Bioinformatics Commons^™

Exploring The Role Of Microrna-1 (Mir-1) On Skeletal Muscle Hypertrophy, Shengyi Fei

College of Education and Human Sciences: Dissertations, Theses, and Student Research

Skeletal muscle hypertrophy is a complex process that involves a range of signaling pathways and transcriptional regulators. Many hormones and growth factors can activate key signaling pathways, such as the PI3K/AKT/mTOR, MAPK, and cAMP pathways, which play a crucial role in the regulation of muscle hypertrophy. In Chapter 1, we reviewed some of the hormones and growth factors known to be associated with skeletal muscle hypertrophy, as well as the function of these key signaling pathways, and revealed some unresolved issues. In Chapter 2, we explored the role of microRNA-1 (miR-1) in skeletal muscle hypertrophy and aimed to determine the …

Adjusting For Gene-Specific Covariates To Improve Rna-Seq Analysis, Hyeongseon Jeon, Kyu-Sang Lim, Yet Nguyen, Dan Nettleton

Mathematics & Statistics Faculty Publications

Summary

This paper suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by employing two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey's q-value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning …

Improved Radiation Expression Profiling In Blood By Sequential Application Of Sensitive And Specific Gene Signatures, Eliseos J. Mucaki, Ben C. Shirley, Peter K. Rogan

Biochemistry Publications

Purpose. Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning based signatures (with 8 to 20% misclassification rates). These signatures can quantify therapeutically-relevant as well as accidental radiation exposures. The prodromal symptoms of Acute Radiation Syndrome (ARS) overlap those present in Influenza and Dengue Fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy.

Methods. This study investigated recall by previous and novel radiation signatures independently derived …

Random Forest-Integrated Analysis In Ad And Late Brain Transcriptome-Wide Data To Identify Disease-Specific Gene Expression, Xinxing Wu, Chong Peng, Peter T. Nelson, Qiang Cheng

Sanders-Brown Center on Aging Faculty Publications

Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects thinking, memory, and behavior. Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently identified common neurodegenerative disease that mimics the clinical symptoms of AD. The development of drugs to prevent or treat these neurodegenerative diseases has been slow, partly because the genes associated with these diseases are incompletely understood. A notable hindrance from data analysis perspective is that, usually, the clinical samples for patients and controls are highly imbalanced, thus rendering it challenging to apply most existing machine learning algorithms to directly analyze such datasets. Meeting this data analysis challenge is …

Expression Of Cytokines And Chemokines As Predictors Of Stroke Outcomes In Acute Ischemic Stroke, Sarah R. Martha, Qiang Cheng, Justin F. Fraser, Liyu Gong, Lisa A. Collier, Stephanie M. Davis, Doug Lukins, Abdulnasser Alhajeri, Stephen Grupke, Keith R. Pennypacker

Institute for Biomedical Informatics Faculty Publications

Introduction: Ischemic stroke remains one of the most debilitating diseases and is the fifth leading cause of death in the US. The ability to predict stroke outcomes within the acute period of stroke would be essential for care planning and rehabilitation. The Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC; clinicaltrials.gov NCT03153683) study collects arterial blood immediately distal and proximal to the intracranial thrombus at the time of mechanical thrombectomy. These blood samples are an innovative resource in evaluating acute gene expression changes at the time of ischemic stroke. The purpose of this study was to identify inflammatory genes and …

Advances In Gene Ontology Utilization Improve Statistical Power Of Annotation Enrichment, Eugene Waverly Hinderer Iii, Robert M. Flight, Rashmi Dubey, James N. Macleod, Hunter N. B. Moseley

Maxwell H. Gluck Equine Research Center Faculty Publications

Gene-annotation enrichment is a common method for utilizing ontology-based annotations in gene and gene-product centric knowledgebases. Effective utilization of these annotations requires inferring semantic linkages by tracing paths through edges in the ontological graph, referred to as relations. However, some relations are semantically problematic with respect to scope, necessitating their omission or modification lest erroneous term mappings occur. To address these issues, we created the Gene Ontology Categorization Suite, or GOcats—a novel tool that organizes the Gene Ontology into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. Here, we demonstrate the …

Biological Pathway Involvement In Melanoma Heterogeneity And Drug-Induced Resistance, Sarah V. Pack

STAR Program Research Presentations

Tumors develop resistance to numerous drug therapies, and this remains a major obstacle in treating many types of non-surgical cancers. Melanoma provides a good model system for studying drug resistance in cancer due to its high propensity to incur resistance after a significant initial response to a drug. Genes that are highly expressed in melanoma cancer cells have been studied, but in order to further understand the collective function of these highly expressed genes we must analyze gene sets, or pathways. A single gene’s function is rarely independent of other genes, and pathway analysis takes this into account.

