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The Low Abundance Of Cpg In The Sars-Cov-2 Genome Is Not An Evolutionarily Signature Of Zap, Ali Afrasiabi, Hamid Alinejad-Rokny, Azad Khosh, Mostafa Rahnama, Nigel Lovell, Zhenming Xu, Diako Ebrahimi
The Low Abundance Of Cpg In The Sars-Cov-2 Genome Is Not An Evolutionarily Signature Of Zap, Ali Afrasiabi, Hamid Alinejad-Rokny, Azad Khosh, Mostafa Rahnama, Nigel Lovell, Zhenming Xu, Diako Ebrahimi
Plant Pathology Faculty Publications
The zinc finger antiviral protein (ZAP) is known to restrict viral replication by binding to the CpG rich regions of viral RNA, and subsequently inducing viral RNA degradation. This enzyme has recently been shown to be capable of restricting SARS-CoV-2. These data have led to the hypothesis that the low abundance of CpG in the SARS-CoV-2 genome is due to an evolutionary pressure exerted by the host ZAP. To investigate this hypothesis, we performed a detailed analysis of many coronavirus sequences and ZAP RNA binding preference data. Our analyses showed neither evidence for an evolutionary pressure acting specifically on CpG …
Identifying The Cell Composition And Clonal Diversity Of Supratentorial Ependymoma Using Single Cell Rna-Sequencing, James He
University Scholar Projects
Ependymoma is a primary solid tumor of the central nervous system. Supratentorial ependymoma (ST-EPN), a subtype of ependymomas, is driven by an oncogenic fusion between the ZFTA and RELA genes in 70% of cases. We introduced this fusion into neural progenitor cells of mice embryos via in utero electroporation of a non-viral binary piggyBac transposon system containing ZFTA-RELA. From preliminary data in the LoTurco lab, inducing the expression of ZFTA-RELA into different neural progenitor cells produces tumors of varying lethality and cellular composition. To define the cellular composition and subclonal diversity of ST-EPN tumors, we used single cell RNA-sequencing to …
Identifying The Cell Composition And Clonal Diversity Of Supratentorial Ependymoma Using Single Cell Rna-Sequencing, James He
Honors Scholar Theses
Ependymoma is a primary solid tumor of the central nervous system. Supratentorial ependymoma (ST-EPN), a subtype of ependymomas, is driven by an oncogenic fusion between the ZFTA and RELA genes in 70% of cases. We introduced this fusion into neural progenitor cells of mice embryos via in utero electroporation of a non-viral binary piggyBac transposon system containing ZFTA-RELA. From preliminary data in the LoTurco lab, inducing the expression of ZFTA-RELA into different neural progenitor cells produces tumors of varying lethality and cellular composition. To define the cellular composition and subclonal diversity of ST-EPN tumors, we used single cell RNA-sequencing …
Connections In The Underworld: A Morphological And Molecular Study Of Diversity And Connectivity Among Anchialine Shrimp., Robert Eugene Ditter
Connections In The Underworld: A Morphological And Molecular Study Of Diversity And Connectivity Among Anchialine Shrimp., Robert Eugene Ditter
FIU Electronic Theses and Dissertations
This research investigates the distribution and population structure of crustaceans, endemic to anchialine systems in the tropical western Atlantic focusing on cave-dwelling shrimp from the family Barbouriidae. Taxonomic and molecular tools (genetic and genomic) are utilized to examine population dynamics and the presence of phenotypic hypervariation (PhyV) of the critically endangered species Barbouria cubensis (von Martens, 1872). The presence of PhyV and its geographic distribution is investigated among anchialine populations of B. cubensis from 34 sites on Abaco, Eleuthera, and San Salvador, Bahamas. Examination of 54 informative morphological characters revealed PhyV present in nearly 90% (n=463) of specimens with no …
Surveying Apicomplexan Diversity And Dynamics In Narragansett Bay, Evelyn Spencer
Surveying Apicomplexan Diversity And Dynamics In Narragansett Bay, Evelyn Spencer
Senior Honors Projects
Parasites play an important role in marine ecosystems and their diversity is generally understudied. Apicomplexans, a group of parasitic protists in the phylum Alveolata, infect a wide variety of animal hosts and are abundant in ecosystems spanning from Polar Regions to Neotropical rainforests. Previous data generated from marine sediments in Antarctica, Naples Bay, and off the coast of Oslo, exhibit high diversity and numbers of apicomplexans. Abundance and diversity of these protists are unknown for Narragansett Bay, despite the fact that they infect many commercially important species. The aim of my study was to obtain abundance data and understand genetic …
Confirming World-Wide Distribution Of An Agriculturally Important Lacewing, Chrysoperla Zastrowi Sillemi, Using Songs, Morphology, Mitochondrial Gene Sequencing, And Phylogenetic Reconstruction, Zoe Mandese
Honors Scholar Theses
