Computational Biology Commons^™

Data Curation And Integration For Cancer Cell Line Pharmacogenomics Analysis, Meric Kinali

Graduate Masters Theses

Cancer cell lines are essential resources for connecting genomic and molecular features with drug response and identifying biomarkers. Therefore, large-scale cancer cell line resources have been growing, but this introduces challenges such as data standardization, consistent annotation, matching identifiers, cross-database integration, and a reproducible computational framework. CellMiner Cross Database (CellMinerCDB) integrates pharmacogenomic datasets from multiple resources and provides a standardized analysis of cell lines across datasets. This thesis examines cancer cell line cross-databases and their importance in pharmacogenomics, with a particular focus on CellMinerCDB. As part of this work, I developed an R package for the gCSI (Genentech Cell Line …

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 …

Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization, Marwa Talal

Theses and Dissertations

Background: Metabolic syndrome (MetS) is a complex cluster of interrelated metabolic abnormalities associated with elevated cardiometabolic risk. While diagnosis is based on well-established five clinical criteria, these may overlook early or atypical metabolic alterations. Large-scale metabolomic profiling offers an opportunity to identify biochemical signatures of MetS beyond diagnostic bias and to evaluate their relative importance across different presentations of the syndrome.

Methods: Data from 117,147 UK Biobank participants were analyzed in a cross-sectional design. High-throughput NMR quantified 75 circulating metabolites, for. Univariate analyses, MetS subtype stratification, and elastic net models with SHAP interpretation were applied to assess feature …

Generating Predictive Gene Expression Signatures For Alzheimer's Disease Using Postmortem Brain Tissue, Ashley Duche

Pharmaceutical Sciences (PhD) Dissertations

Background: Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of amyloid-beta (Aβ) plaques and tau protein aggregates. These pathological features develop in specific brain regions, but why some areas are more vulnerable to early AD-related changes remains unclear. To address this, predictive gene expression signatures were developed to explore the molecular mechanisms underlying regional susceptibility to AD pathology.

Methods: This was performed using postmortem brain (PMB) tissue from participants in the Religious Orders Study and Memory and Aging Project (ROSMAP), Mayo Clinic, and Mount Sinai Brain Bank (MSBB) to generate gene expression signatures from six brain …

Ai-Powered Multi-Omics Integration For Predictive Modeling Of Genotype-Environment-Phenotype Relationships, You Wu

Dissertations, Theses, and Capstone Projects

This dissertation presents a series of machine learning frameworks for modeling genotype–environment–phenotype relationships through integrative predictive modeling of multi-omics data. The work addresses three major axes of biological complexity: modeling biological information transmission cross-levels from genes to proteins to phenotypes, predicting molecular features cross-scale from cells to tissues to organisms, and translating phenotypes cross-species from model systems to humans. Each proposed method also tackles key machine learning (ML) challenges in the biomedical domain, including data scarcity, domain shift, out-of-distribution (OOD) generalization, and hierarchical modeling. Specifically, this dissertation introduces five novel deep learning algorithms: MultiDCP predicts drug-induced transcriptomic and viability responses …

Cloning And Expression Of Scamp3 In Escherichia Coli Bl21(De3) With In Silico Sequence-Based Cancer Epitopes Prediction, Selly Setiati Rajagukguk, Sabar Pambudi, Astari Dwiranti, Doddy Irawan Setyo Utomo, Anom Bowolaksono

Makara Journal of Science

Secretory carrier membrane protein 3 (SCAMP3) is a crucial membrane protein involved in intracellular vesicle traffick-ing and exocytosis. The SCAMP3 expression has been observed in diverse cancer types, such as melanoma, glioma, hepatocellular and breast cancer. Increased SCAMP3 expression has been reported in certain cancer cells relative to that in normal cells, suggesting the potential role of SCAMP3 in cancer development or progression. In this study, we successfully cloned and expressed SCAMP3 in Escherichia coli strain BL21(DE3). SCAMP3 was amplified and insert-ed directionally into the prokaryotic expression vector pET21d(+). The transformation of recombinant plasmid into E. coli BL21(DE3) cells were …

Screening For Penicillin G Acylase (Pga)-Producing Bacteria And Gene Cloning Using Degenerate Oligonucleotide Primed-Pcr, Masdalifah Masdalifah, Sri Rezeki Wulandari, Gabriela Christy Sabbathini, Maria Ulfah, Dini Achnafani, Ahmad Wibisana, Feronika Heppy Sriherfyna, Is Helianti, Niknik Nurhayati

