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Advances in next-generation sequencing (NGS) technologies have resulted in significant reduction of cost per sequenced base pair and increase in sequence data volume. On the other hand, most currently used NGS technologies produce relatively short sequence reads (50 - 150 bp) compared to Sanger sequencing (~700 bp). This represents an additional challenge in data analysis, because shorter reads are more difficult to assemble. At this point, production of sequencing data outpaces our capacity to analyze them. Newer NGS technologies capable of producing longer reads are emerging, which should simplify and speed up genome assembly. However, this will only increase the number of sequenced genomes without structural and functional annotation. In addition to multiple scientific initiatives to sequence thousands of genomes, personalized medicine centered on sequencing and analysis of individual human genomes will become more available. This poses a challenge for computer science and emphasizes the importance of developing new computational algorithms, methodology, tools, and pipelines. This dissertation focuses on development of these software tools, methodologies, and resources to help address the need for processing of volumes of data generated by new sequencing technologies. The research concentrated on genome structure analysis, individual variation, and comparative biology. This dissertation presents: (1) the Short Read Classification Pipeline (SRCP) for preliminary genome characterization of unsequenced genomes; (2) a novel methodology for phylogenetic analysis of closely related organisms or strains of the same organism without a sequenced genome; (3) a centralized online resource for standardized gene nomenclature. Utilizing the SRCP and the methodology for initial phylogenetic analysis developed in this dissertation enables positioning the organism in the evolutionary context. This should facilitate identification of orthologs between the species and paralogs within the species even in the initial stage of the analysis when only exome is sequenced and, thus, enable functional annotation by transferring gene nomenclature from well-annotated 1:1 orthologs, as required by the online standardized gene nomenclature resource developed in this dissertation. Thus, the tools, methodology, and resources presented here are tied together in following the initial analysis workflow for structural and functional annotation.

Emerging sequencing approaches have revolutionized the way we can collect DNA sequence data for applications in bioforensics and biosurveillance. In this research, we present an approach to construct a database of known biological agents and use this database to develop a statistical framework to analyze raw reads from next-generation sequence data for species identification and strain attribution. Our method capitalizes on a Bayesian statistical framework that accommodates information on sequence quality, mapping quality and provides posterior probabilities of matches to a known database of target genomes. Importantly, our approach also incorporates the possibility that multiple species can be present in the sample or that the target strain is not even contained within the reference database. Furthermore, our approach can accurately discriminate between very closely related strains of the same species with very little coverage of the genome and without the need for genome assembly - a time consuming and labor intensive step. We demonstrate our approach using genomic data from a variety of known bacterial agents of bioterrorism and agents impacting human health.

Yes / Introduction: Mobile phones are used the world over, including in healthcare settings. This study aimed to investigate the viable microbial colonisation of mobile phones used by healthcare personnel. Methods: Swabs collected on the same day from 30 mobile phones belonging to healthcare workers from three separate paediatric wards of an Australian hospital were cultured on five types of agar plate, then colonies from each phone were pooled, extracted and sequenced by shotgun metagenomics. Questionnaires completed by staff whose phones were sampled assisted in the analysis and interpretation of results. Results and discussion: All phones sampled cultured viable bacteria. Overall, 399 bacterial operational taxonomic units were identified from 30 phones, with 1432 cumulative hits. Among these were 58 recognised human pathogenic and commensal bacteria (37 Gram-negative, 21 Gram-positive). The total number of virulence factor genes detected was 347, with 1258 cumulative hits. Antibiotic resistance genes (ARGs) were detected on all sampled phones and overall, 133 ARGs were detected with 520 cumulative hits. The most important classes of ARGs detected encoded resistance to beta-lactam, aminoglycoside and macrolide antibiotics and efflux pump mediated resistance mechanisms. Conclusion: Mobile phones carry viable bacterial pathogens and may act as fomites by contaminating the hands of their users and indirectly providing a transmission pathway for hospital-acquired infections and dissemination of antibiotic resistance. Further research is needed, but meanwhile adding touching mobile phones to the five moments of hand hygiene is a simple infection control strategy worth considering in hospital and community settings. Additionally, the implementation of practical and effective guidelines to decontaminate mobile phone devices would likely be beneficial to the hospital population and community at large.

