Computational Biology Commons^™

Regulation Of Müllerian Duct Mesenchyme Transcription During Mammalian Sex Differentiation, Haowen Li, Richard R Behringer, Rachel D Mullen

Dissertations and Theses (Open Access)

Sp7/Osterix (Osx) encodes a zinc-finger transcription factor of the Specificity-protein family discovered by Nakashima et al. at the MD Anderson Cancer Center. While primarily recognized for its role in osteogenesis, Osx has also been implicated in mammalian reproductive development, particularly in male sex differentiation, where Müllerian Duct (MD) regression occurs, mediated by anti-Müllerian hormone (AMH) signaling. AMH-induced regression signals are transduced by the mesenchymal tissue surrounding the ductal structure, known as the Müllerian Duct mesenchyme (MDM). It was discovered that AMH signaling is necessary and sufficient for driving Osx expression in MDM. A previous transgenic mouse reporter …

Quantifying Genetic Diversity In Nematode Feeding Rate, Jacob King

Honors Theses

Understanding how populations persist under changing environmental conditions is a central question in ecology and evolutionary biology. Variation in traits related to resource acquisition may play a key role in determining population survival when resources are limited or fluctuate over time. In this study, I investigated how variation in feeding-related traits influences population persistence using a combination of computational modeling and laboratory experiments. I developed a stochastic, individual-based consumer–resource model to simulate population dynamics across varying levels of trait variance and resource regimes. Across 60,000 simulated populations, increased trait variation consistently reduced extinction risk and extended persistence time, although the …

Toward Interpretable Multi-Omics Multimodal Biomedical Artificial Intelligence, Yanjun Lyu

Computer Science and Engineering Dissertations

The complexity of human disease arises from biological processes that unfold across multiple scales, from molecular variation through cellular function, tissue organisation, brain phenotypes, each of which is associated with distinct measurement modalities, regularities, and characteristic. Contemporary biomedical artificial intelligence has brought the opportunity to reveal the complexity with in; however, its methodological default, in which models are trained on most readily available modality, does not adequately engage with the multi-scale connected structure by which biological meaning is constituted. The research area of multi-omics and multi-modal AI for biomedicine remains at an early exploratory stage, and the work presented in …

A Framework For Characterizing The Peripheral Immune Isonome Using Long-Read Single-Cell Rna Sequencing And Its Relevance To Neurological Disease, Patricia Hayes Doyle

Theses and Dissertations--Neuroscience

Long-read single-cell RNA sequencing provides an opportunity to understand human health and disease at isoform resolution, revealing cellular diversity and disease mechanisms difficult to resolve with bulk or short-read methodologies.

Using a modified PIPseq workflow and computational pipeline adapted for Oxford Nanopore (ONT) sequencing, we profiled isoform usage across immune cells, integrating marker expression and isoform discovery, generating the largest long-read single-cell dataset of human immune cells from a single individual to date. We identified non-canonical protein-coding variants of GZMB and CD3G enriched in unexpected cell types. We also discovered novel transcripts from CMC1 and LYAR with cell-type-specific signatures that …

Computational And Ai Frameworks For Identifying Key Regulatory Genes And Their Target Genes In Plants And Humans, Md Khairul Islam

Dissertations, Master's Theses and Master's Reports

This dissertation presents computational and AI-driven frameworks for identifying key regulatory genes and their downstream targets across plant and human biological systems. Three studies address distinct challenges in genomic regulation using advanced machine learning and bioinformatics approaches.

The first study introduces DyGAF (Dynamic Gene Attention Focus), a dual-attention transformer framework that identifies and ranks disease-relevant biomarker genes by simultaneously modeling independent molecular responses and interdependent regulatory network behavior. Two attention models provide complementary perspectives on gene importance and are fused through a novel combination metric. Applied to COVID-19 nasopharyngeal swab profiles, the attention-weighted representations achieved 94.23% classification accuracy, high sensitivity, …

Dosage Sensitivity And The Evolution Of Dosage Compensation: Tests Of The Insensitive Sex Chromosome Hypothesis In Flour Beetles, Shana Pau

Biology Dissertations

Sex chromosome evolution generates imbalances in gene dosage that can disrupt gene expression and organismal function. These imbalances are often resolved through dosage compensation mechanisms, yet the factors that drive the emergence and diversity of these systems remain poorly understood. This dissertation addresses a central question in evolutionary genomics: what governs the evolution of dosage compensation?

