Bioinformatics

The Cancer Genome Anatomy Project (CGAP) database of the National Cancer Institute contains thousands of expressed sequences, both known and novel, derived from diverse sets of normal, precancerous, and tumor cDNA libraries. This offers the possibility of using this database as a rational starting point for bioinformatics-based cancer gene discovery. Using the Digital Differential Display tool of the CGAP database, a hypothesis-driven gene discovery approach was undertaken to analyze differential expression of various solid tumor types. Two hundred known genes and five hundred novel sequences were discovered to be differentially expressed, and a comprehensive database was established to facilitate identification of cancer diagnostic and therapeutic targets. To validate the use of bioinformatics in discovering genes with organ- and tumor-selectivity, novel ESTs predicted to be colon tumor-specific were analyzed further for expression specificity. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) analysis using matched sets of colon normal- and tumor-derived cDNAs identified one EST to be specifically expressed in the majority of colon tumors and normal small intestine. Due to this apparent specificity, the gene was termed Colon Carcinoma Related Gene (CCRG). Based on protein sequence analysis, CCRG belongs to a novel class of secreted factors. Another gene identified in this study showed homology to Single Minded 2 gene (SIM2). Involvement between SIM2 and cancer has not yet been reported. Isoform-specific expression of SIM2 short-form (SIM2-s) was seen in colon, pancreas, and prostate carcinomas but not in most normal tissues. Using a large collection of paraffin sections from colon, pancreas, and prostate tumor and normal tissues, elevated protein expression was seen in tumors compared to normal tissue specimens, demonstrating the diagnostic potential of SIM2-s. Antisense inhibition of SIM2-s expression in colon and pancreatic cancer cell lines caused inhibition of gene expression, growth inhibition, and apoptosis. Administration of SIM2-s antisense in nude mice caused inhibition of colon tumor growth without pronounced gross toxicity. Using GeneChipRTM technology, a gene expression profile indicative of apoptosis was observed in the colon cancer model. CCRG and SIM2-s offer both a diagnostic and therapeutic potential in select cancers and validate the use of bioinformatics approaches in the gene discovery paradigm.

This research is concerned with analyzing a set of viral genomes to elucidate the underlying characteristics and determine the information-theoretic aspects of the genomic signatures. The goal of this study thereof, is tailored to address the following: (i) Reviewing various methods available to deduce the features and characteristics of genomic sequences of organisms in general, and particularly focusing on the genomes pertinent to viruses; (ii) applying the concepts of information-theoretics (entropy principles) to analyze genomic sequences; (iii) envisaging various aspects of biothermodynamic energetics so as to determine the framework and architecture that decide the stability and patterns of the subsequences in a genome; (iv) evaluating the genomic details using spectral-domain techniques; (v) studying fuzzy considerations to ascertain the overlapping details in genomic sequences; (vi) determining the common subsequences among various strains of a virus by logistically regressing the data obtained via entropic, energetics and spectral-domain exercises; (vii) differentiating informational profiles of coding and non-coding regions in a DNA sequence to locate aberrant (cryptic) attributes evolved as a result of mutational changes and (viii) finding the signatures of CDS of genomes of viral strains toward rationally conceiving plausible designs of vaccines. Commensurate with the topics indicated above, necessary simulations are proposed and computational exercises are performed (with MatLabTM R2009b and other software as needed). Extensive data gathered from open-literature are used thereof and, simulation results are verified. Lastly, results are discussed, inferences are made and open-questions are identified for future research.

Though several clinical monitoring ways exist and have been applied to detect cardiac atril fibrillation (A-Fib) and other arrhythmia, these medical interventions and the ensuing clinical treatments are after the fact and costly. Current portable healthcare monitoring systems come in the form of Ambulatory Event Monitors. They are small, battery-operated electrocardiograph devices used to record the heart's rhythm and activity. However, they are not energy-aware ; they are not personalized ; they require long battery life, and ultimately fall short on delivering real-time continuous detection of arrhythmia and specifically progressive development of cardiac A-Fib. The focus of this dissertation is the design of a class of adaptive and efficient energy-aware real-time detection models for monitoring, early real-time detection and reporting of progressive development of cardiac A-Fib.... The design promises to have a greater positive public health impact from predicting A-Fib and providing a viable approach to meeting the energy needs of current and future real-time monitoring, detecting and reporting required in wearable computing healthcare applications that are constrained by scarce energy resources.

The efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image in binary format is compared against a reference image so as to recognize the differential features that reside locally in the images being compared 2. SW and NW algorithms based binary comparison involves conversion of one-dimensional sequence alignment procedure (indicated traditionally for molecular sequence comparison adopted in bioinformatics) to 2D-image matrix 3. Relevant algorithms specific to computations are implemented as MatLabTM codes 4. Test-images considered are: Real-world bio-/medical-images, synthetic images, microarrays, biometric finger prints (thumb-impressions) and handwritten signatures. Based on the results, conclusions are enumerated and inferences are made with directions for future studies.

Transcription factors are macromolecules that are involved in transcriptional regulation by interacting with specific DNA regions, and they can cause activation or silencing of their target genes. Gene regulation by transcriptional control explains different biological processes such as development, function, and disease. Even though transcriptional control has been of great interest for molecular biology, much still remains unknown. This study was designed to generate the most current list of human transcription factor genes. Unique entries of transcription factor genes were collected and entered into Microsoft Office 2007 Access Database along with information about each gene. Microsoft Office 2007 Access tools were used to analyze and group collected entries according to different properties such as activator or repressor record, or presence of certain protein domains. Furthermore, protein sequence alignments of members of different groups were performed, and phylogenetic trees were used to analyze relationship between different members of each group. This work contributes to the existing knowledge of transcriptional regulation in humans.