Recently, genome-wide surveys for non-coding RNAs have provided evidence for tens of thousands of previously undescribed evolutionary conserved RNAs with distinctive secondary structures. The annotation of these putative ncRNAs, however, remains a difficult problem. Here we describe an SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of efficiently recognizing microRNA precursors in multiple sequence alignments. The software was applied to recent genome-wide RNAz surveys of mammals, urochordates, and nematodes.

Background: A small class of RNA molecules, in particular the tiny genomes of viroids, are circular. Yet most structure prediction algorithms handle only linear RNAs. The most straightforward approach is to compute circular structures from ‘internal’ and ‘external’ substructures separated by a base pair. This is incompatible, however, with the memory-saving approach of the Vienna RNA Package which builds a linear RNA structure from shorter (internal) structures only. Result: Here we describe how circular secondary structures can be obtained without additional memory requirements as a kind of ‘post-processing’ of the linear structures.

The genomes of RNA viruses often carry conserved RNA structures that perform vital functions during the life cycle of the virus. Such structures can be detected using a combination of structure prediction and co-variation analysis. Here we present results from pilot studies on a variety of viral families performed during bioinformatics computer lab courses in past years.

MicroRNAs (miRNAs) are a group of small, ∼21 nt long, riboreg-ulators inhibiting gene expression at a post-transcriptional level. Their most distinctive structural feature is the foldback hairpin of their precursor pre-miRNAs. Even though each pre-miRNA deposited in miRBase has its secondary structure already predicted, little is known about the patterns of structural conservation among pre-miRNAs. We address this issue by clustering the human pre-miRNA sequences based on pairwise, sequence and secondary structure alignment using FOLDALIGN, followed by global multiple alignment of obtained clusters by WAR. As a result, the common secondary structure was successfully determined for four FOLDALIGN clusters: the RF00027 structural family of the Rfam database and three clusters with previously undescribed consensus structures.

Motivation: Recently novel classes of functional RNAs, most prominently the miRNAs have been discovered, strongly suggesting that further types of functional RNAs are still hidden in the recently completed genomic DNA sequences. Only few techniques are known, however, to survey genomes for such RNA genes. When sufficiently similar sequences are not available for comparative approaches the only known remedy is to search directly for structural features. Results: We present here efficient algorithms for computing locally stable RNA structures at genome-wide scales. Both the minimum energy structure and the complete matrix of base pairing probabilities can be computed in 𝒪(N × L2) time and 𝒪(N + L2) memory in terms of the length N of the genome and the size L of the largest secondary structure motifs of interest. In practice, the 100 Mb of the complete genome of Caenorhabditis elegans can be folded within about half a day on a modern PC with a search depth of L = 100. This is sufficient example for a survey for miRNAs.

Most multi-alignment methods are fully automated, i.e. they are based on a fixed set of mathematical rules. For various reasons, such methods may fail to produce biologically meaningful alignments. Herein, we describe a semi-automatic approach to multiple sequence alignment where biological expert knowledge can be used to influence the alignment procedure. The user can specify parts of the sequences that are biologically related to each other; our software program uses these sites as anchor points and creates a multiple alignment respecting these user-defined constraints. By using known functionally, structurally or evolutionarily related positions of the input sequences as anchor points, our method can produce alignments that reflect the true biological relationships among the input sequences more accurately than fully automated procedures can do.