Focusing on dosage sensitivity as a potential driver, I evaluate the Insensitive Sex Chromosome Hypothesis (ISCH), which predicts that chromosome-wide compensation is more likely to evolve in genomic contexts that are not depleted of dosage-sensitive genes. Using flour beetles (Tribolium spp.) as a …

Interacting With Ideas: How To Engage Stem Students In Active Learning Of Theory Using Technology, Jessica Elizabeth Whitney, Keith Brian Morris

2026 Scholarly Teaching Conference: Concurrent Session Papers

STEM education in the modern age has been subject to much reform – from the integration of technology to an emphasis on student-centered teaching strategies, such as active learning. However, in the wake of virtual and blended-learning environments, student engagement and teacher assessment of student success have been challenged. Tools such as KAHOOT! and iClicker have been promoted to foster an active learning environment while sometimes falling short in regards to student retention of course material. In light of this technological educational revolution, instructors need to be able to determine the most effective tools for their discipline to aid in …

Recurrence And Co-Occurrence Of Enhancer-Promoter Interactions Across Human Samples, Satvik Gunjala

Honors Undergraduate Theses

Experimental mapping of enhancer-promoter interactions (EPIs) is resource-intensive, and current computational prediction methods struggle with intrinsic genomic complexity and reliance on limited training data. To address this bottleneck and provide insights for improved computational methods, this study systematically analyzed chromatin contact datasets to investigate the recurrence and co-occurrence of enhancer-promoter interactions across human samples. Putative interactions were evaluated across two HiChIP datasets comprising 218 total samples and one Hi-C dataset comprising 266 samples to assess recurrence across samples and assess sequencing depth related to unique EPIs. Additionally, a preliminary item-based collaborative filtering recommender model was developed to assess co-occurrence patterns …

Population Structure Analysis Of Four Basal Higher-Attines Using Bioinformatic Approaches, Gabriel Mcdanield

Biology Theses

Metapopulations within the family Formicidae are unique among other animals due to the large colonies they build, and the mating strategies required to fertilize a specialized reproductive caste that will often produce young for the life of the colony. One group of ants, the fungus-gardeners (tribe Attini). The population structure of North American non-leafcutting, fungus-gardening ants has been understudied, especially in the southwest of the United States. Additionally, not much is known about their dispersal biology, so the dynamics of dispersal of these species and how they affect population structure is likewise not well known. To shed light on the …

Bioinformatic Analysis Of Pogz Variants In Relation To White Sutton Syndrome, Hannah Rollins

Theses

White-Sutton syndrome (WHSUS) is a rare neurodevelopmental disorder caused by mutations in the Pogo Transposable Element with ZNF Domain (POGZ) gene, which encodes pogo-transposable element with ZNF domain, a chromatin regulator essential for proper mitotic progression and DNA repair. This study uses a bioinformatic framework to evaluate the structural and functional impact of missense mutations in the conserved amino acid region (positions 500–800) of the POGZ protein. Protein modeling, variant effect prediction, conservation analysis, and molecular dynamics simulations were employed to gain an understanding of the effects of POGZ missense mutations on protein structure and movement with specific emphasis on …

Exploring The Role Of Ecological Traits In Shaping Spatial Patterns Of Genetic Diversity, Rilquer Mascarenhas Da Silva

Dissertations, Theses, and Capstone Projects

The field of comparative phylogeography aims at uncovering common underlying causes for shared patterns of diversity and diversification at the population level. However, it is now widely accepted that intraspecific genetic diversity patterns can differ considerably across co-occurring taxa due to ecological processes acting at the population level (and influencing individual movement and abundance). To model the processes underlying lineage diversification and demographic shifts in response to environmental changes, phylogeographers and population geneticists are now faced with the challenge of incorporating measurements of species ecological traits into their molecular studies. This dissertation aimed at investigating how different types of ecological …

Performance Analysis Of Computational Methods For Predicting Protein Function In Rare Diseases, Aichetou Mohamed Sidiya, Hanin Alzaher, Razan Almahdi, Tayeb Brahimi

Effat Undergraduate Research Journal

Protein function prediction is crucial for understanding the underlying mechanisms of rare diseases. With the increasing availability of computational methods including machine learning-based approaches, network-based methods, and sequence-based methods, predicting protein functions has become more accessible. However, it is not clear which of these methods performs better or how they compare to each other in terms of accuracy, efficiency, and scalability. In this study, we evaluate several computational methods for predicting protein functions in rare diseases using key performance indicators (KPIs). We analyze the strengths and weaknesses of each method and provide recommendations for researchers and clinicians interested in using …