Makara Journal of Science

The growing concern over antibiotic resistance has driven global efforts to explore innovative solutions, including the use of Penicillin G acylase (PGA) to produce semisynthetic β-lactam antibiotics. This study screened four potential in-tracellular PGA-producing bacteria: Alcaligenes faecalis InaCC B444 (AfPGA), Kluyvera cryocrescens InaCC B850 (KcPGA), Providencia rettgeri InaCC B25 (Pr25PGA), and P. rettgeri InaCC B466 (Pr466PGA). Penicillin G Acylase encoding genes (pgas) were isolated from them using a Degenerate Oligonucleotide Primed-PCR (DOP-PCR) approach and sequenced. Microbiological assays confirmed all tested crude extracts to exhibit inhibitory effects. Penicillin G was used for evaluating hydrolytic activity and 6-Amino Penicillanic Acid (6-APA) coupled …

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, …

Timigp: A Computational Framework To Determine The Tumor Immune Microenvironment Associated With Prognosis And Immunotherapy Response, Chenyang Li

Dissertations and Theses (Open Access)

Accumulating evidence has suggested that the tumor immune microenvironment (TIME) drastically impacts cancer patients’ clinical outcomes, including prognosis and immunotherapy response. However, understanding TIME remains challenging due to its complexity and heterogeneity. In this dissertation, we introduce TimiGP (Tumor Immune Microenvironment Illustration based on Gene Pairs), a computational framework designed to address this challenge. Leveraging single-cell RNA-seq (scRNA-seq) and bulk gene expression data alongside clinical information, TimiGP constructs a cell-cell interaction network that elucidates the relationship between immune cell function and relevant clinical outcomes, such as prognosis and treatment response. With immunological insights, these cell-cell interactions also facilitate the development …

Advancement In In-Silico Drug Discovery From Virtual Screening Molecular Dockings To De-Novo Drug Design Transformer-Based Generative Ai And Reinforcement Learning, Dony Ang

Computational and Data Sciences (PhD) Dissertations

The field of drug discovery has seen remarkable advancements over the past few decades, transitioning from traditional experimental methods to highly sophisticated computational approaches. One of the pivotal techniques in this evolution is virtual screening, which utilizes molecular docking to predict the interaction between small molecules and target proteins. This method has significantly accelerated the initial stages of drug discovery by enabling the high-throughput screening of large chemical libraries. By simulating the binding affinity and stability of potential drug candidates, virtual screening has become a cornerstone in identifying promising compounds for further development.

A notable application of virtual screening was …

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 …

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 …

In Silico Analysis Of C-Type Lectins As Co-Infection Receptors Of Dengue And Chikungunya Viruses In Aedes Aegypti, Munawir Sazali, R. C. Hidayat Soesilohadi, Nastiti Wijayanti, Tri Wibawa, Arif Nur Muhammad Ansori

Makara Journal of Science

Aedes aegypti is a primer vector of dengue virus (DENV) and chikungunya virus (CHIKV). The susceptibility of mosquitoes to DENV and CHIKV depends on their recognition receptor of pathogens. C-type lectins (CTLs) are an important mediator of virus infection in A. aegypti. This study aims to identify potential receptors and determine the binding affinity between ligand–receptor interaction, CTLs and virus envelopes (DENV-1, 2, 3, and 4 and CHIKV) interaction based on in silico analysis. Sample sequences were obtained from GenBank (NCBI), and 10 CTLs were acquired from VectorBase. Homology modeling based on a minimum standard of 20% was processed …

A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes

Graduate Industrial Research Symposium

The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute it into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects, but does not necessarily indicate the initial point of interference within the network. The objective of this project is to take advantage of large scale and genome-wide perturbational datasets by using them to train a tuned machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of …

Contemporary Vs. Traditional Dna-Based Mutation Testing To Detect Hereditary Breast And Ovarian Cancer Syndrome (Hboc) In Women: A Meta Narrative Review, Hannah Olivia Cantu, Jacob Hipp, Nyela Y. Lopez, Duc Duy Nguyen

Research Methods Poster Session 2024

No abstract provided.

Developing Cellular Systems To Elucidate Rna Structural Dynamics Of Cag Expansion Transcripts In Spinocerebellar Ataxias, Victoria Demeo

Electronic Theses & Dissertations (2024 - present)

Spinocerebellar ataxias (SCAs) are a diverse group of over 40 genetically heterogeneous neurodegenerative disorders, many of which are caused by a trinucleotide CAG repeat expansion in the coding region of specific genes. These expansions lead to the production of polyglutamine (polyQ) tracts that interfere with normal protein function, triggering cellular dysfunction and contributing to disease pathogenesis. The most well-known of these SCAs, such as SCA1, SCA2, and SCA3, exhibit progressive neurodegeneration, yet the precise mechanisms through which these mutations cause disease remain poorly understood.

