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search.filters.applied.f.advisor: Jordan, I. King × End date: 2022 × Start date: 2020 ×

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    Differential Gene Co-Expression Network Characteristics Of Cancer
    (Georgia Institute of Technology, 2022-12-13) Arshad, Zainab
    The transformation from a healthy state to a disease state in cancer is dictated in large part by structural and regulatory abnormalities in genes. While the molecular features underlying this transition have been investigated for some time, allowing groundbreaking advancements in cancer research, a majority of these efforts are focused on mutational and expression changes of individual genes. The recent advancement of network-based analytic methods affords an additional route through which disease pathophysiology and biologic regulation can be investigated. Furthermore, with the development of high-throughput technologies and the availability of large biobanks, gene interaction changes, and their functional consequences can be reliably interpreted from a systemic perspective, in a context specific manner. Towards this end, my research investigates gene co-expression changes, derived from transcriptomic case-control data, that underlie cancer onset and progression relative to healthy tissue. For the first study, global network changes associated with cancers of nine different tissues of origin were investigated. Network complexity generally dropped in the transition from normal precursor tissues to corresponding primary tumors, whereas cross-tissue cancer network similarity overall increased in early-stage cancers followed by a subsequent loss in similarity as tumors reacquire cancer-specific network complexity in late-stage cancers. In addition, gene-gene connections remaining stable through cancer development were found enriched for ‘‘housekeeping’’ gene functions, whereas newly acquired interactions were associated with established cancer-promoting functions. For the second study, gene-network characteristics of the molecular subtypes (Luminal A, Luminal B and Basal) of Breast Cancer (BC) were outlined based on a comparative analysis relative to precursor normal breast tissue. Basal was identified as the most highly connected yet dissimilar subtype to normal control. We discovered eight extensively connected network modules acquired in Basal BCs that harbored 19 genes found significantly associated with survival and encoding cancer hallmark functions including regulation of cell proliferation and motility, as well as neural pathways that have not been previously associated with basal BCs. Finally, the consensus approach of network construction for an unbiased differential analysis of gene co-expression networks used in these studies was published as a step-by-step protocol. Altogether, this thesis highlights gene-network changes characteristic of individual cancer types, molecular subtypes and disease stages that informs their diverse progression patterns and clinical outcomes. Furthermore, it underscores the importance and demonstrates the utility of gene co-expression networks in identifying key genes, gene interactions and functional characteristics of cancers that maybe undiscovered by standard molecular analysis approaches.
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    Efficient alignment-free software applications for next generation sequencing-based molecular epidemiology
    (Georgia Institute of Technology, 2020-01-09) Espitia Navarro, Hector Fabio
    Public health agencies increasingly couple next generation sequencing (NGS) characterization of microbial genomes with bioinformatics analysis methods for molecular epidemiology. The overhead associated with the bioinformatics methods used for this purpose, in terms of both the required human expertise and computational resources, represents a critical bottleneck that limits the potential impact of microbial genomics on public health. This is particularly true for local public health agency laboratories, which are typically staffed with microbiologists who may not have substantial bioinformatics expertise or ready access to high-performance computational resources. There is a pressing need for bioinformatics solutions to genome-enabled molecular epidemiology that is accurate, easy to use, fast, and computationally efficient. This thesis research is focused on the development of an alignment-free algorithm for NGS data analysis and its implementation into turn-key software applications tailored explicitly for genome-enabled molecular epidemiology and environmental microbial genomics. I explored a computational strategy based on k-mer frequencies to distinguish among sequences of interest in NGS read samples. By combining this strategy with the efficient data structure Enhanced Suffix Array (ESA), I developed a base algorithm for the rapid analysis of unprocessed NGS reads. I further adapted and implemented this algorithm into a suite of software applications for sequence typing, gene detection, and gene-based taxonomic read classification. Benchmarking and validation analyses showed that STing is an ultrafast and accurate solution for genome-enabled molecular epidemiology, which performs better than existing bioinformatics methods for sequence typing and gene detection. To overcome the limitation of bioinformatics infrastructure and expertise in public health laboratories, I developed WebSTing, a Web-platform that uses the STing algorithm to provide easy access to the accurate and rapid alignment-free automated characterization of whole genome sequencing (WGS) samples of bacterial isolates. Finally, to demonstrate the utility of the STing in problems beyond simple sequence typing and gene detection, I applied the alignment-free algorithm to two different areas: (1) public health, with the virulence gene profiling of Shiga toxin-producing Escherichia coli (STEC) isolates, and (2) environmental microbial genomics, with the nifH gene-based taxonomy classification of amplicon sequencing reads. I showed that STing performs better that the gold-standard method for STEC isolate characterization, and that it correctly classifies amplicon sequencing reads on simulated communities of nitrogen-fixing organisms.