Genetics and Genomics Commons^™

Mutation At The Human D1s80 Minisatellite Locus, Kuppareddi Balamurugan

Faculty Publications

Little is known about the general biology of minisatellites. The purpose of this study is to examine repeat mutations from the D1S80 minisatellite locus by sequence analysis to elucidate the mutational process at this locus. This is a highly polymorphic minisatellite locus, located in the subtelomeric region of chromosome 1. We have analyzed 90,000 human germline transmission events and found seven (7) mutations at this locus. The D1S80 alleles of the parentage trio, the child, mother, and the alleged father were sequenced and the origin of the mutation was determined. Using American Association of Blood Banks (AABB) guidelines, we found …

Functional Dissection Of The Glucose Signaling Pathways That Regulate The Yeast Glucose Transporter Gene (Hxt) Repressor Rgt1, David J. Jouandot Ii, Adhiraj Roy, Jeong-Ho Kim

Faculty Publications

The yeast Rgt1 repressor is a bifunctional protein that acts as a transcriptional repressor and activator. Under glucose-limited conditions, Rgt1 induces transcriptional repression by forming a repressive complex with its corepressors Mth1 and Std1. Here, we show that Rgt1 is converted from a transcriptional repressor into an activator under high glucose conditions and this occurs through two independent but consecutive events mediated by two glucose signaling pathways: (1) disruption of the repressive complex by the Rgt2/Snf3 pathway; (2) phosphorylation of Rgt1 by the cAMP-dependent protein kinase (cAMP-PKA) pathway. Rgt1 is phosphorylated by PKA at four serine residues within its amino-terminal …

The Nuclear Pore Complex Mediates Binding Of The Mig1 Repressor To Target Promoters, Nayan J. Sarma, Thomas D. Buford, Terry Haley, Kellie Barbara-Haley, George M. Santangelo, Kristine A. Willis

Faculty Publications

All eukaryotic cells alter their transcriptional program in response to the sugar glucose. In Saccharomyces cerevisiae, the best-studied downstream effector of this response is the glucose-regulated repressor Mig1. We show here that nuclear pore complexes also contribute to glucose-regulated gene expression. NPCs participate in glucose-responsive repression by physically interacting with Mig1 and mediating its function independently of nucleocytoplasmic transport. Surprisingly, despite its abundant presence in the nucleus of glucose-grown nup120Δ or nup133Δ cells, Mig1 has lost its ability to interact with target promoters. The glucose repression defect in the absence of these nuclear pore components therefore appears …

Microarray Data Mining And Gene Regulatory Network Analysis, Ying Li

Dissertations

The novel molecular biological technology, microarray, makes it feasible to obtain quantitative measurements of expression of thousands of genes present in a biological sample simultaneously. Genome-wide expression data generated from this technology are promising to uncover the implicit, previously unknown biological knowledge. In this study, several problems about microarray data mining techniques were investigated, including feature(gene) selection, classifier genes identification, generation of reference genetic interaction network for non-model organisms and gene regulatory network reconstruction using time-series gene expression data. The limitations of most of the existing computational models employed to infer gene regulatory network lie in that they either suffer …

A New Approach To Construct Pathway Connected Networks And Its Application In Dose Responsive Gene Expression Profiles Of Rat Liver Regulated By 2,4dnt, Sudhir Chowbina, Youping Deng, Junmei Ai, Xiaogang Wu, Xin Guan, Mitchell S. Wilbanks, Barbara Lynn Escalon, Edward J. Perkins, Jake Y. Chen

Faculty Publications

Background: Military and industrial activities have lead to reported release of 2,4-dinitrotoluene (2,4DNT) into soil, groundwater or surface water. It has been reported that 2,4DNT can induce toxic effects on humans and other organisms. However the mechanism of 2,4DNT induced toxicity is still unclear. Although a series of methods for gene network construction have been developed, few instances of applying such technology to generate pathway connected networks have been reported.