Network Analysis Of Antimicrobial Resistance In Staphylococcus Aureus: Characterization Of Hub Genes And Their Functional Implications, Md Imran Hasan, Davida Smyth, Jeong Yang, Ashley Teufel

Masters Theses (Archived)

Antimicrobial resistance is a major cause of morbidity and mortality in patients with S. aureus infections. In this study, we analyzed genes, molecular mechanisms, and pathways driving drug resistance in S. aureus using network analysis. Using whole-genome sequencing (WGS) data and systems biology approaches, we identified 229 AMR-associated genes and constructed a protein-protein interaction network among these genes. Through network topology and functional enrichment analyses, we not only confirmed their association with resistance, but also highlighted the central roles of these genes in resistance pathways, such as efflux, target replacement, and target protection, which are directly linked to multiple drug …

Investigating The Effects Of Transcription Factor Binding And Genetic Variants In The Striatum Of Post-Mortem Cohorts With Opioid Use Disorder, Rajashree Chakraborty

Theses & Dissertations

The opioid crisis has emerged as one of the most pressing public health challenges of our time, with Opioid Use Disorder (OUD) affecting millions of lives across the globe. Studying OUD is not merely an academic pursuit but a critical necessity in addressing this multifaceted epidemic. The urgency of this research is underscored by the staggering prevalence of OUD, with an estimated 3.7% of U.S. adults requiring treatment in 2022 alone. Despite the availability of effective medications for OUD, a significant treatment gap persists, with only a quarter of those in need receiving these life-saving interventions. The far-reaching consequences of …

Developing A Small Molecule To Inhibit Hsf1 Expression In Cancer And Evaluating Natural Genetic Variation In Small Molecule Toxicity., Michaela Kendal Foley

Theses and Dissertations

Each year cancer affects nearly 20 million people worldwide and genetic differences across populations can impact cancer onset and progression. Specifically, tumors with high levels of HSF1, the master regulator of the cytoprotective heat shock response (HSR), are correlated with poor patient outcomes in multiple cancers such as prostate, breast, and melanoma. Subsequently, the development of pharmacological inhibitors of HSF1 represents a promising strategy for anticancer therapeutics. Using a luciferase-based transcriptional reporter, two small molecule libraries were screened for inhibitors of HSF1 expression in human embryonic kidney cells, yielding ten compounds that decrease HSF1 expression. To identify if cancer lines …

Elucidating The Multi-Omics Of Early-Onset Colorectal Cancer, Jumanah Alshenaifi

Dissertations and Theses (Open Access)

The incidence and mortality rates of sporadic early-onset colorectal cancer have increased in recent decades, but there is no clear etiological basis for this trend. EOCRC is commonly defined as colon and rectal cancers diagnosed before the age of 50 years. The rising incidence of EOCRC has made it the second most common cancer and the third leading cause of cancer death in this age group. The rising incidence of EOCRC is also documented internationally in more than 20 countries across different continents. Clinically, EOCRC has a distinct, more aggressive clinical profile than LOCRC. While approximately 15% of EOCRC cases …

Leveraging Attention Mechanism To Unlock Gene And Protein Attributes, Ala Jararweh

Computer Science ETDs

Advancing personalized medicine depends on effectively integrating and interpreting the vast, heterogeneous landscape of biological data, from genomic sequences and transcriptomics to the insights embedded in scientific literature. Current machine learning models often focus on single data modalities, limiting their capacity to capture the multifaceted nature of biological systems. We address this gap by developing three attention-based machine-learning models integrating diverse data modalities. Firstly, DeepVul is a multi-task model that leverages cancer transcriptome data to predict genes critical for cancer survival and their corresponding drugs. Subsequently, LitGene refines gene representations by integrating textual information from the scientific literature. Finally, Protein2Text …

Identification Of Novel Argonaute Proteins Using A Metagenomic Mining Approach, Lobna Abdallah Ghonaim

Theses and Dissertations

Gene editing is one of the most promising tools in science. It enables precise modifications of an organism's genetic material. Metagenomics is considered a powerful tool that unlocks the broad genetic potential found in uncultured microbial communities. Exploring the genetic diversity of uncultured microbial communities helps identify novel functional proteins with unique properties and make the best use of these diverse microbial ecosystems.

