Bioinformatics Commons^™

A Machine Learning Approach For Predicting Patient Mortality With Heart Rate Variability Statistics, Matthew Thiele, Dario Ghersi

Theses/Capstones/Creative Projects

The prediction of patient mortality in the healthcare system provides a metric by which hospitals can better manage patient care and assess the needs of each individual patient. As such, the development of better predictive methods is vital for improving patient outcomes and overall quality of care. Heart rate variability (HRV) is a measure of the heart’s complex beating patterns, giving medical professionals additional insight into patient health. Previous research has demonstrated the potential use of heart rate variability as a metric for patient mortality prediction for various conditions, however more work is necessary to validate HRV as a metric …

A Bioinformatics Analysis Of Microbial Diversity And Its Correlation With Human Lifestyle, Diet, And Health Variables, Alivia Ankrum, Kate Cooper

Theses/Capstones/Creative Projects

The abundant impact of microbiota on human physiology suggests a need for exploration into their impact on human health and disease. The American Gut Project (AGP) was established to aggregate microbiome sequencing data as well as health, diet, and lifestyle metadata. This study proposes to identify taxonomic species and build a phylogenetic tree representation from the AGP participant sample collection as well as find their respective alpha and beta diversity of all metadata variables based on patient questionnaire data. Additionally, this study will involve a chimeric sequence extraction from the 16S rRNA sequences of the AGP. The expected results are …

Investigation Of Microbe And Host Tissue Interactions Contributing To The Pathogenesis Of Colorectal Cancer, Ryan Chapman, Dhundy Bastola

Theses/Capstones/Creative Projects

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. The pathogenesis of this disease can fall under broad categories; however, the specific precursory mechanism of CRC pathogenesis is still unknown. Dysregulations of the gut microbiome have been identified in the CRC tissue environment. Additionally, CRC tissue gene expression has been observed to differ from that of healthy tissue. Despite these noticeable changes, few studies have directly compared the microorganism composition to the gene expression of CRC tissue. Doing so may identify whether the differentially abundant microorganisms influence the changes in gene expression. The goal of this …

Identification Of Synonymous Genes And Pathways Implicated In Irritable Bowel Disease And Pancreatic Duct Adenocarcinoma, Lavanya Uppala

Theses/Capstones/Creative Projects

Better understanding and genetic characterization of the gut microbiome will allow for the identification of clinically distinct gastrointestinal diseases. Facilitated by high throughput technologies, intestinal flora analyses have elucidated a broad spectrum of neuropsychiatric, immunological, and allergic disorders linked with this organ system. Microbiome research especially has shed light on underlying factors of intestinal disorders. This interplay of environmental bacteria versus host tissue gene expression may have implications for disease pathogenicity and etiological determination. For instance, pancreatic disorders are common symptoms of irritable bowel disease (IBD), which is thought to affect approximately 7% to 21% of the population [1]. However, …

Ingredient Classification Using Food Ontology, Ricky Flores

UNO Student Research and Creative Activity Fair

A food label provides some of the most crucial information for a food product. The food label is a key resource for many health-conscious consumers for understanding ingredients. It is also vital for individuals to avoid food allergens or help patients follow dietary recommendations. While the food labels in the United States are regulated by the Food and Drug Administration (FDA) many labels contain additional information or statements that are not regulated. Moreover, the food label may be complex or contain terminology that the layperson may not understand. Evidence has indicated that consumers often find nutrition labels confusing, especially when …

Comparative Analysis Of Metabolic Pathways Of Bacteria Used In Fermented Food, Keanu Hoang, Kiran Bastola

Theses/Capstones/Creative Projects

This study presents a novel methodology for analyzing metabolic pathways. Utilizing KEGG REST API through a Biopython package and file parser, data about whether or not a bacteria has an enzyme or not was extracted. The results found that differences in metabolic pathway enrichment values follow along the lines of genera and pathway type. In particular, bacteria found in food spoilage and commercial nitrogen fixing products had high values of enrichment.

Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, And Birch, Martin C. Nwadiugwu

Interdisciplinary Informatics Faculty Publications

The current study seeks to compare 3 clustering algorithms that can be used in gene-based bioinformatics research to understand disease networks, protein-protein interaction networks, and gene expression data. Denclue, Fuzzy-C, and Balanced Iterative and Clustering using Hierarchies (BIRCH) were the 3 gene-based clustering algorithms selected. These algorithms were explored in relation to the subfield of bioinformatics that analyzes omics data, which include but are not limited to genomics, proteomics, metagenomics, transcriptomics, and metabolomics data. The objective was to compare the efficacy of the 3 algorithms and determine their strength and drawbacks. Result of the review showed that unlike Denclue and …

High-Throughput Sequencing Pipeline Tool Analysis - Staphylococcus Aureus, Justin Fay

UNO Student Research and Creative Activity Fair

In this project, I compared the tools used at each step of the high-throughput sequencing pipeline used to detect single-nucleotide polymorphisms (SNP’s). There are 11 sequences I utilized from my mentor, Dr. Kate Cooper, on this project (pertaining to Staphylococcus aureus) and I used those in the sequencing pipeline. I evaluated different tools by using different ones at different steps of the pipeline. By doing this, I was able to see how close or far the outputs are by only changing one factor. It is also important to note that, with each online tool comes its own sets of parameters. …

A Compartmental Network Model For The Spread Of Whooping Cough, Kimia Ameri

UNO Student Research and Creative Activity Fair

Outbreaks of pertussis have increased over the past few years, drawing the attention of health care providers. Understanding the transmission mechanisms of contagious disease is critically important, but depends on many intricate factors including pathogen and host environment, exposed population, and their activities. In this work, we try to improve upon the prediction model for the exposed population. The number of whooping cough reported cases in Nebraska between 2000-2017 was gathered. The standard SEIR model is used to predict the infected numbers. The results show that the Susceptible-Exposed-Infected-Recovered (SEIR) model prediction for the number of infected individuals is much higher …

Precision Medicine: Bioinformatics Assists In Finding Accurate Treatment For Her2+ Breast Cancer In Humans, Elizabeth Russman

UNO Student Research and Creative Activity Fair

Human epidermal growth factor receptor 2 (HER2) is a gene located on chromosome 17q12 (Ferrari et al., 2016). A HER2 mutation is known to cause breast cancer, and is responsible for approximately 20% of all breast cancers.

In this project, HER2 positive breast cancer will be examined. The goal of this project is to understand the complicated gene, HER2, and how treatment needs to be more individualized and precise using bioinformatics.

Analysis Of Clustering Algorithms, Ethan Summers

UNO Student Research and Creative Activity Fair

In Bioinformatics, choosing the right algorithm for a problem is very important. Choosing the wrong algorithm or one that is less efficient can make or break a project. Analyzing algorithms beforehand is key. The goal of this project is to analyze three clustering algorithms for protein protein interaction networks and compare their function and results. A clustering algorithm takes a dataset, in this case a simulated PPI (protein-protein interaction) network and groups together similar data points based on some similarity criteria. It is important to know the difference between these algorithms to get the desired results.

Data Analytics Pipeline For Rna Structure Analysis Via Shape, Quinn Nelson

UNO Student Research and Creative Activity Fair

Coxsackievirus B3 (CVB3) is a cardiovirulent enterovirus from the family Picornaviridae. The RNA genome houses an internal ribosome entry site (IRES) in the 5’ untranslated region (5’UTR) that enables cap-independent translation. Ample evidence suggests that the structure of the 5’UTR is a critical element for virulence. We probe RNA structure in solution using base-specific modifying agents such as dimethyl sulfate as well as backbone targeting agents such as N-methylisatoic anhydride used in Selective 2’-Hydroxyl Acylation Analyzed by Primer Extension (SHAPE). We have developed a pipeline that merges and evaluates base-specific and SHAPE data together with statistical analyses that provides confidence …