The Chrysoperla carnea-group of green lacewings is a cryptic species complex. Species within the group are morphologically similar, yet isolated from one another via reproductive mating song. Chrysoperla zastrowi, a species within the carnea-group, is currently described with a distribution ranging from South Africa to the Middle East and India. However, recent collections of carnea-group lacewings from Guatemala and California were preliminarily identified as Chrysoperla zastrowi based upon similarities in their vibrational courtship songs. This analysis aims to place six specimens, collected by collaborators in Guatemala, Armenia, Iran, and California, into a pre-existing phylogeny of the …
Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore
Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore
Dartmouth Scholarship
Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes …
Assessment Of A Metaviromic Dataset Generated From Nearshore Lake Michigan, Siobhan C. Watkins, Neil Kuehnle, C Anthony Ruggeri, Kema Malki, Katherine Bruder, Jinan Elayyan, Kristina Damisch, Naushin Vahora, Paul O'Malley, Brianne Ruggles-Sage, Zachary Romer, Catherine Putonti
Assessment Of A Metaviromic Dataset Generated From Nearshore Lake Michigan, Siobhan C. Watkins, Neil Kuehnle, C Anthony Ruggeri, Kema Malki, Katherine Bruder, Jinan Elayyan, Kristina Damisch, Naushin Vahora, Paul O'Malley, Brianne Ruggles-Sage, Zachary Romer, Catherine Putonti
Bioinformatics Faculty Publications
Bacteriophages are powerful ecosystem engineers. They drive bacterial mortality rates and genetic diversity, and affect microbially mediated biogeochemical processes on a global scale. This has been demonstrated in marine environments; however, phage communities have been less studied in freshwaters, despite representing a potentially more diverse environment. Lake Michigan is one of the largest bodies of freshwater on the planet, yet to date the diversity of its phages has yet to be examined. Here, we present a composite survey of viral ecology in the nearshore waters of Lake Michigan. Sequence analysis was performed using a web server previously used to analyse …
A Classification And Characterization Of Two-Locus, Pure, Strict, Epistatic Models For Simulation And Detection, Ryan J. Urbanowicz, Ambrose L. S. Granizo-Mackenzie, Jeff Kiralis, Jason H Moore
A Classification And Characterization Of Two-Locus, Pure, Strict, Epistatic Models For Simulation And Detection, Ryan J. Urbanowicz, Ambrose L. S. Granizo-Mackenzie, Jeff Kiralis, Jason H Moore
Dartmouth Scholarship
BackgroundThe statistical genetics phenomenon of epistasis is widely acknowledged to confound disease etiology. In order to evaluate strategies for detecting these complex multi-locus disease associations, simulation studies are required. The development of the GAMETES software for the generation of complex genetic models, has provided the means to randomly generate an architecturally diverse population of epistatic models that are both pure and strict, i.e. all n loci, but no fewer, are predictive of phenotype. Previous theoretical work characterizing complex genetic models has yet to examine pure, strict, epistasis which should be the most challenging to detect. This study addresses three goals: …
An Examination Of The Phylogenetic Diversity Of Green Algae (Chlorophyceae) That Symbiose With Spotted Salamanders (Ambystoma Maculatum) In The Egg Stage., Crystal Xue
Honors Scholar Theses
In 1909, the species Oophila amblystomatis Lambert ex Wille was described for green algae that symbiose with salamanders in the egg stage (Wille). There are two hypotheses about the source of algae: 1) that algae enter from the surrounding water once the egg clutch is laid in a pond, and 2) that they are acquired from the maternal reproductive tract. We developed a third hypothesis developed to account for the salamander reproductive cycle. Male salamanders lay spermatophores, which are protein-filled capsules, on plant matter in and around ponds. Spermatophores are exposed to the environment before use by females in internal …
Engaging Students In A Bioinformatics Activity To Introduce Gene Structure And Function, Barbara J. May
Engaging Students In A Bioinformatics Activity To Introduce Gene Structure And Function, Barbara J. May
Biology Faculty Publications
Bioinformatics spans many fields of biological research and plays a vital role in mining and analyzing data. Therefore, there is an ever-increasing need for students to understand not only what can be learned from this data, but also how to use basic bioinformatics tools. This activity is designed to provide secondary and undergraduate biology students to a hands-on activity meant to explore and understand gene structure with the use of basic bioinformatic tools. Students are provided an “unknown” sequence from which they are asked to use a free online gene finder program to identify the gene. Students then predict the …
Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit
Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit
Dartmouth Scholarship
There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.
