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Description
Rewired biological pathways and/or rewired microRNA (miRNA)-mRNA interactions might also influence the activity of biological pathways. Here, rewired biological pathways is defined as differential (rewiring) effect of genes on the topology of biological pathways between controls and cases. Similarly, rewired miRNA-mRNA interactions are defined as the differential (rewiring) effects of

Rewired biological pathways and/or rewired microRNA (miRNA)-mRNA interactions might also influence the activity of biological pathways. Here, rewired biological pathways is defined as differential (rewiring) effect of genes on the topology of biological pathways between controls and cases. Similarly, rewired miRNA-mRNA interactions are defined as the differential (rewiring) effects of miRNAs on the topology of biological pathways between controls and cases. In the dissertation, it is discussed that how rewired biological pathways (Chapter 1) and/or rewired miRNA-mRNA interactions (Chapter 2) aberrantly influence the activity of biological pathways and their association with disease.

This dissertation proposes two PageRank-based analytical methods, Pathways of Topological Rank Analysis (PoTRA) and miR2Pathway, discussed in Chapter 1 and Chapter 2, respectively. PoTRA focuses on detecting pathways with an altered number of hub genes in corresponding pathways between two phenotypes. The basis for PoTRA is that the loss of connectivity is a common topological trait of cancer networks, as well as the prior knowledge that a normal biological network is a scale-free network whose degree distribution follows a power law where a small number of nodes are hubs and a large number of nodes are non-hubs. However, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the scale-free structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal samples. Hence, it is hypothesized that if the number of hub genes is different in a pathway between normal and cancer, this pathway might be involved in cancer. MiR2Pathway focuses on quantifying the differential effects of miRNAs on the activity of a biological pathway when miRNA-mRNA connections are altered from normal to disease and rank disease risk of rewired miRNA-mediated biological pathways. This dissertation explores how rewired gene-gene interactions and rewired miRNA-mRNA interactions lead to aberrant activity of biological pathways, and rank pathways for their disease risk. The two methods proposed here can be used to complement existing genomics analysis methods to facilitate the study of biological mechanisms behind disease at the systems-level.
ContributorsLi, Chaoxing (Author) / Dinu, Valentin (Thesis advisor) / Kuang, Yang (Thesis advisor) / Liu, Li (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2017
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Description
Childhood Apraxia of Speech (CAS) is a severe motor speech disorder that is difficult to diagnose as there is currently no gold-standard measurement to differentiate between CAS and other speech disorders. In the present study, we investigate underlying biomarkers associated with CAS in addition to enhanced phenotyping through behavioral testing.

Childhood Apraxia of Speech (CAS) is a severe motor speech disorder that is difficult to diagnose as there is currently no gold-standard measurement to differentiate between CAS and other speech disorders. In the present study, we investigate underlying biomarkers associated with CAS in addition to enhanced phenotyping through behavioral testing. Cortical electrophysiological measures were utilized to investigate differences in neural activation in response to native and non-native vowel contrasts between children with CAS and typically developing peers. Genetic analysis included full exome sequencing of a child with CAS and his unaffected parents in order to uncover underlying genetic variation that may be causal to the child’s severely impaired speech and language. Enhanced phenotyping was completed through extensive behavioral testing, including speech, language, reading, spelling, phonological awareness, gross/fine motor, and oral and hand motor tasks. Results from cortical electrophysiological measures are consistent with previous evidence of a heightened neural response to non-native sounds in CAS, potentially indicating over specified phonological representations in this population. Results of exome sequencing suggest multiple genetic variations contributing to the severely affected phenotype in the child and provide further evidence of heterogeneous genomic pathways associated with CAS. Finally, results of behavioral testing demonstrate significant impairments evident across tasks in CAS, suggesting underlying sequential processing deficits in multiple domains. Overall, these results have the potential to delineate functional pathways from genetic variations to the brain to observable behavioral phenotypes and motivate the development of preventative and targeted treatment approaches.
ContributorsVose, Caitlin (Author) / Peter, Beate (Thesis advisor) / Liu, Li (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2018
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Description
In species with highly heteromorphic sex chromosomes, the degradation of one of the sex chromosomes can result in unequal gene expression between the sexes (e.g., between XX females and XY males) and between the sex chromosomes and the autosomes. Dosage compensation is a process whereby genes on the sex chromosomes

In species with highly heteromorphic sex chromosomes, the degradation of one of the sex chromosomes can result in unequal gene expression between the sexes (e.g., between XX females and XY males) and between the sex chromosomes and the autosomes. Dosage compensation is a process whereby genes on the sex chromosomes achieve equal gene expression which prevents deleterious side effects from having too much or too little expression of genes on sex chromsomes. The green anole is part of a group of species that recently underwent an adaptive radiation. The green anole has XX/XY sex determination, but the content of the X chromosome and its evolution have not been described. Given its status as a model species, better understanding the green anole genome could reveal insights into other species. Genomic analyses are crucial for a comprehensive picture of sex chromosome differentiation and dosage compensation, in addition to understanding speciation.

