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The Genome Reverse Compiler (GRC) is an annotation tool for prokaryotic genomes. Its name and philosophy are based on analogy with a high-level programming language compiler. In this analogy, the genome is a program in a certain low-level language that humans cannot understand. Given the sequence of any prokaryotic genome, GRC produces its corresponding "high-level program"--its annotation. GRC works in a completely automatic manner, using standard input and output formats. The goal is to provide an open-source, easy-to-run, very efficient annotation program. / Master of Science

"Host resolution of viral infection is dependent upon components of the innate and acquired immune system. The mammalian protein Apobec3 plays an important role as part of the immune system’s innate defenses through its modification of reverse transcribed viral DNA. Recently, Apobec3 was found to directly inhibit HIV-1 and HBV replication through deaminating newly transcribed deoxycytidine residues to deoxyuridine. The ability of mouse and simian Apobec3 variants to inhibit human retroviruses and vice versa highlights the utility of analyzing cross-species homologues. To better understand this editing enzyme, differentially pathogen-susceptible inbred mice were used as an experimental model. The purpose of this project is to examine the effects of murine Apobec3 (muA3) alternative splicing on its DNA-editing characteristics. Three distinct Apobec3 isoforms were isolated from pathogen-susceptible BALB/cByJ (“C”) inbred mice, and two Apobec3 isoforms came from pathogen-resistant C57BL/6ByJ (“Y”) mice. The five muA3 isoforms were cloned, sequenced, and expressed from a constitutive promoter in a haploid Saccharomyces cerevisia strain. MuA3 DNA-editing activity was measured via the CAN1 forward mutation assay. The five isoforms studied in this project were discovered to be strain-specific. One isoform from each mouse strain mutated the yeast CAN1 locus significantly. Additionally, both muA3 isoform mRNAs derived from the pathogen-resistant Y mice were found to persist at a higher level (2.7 -12.4 fold) than any of the C mouse isoforms. This suggests that the absence of exon 5 or some other signal in the Y mice may influence transcript stability. Evidence also suggests that the murine Apobec3 start codon is actually 33bp upstream of its reference start, with implications for previous research performed using muA3. Sequencing analysis of genomic DNA revealed the presence of a 4bp insertion in a region of BALB/cByJ muA3 which may have disrupted an intronic splicing enhancer signal. Furthermore, a novel BALB/cByJ Apobec3 isoform was characterized. This is the first report of strain-specific processing with regard to muA3."

Down syndrome is one of the most common genetic disorders, resulting in a range of neurological and neuromuscular disabilities. Although the presence of specific disabilities varies among individuals with Down syndrome, all individuals with Down syndrome are born with hypotonia (low muscle tone) and over half with congenital heart defects. Later in life, all individuals demonstrate intellectual disabilities to varying degrees, while many also develop early-onset Alzheimer’s disease. While the cause of Down syndrome is known to be a triplication of the 21st chromosome, it is unknown how this extraneous genetic material causes the development of these phenotypes. We have begun research into the biological basis of these disabilities using the tiny nematode, Caenorhabditis elegans as a genetic model. We used the technique RNA interference (RNAi), which allows us to study the in vivo function of genes by knocking down their expression one at a time in a living, behaving animal. We have used this technique to systematically study the in vivo function for genes involved in Down syndrome. To this end, we identified and knocked down C. elegans genes with sequence similarity to 67% of genes on the human 21st chromosome genes. We used these RNAi-treated worms to investigate the neuromuscular function of human chromosome 21 gene equivalents by assaying locomotion and pharyngeal pumping in a blinded screen. We used locomotion as a measure of neurological and neuromuscular function, while we used pharyngeal pumping as a model for cardiac function. We also performed an aldicarb screen to examine the role of some of these genes in the function of the synapse. Our experiments have provided valuable insight into the in vivo function of the vast majority of genes on the human 21st chromosome. This will be vital to identify genes that are potentially involved in eliciting Down syndrome-related phenotypes, laying the groundwork for further studies into the neurobiology of Down Syndrome. / text

The complex molecular machinery of the cell controls its response to various signals and environmental conditions. A natural approach to study these molecular mechanisms and cellular processes is with protein interaction networks. Due to the complexity of these networks, sophisticated computational techniques are required to extract biological insights from them. In this thesis, I develop and apply network-based algorithms for three different challenges. 1. I develop a novel, highly-scalable algorithm for network-based label prediction methods that enables the integration of functional annotations and interaction networks across many species in order to predict the functions of genes in newly-sequenced bacteria. 2. To overcome the limitations of experimental approaches to find human proteins and processes that are hijacked by SARS-CoV-2, I adapt network propagation approaches for predicting human interactors of the virus. 3. Large-scale experimental techniques to screen chemicals for toxicity have tested their effects on many individual proteins. I integrate human protein-protein interactions with this data to gain insights into the molecular networks those chemicals affect. For each of these research problems, I perform comprehensive evaluations and downstream analyses to demonstrate both the accuracy of our approaches and their utility in obtaining a broader understanding of the molecular systems in question. / Doctor of Philosophy / The functions of all living cells are governed by complex networks of molecular interactions. A major goal of systems biology is to understand the components of this machinery and how they regulate each other to control the cell's response to various conditions and signals. Advances in experimental techniques to understand these systems over the past couple of decades have led to an explosion of data that probe various aspects of a cell such as genome sequencing, which reads the DNA blueprint, gene expression, which measures the amount of each gene's products in the cell, and the interactions between those products (i.e., proteins). To extract biological insights from these datasets, increasingly sophisticated computational methods are required. A powerful approach is to model the datasets as networks where the individual molecules are the nodes and the interactions between them are the edges. In this thesis, I develop and apply network-based algorithms to utilize molecular systems data for three related problems: (i) predicting the functions of genes in bacterial species, (ii) predicting human proteins and processes that are hijacked by the SARS-CoV-2 virus, and (iii) suggesting cellular signaling pathways affected by exposure to a chemical. Developments such as those presented in these three projects are critical to obtaining a broader understanding of the functions of genes in the cell. Therefore, I make the methods and results for each project easily accessible to aid other researchers in their efforts.