Life In The Phyllobiome: Functional Adaptations In Novosphingobium Sp. ‘Leaf2’, A Leaf-Borne Alphaproteobacteria, Katherine Sindelar

UNO Student Research and Creative Activity Fair

Plant-associated microbiomes have emerged as a significant influence on host health and development, driving interest into the functional repertoires of constituent organisms and the mechanisms of host selection on resident microbial populations. Research into plant commensal bacteria have largely focused on rhizospheric milieus - the leaf-surface phyllobiome presents a more punishing environment, where microbia are subjected to high levels of UV radiation, low water and nutrient availability, and foliar agricultural chemicals in food crops. To investigate adaptations towards success in this harsh environment, a comparative genomics analysis across a cohort of Novosphingobium species was conducted using public bioinformatics resources and …

Phr: Patient Health Record, Quinn Nelson

Theses/Capstones/Creative Projects

The rapid development of information technology systems has expanded into multiple disciplines and results in systems that are limited by initial design and implementation: the Healthcare Information Technology (HIT) space is no different. The introduction of the Electronic Health Record (EHR) system has changed the way healthcare operates. Initial designs of these systems were focused on serving the needs of insurance companies and healthcare billing departments. Research shows that the design of EHR systems negatively impact provider-patient interactions and the care they receive. This capstone project capitalizes on the collaboration efforts between UNO and UNMC – by joining a research …

Trimethyltin-Induced Cerebellar Damage On Adult Male Wistar Rats. Trimetil Estaño Induce Daño Cerebral En Ratas Machos Adultas Wistar., M. S. Ajao, A. Okesina, Martin C. Nwadiugwu

Interdisciplinary Informatics Faculty Publications

Abstract: This research work was done to investigate the acute toxicological effect of trimethyltin chloride on the cerebellum of Wistar rat. Ten adult male Wistar rats were used for the study. The animals were grouped into two: Group A and B, with five adult male Wistar rats in each group. Group A serves as the trimethyltin (TMT) group, while group B serves as the normal saline (NS) group. 3mg/kg of trimethyltin chloride was administered to animals in the TMT group, while 1.0mls of normal saline was administered to the animals in the NS group via intraperitoneal route for 3 …

Identification Of Genes Involved In Diauxic Shift Of Saccharomyces Cerevisiae Through Gateway Node Analysis., Emily Pachunka

UNO Student Research and Creative Activity Fair

The use of high-throughput assays, or experiments yielding large data sets, in biological research has become a standard practice in laboratories throughout the world. Because such investigations have the ability to produce high volume and comprehensive data sets, it is then important to develop methods that allow researchers to quickly pull meaningful information from an overwhelming amount of data. Network modeling has become a popular technique for visualizing and analyzing large biological data sets. A network is a basic graph with nodes and edges (i.e. social networks) that also integrates complex principles of graph theory for deeper analysis and pattern …

Identification Of Optimal Parameter Ranges In Building And Assessing Correlation Networks Built From Gene Expression., Qianran Li

UNO Student Research and Creative Activity Fair

In this project, I investigate and define the range of acceptable outputs of a gene expression correlation network model. Gene expression refers to the amount of product made by a gene under a given biological condition. A correlation network is a graphical model where the nodes represent genes in an organism and the edges represent the amount of correlation between genes, based on their expression. Correlation network modeling has been used in cellular and biomedical domains to identify functional relationships between genes. The network model in general is a wonderful tool for showcasing relationships, but often times they are misused …

A Network Mdoel To Investigate Robustness Of Gene Expressions, Naresh Pasupuleti

UNO Student Research and Creative Activity Fair

Correlation networks are ideal to describe the relationship between the expression profiles of genes. Gene expression is a characteristic exhibited by a particular gene. Our body has thousands of genes; each of them expresses differently, and each one of them has a particular function associated with them. When genes corresponding to a particular part of the body becomes non-functional, i.e., not expressed, then the function corresponding to that part of the body does not happen, thereby causing impairment or mutations. Co-regulation is a method involved in clustering analysis to find genes that perform similar functions. We want to identify genes …