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Introduction: Sepsis is a major source of morbidity and mortality in neonates; however, identification of the causative pathogens can be challenging. Next generation sequencing (NGS) is a high-throughput, parallel sequencing technique for DNA. Pathogen-targeted enrichment followed by NGS has the potential to be more sensitive and faster than current gold-standard blood culture. In this pilot study, we will test the feasibility and pathogen detection patterns of pathogen-targeted NGS in neonates with suspected sepsis. Additionally, the distribution and diagnostic accuracy of cell-free DNA and protein C levels at two time points will be explored. Methods: We will conduct a prospective, pilot observational study. Neonates over 1 kg with suspected sepsis from a single tertiary care children’s hospital will be recruited for the study. Recruitment will be censored at 200 events or 6 months duration. Two blood study samples will be taken: the first simultaneous to the blood culture (time = 0 hr, for NGS and biomarkers) via an exception to consent (deferred consent) and another 24 hours later after prospective consent (biomarkers only). Neonates will be adjudicated into those with clinical sepsis, culture-proven sepsis and without sepsis based on clinical criteria. Feasibility parameters (e.g. recruitment) and NGS process time will be reported. Analysis: NGS results will be described in aggregate, compared to the simultaneous blood culture (sensitivity and specificity) and reviewed via expert panel for plausibility. Pilot data for biomarker distribution and diagnostic accuracy (sensitivity and specificity) for distinguishing between septic and non-septic neonates will be reported. Study amendment and interim results: After obtaining ethics approval, study enrolment started October 15, 2020. Interim feasibility results showed successful deferred consent, but low enrolment. A study amendment was used to increase enrolment, create pre-packaged blood kits and implement a substitute decision maker Notification form. / Thesis / Master of Health Sciences (MSc)

Chaperone proteins are essential components in the maintenance and turnover of the proteome. Many chaperones play integral functions in the folding and unfolding of cellular substrates under many conditions, including heat stress. Most chaperones can be characterized into two categories; the typical ATP dependent chaperones and the ATP independent chaperones. One ATP independent chaperone class it the Small Heat Shock Proteins (sHSPs), which as molecular life vests and are thought to protect misfolding proteins from irreversible aggregation. One such organism, the cyanobacterium Synechocystis sp. PCC 6803, is an excellent model for the study and understanding of these proteins and their functions in vivo. The genome of Synechocystis encodes only one sHSP, Hsp16.6, and it has be shown to be essential for acquired thermotolerance. Two mutant derivatives of Hsp16.6 with single amino acid substitutions in the N-terminal arm (L9P and E25K) have loss-of-function phenotypes similar to knock out strains, but each has very different biochemical properties. The mutant L9P has an inability to interact with putative substrates during heat stress in vivo, while the mutant E25K appears unable to release substrates. Using a directed evolution approach, suppressors have been isolated that recover the lost thermotolerance of their respective parent strains, either L9P (16 suppressors) or E25K (10 suppressors). Illumina sequencing and comparative genomics have been used to identify alterations in the genomes of the suppressor strains in order to define genetic circuits involved in thermotolerance.

The epigenome carries dynamic information that controls gene expression and maintains cell identity during both disease and normal development. The inherent plasticity of the epigenome paves new avenues for developing diagnostic and therapeutic tools for human diseases. Microfluidic technology has improved the sensitivity and resolution of epigenomic analysis due to its outstanding ability to manipulate nanoliter-scale liquid volumes. In this thesis, I report three projects focusing on low-input, cell-type-specific and spatially resolved histone modification profiling on microfluidic platforms. First, I applied Microfluidic Oscillatory Washing-based Chromatin Immunoprecipitation followed by sequencing (MOWChIP-seq) to study the effect of culture dimensionality, hypoxia stress and bacterium infection on histone modification landscapes of brain tumor cells. I identified differentially marked regions between different culture conditions. Second, I adapted indexed ChIPmentation and introduced mu-CM, a low-input microfluidic device capable of performing 8 assays in parallel on different histone marks using as few as 20 cells in less than 7 hours. Last, I investigated the spatially resolved epigenome and transcriptome of neuronal and glial cells from coronal sections of adult mouse neocortex. I applied unsupervised clustering to identify distinct spatial patterns in neocortex epigenome and transcriptome that were associated with central nervous system development. I demonstrated that our method is well suited for scarce samples, such as biopsy samples from patients in the context of precision medicine. / Doctor of Philosophy / Epigenetic is the study of alternations in organisms not caused by alternation of the genetic codes. Epigenetic information plays pivotal role during growth, aging and disease. Epigenetic information is dynamic and modifiable, and thus serves as an ideal target for various diagnostic and therapeutic strategies of human diseases. Microfluidics is a technology that manipulates liquids with extremely small volumes in miniaturized devices. Microfluidics has improved the sensitivity and resolution of epigenetic analysis. In this thesis, I report three projects focusing on low-input, cell-type-specific and spatially resolved histone modification profiling on microfluidic platforms. Histone modification is one type of epigenetic information and regulates gene expression. First, we studied the influence of culture condition and bacterium infection on histone modification profile of brain tumor cells. Second, we introduced mu-CM, combining a low-input microfluidic device with indexed ChIPmentation and is capable of performing 8 assays in parallel using as few as 20 cells. Last, we investigated spatial variations in the epigenome and transcriptome across adult mouse neocortex, the outer layer of brain involving in higher-order function, such as cognition. I identified distinct spatial patterns responsible for central nervous system development using machine learning algorithm. Our method is well suited for studying scarce samples, such as cells populations isolated from patients in the context of precision medicine.

