Bioinformatics Commons^™

Distributional Properties Of Inversions And Segmentation Algorithms For Rna Sequences, Sameera Dhananjaya Viswakula

Open Access Theses & Dissertations

Ribonucleic acid (RNA) is a long single stranded molecule made up of four types of nucleotide bases: Adenine (A), Cytosine(C), Guanine (G) and Uracil (U). It folds back on itself and forms C-G and A-U complementary base pairs. The set of such hydrogen-bonded pairs in an RNA molecule is called its secondary structure. Knowing the secondary structure of RNA is useful for understanding its biological function. Prediction of RNA secondary structure from the nucleotide sequence has been an important bioinformatics problem for over two decades.

The work in this thesis is motivated by the need to improve the secondary structure …

Computational Tool For Automated Large-Scale Gpiomic Analysis, Juan Clemente Aguilar

Open Access Theses & Dissertations

Liquid chromatography-tandem mass spectrometry (LC-MS/MS or MS/MS) is the most efficient tool today for the identification of glycosylphosphatidylinositol (GPI) molecules. The amount of data produced in each MS/MS experiment is a major bottleneck in high-throughput GPIomic (the entire collection of free and protein-linked GPIs) projects. Efficient computational tools can significantly reduce the amount of time analyzing MS/MS data; however, at present the automatic interpretation of these data to annotate GPI structures is absent. We propose a library-based tool to identify GPI structures by matching fragment peaks in the spectra with data derived from a theoretical database of GPI structures that …

Modeling And Quantitative Analysis Of White Matter Fiber Tracts In Diffusion Tensor Imaging, Xuwei Liang

University of Kentucky Doctoral Dissertations

Diffusion tensor imaging (DTI) is a structural magnetic resonance imaging (MRI) technique to record incoherent motion of water molecules and has been used to detect micro structural white matter alterations in clinical studies to explore certain brain disorders. A variety of DTI based techniques for detecting brain disorders and facilitating clinical group analysis have been developed in the past few years. However, there are two crucial issues that have great impacts on the performance of those algorithms. One is that brain neural pathways appear in complicated 3D structures which are inappropriate and inaccurate to be approximated by simple 2D structures, …

Evaluating Methods For The Analysis Of Rare Variants In Sequence Data, Alexander Luedtke, Scott Powers, Ashley Petersen, Alexandra Sitarik, Airat Bekmetjev, Nathan L. Tintle

Faculty Work Comprehensive List

A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying …

Inflated Type I Error Rates When Using Aggregation Methods To Analyze Rare Variants In The 1000 Genomes Project Exon Sequencing Data In Unrelated Individuals: Summary Results From Group 7 At Genetic Analysis Workshop 17, Nathan L. Tintle, Hugues Aschard, Inchi Hu, Nora Nock, Haitian Wang, Elizabeth Pugh

Faculty Work Comprehensive List

As part of Genetic Analysis Workshop 17 (GAW17), our group considered the application of novel and standard approaches to the analysis of genotype-phenotype association in next-generation sequencing data. Our group identified a major issue in the analysis of the GAW17 next-generation sequencing data: type I error and false-positive report probability rates higher than those expected based on empirical type I error levels (as high as 90%). Two main causes emerged: population stratification and long-range correlation (gametic phase disequilibrium) between rare variants. Population stratification was expected because of the diverse sample. Correlation between rare variants was attributable to both random causes …

Identification Of Genetic Association Of Multiple Rare Variants Using Collapsing Methods, Yan V. Sun, Yun Ju Sung, Nathan L. Tintle, Andreas Ziegler

Faculty Work Comprehensive List

Next-generation sequencing technology allows investigation of both common and rare variants in humans. Exomes are sequenced on the population level or in families to further study the genetics of human diseases. Genetic Analysis Workshop 17 (GAW17) provided exomic data from the 1000 Genomes Project and simulated phenotypes. These data enabled evaluations of existing and newly developed statistical methods for rare variant sequence analysis for which standard statistical methods fail because of the rareness of the alleles. Various alternative approaches have been proposed that overcome the rareness problem by combining multiple rare variants within a gene. These approaches are termed collapsing …

Evaluating Methods For Combining Rare Variant Data In Pathway-Based Tests Of Genetic Association, Ashley Petersen, Alexandra Sitarik, Alexander Luedtke, Scott Powers, Airat Bekmetjev, Nathan L. Tintle