Correlation Networks: Causative Relationships From Gene Expression Data, Grogan W. Huff

UNO Student Research and Creative Activity Fair

Genes that share expression conditions show a biological correlation, and no modern method of visualization displays these intricate co-expression patterns better than a graph. Structural observations about a co-expression graph can reveal the secrets of the biological system that it models, but experimentally validated co-expression graphs are pain-staking work to produce. Present day correlation network analysis shows potential for drawing conclusions from large volumes of biological systems data in an inexpensive and easy-to-produce way, however, work remains to confirm the appropriateness and scope of such methods for specific, scientific application. Toward this effort, we generated a Pearson correlation network from …

A Dynamic Run-Profile Energy-Aware Approach For Scheduling Computationally Intensive Bioinformatics Applications, Sachin Pawaskar, Hesham Ali

Computer Science Faculty Proceedings & Presentations

High Performance Computing (HPC) resources are housed in large datacenters, which consume exorbitant amounts of energy and are quickly demanding attention from businesses as they result in high operating costs. On the other hand HPC environments have been very useful to researchers in many emerging areas in life sciences such as Bioinformatics and Medical Informatics. In an earlier work, we introduced a dynamic model for energy aware scheduling (EAS) in a HPC environment; the model is domain agnostic and incorporates both the deadline parameter as well as energy parameters for computationally intensive applications. Our proposed EAS model incorporates 2-phases. In …

A Novel Approach To Identify Shared Fragments In Drugs And Natural Products, Ashkay Balasubramanya, Ishwor Thapa, Dhundy Raj Bastola, Dario Ghersi

Interdisciplinary Informatics Faculty Proceedings & Presentations

Fragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are …

Genome-Wide Detection And Analysis Of Multifunctional Genes, Yuri Pritykin, Dario Ghersi, Mona Singh

Interdisciplinary Informatics Faculty Publications

Many genes can play a role in multiple biological processes or molecular functions. Identifying multifunctional genes at the genome-wide level and studying their properties can shed light upon the complexity of molecular events that underpin cellular functioning, thereby leading to a better understanding of the functional landscape of the cell. However, to date, genome-wide analysis of multifunctional genes (and the proteins they encode) has been limited. Here we introduce a computational approach that uses known functional annotations to extract genes playing a role in at least two distinct biological processes. We leverage functional genomics data sets for three organisms—H. sapiens, …

Is Trust Always Better Than Distrust? The Potential Value Of Distrust In Newer Virtual Teams Engaged In Short-Term Decision-Making, Paul Benjamin Lowry, Ryan M. Schuetzler, Justin Scott Giboney, Thomas A. Gregory

Information Systems and Quantitative Analysis Faculty Publications

The debate on the benefits of trust or distrust in groups has generated a substantial amount of research that points to the positive aspects of trust in groups, and generally characterizes distrust as a negative group phenomenon. Therefore, many researchers and practitioners assume that trust is inherently good and distrust is inherently bad. However, recent counterintuitive evidence obtained from face-to-face (FtF) groups indicates that the opposite might be true; trust can prove detrimental, and distrust instrumental, to decision-making in groups. By extending this argument to virtual teams (VTs), we examined the value of distrust for VTs completing routine and non-routine …

On The Comparison Of State- And Transition-Based Analysis Of Biological Relevance In Gene Co-Expression Networks, Kathryn Dempsey Cooper, Prasuna Vemuri, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Traditional correlation network analysis typically involves creating a network using gene expression data and then identifying biologically relevant clusters from that network by enrichment with Gene Ontology or pathway information. When one wants to examine these networks in a dynamic way - such as between controls versus treatment or over time - a "snapshot" approach is taken by comparing network structures at each time point. The biological relevance of these structures are then reported and compared. In this research, we examine the same "snapshot" networks but focus on the enrichment of changes in structure to determine if these results give …

Bioinformatics And Biomedical Engineering, Francisco Ortuño, Ignacio Rojas, Kathryn Dempsey Cooper, Sachin Pawaskar, Hesham Ali

Faculty Books and Monographs

Editors: Francisco Ortuño, Ignacio Rojas

Chapter, Identification of Biologically Significant Elements Using Correlation Networks in High Performance Computing Environments, co-authored by Kathryn Dempsey Cooper, Sachin Pawaskar, and Hesham Ali, UNO faculty members.