In order to address this, multiple comparative genomics and bioinformatics analyses were conducted to elucidate patterns of evolution in the green anole and across multiple anole species. Comparative genomics analyses were used to infer additional X-linked loci in the green anole, RNAseq data from male and female samples were anayzed to quantify patterns of sex-biased gene expression across the genome, and the extent of dosage compensation on the anole X chromosome was characterized, providing evidence that the sex chromosomes in the green anole are dosage compensated.

In addition, X-linked genes have a lower ratio of nonsynonymous to synonymous substitution rates than the autosomes when compared to other Anolis species, and pairwise rates of evolution in genes across the anole genome were analyzed. To conduct this analysis a new pipeline was created for filtering alignments and performing batch calculations for whole genome coding sequences. This pipeline has been made publicly available.
ContributorsRupp, Shawn Michael (Author) / Wilson Sayres, Melissa A (Thesis advisor) / Kusumi, Kenro (Committee member) / DeNardo, Dale (Committee member) / Arizona State University (Publisher)
Created2016
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Description
High throughput transcriptome data analysis like Single-cell Ribonucleic Acid sequencing (scRNA-seq) and Circular Ribonucleic Acid (circRNA) data have made significant breakthroughs, especially in cancer genomics. Analysis of transcriptome time series data is core in identifying time point(s) where drastic changes in gene transcription are associated with homeostatic to non-homeostatic cellular

High throughput transcriptome data analysis like Single-cell Ribonucleic Acid sequencing (scRNA-seq) and Circular Ribonucleic Acid (circRNA) data have made significant breakthroughs, especially in cancer genomics. Analysis of transcriptome time series data is core in identifying time point(s) where drastic changes in gene transcription are associated with homeostatic to non-homeostatic cellular transition (tipping points). In Chapter 2 of this dissertation, I present a novel cell-type specific and co-expression-based tipping point detection method to identify target gene (TG) versus transcription factor (TF) pairs whose differential co-expression across time points drive biological changes in different cell types and the time point when these changes are observed. This method was applied to scRNA-seq data sets from a SARS-CoV-2 study (18 time points), a human cerebellum development study (9 time points), and a lung injury study (18 time points). Similarly, leveraging transcriptome data across treatment time points, I developed methodologies to identify treatment-induced and cell-type specific differentially co-expressed pairs (DCEPs). In part one of Chapter 3, I presented a pipeline that used a series of statistical tests to detect DCEPs. This method was applied to scRNA-seq data of patients with non-small cell lung cancer (NSCLC) sequenced across cancer treatment times. However, this pipeline does not account for correlations among multiple single cells from the same sample and correlations among multiple samples from the same patient. In Part 2 of Chapter 3, I presented a solution to this problem using a mixed-effect model. In Chapter 4, I present a summary of my work that focused on the cross-species analysis of circRNA transcriptome time series data. I compared circRNA profiles in neonatal pig and mouse hearts, identified orthologous circRNAs, and discussed regulation mechanisms of cardiomyocyte proliferation and myocardial regeneration conserved between mouse and pig at different time points.
ContributorsNyarige, Verah Mocheche (Author) / Liu, Li (Thesis advisor) / Wang, Junwen (Thesis advisor) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Understanding intratumor heterogeneity and their driver genes is critical to

designing personalized treatments and improving clinical outcomes of cancers. Such

investigations require accurate delineation of the subclonal composition of a tumor, which

to date can only be reliably inferred from deep-sequencing data (>300x depth). The

resulting algorithm from the work presented here, incorporates an

Understanding intratumor heterogeneity and their driver genes is critical to

designing personalized treatments and improving clinical outcomes of cancers. Such

investigations require accurate delineation of the subclonal composition of a tumor, which

to date can only be reliably inferred from deep-sequencing data (>300x depth). The

resulting algorithm from the work presented here, incorporates an adaptive error model

