Development of computational tools for the integrated analysis of DNA microarray data with applications in cancer research

The MAP-i Doctoral Program of the Universities of Minho, Aveiro and Porto

Integrating data from heterogeneous DNA microarray platforms

DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.

Discrimination of bacteriophage infected cells using locked nucleic acid fluorescent in situ hybridization (LNA-FISH)

Bacteriophage-host interaction studies in biofilm structures are still challenging due to the technical limitations of traditional methods. The aim of this study was to provide a direct fluorescence in situ hybridization (FISH) method based on locked nucleic acid (LNA) probes, which targets the phage replication phase, allowing the study of population dynamics during infection. Bacteriophages specific for two biofilm-forming bacteria, Pseudomonas aeruginosa and Acinetobacter, were selected. Four LNA probes were designed and optimized for phage-specific detection and for bacterial counterstaining. To validate the method, LNA-FISH counts were compared with the traditional plaque forming unit (PFU) technique. To visualize the progression of phage infection within a biofilm, colony-biofilms were formed and infected with bacteriophages. A good correlation (r=0.707) was observed between LNA-FISH and PFU techniques. In biofilm structures, LNA-FISH provided a good discrimination of the infected cells and also allowed the assessment of the spatial distribution of infected and non-infected populations.