The two volume set LNCS 9043 and 9044 constitutes the refereed proceedings of the Third International Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2015, held in Granada, Spain in April 2015. The 134 papers presented were carefully reviewed and selected from 268 submissions. The scope of the conference spans the following areas: bioinformatics for healthcare and diseases, biomedical engineering, biomedical image analysis, biomedical signal analysis, computational genomics, computational …

Molblocks: Decomposing Small Molecule Sets And Uncovering Enriched Fragments, Dario Ghersi, Mona Singh

Interdisciplinary Informatics Faculty Publications

The chemical structures of biomolecules, whether naturally occurring or synthetic, are composed of functionally important building blocks. Given a set of small molecules—for example, those known to bind a particular protein—computationally decomposing them into chemically meaningful fragments can help elucidate their functional properties, and may be useful for designing novel compounds with similar properties. Here we introduce molBLOCKS, a suite of programs for breaking down sets of small molecules into fragments according to a predefined set of chemical rules, clustering the resulting fragments, and uncovering statistically enriched fragments. Among other applications, our software should be a great aid in large-scale …

Identifying Aging-Related Genes In Mouse Hippocampus Using Gateway Nodes, Kathryn Dempsey Cooper, Hesham Ali

Interdisciplinary Informatics Faculty Publications

Background: High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph …

Interaction-Based Discovery Of Functionally Important Genes In Cancers, Dario Ghersi, Mona Singh

Interdisciplinary Informatics Faculty Publications

A major challenge in cancer genomics is uncovering genes with an active role in tumorigenesis from a potentially large pool of mutated genes across patient samples. Here we focus on the interactions that proteins make with nucleic acids, small molecules, ions and peptides, and show that residues within proteins that are involved in these interactions are more frequently affected by mutations observed in large-scale cancer genomic data than are other residues. We leverage this observation to predict genes that play a functionally important role in cancers by introducing a computational pipeline (http://canbind.princeton.edu) for mapping large-scale cancer exome data …

Lack Of Rbl1/P107 Effects On Cell Proliferation And Maturation In The Inner Ear, Sonia M. Rocha-Sanchez, Laura R. Scheetz, Sabrina Siddiqi, Michael W. Weston, Lynette M. Smith, Kate Dempsey, Hesham Ali, Joann Mcgee, Edward J. Walsh

Information Systems and Quantitative Analysis Faculty Publications

Loss of postnatal mammalian auditory hair cells (HCs) is irreversible. Earlier studies have highlighted the importance of the Retinoblastoma family of proteins (pRBs) (i.e., Rb1, Rbl1/p107, and Rbl2/p130) in the auditory cells’ proliferation and emphasized our lack of information on their specific roles in the auditory system. We have previously demonstrated that lack of Rbl2/p130 moderately affects HCs’ and supporting cells’ (SCs) proliferation. Here, we present evidence supporting multiple roles for Rbl1/p107 in the developing and mature mouse organ of Corti (OC). Like other pRBs, Rbl1/p107 is expressed in the OC, particularly in the Hensen’s and Deiters’ cells. Moreover, Rbl1/p107 …

On Identifying And Analyzing Significant Nodes In Protein-­Protein Interaction Networks, Rohan Khazanchi, Kathryn Dempsey Cooper, Ishwor Thapa, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Network theory has been used for modeling biological data as well as social networks, transportation logistics, business transcripts, and many other types of data sets. Identifying important features/parts of these networks for a multitude of applications is becoming increasingly significant as the need for big data analysis techniques grows. When analyzing a network of protein-protein interactions (PPIs), identifying nodes of significant importance can direct the user toward biologically relevant network features. In this work, we propose that a node of structural importance in a network model can correspond to a biologically vital or significant property. This relationship between topological and …