Our objective …

Highly Conserved Molecular Pathways, Including Wnt Signaling, Promote Functional Recovery From Spinal Cord Injury In Lampreys, Paige E. Herman, Angelos Papatheodorou, Stephanie A. Bryant, Courtney K. M. Waterbury, Joseph R. Herdy, Anthony A. Arcese, Joseph D. Buxbaum, Jeramiah J. Smith, Jennifer R. Morgan, Ona Bloom

Biology Faculty Publications

In mammals, spinal cord injury (SCI) leads to dramatic losses in neurons and synaptic connections, and consequently function. Unlike mammals, lampreys are vertebrates that undergo spontaneous regeneration and achieve functional recovery after SCI. Therefore our goal was to determine the complete transcriptional responses that occur after SCI in lampreys and to identify deeply conserved pathways that promote regeneration. We performed RNA-Seq on lamprey spinal cord and brain throughout the course of functional recovery. We describe complex transcriptional responses in the injured spinal cord, and somewhat surprisingly, also in the brain. Transcriptional responses to SCI in lampreys included transcription factor networks …

Rt-Qpcr Demonstrates Light-Dependent Atrbcs1a And Atrbcs3b Mrna Expressions In Arabidopsis Thaliana Leaves, Ming-Mei Chang, Anna Li, Robert Feissner, Talal Ahmad

Biology

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is widely used in diagnosis and research to determine specific mRNA expressions in cells. As RT-qPCR applications increase, it’s necessary to provide undergraduates hands-on experience of this modern technique. Here, we report a 3-week laboratory exercise using RT-qPCR to demonstrate the light-dependent expressions of AtRBCS1A and AtRBCS3B genes encoding two Arabidopsis thaliana small subunits of the ribulose 1,5-bisphosphate carboxylase/ oxygenase (Rubisco). In the first week, students purified and quantified total RNA from leaves of A. thaliana pretreated in the dark for 96 hr and untreated controls. In the second week, RNA samples were …

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, …

A Gene-Based Association Method For Mapping Traits Using Reference Transcriptome Data, Eric R. Gamazon, Heather Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler, Joshua C. Denny, Gtex Consortium, Dan L. Nicolae, Nancy J. Cox, Hae Kyung Im

Bioinformatics Faculty Publications

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual’s genetic profile and correlates ‘imputed’ gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys …

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 …

Identification And Comparative Analysis Of Subolesin/Akirin Ortholog From Ornithodoros Turicata Ticks, Hameeda Sultana, Unnati Patel, Daniel E. Sonenshine, Girish Neelakanta

Biological Sciences Faculty Publications

Background: Subolesin is an evolutionary conserved molecule in diverse arthropod species that play an important role in the regulation of genes involved in immune responses, blood digestion, reproduction and development. In this study, we have identified a subolesin ortholog from soft ticks Ornithodoros turicata, the vector of the relapsing fever spirochete in the United States.

Methods: Uninfected fed or unfed O. turicata ticks were used throughout this study. The subolesin mRNA was amplified by reverse transcription polymerase chain reaction (RT-PCR) and sequenced. Quantitative-real time PCR (QRT-PCR) was performed to evaluate subolesin mRNA levels at different O. turicata developmental stages …

Using Phylogenetically-Informed Annotation (Pia) To Search For Light-Interacting Genes In Transcriptomes From Non-Model Organisms, Daniel L. Speiser, Molly S. Pankey, Alexander K. Zaharoff, Barbara A. Battelle, Heather D. Bracken-Grissom, Jesse W. Breinholt, Seth M. Bybee, Thomas W. Cronin, Anders Garm, Annie R. Lindgren, Nipam H. Patel, Megan L. Porter, Meredith E. Protas, Ajna S. Rivera, Jeanne M. Serb, Kirk S. Zigler, Keith A. Crandall, Todd H. Oakley

Biology Faculty Publications and Presentations

Background: Tools for high throughput sequencing and de novo assembly make the analysis of transcriptomes (i.e. the suite of genes expressed in a tissue) feasible for almost any organism. Yet a challenge for biologists is that it can be difficult to assign identities to gene sequences, especially from non-model organisms. Phylogenetic analyses are one useful method for assigning identities to these sequences, but such methods tend to be time-consuming because of the need to re-calculate trees for every gene of interest and each time a new data set is analyzed. In response, we employed existing tools for phylogenetic analysis to …

Regulation Of Human Pax6 Expression By Mir-7, M Needhamsen, Robert B. White, Keith M. Giles, Sarah A. Dunlop, Meghan G. Thomas