A significant challenge in studying CAG repeat expansion disorders lies in the complexity of the disease …

Identifying Non-Traditional Slippery Sequences Associated With Translational Frameshifts, Aaron J. Gin, Kari Lynn Clase

Graduate Industrial Research Symposium

Genetic frameshifts are a mutation in which
a nucleotide skip leads to a shift in the
reading frame. In viruses, these frameshifts
can be programmed using a slippery
sequence to bypass the stop codon
associated with the initial protein. This
allows for variable control of protein
expression. In bacteriophages, translational
frameshifts have been identified but only a
few have been proven experimentally. Using
experimental data and comparative
genomics, non-traditional slippery
sequences can be identified as assisting in
controlling the protein coding throughout
viruses. Novel slippery sequences can aid in
the understanding of protein expression in
biological environments and further the …

Characterizing Endogenous Dicer Products To Unravel Novel Rnai Biogenesis Pathways, Jacob Oche Peter

Dissertations

ABSTRACT

RNA interference (RNAi) is a pervasive gene regulatory mechanism in eukaryotes based on the action of multiple classes of small RNA (sRNA). Exploiting RNAi pathways in non-model systems have great potential for creating potent RNAi technologies. Here, we accessed RNAi-mediated control of gene expression in the two-spotted spider mite, Tetranychus urticae (T. urticae) using engineered dsRNA designed to modulate the host RNAi pathway and increase RNAi efficacy. Analysis of Dicer (Dcr) generated fragments revealed how exogenous RNAs access the host RNAi pathway in this animal, opening avenues for designing RNAi technology for their control. Further, some organisms …

Alterations Of The Gut Mycobiome In Patients With Ms - A Bioinformatic Approach, Saumya Shah

Honors Scholar Theses

The mycobiome is the fungal component of the gut microbiome and is implicated in several autoimmune diseases. However, its role in multiple sclerosis (MS) has not been studied. We performed descriptive and formal statistical tests using the R language to characterize the gut mycobiome in people with MS (pwMS) and healthy controls. We found that the microbiome composition of multiple sclerosis patients is different from healthy people. The mycobiome had significantly higher alpha diversity and inter-subject variation in pwMS than controls. Additionally, Saccharomyces and Aspergillus were over-represented in pwMS. Different mycobiome profiles, defined as mycotypes, were associated with different bacterial …

Annual Faculty Research Symposium 2022, Oakwood University

Proceedings

No abstract provided.

Comparative Modeling And Evolutionary Comparison Of Serine Protease, A Timber Rattlesnake Venom Protein, Qawer Ayaz

Theses

The aim of this study is to create a homology model of VG35 serine protease and evaluate the evolutionary comparison of secondary structure on basis of protein model using YASARA. This method was furthermore used to predict the potential epitopes which can help in the investigation of future studies.

The VG35 was used to run a BLAST search which gave most resembled serine protease of different species which was then translated and modeled in YASARA. The modeled protein data was then used to determine the secondary structure. This was then used for evolutionary comparison of all proteins to VG35. Then …

Characterization Of The Growth Factor Receptor Network Oncogenes In Lung Cancer, Ashley Duche

Pharmaceutical Sciences (MS) Theses

Lung cancer remains the leading cause of cancer related deaths worldwide, reportedly contributing to 1.8 million of the 10.0 million mortalities documented in the year 2020. Although advancements have been made in therapeutics and diagnostic methods, formulation of effective treatments and development of drug resistance continues to be a challenge. These challenges arise from our lack of understanding of intricate signaling pathways, such as the Growth Factor Receptor Network (GFRN), which contributes to complex lung tumor heterogeneity allowing for drug resistance development. In this study, gene expression signatures of six GFRN oncogenes overexpressed in human mammary epithelial cells (HMECs) were …

Comparative Genomics Methods And Applications, Emily N. Alden

Biomedical Sciences ETDs

Virtually all fields of biology have benefited from the advancements in comparative genomics technologies, specifically in the study of evolution. In this dissertation I develop and use comparative genomic technologies to investigate the novel SARS-CoV-2 virus, assembly the first genome of the black lace domestic angelfish and identify germline genetic variants associated with altered breast cancer-specific survival. Our genome tiling array for the novel coronavirus presents a rapid and cost-effective method to sequence the entire viral genome and can be used to track the rapid evolution of viral variants in the population. The domestic angelfish is a member of the …

Biases And Blind-Spots In Genome-Wide Crispr-Cas9 Knockout Screens, Merve Dede

Dissertations and Theses (Open Access)