Results: Microarray analyses were conducted using liver tissue of rats collected 24h after exposure to a single oral gavage with one of five concentrations of 2,4DNT. We observed …

Discrete Diffusion Models To Study The Effects Of Mg2+ Concentration On The Phopq Signal Transduction System, Preetam Ghosh, Samik Ghosh, Kalyan Basu, Sajal K. Das, Chaoyang Zhang

Faculty Publications

Background: The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain …

Toxicogenomics Analysis Of Non-Model Transcriptomes Using Next-Generation Sequencing And Microarray, Arun Rawat

Dissertations

With the advent of next generation technologies like Roche/454 Life Sciences that require low cost and less time for sequencing will help in providing a workable draft of non-model species genomes. Availability of high throughput microarray technologies for gene expression profiling provides low-cost tools for investigation of highly-integrated responses to various stimuli. These advancements along with bioinformatics processing have led to an increasing number of non-model species having well-annotated transcriptomes. The project focuses on the life cycle of development, functional annotation, and utilization of genomic tools for the avian wildlife species to determine the molecular impacts of exposure to munitions …

Development Of Representative Species-Level Molecular Markers And Morphological Character Analysis Of Leucothoid Amphipods (Crustacea: Amphipoda), Kristine Nicolle White

Dissertations

Leucothoid amphipods were investigated using morphology and molecular rDNA gene sequence fragments. The morphological diagnostic characters for traditional taxonomy have been clarified, a molecular marker for representative species has been developed, and one of the current anamorph-leucomorph connections has been confirmed with molecular sequence data. Ultimately this study has combined traditional morphological and modern molecular methods to clarify the taxonomy and to propose a preliminary phylogeny of the Leucothoidae. Analysis of 18S rDNA gene fragments from 13 species in two genera supported the current morphological species designations and the separation of the family into two clades. Combined analysis of 18S …

The Tbx20 Homologs Midline And H15 Specify Ventral Fate In The Drosophila Melanogaster Leg, Pia C. Svendson, Ann Formaz-Preston, Sandra M. Leal, William J. Brook

Faculty Publications

Regional fates in the developing limbs of Drosophila melanogaster are controlled by selector gene transcription factors. Ventral fate in the fly leg is specified by the expression of the ligand Wingless. We present evidence that midline and H15, members of the Tbx20 class of T-box transcription factors, are key mediators of the Wingless signal in the formation of the ventral region of the fly leg. midline and H15 are restricted to identical ventral domains of expression through activation by Wingless and repression by the dorsal signal Decapentaplegic. midline and H15 function redundantly and cell autonomously in the formation of …

Bridging Functional Genomics And Toxicogenomics Through Dna Microarrays In A Fish Model, Shuzhao Li

Dissertations

In a case study of finding gene expression signatures for environmental stressors in Cyprinodon variegatus, this dissertation examines several important issues of applying DNA microarray technology to fish toxicogenomics. The most relevant disciplines, fish toxicogenomics and computational systems biology are reviewed in Chapter 1. Chapter 2 reviews major aspects of DNA microarray technology.

On DNA microarrays, even for probes that target the same transcript, large variations are seen in the probe signals. These variations are partly dependent and partly independent on probe sequences. Chapter 3 estimates the sequence independent variation by combining experimental and computational approaches. Chapter 4 and …

Reverse Engineering Of Gene Regulatory Networks For Discovery Of Novel Interactions In Pathways Using Gene Expression Data, Tanwir Habib

Dissertations

A variety of chemicals in the environment have the potential to adversely affect the biological systems. We examined the responses of Rat (Rattus norvegicus) to the RDX exposure and female fathead minnows (FHM, Pimephales promelas) to a model aromatase inhibitor, fadrozole, using a transcriptional network inference approach. Rats were exposed to RDX and fish were exposed to 0 or 30mg/L fadrozole for 8 days. We analyzed gene expression changes using 8000 probes microarrays for rat experiment and 15,000 probe microarrays for fish. We used these changes to infer a transcriptional network. The central nervous system is remarkably plastic in its …

Inferring Gene Regulatory Networks From Time Series Microarray Data, Peng Li

Dissertations

The innovations and improvements in high-throughput genomic technologies, such as DNA microarray, make it possible for biologists to simultaneously measure dependencies and regulations among genes on a genome-wide scale and provide us genetic information. An important objective of the functional genomics is to understand the controlling mechanism of the expression of these genes and encode the knowledge into gene regulatory network (GRN). To achieve this, computational and statistical algorithms are especially needed.