We developed and employed a metagenomic-based approach to mine more than 1000 metagenomes for prokaryotic argonaute proteins (pAgos), a potential gene editing machinery encoded in bacterial and archaeal genomes. Our workflow involved strict quality control, sequence assembly, …

The Role Of Secondary And Tertiary Structure In The Cap-Independent Translation Of Fgf-9 And Hif-1-Alpha, Amanda Michelle Whittaker

Dissertations, Theses, and Capstone Projects

Under normoxic conditions, eukaryotes initiate translation of RNA through eIF4E recognition of the 5’ cap. However, under cellular stress, eukaryotic translation must be initiated through a 4E-independent, or “cap-independent” mechanism, involving eukaryotic initiation factor 4G (eIF4G) binding directly to the 5’ untranslated regions (5’ UTR) of the RNA. eIF4G binding then recruits the ribosome to the transcript. While this mechanism is useful for translation of apoptotic transcripts and transcripts involved in cell survival, cap-independent translation is also utilized by oncogenic RNA for tumorigenesis. Previous work by our lab and others has categorized this recruitment and initiation mechanism as either internal-ribosome-entry-site …

18s Metabarcode Analyses Of Eukaryotic Species In The Respiratory Microbiomes Of Wild Canids From New Hampshire, Collin Sinclair Blake

Honors Theses and Capstones

This study is of an exploratory nature and focuses on characterizing the eukaryotic microbiota present in the respiratory tissues of six wild canids and one domestic canine. The contents of this document largely pertain to dry lab analyses of 18S barcodes in bioinformatics programs – primarily QIIME2 running in the GitBash command line, service for which was hosted by the UNH Ron Bioinformatics training server. All procedures listed within the section below were performed by second parties at the UNH Hubbard Center for Genomics Studies (HCGS), the New Hampshire Veterinary Diagnostics Lab (NHVDL), and the UNH Microbial Ecology and Emerging …

Cath-Ddg: Towards Robust Mutation Effect Prediction On Protein-Protein Interactions Out Of Cath Homologous Superfamily, Guanglei Yu, Xuehua Bi, Teng Ma, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and disease mechanistic studies. Recently, deep learning models using protein 3D structures have become predominant for predicting mutation effects. However, significant challenges remain in practical applications, in part due to the considerable disparity in generalization capabilities between easy and hard mutations. Specifically, a hard mutation is defined as one with its maximum TM-score < 0.6 when compared to the training set. Additionally, compared to physics-based approaches, deep learning models may overestimate performance due to potential data leakage.

Results: We propose new training/test splits that mitigate data leakage according to the CATH homologous superfamily. Under the constraints of physical …

Copula-Based Bayesian Model For Detecting Differential Gene Expression, Prasansha Liyanaarachchi, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

Deoxyribonucleic acid, more commonly known as DNA, is a fundamental genetic material in all living organisms, containing thousands of genes, but only a subset exhibit differential expression and play a crucial role in diseases. Microarray technology has revolutionized the study of gene expression, with two primary types available for expression analysis: spotted cDNA arrays and oligonucleotide arrays. This research focuses on the statistical analysis of data from spotted cDNA microarrays. Numerous models have been developed to identify differentially expressed genes based on the red and green fluorescence intensities measured using these arrays. We propose a novel approach using a Gaussian …

Hidden Markov Model For Identifying Local Variants In Human Genomes Using Simulated Data, Scott Mccallum

Electronic Theses and Dissertations

Identifying adaptive mutations in genetic data is challenging due to the low frequency of occurrence of such events, and because signatures of selection are intertwined with the footprints of various other evolutionary forces that shape our genomes. Even when a larger region appears to be under selection, genomic sites that are linked to adaptive mutations have similar statistical signals, and thus can obfuscate the identification of the actual adaptive mutation. The new method described here uses a Hidden Markov Model that allows for classification of neutral, linked, and sweep (adaptive mutation) genomic sites. This model is general and can be …

The Influence Of Environmental Change On Genetic Diversity Across Spatial And Taxonomic Scales, Connor M. French

Dissertations, Theses, and Capstone Projects

The spatial distribution of genetic diversity is of interest to biodiversity scientists and conservationists and is a fundamental metric of biodiversity. Genetic diversity patterns across spatial and taxonomic scales contain information about population and assemblage dynamics that can convey their resilience to environmental change. Ectotherms are especially linked to their environments and may be especially sensitive to fluctuations in the environment over time. Herein, I investigate global and regional patterns of genetic diversity in two groups of ectotherms, insects and lizards, to understand the relationship between environmental change and genetic diversity, from populations to assemblages. Overall, my research aims to …

Regulation Of Serpina1 Mrna Expression By Environmental Conditions In Hepatocyte Cells, Fnu Jiamutai