into statistical decomposition of mixed populations, which corrects the mean-variance

dependency of sequencing data at the subclonal level and enables accurate subclonal

discovery in tumors sequenced at standard depths (30-50x). Tested on extensive computer

simulations and real-world data, this new method, named model-based adaptive grouping

of subclones (MAGOS), consistently outperforms existing methods on minimum

sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports

subclone analysis using single nucleotide variants and copy number variants from one or

more samples of an individual tumor. GUST algorithm, on the other hand is a novel method

in detecting the cancer type specific driver genes. Combination of MAGOS and GUST

results can provide insights into cancer progression. Applications of MAGOS and GUST

to whole-exome sequencing data of 33 different cancer types’ samples discovered a

significant association between subclonal diversity and their drivers and patient overall

survival.
ContributorsAhmadinejad, Navid (Author) / Liu, Li (Thesis advisor) / Maley, Carlo (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2019
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Description
The severity of the health and economic devastation resulting from outbreaks of viruses such as Zika, Ebola, SARS-CoV-1 and, most recently, SARS-CoV-2 underscores the need for tools which aim to delineate critical disease dynamical features underlying observed patterns of infectious disease spread. The growing emphasis placed on genome sequencing to

The severity of the health and economic devastation resulting from outbreaks of viruses such as Zika, Ebola, SARS-CoV-1 and, most recently, SARS-CoV-2 underscores the need for tools which aim to delineate critical disease dynamical features underlying observed patterns of infectious disease spread. The growing emphasis placed on genome sequencing to support pathogen outbreak response highlights the need to adapt traditional epidemiological metrics to leverage this increasingly rich data stream. Further, the rapidity with which pathogen molecular sequence data is now generated, coupled with advent of sophisticated, Bayesian statistical techniques for pathogen molecular sequence analysis, creates an unprecedented opportunity to disrupt and innovate public health surveillance using 21st century tools. Bayesian phylogeography is a modeling framework which assumes discrete traits -- such as age, location of sampling, or species -- evolve according to a continuous-time Markov chain process along a phylogenetic tree topology which is inferred from molecular sequence data.

While myriad studies exist which reconstruct patterns of discrete trait evolution along an inferred phylogeny, attempts to translate the results of phyloegographic analyses into actionable metrics that can be used by public health agencies to direct the development of interventions aimed at reducing pathogen spread are conspicuously absent from the literature. In this dissertation, I focus on developing an intuitive metric, the phylogenetic risk ratio (PRR), which I use to translate the results of Bayesian phylogeographic modeling studies into a form actionable by public health agencies. I apply the PRR to two case studies: i) age-associated diffusion of influenza A/H3N2 during the 2016-17 US epidemic and ii) host associated diffusion of West Nile virus in the US. I discuss the limitations of this (and Bayesian phylogeographic) approaches when studying non-geographic traits for which limited metadata is available in public molecular sequence databases and statistically principled solutions to the missing metadata problem in the phylogenetic context. Then, I perform a simulation study to evaluate the statistical performance of the missing metadata solution. Finally, I provide a solution for researchers whom are interested in using the PRR and phylogenetic UTMs in their own genomic epidemiological studies yet are deterred by the idiosyncratic, error-prone processes required to implement these methods using popular Bayesian phylogenetic inference software packages. My solution, Build-A-BEAST, is a publicly available, object-oriented system written in python which aims to reduce the complexity and idiosyncrasy of creating XML files necessary to perform the aforementioned analyses. This dissertation extends the conceptual framework of Bayesian phylogeographic methods, develops a method to translates the output of phylogenetic models into an actionable form, evaluates the use of priors for missing metadata, and, finally, provides a solution which eases the implementation of these methods. In doing so, I lay the foundation for future work in disseminating and implementing Bayesian phylogeographic methods for routine public health surveillance.
ContributorsVaiente, Matteo (Author) / Scotch, Matthew (Thesis advisor) / Mubayi, Anuj (Committee member) / Liu, Li (Committee member) / Arizona State University (Publisher)
Created2020
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Description
All biological processes like cell growth, cell differentiation, development, and aging requires a series of steps which are characterized by gene regulation. Studies have shown that gene regulation is the key to various traits and diseases. Various factors affect the gene regulation which includes genetic signals, epigenetic tracks, genetic variants,

