<p> Biological, biomedical, and radiological data tend to be large, complex, and noisy. Gene expression studies contain expression levels for thousands of genes and hundreds or thousands of patients. Chest Computed Tomography images used for diagnosing lung cancer consist of hundreds of 2-D image &rdquo;slices&rdquo;, each containing hundreds of thousands of pixels. Beneath the size and apparent complexity of many of these data are simple and sparse structures. These low complexity structures can be leveraged into new approaches to biological, biomedical, and radiological data analyses. Two examples are presented here. First, a new framework SparRec (Sparse Recovery) for imputation of GWAS data, based on a matrix completion (MC) model taking advantage of the low-rank and low number of co-clusters of GWAS matrices. SparRec is flexible enough to impute meta-analyses with multiple cohorts genotyped on different sets of SNPs, even without a reference panel. Compared with Mendel-Impute, another MC method, our low-rank based method achieves similar accuracy and efficiency even with up to 90% missing data; our co-clustering based method has advantages in running time. MC methods are shown to have advantages over statistics-based methods, including Beagle and fastPhase. Second, we demonstrate NoduleX, a method for predicting lung nodule malignancy from chest Computed Tomography (CT) data, based on deep convolutional neural networks. For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort and compare our results with classifications provided by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of up to 0.99, commensurate with the radiologists&rsquo; analysis. Whether they are leveraged directly or extracted using mathematical optimization and machine learning techniques, low complexity structures provide researchers with powerful tools for taming complex data. </p><p>

<p> Dengue virus (DENV) is a serious worldwide health concern putting about 2.5 billion people in more than 100 countries at-risk Dengue is a member of the flaviviridae family, is transmitted to human via mosquitos. Dengue is a deadly viral disease. Unfortunately, there are no vaccines or antiviral that can prevent this infection and that is why researchers are diligently working to find cures. The DENV genome codes for multiple nonstructural proteins one of which is the NS3 enzyme that participates in different steps of the viral life cycle including viral replication, viral RNA genome synthesis and host immune mechanism. Recent studies suggest the role of fatty acid biogenesis during DENV infection, including posttranslational protein modification. Phosphorylation is among the protein post translational modifications and plays essential roles in protein folding, interactions, signal transduction, survival and apoptosis. </p><p> In silico study provides a powerful approach to gain a better understanding of the biological systems at the gene level. NS3 has the potential to be phosphorylated by any of the &sim;500 human kinases. We predicted potential kinases that might phosphorylate NS3 and calculated Dena ranking score using neural network and other machine learning based webserver programs. These scores enabled us to investigate and identify the top kinases that phosphorylate DENV NS3. We hypothesize that preventing the phosphorylation of NS3 may interrupt the viral replication and participate in antiviral evasion. Using multiple sequence alignment bioinformatics tools we verified the results of the highly conserved residues and the residues around active sites whose phosphorylation may have a potential effect on viral replication. We further verified the results with multiple bioinformatics tools. Moreover, we included the Zika virus in our research and analysis taking into consideration the facts that Zika is related to the dengue virus because it belongs to the same Flavivirus genus affecting humans which might lead to a lot of similarities between Zika and Dengue, and that Zika is available for <i>in vitro</i> testing. </p><p> Our studies propose that the Host-Mediated Phosphorylation of NS3 would affect its capability to interact with NS5 and knocking out one of the interacting proteins may inhibit viral replication. These results will open new doors for further investigation and future work is expected to help identify the key inhibition mechanisms.</p><p>

<p> Humoral immunity is driven by the expansion, somatic hypermutation, and selection of B cell clones. Each clone is the progeny of a single B cell responding to antigen. with diversified Ig receptors. The advent of next-generation sequencing technologies enables deep profiling of the Ig repertoire. This large-scale characterization provides a window into the micro-evolutionary dynamics of the adaptive immune response and has a variety of applications in basic science and clinical studies. Clonal relationships are not directly measured, but must be computationally inferred from these sequencing data. In this dissertation, we use a combination of human experimental and simulated data to characterize the performance of hierarchical clustering-based methods for partitioning sequences into clones. Our results suggest that hierarchical clustering using single linkage with nucleotide Hamming distance identifies clones with high confidence and provides a fully automated method for clonal grouping. The performance estimates we develop provide important context to interpret clonal analysis of repertoire sequencing data and allow for rigorous testing of other clonal grouping algorithms. We present the clonal grouping tool as well as other tools for advanced analyses of large-scale Ig repertoire sequencing data through a suite of utilities, Change-O. All Change-O tools utilize a common data format, which enables the seamless integration of multiple analyses into a single workflow. We then apply the Change-O suite in concert with the nucleotide coding se- quences for WNV-specific antibodies derived from single cells to identify expanded WNV-specific clones in the repertoires of recently infected subjects through quantitative Ig repertoire sequencing analysis. The method proposed in this dissertation to computationally identify B cell clones in Ig repertoire sequencing data with high confidence is made available through the Change-O suite and can be applied to provide insight into the dynamics of the adaptive immune response.</p><p>