Inference of GRN is a very challenging task for computational biologists because the degree of freedom of the parameters is redundant. Various computational approaches have been proposed for …

A Distribution-Free Convolution Model For Background Correction Of Oligonucleotide Microarray Data, Zhongxue Chen, Monnie Mcgee, Qingzhong Liu, Megan Kong, Youping Deng, Richard H. Scheuermann

Faculty Publications

Introduction

Affymetrix GeneChip^® high-density oligonucleotide arrays are widely used in biological and medical research because of production reproducibility, which facilitates the comparison of results between experiment runs. In order to obtain high-level classification and cluster analysis that can be trusted, it is important to perform various pre-processing steps on the probe-level data to control for variability in sample processing and array hybridization. Many proposed preprocessing methods are parametric, in that they assume that the background noise generated by microarray data is a random sample from a statistical distribution, typically a normal distribution. The quality of the final results depends …

Comparing 2-Nt 3' Overhangs Against Blunt-Ended Sirnas: A Systems Biology Based Study, Preetam Ghosh, Robert Dullea, James E. Fischer, Tom G. Turi, Ronald W. Sarver, Chaoyang Zhang, Kalyan Basu, Sajal K. Das

Faculty Publications

In this study, we formulate a computational reaction model following a chemical kinetic theory approach to predict the binding rate constant for the siRNA-RISC complex formation reaction. The model allowed us to study the potency difference between 2-nt 3' overhangs against blunt-ended siRNA molecules in an RNA interference (RNAi) system. The rate constant predicted by this model was fed into a stochastic simulation of the RNAi system (using the Gillespie stochastic simulator) to study the overall potency effect. We observed that the stochasticity in the transcription/translation machinery has no observable effects in the RNAi pathway. Sustained gene silencing using siRNAs …

An Ensemble Learning Approach To Reverse-Engineering Transcriptional Regulatory Networks From Time-Series Gene Expression Data, Jianhua Ruan, Youping Deng, Edward J. Perkins, Weixiong Zhang

Faculty Publications

Background

One of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations.

Results

Here we propose a supervised machine learning approach to address these questions. We focus our study on the gene transcriptional regulation of the cell cycle in the budding yeast, thanks to the large amount of data available and relatively well-understood biology, although the main ideas …

High-Throughput Next-Generation Sequencing Technologies Foster New Cutting-Edge Computing Techniques In Bioinformatics, Mary Qu Yang, Brian D. Athey, Hamid R. Arabnia, Andrew H. Sung, Qingzhong Liu, Jack Y. Yang, Jinghe Mao, Youping Deng

Faculty Publications

The advent of high-throughput next generation sequencing technologies have fostered enormous potential applications of supercomputing techniques in genome sequencing, epi-genetics, metagenomics, personalized medicine, discovery of non-coding RNAs and protein-binding sites. To this end, the 2008 International Conference on Bioinformatics and Computational Biology (Biocomp) - 2008 World Congress on Computer Science, Computer Engineering and Applied Computing (Worldcomp) was designed to promote synergistic inter/multidisciplinary research and education in response to the current research trends and advances. The conference attracted more than two thousand scientists, medical doctors, engineers, professors and students gathered at Las Vegas, Nevada, USA during July 14-17 and received great …

Reverse Recruitment: Activation Of Yeast Genes At The Nuclear Periphery, Terry Marvin Haley

Dissertations

The regulation of genes at the nuclear periphery is an evolutionarily conserved phenomenon in eukaryotes. The reverse-recruitment model of transcriptional activation postulates that genes are activated by moving to and contacting transcription machinery located at subnuclear structures. In Saccharomyces cerevisiae it has been reported that this platform for gene regulation may reside at the nuclear periphery. To test this hypothesis, I utilized a GFP-gene tagging technique, which uses LacI-GFP to visualize a tandem array of its DNA-binding sequence, to monitor localization ofSUC2 and GALL I found that both genes preferentially localized to the nuclear periphery when transcriptionally active. By developing …

Sammd: Staphylococcus Aureus Microarray Meta-Database, Vijayaraj Nagarajan, Mohamed O. Elasri

Faculty Publications

Background

Staphylococcus aureus is an important human pathogen, causing a wide variety of diseases ranging from superficial skin infections to severe life threatening infections. S. aureus is one of the leading causes of nosocomial infections. Its ability to resist multiple antibiotics poses a growing public health problem. In order to understand the mechanism of pathogenesis of S. aureus, several global expression profiles have been developed. These transcriptional profiles included regulatory mutants of S. aureus and growth of wild type under different growth conditions. The abundance of these profiles has generated a large amount of data without a uniform annotation …

A Comparative Study Of Different Machine Learning Methods On Microarray Gene Expression Data, Mehdi Pirooznia, Jack Y. Yang, Mary Qu Yang, Youping Deng

Faculty Publications

Background

Several classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. The accuracy of these methods has been calculated with validation methods such as v-fold validation. However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results.