Insertion and deletion (indel), a common form of genetic variation, has been shown to cause or contribute to human genetic diseases and cancer. Despite this importance and being the second most abundant variant type in the human genome, indels have not been studied as much as the single nucleotide polymorphism (SNP). With the advance of next-generation sequencing technology, many indel calling tools have been developed. However, performance comparison of commonly used tools has shown that (1) the tools have limited power in identifying indels and there are significant number of indels undetected, and (2) there is significant disagreement among the indel sets produced by the tools. These findings indicate the necessity of improving the existing tools or developing new algorithms to achieve reliable and consistent indel calling results. Two indels are biologically equivalent if the resulting sequences are the same. Storing biologically equivalent indels as distinct entries in databases causes data redundancy and misleads downstream analysis. It is thus desirable to have a unified system for identifying and representing equivalent indels. This dissertation describes UPS-indel, a utility tool that creates a universal positioning system for indels so that equivalent indels can be uniquely determined by their coordinates in the new system. Results show that UPS-indel identifies more redundant indels than existing algorithms. While mapping short reads to the reference genome, a significant number of short reads are unmapped and excluded from downstream analyses, thereby causing information loss in the subsequent variant calling. This dissertation describes Genesis-indel, a computational pipeline that explores the unmapped reads to identify missing novel indels. Results analyzing sequence alignment of 30 breast cancer patients show that Genesis-indel identifies many novel indels that also show significant enrichment in oncogenes and tumor suppressor genes, demonstrating the importance of rescuing indels hidden in the unmapped reads in cancer and disease studies. Somatic mutations play a vital role in transforming healthy cells into cancer cells. Therefore, accurate identification of somatic mutations is essential. Many somatic mutations callers are available with different strengths and weaknesses. An ensemble approach integrating the power of the callers is warranted. This dissertation describes SomaticHunter, an ensemble of two callers, namely Platypus and VarDict. Results on synthetic tumor data show that for both SNPs and indels, SomaticHunter achieves recall comparable to the state-of-the-art somatic mutation callers and the highest precision, resulting in the highest F1 score. / Doctor of Philosophy / Insertion and deletion (indel), a common form of genetic variation in the human genome, is associated with genetic diseases and cancer. However, indels are heavily understudied due to experimental and computational challenges. This dissertation addresses the computational challenges in three aspects. First, the current approach of representing indels is ambiguous and causes significant database redundancy. A universal positioning system, UPS-indel, is proposed to represent equivalent indels unambiguously and the UPS-indel algorithm is theoretically proven to find all equivalent indels and is thus exhaustive. Second, a significant number of indels are hidden in DNA reads not mapped to the reference genome. Genesis-indel, a computational pipeline that explores the unmapped reads to identify novel indels that are initially missed, is developed. Genesis-indel has been shown to uncover indels that can be important genetic markers for breast cancer. Finally, mutations occurring in somatic cells play a vital role in transforming healthy cells into cancer cells. Therefore, accurate identification of somatic mutation is essential for a better understanding of cancer genomes. SomaticHunter, an ensemble of two sensitive variant callers, is developed. Simulated studies using whole genome and whole exome sequences have shown that SomaticHunter achieves recall comparable to state-of-the-art somatic mutation callers while delivering the highest precision and therefore resulting in the highest F1 score among all the callers compared.

Next-generation sequencing (NGS) technologies have made it possible to rapidly sequence the human genome, heralding a new era of health-care innovations based on personalized genetic information. However, these NGS technologies generate data at a rate that far outstrips Moore\'s Law. As a consequence, analyzing this exponentially increasing data deluge requires enormous computational and storage resources, resources that many life science institutions do not have access to. As such, cloud computing has emerged as an obvious, but still nascent, solution. This thesis intends to investigate and design an efficient framework for running and managing large-scale data-intensive scientific applications in the cloud. Based on the learning from our parallel implementation of a genome analysis pipeline in the cloud, we aim to provide a framework for users to run such data-intensive scientific workflows using a hybrid setup of client and cloud resources. We first present SeqInCloud, our highly scalable parallel implementation of a popular genetic variant pipeline called genome analysis toolkit (GATK), on the Windows Azure HDInsight cloud platform. Together with a parallel implementation of GATK on Hadoop, we evaluate the potential of using cloud computing for large-scale DNA analysis and present a detailed study on efficiently utilizing cloud resources for running data-intensive, life-science applications. Based on our experience from running SeqInCloud on Azure, we present CloudFlow, a feature rich workflow manager for running MapReduce-based bioinformatic pipelines utilizing both client and cloud resources. CloudFlow, built on the top of an existing MapReduce-based workflow manager called Cloudgene, provides unique features that are not offered by existing MapReduce-based workflow managers, such as enabling simultaneous use of client and cloud resources, automatic data-dependency handling between client and cloud resources, and the flexibility of implementing user-defined plugins for data transformations. In-general, we believe that our work attempts to increase the adoption of cloud resources for running data-intensive scientific workloads. / Master of Science