Computational Biology Commons^™

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.

A Case Of Incipient Budding Speciation In The California Floristic Province, Infraspecific Divergence In Abronia Villosa, Eli J. Allen

Cal Poly Humboldt theses and projects

Physical barriers to gene flow are the traditional evidence for species divergence. Conversely, there has been increasing acknowledgment of speciation in the face of gene flow as an evolutionary process. Budding speciation involves peripheral populations adapting to local ecological conditions, thereby budding off from a widespread progenitor species. Budding speciation is distinguished by ecological divergence and is generally evidenced by asymmetrical range size and nested phylogenetic relationships of sister species. The narrow endemic Abronia villosa var. aurita is adapted to montane sandy washes adjacent to its widespread sister variety, the desert dwelling var. villosa. Here, I tested the hypothesis …

The Evolution Of The Metazoan Circadian Molecular Mechanism And The Conservation Of Canonical Circadian Elements, Olivia Waldridge

Theses and Dissertations--Biology

The temporal organization of biological activities through circadian rhythms is pivotal for the survival of organisms. Significant progress has been made in understanding the molecular foundations of animal circadian clocks through the characterization of key components such as Clock, Bmal1/Cycle(Cyc), Period (Per), Timeless, and Cryptochrome (Cry) in several model organisms. To determine the universality of this conservation, we investigated the sequences of these genes and their paralogs across 46 animal species that encompass multiple phyla in the metazoan kingdom to resolve the relative timing of duplication and loss events that have diversified core clock components in the metazoan kingdom. Using …

Ai And Ml-Based Risk Assessment Of Chemicals: Predicting Carcinogenic Risk From Chemical-Induced Genomic Instability, Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, Prachi Pradeep, Madleen Busse, Andreas Luch, Akihiko Hirose, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, …

From Code To Crops: Harnessing Bioinformatics And Artificial Intelligence (Ai) In Agricultural Omics, Lakshay Anand

Theses and Dissertations--Plant and Soil Sciences

Global agricultural faces numerous challenges, such as climate change, resource limitations, novel pests and diseases, increasing costs, and the ever-increasing human population. To tackle these challenges, we need innovative strategies that combine new technologies and data analytics approaches to enhance agricultural output, promote sustainable methods, and optimize resource allocation. The key to this innovation lies in understanding the complex molecular web within plants that governs their growth, defense, and adaptability mechanisms. By mastering this molecular network, we can cultivate crops that are more resilient, sustainable, and suitable for different climatic terrains. Moreover, studying the symbiotic relationship between plants and microorganisms …

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 …

Sccad: Cluster Decomposition-Based Anomaly Detection For Rare Cell Identification In Single-Cell Expression Data, Yunpei Xu, Shaokai Wang, Qilong Feng, Jiazhi Xia, Yaohang Li, Hong-Dong Li, Jianxin Wang

Computer Science Faculty Publications

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate …

Identifying New Cancer Genes Based On The Integration Of Annotated Gene Sets Via Hypergraph Neural Networks, Chao Deng, Hong-Dong Li, Li-Shen Zhang, Yiwei Liu, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation

Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.

Results

Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of …

Machine Learning And Rna Bioinformatics, Jason Rafe Miller

Graduate Theses, Dissertations, and Problem Reports (ETD)

The applied science of bioinformatics encompasses computational analysis of molecular biology data. Advances in genomics and DNA sequencing technology have enabled computational analysis of ribonucleic acids (RNAs), which play diverse and critical roles in most cells. To assist the study of human RNA, we trained machine learning models on RNA nucleotide sequences, devoid of domain knowledge. We built models that distinguish long non-coding lncRNA from protein-coding mRNA, and models that predict the cytoplasmic vs. nuclear preferences of lncRNAs. In a review of published lncRNA subcellular localization classifiers, we show that the commonly used validation protocol generates optimistic performance measures, and …

Model-Based Deep Autoencoders For Clustering Single-Cell Rna Sequencing Data With Side Information, Xiang Lin