Results

In this study, we compared the efficiency of the classification methods including; SVM, RBF Neural Nets, …

A Hybrid Machine Learning-Based Method For Classifying The Cushing's Syndrome With Comorbid Adrenocortical Lesions, Jack Y. Yang, Mary Qu Yang, Zuojie Lao, Yan Ma, Jianling Li, Youping Deng, Xudong Huang

Faculty Publications

Background

The prognosis for many cancers could be improved dramatically if they could be detected while still at the microscopic disease stage. It follows from a comprehensive statistical analysis that a number of antigens such as hTERT, PCNA and Ki-67 can be considered as cancer markers, while another set of antigens such as P27KIP1 and FHIT are possible markers for normal tissue. Because more than one marker must be considered to obtain a classification of cancer or no cancer, and if cancer, to classify it as malignant, borderline, or benign, we must develop an intelligent decision system that can fullfill …

Improving Prediction Accuracy Of Tumor Classification By Reusing Genes Discarded During Gene Selection, Jack Y. Yang, Guo-Zheng Li, Hao-Hua Meng, Mary Qu Yang, Youping Deng

Faculty Publications

Background

Since the high dimensionality of gene expression microarray data sets degrades the generalization performance of classifiers, feature selection, which selects relevant features and discards irrelevant and redundant features, has been widely used in the bioinformatics field. Multi-task learning is a novel technique to improve prediction accuracy of tumor classification by using information contained in such discarded redundant features, but which features should be discarded or used as input or output remains an open issue.

Results

We demonstrate a framework for automatically selecting features to be input, output, and discarded by using a genetic algorithm, and propose two algorithms: GA-MTL …

Promoting Synergistic Research And Education In Genomics And Bioinformatics, Jack Y. Yang, Mary Qu Yang, Hamid R. Arabnia, Youping Deng

Faculty Publications

Bioinformatics and Genomics are closely related disciplines that hold great promises for the advancement of research and development in complex biomedical systems, as well as public health, drug design, comparative genomics, personalized medicine and so on. Research and development in these two important areas are impacting the science and technology.

High throughput sequencing and molecular imaging technologies marked the beginning of a new era for modern translational medicine and personalized healthcare. The impact of having the human sequence and personalized digital images in hand has also created tremendous demands of developing powerful supercomputing, statistical learning and artificial intelligence approaches to …

Iloop - A Web Application For Two-Channel Microarray Interwoven Loop Design, Mehdi Pirooznia, Ping Gong, Jack Y. Yang, Mary Qu Yang, Edward J. Perkins, Youping Deng

Faculty Publications

Microarray technology is widely applied to address complex scientific questions. However, there remain fundamental issues on how to design experiments to ensure that the resulting data enables robust statistical analysis. Interwoven loop design has several advantages over other designs. However it suffers in the complexity of design. We have implemented an online web application which allows users to find optimal loop designs for two-color microarray experiments. Given a number of conditions (such as treatments or time points) and replicates, the application will find the best possible design of the experiment and output experimental parameters. It is freely available from http://mcbc.usm.edu/iloop …

Batch Blast Extractor: An Automated Blastx Parser Application, Mehdi Pirooznia, Edward J. Perkins, Youping Deng

Faculty Publications

Motivation

BLAST programs are very efficient in finding similarities for sequences. However for large datasets such as ESTs, manual extraction of the information from the batch BLAST output is needed. This can be time consuming, insufficient, and inaccurate. Therefore implementation of a parser application would be extremely useful in extracting information from BLAST outputs.

Results

We have developed a java application, Batch Blast Extractor, with a user friendly graphical interface to extract information from BLAST output. The application generates a tab delimited text file that can be easily imported into any statistical package such as Excel or SPSS for further …

Genomics, Molecular Imaging, Bioinformatics, And Bio-Nano-Info Integration Are Synergistic Components Of Translational Medicine And Personalized Healthcare Research, Jack Y. Yang, Mary Qu Yang, Hamid R. Arabnia, Youping Deng