A Novel Correlation Networks Approach For The Identification Of Gene Targets, Kathryn Dempsey Cooper, Stephen Bonasera, Dhundy Raj Bastola, Hesham Ali
A Novel Correlation Networks Approach For The Identification Of Gene Targets, Kathryn Dempsey Cooper, Stephen Bonasera, Dhundy Raj Bastola, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Correlation networks are emerging as a powerful tool for modeling temporal mechanisms within the cell. Particularly useful in examining coexpression within microarray data, studies have determined that correlation networks follow a power law degree distribution and thus manifest properties such as the existence of “hub” nodes and semicliques that potentially correspond to critical cellular structures. Difficulty lies in filtering coincidental relationships from causative structures in these large, noise-heavy networks. As such, computational expenses and algorithm availability limit accurate comparison, making it difficult to identify changes between networks. In this vein, we present our work identifying temporal relationships from microarray data …
Minimum Description Length Measures Of Evidence For Enrichment, Zhenyu Yang, David R. Bickel
Minimum Description Length Measures Of Evidence For Enrichment, Zhenyu Yang, David R. Bickel
COBRA Preprint Series
In order to functionally interpret differentially expressed genes or other discovered features, researchers seek to detect enrichment in the form of overrepresentation of discovered features associated with a biological process. Most enrichment methods treat the p-value as the measure of evidence using a statistical test such as the binomial test, Fisher's exact test or the hypergeometric test. However, the p-value is not interpretable as a measure of evidence apart from adjustments in light of the sample size. As a measure of evidence supporting one hypothesis over the other, the Bayes factor (BF) overcomes this drawback of the p-value but lacks …
Powerful Snp Set Analysis For Case-Control Genome Wide Association Studies, Michael C. Wu, Peter Kraft, Michael P. Epstein, Deanne M. Taylor, Stephen J. Chanock, David J. Hunter, Xihong Lin
Powerful Snp Set Analysis For Case-Control Genome Wide Association Studies, Michael C. Wu, Peter Kraft, Michael P. Epstein, Deanne M. Taylor, Stephen J. Chanock, David J. Hunter, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Sparse Linear Discriminant Analysis For Simultaneous Testing For The Significance Of A Gene Set/Pathway And Gene Selection, Michael C. Wu, Lingson Zhang, Zhaoxi Wang, David C. Christiani, Xihong Lin
Sparse Linear Discriminant Analysis For Simultaneous Testing For The Significance Of A Gene Set/Pathway And Gene Selection, Michael C. Wu, Lingson Zhang, Zhaoxi Wang, David C. Christiani, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Estimation And Testing For The Effect Of A Genetic Pathway On A Disease Outcome Using Logistic Kernel Machine Regression Via Logistic Mixed Models, Dawei Liu, Debashis Ghosh, Xihong Lin
Estimation And Testing For The Effect Of A Genetic Pathway On A Disease Outcome Using Logistic Kernel Machine Regression Via Logistic Mixed Models, Dawei Liu, Debashis Ghosh, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Powerful And Flexible Multilocus Association Test For Quantitative Traits, Lydia Coulter Kwee, Dawei Liu, Xihong Lin, Debashis Ghosh, Michael P. Epstein
A Powerful And Flexible Multilocus Association Test For Quantitative Traits, Lydia Coulter Kwee, Dawei Liu, Xihong Lin, Debashis Ghosh, Michael P. Epstein
Harvard University Biostatistics Working Paper Series
No abstract provided.
