas of the world. The mite is carried to the bee brood cell, where it reproduces, by a nurse bee; therefore the selection of the bee stage by the parasite could influence its reproductive success. This study investigates the role of the cuticular hydrocarbons of the European honeybee (Apis mellifera) in host-selection by the mite. Preliminary laboratory bioassays confirmed the preference of the varroa mite for nurse bees over pollen foragers. GC-MS analysis of nurse and pollen bees revealed differences in the cuticular hydrocarbons of the two stages; in particular, it appeared that pollen bees have more (Z)-8-heptadecene than nurse bees. Laboratory experiments showed that treatment of nurse bees with 100 ng of the pure compound makes them repellent to the varroa mite. These results suggest that the mite can exploit the differences in the cuticular composition of its host for a refined selection that allows it to reach a brood cell and start reproduction. The biological activity of the alkene encourages further investigations for the development of novel control techniques based on this compound.

Probe selection algorithms with applications in the analysis of microbial communities.

We propose two efficient heuristics for minimizing the number of oligonucleotide probes needed for analyzing populations of ribosomal RNA gene (rDNA) clones by hybridization experiments on DNA microarrays. Such analyses have applications in the study of microbial communities. Unlike in the classical SBH (sequencing by hybridization) procedure, where multiple probes are on a DNA chip, in our applications we perform a series of experiments, each one consisting of applying a single probe to a DNA microarray containing a large sample of rDNA sequences from the studied population. The overall cost of the analysis is thus roughly proportional to the number of experiments, underscoring the need for minimizing the number of probes. Our algorithms are based on two well-known optimization techniques, i.e. simulated annealing and Lagrangian relaxation, and our preliminary tests demonstrate that both algorithms are able to find satisfactory probe sets for real rDNA data.