Faculty Work Comprehensive List

Analyzing sets of genes in genome-wide association studies is a relatively new approach that aims to capitalize on biological knowledge about the interactions of genes in biological pathways. This approach, called pathway analysis or gene set analysis, has not yet been applied to the analysis of rare variants. Applying pathway analysis to rare variants offers two competing approaches. In the first approach rare variant statistics are used to generate p-values for each gene (e.g., combined multivariate collapsing [CMC] or weighted-sum [WS]) and the gene-level p-values are combined using standard pathway analysis methods (e.g., gene set enrichment analysis or …

Analysis Of Differential Gene Expression And Alternative Splicing In The Liver And Gastrointestinal Tract In The Lactating Rat, Antony Thomas Athippozhy

University of Kentucky Doctoral Dissertations

Rat exon microarrays were utilized to detect changes in mRNA expression and alternative splicing in the liver, duodenum, jejunum, and ileum of the lactating rat when compared to age-matched virgin controls. Analysis of data at the level of gene expression revealed differential expression of genes involved in cholesterol biosynthesis in each tissue examined, suggesting increased Sterol Response Element Binding Protein activity. We also detected decreased mRNA from components of the T-cell signaling pathway in the jejunum and ileum. We characterized expression of solute carrier and adenosine triphosphate binding cassette proteins. In addition to characterizing genes by pathway, we have also …

Analysis On Partial Relationship In Lod, Kalpa Gunaratna, Sarasi Lalithsena, Cory Andrew Henson, Prateek Jain

Kno.e.sis Publications

Relationships play a key role in Semantic Web to connect the dots between entities (concepts or instances) in a way that enables to absorb the real sense of the entities. Some interesting relationships would give proof for the existence of subject and object in triples which in tern can be defined as evidential relationships. Identifying evidential relationships will yield solutions to some existing inference problems and open doors for new applications and research. Part_of relationships are identified as a special kind of an evidential relationship out of membership, causality and etc. Linked Open data as a global data space would …

Demonstration: Real-Time Semantic Analysis Of Sensor Streams, Harshal Kamlesh Patni, Cory Andrew Henson, Michael Cooney, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

The emergence of dynamic information sources - including sensor networks - has led to large streams of real-time data on the Web. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years. With this coming data explosion, real-time analytics software must either adapt or die. This paper focuses on the task of integrating and analyzing multiple heterogeneous streams of sensor data with the goal of creating meaningful abstractions, or features. These features are then temporally aggregated into feature …

A Genetic Optimization Approach For Isolating Translational Efficiency Bias, Douglas W. Raiford, Dan E. Krane, Travis E. Doom, Michael L. Raymer

Kno.e.sis Publications

The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution, phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most well studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both genomic sequence information and …

A Systematic Property Mapping Using Category Hierarchy And Data, Kalpa Gunaratna, Sarasi Lalithsena, Prateek Jain, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

Relationships play a key role in Semantic Web to connect the dots between entities (concepts or instances in a way that enables to absorb the real sense of the entities. Even though relationships are important, it is difficult to categorize or identify them because they consist of complex knowledge in the schema. Therefore systematically identifying relationships yield many advantages and open doors for new research avenues. In this work, we try to identify a specific type of relationship (part of) in a multi-domain dataset and devised an algorithm using Wikipedia to identify patterns of part of relationships in the dataset. …

Citizen Sensor Data Mining, Social Media Analytics And Development Centric Web Applications, Meenakshi Nagarajan, Amit P. Sheth, Selvam Velmuru

Kno.e.sis Publications

With the rapid rise in the popularity of social media (500M+ Facebook users, 100M+ twitter users), and near ubiquitous mobile access (4.1 billion actively-used mobile phones), the sharing of observations and opinions has become common-place (nearly 100M tweets a day, 1.8 trillion SMSs in US last year). This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it towards targeted online content delivery, crisis management, organizing revolutions or promoting social development in underdeveloped and developing countries. This tutorial …

Semantic Social Mashup Approach For Designing Citizen Diplomacy, Amit P. Sheth

Kno.e.sis Publications

Advancement in technology has brought exceptional connectivity, easy and open access to communication mediums via Internet. Everyday millions of people are interactively communicating to each other and sharing multimedia content through Social Media/Networks, Web-based and mobile-based technologies. Social media provides variety of interesting, engaging applications such as Twitter, Facebook, YouTube, Flicker, Blogs. People interested in contributing to global welfare and improving humanity are connected to various NGO's like Red Cross, Ushahidi (www.ushahidi.com), eMoksha (emoksha.org), etc. Social media and NGOs are acting as an excellent medium of communication and sharing, connected diverse people irrespective of their nationality, religion, culture, etc. Social …