Faculty Publications

Supported by National Science Foundation (NSF), International Society of Intelligent Biological Medicine (ISIBM), International Journal of Computational Biology and Drug Design and International Journal of Functional Informatics and Personalized Medicine, IEEE 7th Bioinformatics and Bioengineering attracted more than 600 papers and 500 researchers and medical doctors. It was the only synergistic inter/multidisciplinary IEEE conference with 24 Keynote Lectures, 7 Tutorials, 5 Cutting-Edge Research Workshops and 32 Scientific Sessions including 11 Special Research Interest Sessions that were designed dynamically at Harvard in response to the current research trends and advances. The committee was very grateful for the IEEE Plenary Keynote Lectures …

Analyzing Adjuvant Radiotherapy Suggests A Non Monotonic Radio-Sensitivity Over Tumor Volumes, Jack Y. Yang, Andrzej Niemierko, Mary Qu Yang, Youping Deng

Faculty Publications

Background: Adjuvant Radiotherapy (RT) after surgical removal of tumors proved beneficial in long-term tumor control and treatment planning. For many years, it has been well concluded that radio-sensitivities of tumors upon radiotherapy decrease according to the sizes of tumors and RT models based on Poisson statistics have been used extensively to validate clinical data. Results: We found that Poisson statistics on RT is actually derived from bacterial cells despite of many validations from clinical data. However cancerous cells do have abnormal cellular communications and use chemical messengers to signal both surrounding normal and cancerous cells to develop new blood vessels …

Transcriptomic Analysis Of Rdx And Tnt Interactive Sublethal Effects In The Earthworm Eisenia Fetida, Ping Gong, Xin Guan, Laura S. Anouye, Youping Deng, Mehdi Pirooznia, Edward J. Perkins

Faculty Publications

Background

Explosive compounds such as TNT and RDX are recalcitrant contaminants often found co-existing in the environment. In order to understand the joint effects of TNT and RDX on earthworms, an important ecological and bioindicator species at the molecular level, we sampled worms (Eisenia fetida) exposed singly or jointly to TNT (50 mg/kg soil) and RDX (30 mg/kg soil) for 28 days and profiled gene expression in an interwoven loop designed microarray experiment using a 4k-cDNA array. Lethality, growth and reproductive endpoints were measured.

Results

Sublethal doses of TNT and RDX had no significant effects on the survival …

Investigation Of Transmembrane Proteins Using A Computational Approach, Jack Y. Yang, Mary Qu Yang, Keith A. Dunker, Youping Deng, Xudong Huang

Faculty Publications

Background

An important subfamily of membrane proteins are the transmembrane α-helical proteins, in which the membrane-spanning regions are made up of α-helices. Given the obvious biological and medical significance of these proteins, it is of tremendous practical importance to identify the location of transmembrane segments. The difficulty of inferring the secondary or tertiary structure of transmembrane proteins using experimental techniques has led to a surge of interest in applying techniques from machine learning and bioinformatics to infer secondary structure from primary structure in these proteins. We are therefore interested in determining which physicochemical properties are most useful for discriminating transmembrane …

Supervised Learning Method For The Prediction Of Subcellular Localization Of Proteins Using Amino Acid And Amino Acid Pair Composition, Tanwir Habib, Chaoyang Zhang, Jack Y. Yang, Mary Qu Yang, Youping Deng

Faculty Publications

Background

Occurrence of protein in the cell is an important step in understanding its function. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. Most studied methods for prediction of subcellular localization of proteins are signal peptides, the location by sequence homology, and the correlation between the total amino acid compositions of proteins. Taking amino-acid composition and amino acid pair composition into consideration helps improving the prediction accuracy.

Results

We constructed a dataset of protein sequences from SWISS-PROT database and segmented them into 12 classes based on their subcellular locations. SVM modules were trained to …

Transcriptome Profiling Of Saccharomyces Cerevisiae Mutants Lacking C2h2 Zinc Finger Proteins, Jinghe Mao, Tanwir Habib, Ming Shenwu, Baobin Kang, Wilbur Allen, Lashonda Robertson, Jack Y. Yang, Youping Deng

Faculty Publications

Background

The budding yeast Saccharomyces cerevisiae is a eukaryotic organism with extensive genetic redundancy. Large-scale gene deletion analysis has shown that over 80% of the ~6200 predicted genes are nonessential and that the functions of 30% of all ORFs remain unclassified, implying that yeast cells can tolerate deletion of a substantial number of individual genes. For example, a class of zinc finger proteins containing C2H2 zinc fingers in tandem arrays of two or three is predicted to be transcription factors; however, seven of the thirty-one predicted genes of this class are nonessential, and their functions are poorly understood. In this …