Adaptation of the bacterial CRISPR-Cas9 system to mammalian cells revolutionized the field of functional genomics, enabling genome-scale genetic perturbations to study essential genes, whose loss of function results in a severe fitness defect. There are two types of essential genes in a cell. Core essential genes are absolutely required for growth and proliferation in every cell type. On the other hand, context-dependent essential genes become essential in an environmental or genetic context. The concept of context-dependent gene essentiality is particularly important in cancer, since killing cancer cells selectively without harming surrounding healthy tissue remains a major challenge. The toxicity of …

Applications Of Machine Learning In Microbial Forensics, Ryan B. Ghannam

Dissertations, Master's Theses and Master's Reports

Microbial ecosystems are complex, with hundreds of members interacting with each other and the environment. The intricate and hidden behaviors underlying these interactions make research questions challenging – but can be better understood through machine learning. However, most machine learning that is used in microbiome work is a black box form of investigation, where accurate predictions can be made, but the inner logic behind what is driving prediction is hidden behind nontransparent layers of complexity.

Accordingly, the goal of this dissertation is to provide an interpretable and in-depth machine learning approach to investigate microbial biogeography and to use micro-organisms as …

Single-Fluorophore Sensors For Mechanical Force In Living Cells, Sarah Kricheff

Honors Scholar Theses

Mechanotransduction is the process by which a mechanical stimulus is converted to a cellular signal. This process is heavily influential of cell morphology, differentiation, and behavior. However, altered levels of mechanical stimuli are also found in many pathological contexts. For example, cancerous cells have stiffer surrounding tissue than healthy cells, and research suggests that this alters cell behavior and promotes metastasis. Despite these findings, the cellular processes behind these signaling alterations remain widely unknown. Understanding these cascades is critical, as involved proteins can give us a deeper understanding of the role of mechanotransduction, and certain proteins can potentially be targeted …

Metabolic Network Analysis Of Filamentous Cyanobacteria, Daniel Alexis Norena-Caro

LSU Doctoral Dissertations

Cyanobacteria were the first organisms to use oxygenic photosynthesis, converting CO₂ into useful organic chemicals. However, the chemical industry has historically relied on fossil raw materials to produce organic precursors, which has contributed to global warming. Thus, cyanobacteria have emerged as sustainable stakeholders for biotechnological production. The filamentous cyanobacterium Anabaena sp. UTEX 2576 can metabolize multiple sources of Nitrogen and was studied as a platform for biotechnological production of high-value chemicals (i.e., pigments, antioxidants, vitamins and secondary metabolites). From a Chemical engineering perspective, the biomass generation in this organism was thoroughly studied by interpreting the cell as a microbial …

Deepep: A Deep Learning Framework For Identifying Essential Proteins, Min Zeng, Min Li, Fang-Xiang Wu, Yaohang Li, Yi Pan

Computer Science Faculty Publications

Background: Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results: We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. …

Microrna Profiling And Engineering Of Cho Cell Lines Stably Expressing Difficult-To-Express Lysosomal Protein, Ifeanyi Amadi

KGI Theses and Dissertations

Difficult-to-express (DTE) recombinant proteins like multi-specific proteins, DTE monoclonal antibodies and lysosomal enzymes, have seen difficulties in manufacturability using Chinese hamster ovary (CHO) cells and other mammalian cells as production platforms. CHO cells are preferably used for protein production because of their innate ability to secrete human-like recombinant proteins with post-translational modification, resistance to viral infection and familiarity with drug regulators. However, despite huge progress made in engineering CHO cells for high volumetric productivity, expression of DTE proteins like recombinant lysosomal sulfatase represent one of the poorly understood proteins. Furthermore, there are growing interest in the use of microRNAs (miRNAs) …

Simplicity Diffexpress: A Bespoke Cloud-Based Interface For Rna-Seq Differential Expression Modeling And Analysis, Cintia C. Palu, Marcelo Ribeiro-Alves, Yanxin Wu, Brendan Lawlor, Pavel V. Baranov, Brian Kelly, Paul Walsh

Department of Computer Science Publications

One of the key challenges for transcriptomics-based research is not only the processing of large data but also modeling the complexity of features that are sources of variation across samples, which is required for an accurate statistical analysis. Therefore, our goal is to foster access for wet lab researchers to bioinformatics tools, in order to enhance their ability to explore biological aspects and validate hypotheses with robust analysis. In this context, user-friendly interfaces can enable researchers to apply computational biology methods without requiring bioinformatics expertise. Such bespoke platforms can improve the quality of the findings by allowing the researcher to …