All biological processes like cell growth, cell differentiation, development, and aging requires a series of steps which are characterized by gene regulation. Studies have shown that gene regulation is the key to various traits and diseases. Various factors affect the gene regulation which includes genetic signals, epigenetic tracks, genetic variants, etc. Deciphering and cataloging these functional genetic elements in the non-coding regions of the genome is one of the biggest challenges in precision medicine and genetic research. This thesis presents two different approaches to identifying these elements: TreeMap and DeepCORE. The first approach involves identifying putative causal genetic variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. TreeMap performs an organized search for individual and multiple causal variants using a tree guided nested machine learning method. DeepCORE on the other hand explores novel deep learning techniques that models the relationship between genetic, epigenetic and transcriptional patterns across tissues and cell lines and identifies co-operative regulatory elements that affect gene regulation. These two methods are believed to be the link for genotype-phenotype association and a necessary step to explaining various complex diseases and missing heritability.
ContributorsChandrashekar, Pramod Bharadwaj (Author) / Liu, Li (Thesis advisor) / Runger, George C. (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2020
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Description
This dissertation presents three novel algorithms with real-world applications to genomic oncology. While the methodologies presented here were all developed to overcome various challenges associated with the adoption of high throughput genomic data in clinical oncology, they can be used in other domains as well. First, a network informed feature

This dissertation presents three novel algorithms with real-world applications to genomic oncology. While the methodologies presented here were all developed to overcome various challenges associated with the adoption of high throughput genomic data in clinical oncology, they can be used in other domains as well. First, a network informed feature ranking algorithm is presented, which shows a significant increase in ability to select true predictive features from simulated data sets when compared to other state of the art graphical feature ranking methods. The methodology also shows an increased ability to predict pathological complete response to preoperative chemotherapy from genomic sequencing data of breast cancer patients utilizing domain knowledge from protein-protein interaction networks. Second, an algorithm that overcomes population biases inherent in the use of a human reference genome developed primarily from European populations is presented to classify microsatellite instability (MSI) status from next-generation-sequencing (NGS) data. The methodology significantly increases the accuracy of MSI status prediction in African and African American ancestries. Finally, a single variable model is presented to capture the bimodality inherent in genomic data stemming from heterogeneous diseases. This model shows improvements over other parametric models in the measurements of receiver-operator characteristic (ROC) curves for bimodal data. The model is used to estimate ROC curves for heterogeneous biomarkers in a dataset containing breast cancer and cancer-free specimen.
ContributorsSaul, Michelle (Author) / Dinu, Valentin (Thesis advisor) / Liu, Li (Committee member) / Wang, Junwen (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Advancements in high-throughput biotechnologies have generated large-scale multi-omics datasets encompassing diverse dimensions such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and phenomics. Traditionally, statistical and machine learning-based approaches utilize single-omics data sources to uncover molecular signatures, dissect complicated cellular mechanisms, and predict clinical results. However, to capture the multifaceted pathological

Advancements in high-throughput biotechnologies have generated large-scale multi-omics datasets encompassing diverse dimensions such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and phenomics. Traditionally, statistical and machine learning-based approaches utilize single-omics data sources to uncover molecular signatures, dissect complicated cellular mechanisms, and predict clinical results. However, to capture the multifaceted pathological mechanisms, integrative multi-omics analysis is needed that can provide a comprehensive picture of the disease. Here, I present three novel approaches to multi-omics integrative analysis. I introduce a single-cell integrative clustering method, which leverages multi-omics to enhance the resolution of cell subpopulations. Applied to a Cellular Indexing of Transcriptomes and Epitopes (CITE-Seq) dataset from human Acute Myeloid Lymphoma (AML) and control samples, this approach unveiled nuanced cell populations that otherwise remain elusive. I then shift the focus to a computational framework to discover transcriptional regulatory trios in which a transcription factor binds to a regulatory element harboring a genetic variant and subsequently differentially regulates the transcription level of a target gene. Applied to whole-exome, whole-genome, and transcriptome data of multiple myeloma samples, this approach discovered synergetic cis-acting and trans-acting regulatory elements associated with tumorigenesis. The next part of this work introduces a novel methodology that leverages the transcriptome and surface protein data at the single-cell level produced by CITE-Seq to model the intracellular protein trafficking process. Applied to COVID-19 samples, this approach revealed dysregulated protein trafficking associated with the severity of the infection.
ContributorsMudappathi, Rekha (Author) / Liu, Li (Thesis advisor) / Dinu, Valentin (Committee member) / Sun, Zhifu (Committee member) / Arizona State University (Publisher)
Created2023