Dissertations

Clustering analysis has been conducted extensively in single-cell RNA sequencing (scRNA-seq) studies. scRNA-seq can profile tens of thousands of genes' activities within a single cell. Thousands or tens of thousands of cells can be captured simultaneously in a typical scRNA-seq experiment. Biologists would like to cluster these cells for exploring and elucidating cell types or subtypes. Numerous methods have been designed for clustering scRNA-seq data. Yet, single-cell technologies develop so fast in the past few years that those existing methods do not catch up with these rapid changes and fail to fully fulfil their potential. For instance, besides profiling transcription …

Dna Methylation-Based Epigenetic Biomarkers In Cell-Type Deconvolution And Tumor Tissue Of Origin Identification, Ze Zhang

Dartmouth College Ph.D Dissertations

DNA methylation is an epigenetic modification that regulates gene expression and is essential to establishing and preserving cellular identity. Genome-wide DNA methylation arrays provide a standardized and cost-effective approach to measuring DNA methylation. When combined with a cell-type reference library, DNA methylation measures allow the assessment of underlying cell-type proportions in heterogeneous mixtures. This approach, known as DNA methylation deconvolution or methylation cytometry, offers a standardized and cost-effective method for evaluating cell-type proportions. While this approach has succeeded in discerning cell types in various human tissues like blood, brain, tumors, skin, breast, and buccal swabs, the existing methods have major …

Investigating The Impact Of Transcription On Mutation Rates, Sarah Patterson

Theses and Dissertations

tRNA genes are highly transcribed and perform one of the most fundamental cellular functions. Although a universal pattern observed across all three domains of life is that highly transcribed genes tend to evolve slowly, tRNA genes have been shown previously to evolve rapidly. This rapid sequence evolution could result from relaxed selection, increased mutation rate, or a combination of both. Here, we use mutation-accumulation line sequencing data to show that tRNA genes accumulate more mutations than other gene types. Our results indicate that this elevated mutation rate is a consequence of both elevated transcription-associated mutagenesis and a lack of transcription-coupled …

Increased Coding Potential Of Bovine Herpesvirus 1, Victoria Jefferson

Theses and Dissertations

Bovine respiratory disease (BRD) costs the cattle industry millions of dollars in costs in treatment and loss every year in the United States. A significant pathogen often contributes to BRD is Bovine Herpesvirus 1 (BoHV-1), a double stranded DNA virus with the ability to establish latency in the trigeminal ganglia and neurons. Primary infection with BoHV-1 results in immunosuppression that increases the risk of secondary bacterial infection and pneumonia. Because herpesviruses infect their hosts for life and can be reactivated in times of stress, BoHV-1 can present a recurring risk of BRD. The following research aims to expand the knowledge …

Analyzing Extracellular Vesicles For Disease Monitoring In Metastatic Colorectal Cancer Patients, Vahid Bahrambeigi

Dissertations and Theses (Open Access)

Colorectal cancer (CRC) is the second leading cause of cancer deaths in the United States. Metastases are the main cause of cancer-related death, and the most frequent metastatic sites in patients with CRCs are liver and lung. To confirm the diagnosis of metastatic CRC (mCRCs) and to classify mCRCs, tumor biopsy of a suspected metastasis is often required. The consensus molecular subtype (CMS) classification which is based on gene expression profiles of CRC tumor specimens, is a predictive factor for treatment outcomes of standard chemotherapies for mCRCs. The practicality of repeated tumor biopsies for disease monitoring in mCRC patients is …

The Typical Trna Co-Expresses Multiple 5' Trna Halves Whose Sequences And Abundances Depend On Isodecoder And Isoacceptor And Change With Tissue Type, Cell Type, And Disease, Robert Akins, Kayleigh Ostberg, Tess Cherlin, Nikolas Tsiouplis, Phillipe Loher, Isidore Rigoutsos