Assessing Population Level Genetic Instability Via Moving Average, Samuel Mcdaniel, Rebecca Betensky, Tianxi Cai
Assessing Population Level Genetic Instability Via Moving Average, Samuel Mcdaniel, Rebecca Betensky, Tianxi Cai
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Novel Ensemble Learning Method For De Novo Computational Identification Of Dna Binding Sites, Arijit Chakravarty, Jonathan M. Carlson, Radhika S. Khetani, Robert H H. Gross
A Novel Ensemble Learning Method For De Novo Computational Identification Of Dna Binding Sites, Arijit Chakravarty, Jonathan M. Carlson, Radhika S. Khetani, Robert H H. Gross
Dartmouth Scholarship
Despite the diversity of motif representations and search algorithms, the de novo computational identification of transcription factor binding sites remains constrained by the limited accuracy of existing algorithms and the need for user-specified input parameters that describe the motif being sought.ResultsWe present a novel ensemble learning method, SCOPE, that is based on the assumption that transcription factor binding sites belong to one of three broad classes of motifs: non-degenerate, degenerate and gapped motifs. SCOPE employs a unified scoring metric to combine the results from three motif finding algorithms each aimed at the discovery of one of these classes of motifs. …
Assessment Of A Cgh-Based Genetic Instability, David A. Engler, Yiping Shen, J F. Gusella, Rebecca A. Betensky
Assessment Of A Cgh-Based Genetic Instability, David A. Engler, Yiping Shen, J F. Gusella, Rebecca A. Betensky
Harvard University Biostatistics Working Paper Series
No abstract provided.
Survival Analysis With Large Dimensional Covariates: An Application In Microarray Studies, David A. Engler, Yi Li
Survival Analysis With Large Dimensional Covariates: An Application In Microarray Studies, David A. Engler, Yi Li
Harvard University Biostatistics Working Paper Series
Use of microarray technology often leads to high-dimensional and low- sample size data settings. Over the past several years, a variety of novel approaches have been proposed for variable selection in this context. However, only a small number of these have been adapted for time-to-event data where censoring is present. Among standard variable selection methods shown both to have good predictive accuracy and to be computationally efficient is the elastic net penalization approach. In this paper, adaptation of the elastic net approach is presented for variable selection both under the Cox proportional hazards model and under an accelerated failure time …
Power Boosting In Genome-Wide Studies Via Methods For Multivariate Outcomes, Mary J. Emond
Power Boosting In Genome-Wide Studies Via Methods For Multivariate Outcomes, Mary J. Emond
UW Biostatistics Working Paper Series
Whole-genome studies are becoming a mainstay of biomedical research. Examples include expression array experiments, comparative genomic hybridization analyses and large case-control studies for detecting polymorphism/disease associations. The tactic of applying a regression model to every locus to obtain test statistics is useful in such studies. However, this approach ignores potential correlation structure in the data that could be used to gain power, particularly when a Bonferroni correction is applied to adjust for multiple testing. In this article, we propose using regression techniques for misspecified multivariate outcomes to increase statistical power over independence-based modeling at each locus. Even when the outcome …
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Harvard University Biostatistics Working Paper Series
No abstract provided.
Structural Inference In Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Xihong Lin, Donglin Zeng
Structural Inference In Transition Measurement Error Models For Longitudinal Data, Wenqin Pan, Xihong Lin, Donglin Zeng
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin
Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin
A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross
Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross
Dartmouth Scholarship
The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach.
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Dartmouth Scholarship
The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.