<p> Cancer is a complex and multifaceted disease, and a vast amount of time and effort has been spent on characterizing its behaviors, identifying its weaknesses, and discovering effective treatments. Two major obstacles stand in the way of progress toward effective precision treatment for the majority of patients.</p><p> First, cancer's extraordinary heterogeneity&mdash;both between and even within patients&mdash;means that most patients present with a disease slightly different from every previously recorded case. New methods are necessary to analyze the growing body of patient data so that we can classify each new patient with as much accuracy and precision as possible. In chapter 2 I present a method that integrates data from multiple genomics platforms to identify axes of variation across breast cancer patients, and to connect these gene modules to potential therapeutic options. In this work we find modules describing variation in the tumor microenvironment and activation of different cellular processes. We also illustrate the challenges and pitfalls of translating between model systems and patients, as many gene modules are poorly conserved when moving between datasets.</p><p> A second problem is that cancer cells are constantly evolving, and many treatments inevitably lead to resistance as new mutations arise or compensatory systems are activated. To overcome this we must find rational combinations that will prevent resistant adaptation before it can start. Starting in chapter 3 I present a series of projects in which we used a high-throughput proteomics approach to characterize the activity of a large proportion of protein kinases, ending with the discovery of a promising drug combination for the treatment of breast cancer in chapter 8.</p><p>

<p> Forensic scientists routinely use DNA for identification and to match samples with individuals. Although standard approaches are effective on a wide variety of samples in various conditions, issues such as low-template DNA samples and mixtures of DNA from multiple individuals pose significant challenges. Extreme examples of these challenges can be found in the field of ancient DNA, where DNA recovered from ancient remains is highly fragmented and marked by patterns of DNA-damage. Additionally, ancient libraries are often characterized by low endogenous DNA content and contaminating DNA from outside sources. As a result, standard forensics approaches, such as amplification of short-tandem repeats, are not effective on ancient samples. Alternatively, ancient DNA is routinely directly sequenced using high-throughput sequencing to survey the molecules that are present within a library. However, the resulting sequences are not easily compared for the purposes of identification, as each data set represents a random and, in some cases, non-overlapping, sample of the genome.</p><p> In this dissertation, I present two approaches for interpreting shotgun sequences that address two common issues in forensic and ancient DNA: extremely low nuclear genome coverage and mixtures of sequences from multiple individuals. First, I present an approach to test for a common source individual between extremely low-coverage sequence data sets that makes use of the vast number of single-nucleotide polymorphisms (SNPs) discovered by surveys of human genetic diversity. As almost no observed SNP positions will be common to both samples, our method uses patterns of linkage disequilibrium as modeled by a panel of haplotypes to determine whether observations made across samples are consistent with originating from a single individual. I demonstrate the power of this approach using coalescent simulations, downsampled high-throughput sequencing data and published ancient DNA data. Second, I present an approach for interpreting mixtures of mitochondrial DNA sequences from multiple individuals. Mixed DNA samples are common in forensics investigations, either from the direct nature of a case (e.g., a sample containing DNA from both a victim and a perpetrator) or from outside contamination. I describe an expectation maximization approach for detecting the mitochondrial haplogroups contributing to a mixture and partitioning fragments by haplogroup to reconstruct the underlying haplotypes. I demonstrate the approach&rsquo;s feasibility, accuracy, and sensitivity on both <i>in silico</i> and <i>in vitro</i> sequence mixtures. Finally, I present the results of applying our mixture interpretation approach on ancient contact DNA recovered from &sim; 700 year old moccasin and cordage samples.</p>

Pulmonary disease is one of the most common and serious medical conditions in the world, and the correct diagnosis and prediction of incipient pulmonary diseases such as tuberculosis (TB) and lung cancer can greatly decrease the number of pulmonary disease-related deaths. In this thesis, I studied the transcriptome and microbiome difference between pulmonary disease patients and healthy controls, developed and applied several pipelines incorporating bioinformatics methods, statistics and machine learning models to identify patterns in human transcriptome as well as microbiome data for pulmonary disease prediction. On the host transcriptome side, I first evaluated the performance of existing TB disease and TB progression biomarkers, created a bulk RNA-seq gene-expression based biomarker selection pipeline, and then identified a 29-gene signature that can correctly predict TB progression as far as 6 years before the TB diagnosis. On microbiome side, I developed Animalcules, an R package for microbiome data analysis such as diversity comparison and differential abundance analysis, which supports both user graphical interface and command-line functions. I then applied Animalcules for two microbiome case studies: identifying the TB and Asthma related microbes. After working on host transcriptome and microbiome separately, I then discussed the computational framework for identifying host-microbe interactions, and its significant potential for studying pulmonary disease pathogenesis, diagnosis and treatment.

Includes abstract. Includes bibliographical references.

Includes bibliographical references (leaves 92-112). / In humans and other higher eukaryotes the observation of multiple splice isoforms for a given gene is common. However it is not clear whether all of these alternatively spliced isoforms are a product of true alternative splicing or some are due to DNA sequence variations in human populations. Genetic variations that affect splicing have been shown to cause variation in splicing patterns and potentially are an important source of phenotypic variability among humans. Furthermore, variation in disease susceptibility and manifestation between individuals is often associated with genetic polymorphisms that determine the way in which genes are spliced. Hence, identification of genetic polymorphisms that might affect the way in which pre-mRNAs are spliced is an area of great interest.

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