Research outputs 2014 to 2021

The paired box gene 6 (PAX6) is a powerful mediator of eye and brain organogenesis whose spatiotemporal expression is exquisitely controlled by multiple mechanisms, including post-transcriptional regulation by microRNAs (miRNAs). In the present study, we use bioinformatic predictions to identify three candidate microRNA-7 (miR-7) target sites in the human PAX6 3′ untranslated region (3′-UTR) and demonstrate that two of them are functionally active in a human cell line. Furthermore, transient transfection of cells with synthetic miR-7 inhibits PAX6 protein expression but does not alter levels of PAX6 mRNA, suggesting that miR-7 induces translational repression of PAX6. Finally, a comparison of …

A New Essential Protein Discovery Method Based On The Integration Of Protein-Protein Interaction And Gene Expression Data, Min Li, Hanhui Zhang, Jian-Xin Wang, Yi Pan

Computer Science Faculty Publications

The article offers information on a study conducted on the essential protein discovery method, PeC, which is based on the integration of protein-protein interaction and gene expression data. It states that PeC was developed on the basis of the definitions of edge clustering coefficient (ECC) and Pearson's correlation coefficient (PCC). It mentions that a list of essential proteins of Saccharomyces cerevisiae were collected.

Background: Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have …

Linear Methods For Analysis And Quality Control Of Relative Expression Ratios From Quantitative Real-Time Polymerase Chain Reaction Experiments, Robert B. Page, Arnold J. Stromberg

Biology Faculty Publications

Relative expression quantitative real-time polymerase chain reaction (RT-qPCR) experiments are a common means of estimating transcript abundances across biological groups and experimental treatments. One of the most frequently used expression measures that results from such experiments is the relative expression ratio (R_E), which describes expression in experimental samples (i.e., RNA isolated from organisms, tissues, and/or cells that were exposed to one or more experimental or nonbaseline condition) in terms of fold change relative to calibrator samples (i.e., RNA isolated from organisms, tissues, and/or cells that were exposed to a control or baseline condition). Over the past decade, several …

Selecting 'Significant' Differentially Expressed Genes From The Combined Perspective Of The Null And The Alternative, Beatrijs Moerkerke, Els Goetghebeur

Harvard University Biostatistics Working Paper Series

No abstract provided.

Cluster Analysis Of Genomic Data With Applications In R, Katherine S. Pollard, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In this paper, we provide an overview of existing partitioning and hierarchical clustering algorithms in R. We discuss statistical issues and methods in choosing the number of clusters, the choice of clustering algorithm, and the choice of dissimilarity matrix. In particular, we illustrate how the bootstrap can be employed as a statistical method in cluster analysis to establish the reproducibility of the clusters and the overall variability of the followed procedure. We also show how to visualize a clustering result by plotting ordered dissimilarity matrices in R. We present a new R package, hopach, which implements the hybrid clustering method, …

Classification Using Generalized Partial Least Squares, Beiying Ding, Robert Gentleman

Bioconductor Project Working Papers

The advances in computational biology have made simultaneous monitoring of thousands of features possible. The high throughput technologies not only bring about a much richer information context in which to study various aspects of gene functions but they also present challenge of analyzing data with large number of covariates and few samples. As an integral part of machine learning, classification of samples into two or more categories is almost always of interest to scientists. In this paper, we address the question of classification in this setting by extending partial least squares (PLS), a popular dimension reduction tool in chemometrics, in …

Mixture Models For Assessing Differential Expression In Complex Tissues Using Microarray Data, Debashis Ghosh

The University of Michigan Department of Biostatistics Working Paper Series

The use of DNA microarrays has become quite popular in many scientific and medical disciplines, such as in cancer research. One common goal of these studies is to determine which genes are differentially expressed between cancer and healthy tissue, or more generally, between two experimental conditions. A major complication in the molecular profiling of tumors using gene expression data is that the data represent a combination of tumor and normal cells. Much of the methodology developed for assessing differential expression with microarray data has assumed that tissue samples are homogeneous. In this article, we outline a general framework for determining …

Statistical Inference For Simultaneous Clustering Of Gene Expression Data, Katherine S. Pollard, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Current methods for analysis of gene expression data are mostly based on clustering and classification of either genes or samples. We offer support for the idea that more complex patterns can be identified in the data if genes and samples are considered simultaneously. We formalize the approach and propose a statistical framework for two-way clustering. A simultaneous clustering parameter is defined as a function of the true data generating distribution, and an estimate is obtained by applying this function to the empirical distribution. We illustrate that a wide range of clustering procedures, including generalized hierarchical methods, can be defined as …