Virtual Cgh: An Integrative Approach To Predict Genetic Abnormalities From Gene Expression Microarray Data Applied In Lymphoma, Huimin Geng, Javeed Iqbal, Hesham Ali

Information Systems and Quantitative Analysis Faculty Publications

Background: Comparative Genomic Hybridization (CGH) is a molecular approach for detecting DNA Copy Number Alterations (CNAs) in tumor, which are among the key causes of tumorigenesis. However in the post-genomic era, most studies in cancer biology have been focusing on Gene Expression Profiling (GEP) but not CGH, and as a result, an enormous amount of GEP data had been accumulated in public databases for a wide variety of tumor types. We exploited this resource of GEP data to define possible recurrent CNAs in tumor. In addition, the CNAs identified by GEP would be more functionally relevant CNAs in the disease …

Pcr Detection Of Nearly Any Dengue Virus Strain Using A Highly Sensitive Primer ‘Cocktail’, Charul Gijavanekal, Maria Anez-Lingerfelt, Chen Feng, Catherine Putonti, George E. Fox, Aniko Sabo, Yuriy Fofanov, Richard C. Wilson

Bioinformatics Faculty Publications

PCR detection of viral pathogens is extremely useful, but suffers from thechallenge of detecting the many variant strains of a given virus that ariseover time. Here, we report the computational derivation and initial experi-mental testing of a combination of 10 PCR primers to be used in a singlehigh-sensitivity mixed PCR reaction for the detection of dengue virus. Pri-mer sequences were computed such that their probability of misprimingwith human DNA is extremely low. A ‘cocktail’ of 10 primers was shownexperimentally to be able to detect cDNA clones representing the four sero-types and dengue virus RNA spiked into total human whole blood …

Cloudvista: Visual Cluster Exploration For Extreme Scale Data In The Could, Keke Chen, Huiqi Xi, Fengguang Tian, Shumin Guo

Kno.e.sis Publications

The problem of efficient and high-quality clustering of extreme scale datasets with complex clustering structures continues to be one of the most challenging data analysis problems. An innovate use of data cloud would provide unique opportunity to address this challenge. In this paper, we propose the CloudVista framework to address (1) the problems caused by using sampling in the existing approaches and (2) the problems with the latency caused by cloud-side processing on interactive cluster visualization. The CloudVista framework aims to explore the entire large data stored in the cloud with the help of the data structure visual frame and …

Contextual Ontology Alignment Of Lod With An Upper Ontology: A Case Study With Proton, Prateek Jain, Peter Z. Yeh, Kunal Verma, Reymonrod G. Vasquez, Mariana Darnorva, Pascal Hitzler, Amit P. Sheth

Kno.e.sis Publications

The Linked Open Data (LOD) is a major milestone towards realizing the Semantic Web vision, and can enable applications such as robust Question Answering (QA) systems that can answer queries requiring multiple, disparate information sources. However, realizing these applications requires relationships at both the schema and instance level, but currently the LOD only provides relationships for the latter. To address this limitation, we present a solution for automatically finding schema-level links between two LOD ontologies – in the sense of ontology alignment. Our solution, called BLOOMS+, extends our previous solution (i.e. BLOOMS) in two significant ways. BLOOMS+ 1) uses a …

Automated Classification Of The Narrative Of Medical Reports Using Natural Language Processing, Ira J. Goldstein

Legacy Theses & Dissertations (2009 - 2024)

In this dissertation we present three topics critical to the document level classification of the narrative in medical reports: the use of preferred terminology in light of the presence of synonymous terms, the less than optimal performance of classification systems when presented with a non-uniform distribution of classes, and the problems associated with scarcity of labeled data when presented with an imbalance of classes in the data sets.

Hemebind: A Novel Method For Heme Binding Residue Prediction By Combining Structural And Sequence Information, R. Liu, Jianjun Hu

Faculty Publications

Background

Accurate prediction of binding residues involved in the interactions between proteins and small ligands is one of the major challenges in structural bioinformatics. Heme is an essential and commonly used ligand that plays critical roles in electron transfer, catalysis, signal transduction and gene expression. Although much effort has been devoted to the development of various generic algorithms for ligand binding site prediction over the last decade, no algorithm has been specifically designed to complement experimental techniques for identification of heme binding residues. Consequently, an urgent need is to develop a computational method for recognizing these important residues.

Results

Here …