Addressing Challenges in a Graph-Based Analysis of High-Throughput Biological Data

Graph-based methods used in the analysis of DNA microarray technology can be powerful tools in the elucidation of biological relationships. As these methods are developed and applied to various types of data, challenges arise that test the limits of current algorithms. These challenges arise in all phases of data analysis: data normalization, modeling biological networks, and interpreting results. Spectral graph theory methods are investigated as means of threshold selection, a key step in constructing graphical models of biological data. Also important in constructing graphs is the selection of an appropriate gene-gene similarity metric, and an overview of similarity profiles for some biological data sets is present, along with a similarity thresholding method based upon structural properties of random graphs. The identification of altered relationships between two or more conditions is a goal of many microarray gene expression studies. Clique-based methods can identify sets of coexpressed genes within each group, but additional computational methods are required to uncover the differential relationships and sets of genes changing together between groups. Differential filters are reviewed to highlight those changing interactions and sets of changing genes. The effect of various normalization methods on these differential results is also studied. Finally, how methods commonly used in the analysis of gene expression data can be used to investigate relationships in noisy and incomplete historical ecosystem data is explored

Studies on the relationships between oligonucleotide probe properties and hybridization signal intensities

Microarray technology is a commonly used tool in biomedical research for assessing global gene expression, surveying DNA sequence variations, and studying alternative gene splicing. Given the wide range of applications of this technology, comprehensive understanding of its underlying mechanisms is of importance. The focus of this work is on contributions from microarray probe properties (probe secondary structure: ?Gss, probe-target binding energy: ?G, probe-target mismatch) to the signal intensity. The benefits of incorporating or ignoring these properties to the process of microarray probe design and selection, as well as to microarray data preprocessing and analysis, are reported. Four related studies are described in this thesis. In the first, probe secondary structure was found to account for up to 3% of all variation on Affymetrix microarrays. In the second, a dinucleotide affinity model was developed and found to enhance the detection of differentially expressed genes when implemented as a background correction procedure in GeneChip preprocessing algorithms. This model is consistent with physical models of binding affinity of the probe target pair, which depends on the nearest-neighbor stacking interactions in addition to base-pairing. In the remaining studies, the importance of incorporating biophysical factors in both the design and the analysis of microarrays ‘percent bound’, predicted by equilibrium models of hybridization, is a useful factor in predicting and assessing the behavior of long oligonucleotide probes. However, a universal probe-property-independent three-parameter Langmuir model has also been tested, and this simple model has been shown to be as, or more, effective as complex, computationally expensive models developed for microarray target concentration estimation. The simple, platform-independent model can equal or even outperform models that explicitly incorporate probe properties, such as the model incorporating probe percent bound developed in Chapter Three. This suggests that with a “spiked-in” concentration series targeting as few as 5-10 genes, reliable estimation of target concentration can be achieved for the entire microarray