The last decade has been marked by unprecedented growth in both the production of biomedical data and the amount of published literature discussing it. Almost every known or postulated piece of information pertaining to genes, proteins, and their role in biological processes is reported somewhere in the vast amount of published biomedical literature. We believe the ability to rapidly survey and analyze this literature and extract pertinent information constitutes a necessary step toward both the design and the interpretation of any large-scale experiment. Moreover, automated literature mining offers a yet untapped opportunity to integrate many fragments of information gathered by researchers from multiple fields of expertise into a complete picture exposing the interrelated roles of various genes, proteins, and chemical reactions in cells and organisms. In this thesis, we show that functional keywords in biomedical literature, particularly Medline, represent very valuable information and can be used to discover new genomic knowledge. To validate our claim we present an investigation into text mining biomedical literature to assist microarray data analysis, yeast gene function classification, and biomedical literature categorization. We conduct following studies: 1. We test sets of genes to discover common functional keywords among them and use these keywords to cluster them into groups; 2. We show that it is possible to link genes to diseases by an expert human interpretation of the functional keywords for the genes- none of these diseases are as yet mentioned in public databases; 3. By clustering genes based on commonality of functional keywords it is possible to group genes into meaningful clusters that reveal more information about their functions, link to diseases and roles in metabolism pathways; 4. Using extracted functional keywords, we are able to demonstrate that for yeast genes, we can make a better functional grouping of genes in comparison to available public microarray and phylogenetic databases; 5. We show an application of our approach to literature classification. Using functional keywords as features, we are able to extract epidemiological abstracts automatically from Medline with higher sensitivity and accuracy than a human expert.

Simultaneous measurement of the expression levels of thousands to ten thousand genes in multiple tissue types is a result of advancement in microarray technology. These expression levels provide clues about the gene functions and that have enabled better diagnosis and treatment of serious disease like cancer. To solve the mystery of unknown gene functions, biological to statistical mapping is needed in terms of classifying the genes. Here we introduce a novel approach of combining both statistical consistency and biological relevance of the clusters produced by a clustering method. Here we employ two performance measures in combination for measuring statistical stability and functional similarity of the cluster members using a set of gene expressions with known biological functions. Through this analysis we construct a platform to predict about unknown gene functions using the outperforming clustering algorithm.

Simultaneous measurement of the expression levels of thousands to ten thousand genes in multiple tissue types is a result of advancement in microarray technology. These expression levels provide clues about the gene functions and that have enabled better diagnosis and treatment of serious disease like cancer. To solve the mystery of unknown gene functions, biological to statistical mapping is needed in terms of classifying the genes. Here we introduce a novel approach of combining both statistical consistency and biological relevance of the clusters produced by a clustering method. Here we employ two performance measures in combination for measuring statistical stability and functional similarity of the cluster members using a set of gene expressions with known biological functions. Through this analysis we construct a platform to predict about unknown gene functions using the outperforming clustering algorithm.

Investigating mechanisms that regulate genes and the genes' functions are essential to understand a biological system. This dissertation is consists of two specific research projects under these aims, which are for understanding piRNA's regulation mechanism and predicting genes' function computationally. The first project shows a piRNA regulation landscape in C. elegans. piRNAs (Piwi-interacting small RNAs) form a complex with Piwi Argonautes to maintain fertility and silence transposons in animal germlines. In C. elegans, previous studies have suggested that piRNAs tolerate mismatched pairing and in principle could target all transcripts. In this project, by computationally analyzing the chimeric reads directly captured by cross-linking piRNA and their targets in vivo, piRNAs are found to target all germline mRNAs with microRNA-like pairing rules. The number of targeting chimeric reads correlates better with binding energy than with piRNA abundance, suggesting that piRNA concentration does not limit targeting. Further more, in mRNAs silenced by piRNAs, secondary small RNAs are found to be accumulating at the center and ends of piRNA binding sites. Whereas in germline-expressed mRNAs, reduced piRNA binding density and suppression of piRNA-associated secondary small RNAs targeting correlate with the CSR-1 Argonaute presence. These findings reveal physiologically important and nuanced regulation of piRNA targets and provide evidence for a comprehensive post-transcriptional regulatory step in germline gene expression. The second project elaborates a computational model to predict gene function. Predicting genes involved in a biological function facilitates many kinds of research, such as prioritizing candidates in a screening project. Following the “Guilt By Association” principle, multiple datasets are considered as biological networks and integrated together under a multi-label learning framework for predicting gene functions. Specifically, the functional labels are propagated and smoothed using a label propagation method on the networks and then integrated using an “Error correction of code” multi-label learning framework, where a “codeword” defines all the labels annotated to a specific gene. The model is then trained by finding the optimal projections between the code matrix and the biological datasets using canonical correlation analysis. Its performance is benchmarked by comparing to a state-of-art algorithm and a large scale screen results for piRNA pathway genes in D.melanogaster. Finally, piRNA targeting's roles in epigenetics and physiology and its cross-talk with CSR-1 pathway are discussed, together with a survey of additional biological datasets and a discussion of benchmarking methods for the gene function prediction.