Computational Medicine Center Faculty Papers

Transfer RNA-derived fragments (tRFs) are noncoding RNAs that arise from either mature transfer RNAs (tRNAs) or their precursors. One important category of tRFs comprises the tRNA halves, which are generated through cleavage at the anticodon. A given tRNA typically gives rise to several co-expressed 5'-tRNA halves (5'-tRHs) that differ in the location of their 3' ends. These 5'-tRHs, even though distinct, have traditionally been treated as indistinguishable from one another due to their near-identical sequences and lengths. We focused on co-expressed 5'-tRHs that arise from the same tRNA and systematically examined their exact sequences and abundances across 10 different human …

The Detection Of Putative Recessive Lethal Haplotypes In Irish Sheep Populations, Rory Mcauley

ORBioM (Open Research BioSciences Meeting)

In livestock populations, recessive lethal alleles are a known contributor to poor reproductive performance due to embryonic death in homozygous individuals. Despite their lethal effect in the recessive form, these alleles may be maintained at high frequencies among carrier animals because of their positive pleiotropic effects on economically important traits. Although several such recessive alleles have been identified in cattle and pig populations, limited studies have been completed in sheep, and none within Irish sheep populations. Genotype data for 69,034 animals from five major Irish sheep breeds genotyped on a variety of panels was available for this study. Only animals …

Tracing Evolution Of Gene Transfer Agents Using Comparative Genomics, Roman Kogay

Dartmouth College Ph.D Dissertations

The accumulating evidence suggest that viruses and their components can be domesticated by their hosts, equipping them with convenient molecular toolkits for various functions. One of such domesticated system is Gene Transfer Agents (GTAs) that are produced by some bacteria and archaea. GTAs morphologically resemble small phage-like particles and contain random fragments of their host genome. They are produced only by a small fraction of the microbial population and are released through a lysis of the host cell. Bioinformatic analyses suggest that GTAs are especially abundant in the taxonomic class of Alphaproteobacteria, where they are vertically inherited and evolve …

Convolutional Neural Network-Based Gene Prediction Using Buffalograss As A Model System, Michael Morikone

Complex Biosystems Program: Dissertations and Student Research

The task of gene prediction has been largely stagnant in algorithmic improvements compared to when algorithms were first developed for predicting genes thirty years ago. Rather than iteratively improving the underlying algorithms in gene prediction tools by utilizing better performing models, most current approaches update existing tools through incorporating increasing amounts of extrinsic data to improve gene prediction performance. The traditional method of predicting genes is done using Hidden Markov Models (HMMs). These HMMs are constrained by having strict assumptions made about the independence of genes that do not always hold true. To address this, a Convolutional Neural Network (CNN) …

The Role Of Non-Coding Rnas In Myelodysplastic Neoplasms, Vasileios Georgoulis, Epameinondas Koumpis, Eleftheria Hatzimichael

Computational Medicine Center Faculty Papers

Myelodysplastic syndromes or neoplasms (MDS) are a heterogeneous group of myeloid clonal disorders characterized by peripheral blood cytopenias, blood and marrow cell dysplasia, and increased risk of evolution to acute myeloid leukemia (AML). Non-coding RNAs, especially microRNAs and long non-coding RNAs, serve as regulators of normal and malignant hematopoiesis and have been implicated in carcinogenesis. This review presents a comprehensive summary of the biology and role of non-coding RNAs, including the less studied circRNA, siRNA, piRNA, and snoRNA as potential prognostic and/or predictive biomarkers or therapeutic targets in MDS.

Modeling Nonsegmented Negative-Strand Rna Virus (Nnsv) Transcription With Ejective Polymerase Collisions And Biased Diffusion, Felipe-Andres Piedra

Research Symposium

Background: The textbook model of NNSV transcription predicts a gene expression gradient. However, multiple studies show non-gradient gene expression patterns or data inconsistent with a simple gradient. Regarding the latter, several studies show a dramatic decrease in gene expression over the last two genes of the respiratory syncytial virus (RSV) genome (a highly studied NNSV). The textbook model cannot explain these phenomena.

Methods: Computational models of RSV and vesicular stomatitis virus (VSV – another highly studied NNSV) transcription were written in the Python programming language using the Scientific Python Development Environment. The model code is freely available on GitHub: …