Excluding skin cancers, prostate cancer is the most frequently diagnosed cancer in American men. The American Cancer Society estimated 220,800 new prostate cancer cases would be diagnosed in 2015. Prostate cancer is also the second leading cause of cancer-specific mortality at 27,540 deaths estimated in 2015. Of particular concern are the increased incidence, mortality, and aggressive features of prostate cancer seen in African American men. These health disparities are not fully explained by non-biological factors such as socioeconomics, access to care, or treatment. Prostate cancer presents a compelling case for the clinical usefulness of biomarkers. The lack of an assured prostate cancer susceptibility gene necessitates other molecular markers are required for screening. Because of its slow-growing nature, early prostate cancer is asymptomatic so biomarkers that accurately diagnose asymptomatic prostate cancer would be of great value. Additionally, prognostic markers to discriminate indolent and aggressive disease would be highly prized. The racial differences in prostate cancer also suggest that biomarkers could be particularly useful in heavily burdened populations such as African American men. For a myriad of reasons, however, biomarker discovery has not been as fruitful as anticipated in the wake of advances in high-throughput genomic and proteomic technologies. Pathway analysis has emerged as a strategy for identifying molecular changes in prostate cancer and uncovering molecular targets for biomarkers and therapy. The thread uniting the studies presented herein is the application of pathway analysis to human prostate cancer to identify altered mechanisms of prostate cancer tumors development and progression. Study 1 used comprehensive genomic patient data obtained from The Cancer Genome Atlas to identify differentially expressed genes and pathway signatures in prostate cancer. This analysis highlighted the strong association of the "TGF-β signaling" and "Ran regulation of mitotic spindle formation" with prostate cancer and confirmed reported findings from microarray data that suggest "Actin Cytoskeleton Regulation", "Cell Cycle", "MAPK Signaling", and "Calcium Signaling" are also altered in prostate cancer. Study 2 incorporated a similar methodological approach to study paired RC-77 human prostate cancer cell lines. This cell model is one of few models derived from an African American patient. This work completed the first comprehensive proteomic analysis of RC-77 cell lines and found 63 differentially expressed proteins between the malignant RC-77T/E cell line and the non-malignant RC-77N/E, with 18 proteins uniquely detected in RC-77T/E and 2 proteins uniquely detected in RC-77N/E. Many of these differentially expressed proteins fall into the category of structural proteins or have a structural role. A pathway approach was used to provide a context for these differences and revealed correlation of the "Tight Junction", "Cell Adhesion Molecules", "Adherens Junction", "ECM-Receptor interaction", "Focal Adhesion", and "Proteoglycans in Cancer" pathways with either RC-77T/E or RC-77N/E cells. Study 3 applied the pathway analysis to race-, age-, and stage-matched malignant and non-malignant prostate tissues to examine pathway dysregulation in the context of racial health disparities. While this small case study was not able to show racial differences in the expression of individual genes, pathways were differentially associated with African American prostate cancer. Three supplementary files containing the expression data and full analysis results for each project are included with this dissertation: Supplementary File 1 (MyersJS_SuppInfo_GenomicData_TCGA.xlsx), Supplementary File 2 (MyersJS_SuppInfo_ProteomicData_RC77.xlsx), and Supplementary File 3 (MyersJS_SuppInfo_ProteomicData_Tissues.xlsx). / A Dissertation submitted to the Department of Chemistry and Biochemistry in partial fulfillment of the Doctor of Philosophy. / Spring Semester 2016. / March 28, 2016. / genomics, health disparity, pathway analysis, prostate cancer, proteomics / Includes bibliographical references. / Qing-Xiang Sang, Professor Directing Dissertation; Wu-Min Deng, University Representative; Alan G. Marshall, Committee Member; Michael Roper, Committee Member; Michelle Arbeitman, Committee Member.

Alzheimer’s disease is a progressive neurodegenerative disorder and the most common form of dementia. Like many neurological disorders, Alzheimer’s disease has a sex-biased epidemiological profile, affecting approximately twice as many women as men. The cause of this sex difference has yet to be elucidated. To identify molecular correlates of this sex bias, we investigated molecular sex differences in the hippocampus of healthy female and male mice at 1, 2, and 4 months of age. This analysis identifies a host of genes that display sex-biased expression in the developing mouse hippocampus, many of which are heat shock proteins. Using this dataset as a baseline, we investigated molecular pathology in both sexes using the 5XFAD transgenic mouse model of Alzheimer’s disease. We profiled the transcriptome of the mouse hippocampus during early stages of disease development with RNA-sequencing. To supplement our transcriptomic analysis, we performed a series of liquid-chromatography mass spectrometry analyses of protein abundance. This proteomic investigation was refined by an extensive methods-oriented analysis of sample fractionation. The analysis of 5XFAD transgenic mice reveals pathological differences in transcript abundance as early as 2 months of age, prior to observable plaque deposition. At 4 months of age, we detect wide-spread up regulation of transcripts associated with immune function in diseased animals. Interestingly, our data indicate that female transgenic mice show a stronger disease phenotype than their male counterparts as measured by number of differentially expressed genes. We also find elevated expression of the 5XFAD transgenes in females relative to males, which likely accounts for a portion of sex-biased molecular pathology observed in this dataset. Taken together, our analyses identify both innate molecular sex differences in the rodent brain, as well as molecular correlates of sex-biased disease features. The findings enhance our understanding of neural sex-differences, and present potential candidate biomarkers for pharmacological intervention for Alzheimer’s disease. / A Dissertation submitted to the Department of Biomedical Sciences in partial fulfillment of the Doctor of Philosophy. / Fall Semester 2016. / November 10, 2016. / Includes bibliographical references. / Richard S. Nowakowski, Professor Directing Dissertation; Thomas Houpt, University Representative; Michelle Arbeitman, Committee Member; Brian Inouye, Committee Member; James Olcese, Committee Member.

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