All Theses

The SERPINA1 gene encodes the critical protease inhibitor α-1-antitrypsin (A1AT). A1AT represses neutrophil elastase activity to protect lung tissue from inflammatory damage. A deficiency in α-1-antitrypsin can lead to chronic obstructive pulmonary disease (COPD). Pathogenic genetic variants in SERPINA1 are also associated with A1AT protein misfolding and liver cirrhosis. The regulatory mechanisms of SERPINA1 expression are not well understood, but previous studies suggest that alternative polyadenylation in the 3' untranslated region (3'UTR) affects A1AT protein expression. In this study, we used the liver cancer cell line HepG2 to determine how environmental conditions influence SERPINA1 mRNA expression and post-transcriptional regulation. We …

Early Onset Alzheimer’S Disease Markers In Mouse Hippocampus Unveiled By Single-Cell Transcriptomic Analysis Following Cranial Radiotherapy, Tuba Aksoy

Dissertations and Theses (Open Access)

Cranial radiation therapy plays an integral role in the treatment of brain tumors but can lead to progressive cognitive deficits in survivors by mechanisms that are poorly understood. To develop preventive or mitigative strategies, it is crucial to better understand the underlying pathogenesis of radiation-induced cognitive impairments. The study investigated single-cell transcriptomics and DNA methylation changes as potential drivers of persistent cellular dysfunction after radiation exposure, specifically concentrating on the CA1-3 regions of the hippocampus and the prefrontal cortex due to their role in cognitive functions. Thirteen-week-old mice underwent whole-brain radiation at clinically relevant doses. Following whole-brain radiation, an assessment …

Utilizing The In4mer Crispr/Cas12a Multiplex Knockout Platform To Investigate Synthetic Lethality In The Human Genome, Xingdi Ma

Dissertations and Theses (Open Access)

The emergence of high-throughput sequencing technologies and the development of targeted cancer therapies have significantly advanced our understanding of cancer genomics and prolonged patient survival. Despite these advances, durable response remains difficult to achieve in the clinic. The concept of synthetic lethality has gained traction as a promising opportunity to discover novel cancer-specific vulnerabilities and therapeutic targets. Unfortunately, initial technologies for combinatorial genetic perturbation in mammalian cells suffer from inefficiency and are challenging to scale. In this dissertation, I report: 1) paralog selection method to select candidate synthetic lethal paralogs; 2) our Cas12a multiplex platform “IN4MER” that provides superior sensitivity …

Genomic Data Science Approaches For Understanding Human Diseases, Snehal Shah

All Dissertations

The intricate interplay of genetic predisposition, environmental influences, and lifestyle acts as the multifactorial landscape of diseases. Understanding this complexity presents a significant challenge. Molecular insights into disease mechanisms, particularly the interactions of DNA, RNA, and proteins with environmental and lifestyle factors, have revolutionized disease diagnosis, prognosis, and treatment. High-throughput technologies, such as next-generation sequencing, generate large amounts of molecular data, holding a wealth of knowledge. These datasets unveil the roles of genes and their interactions with various factors through analysis, shedding light on previously unknown molecular mechanisms underlying disease pathogenesis. Furthermore, they facilitate the discovery of biomarkers crucial for …

Omani Camels From A Cultural And Genomics Perspective, Al Muatasim Al Zadjali

Electronic Theses and Dissertations

The Dromedarian camel, Camelus dromedarius, is native to the Arabian Peninsula, including the Sultanate of Oman. These camels are used for food, milk, as well as show and racing competitions. Despite their economic and cultural importance research on camels in Oman is limited. The goal of this study was to examine their genomic variation, relationship with camels in other parts of the Arabian Peninsula, and to determine if selective breeding has led to the establishment of distinct breeds in Oman. Information was compiled from multiple sources to produce a comprehensive review on the breeding, management, economic and cultural use, …

Transcriptomic Profiling Of Engineered Human Gene Integration In The Mouse Genome Following In Vitro Gene Editing, Ethan Potts, Made Harumi Padmaswari, Christopher Nelson

Biomedical Engineering Undergraduate Honors Theses

Gene replacement is a promising method of therapy for genetic diseases. However, safety and efficacy are areas that need more research. This experiment aims to use RNA sequencing and bioinformatic techniques to provide answers to these questions and provide direction to future studies to develop a gene replacement therapeutic. C2C12 mouse myoblast cells were transfected with a vector containing a CRISPR-Cas9 system and the Human Factor IX (hF9) gene in order to hijack target genes and integrate the hF9 gene. The two target genes, myoglobin (Mb) and creatine kinase (Ckm), were chosen for their high rate of expression and low …