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Search Results: 1 - 10 of 1437 matches for " Shane McCarthy "
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Nonlinear Self-Duality and Supergravity
Sergei M. Kuzenko,Shane A. McCarthy
Physics , 2002, DOI: 10.1088/1126-6708/2003/02/038
Abstract: The concept of self-dual supersymmetric nonlinear electrodynamics is generalized to a curved superspace of N = 1 supergravity, for both the old minimal and the new minimal versions of N = 1 supergravity. We derive the self-duality equation, which has to be satisfied by the action functional of any U(1) duality invariant model of a massless vector multiplet, and construct a family of self-dual nonlinear models. This family includes a curved superspace extension of the N = 1 super Born-Infeld action. The supercurrent and supertrace in such models are proved to be duality invariant. The most interesting and unexpected result is that the requirement of nonlinear self-duality yields nontrivial couplings of the vector multiplet to Kahler sigma models. We explicitly derive the couplings to general Kahler sigma models in the case when the matter chiral multiplets are inert under the duality rotations, and more specifically to the dilaton-axion chiral multiplet when the group of duality rotations is enhanced to SL(2,R).
Power Weighted Densities for Time Series Data
Daniel M. McCarthy,Shane T. Jensen
Statistics , 2014,
Abstract: While time series prediction is an important, actively studied problem, the predictive accuracy of time series models is complicated by non-stationarity. We develop a fast and effective approach to allow for non-stationarity in the parameters of a chosen time series model. In our power-weighted density (PWD) approach, observations in the distant past are down-weighted in the likelihood function relative to more recent observations, while still giving the practitioner control over the choice of data model. One of the most popular non-stationary techniques in the academic finance community, rolling window estimation, is a special case of our PWD approach. Our PWD framework is a simpler alternative compared to popular state-space methods that explicitly model the evolution of an underlying state vector. We demonstrate the benefits of our PWD approach in terms of predictive performance compared to both stationary models and alternative non-stationary methods. In a financial application to thirty industry portfolios, our PWD method has a significantly favorable predictive performance and draws a number of substantive conclusions about the evolution of the coefficients and the importance of market factors over time.
Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome
Teresia J Buza, Fiona M McCarthy, Shane C Burgess
BMC Genomics , 2007, DOI: 10.1186/1471-2164-8-425
Abstract: We analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the in vivo expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology), we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO) functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines.We demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and inform gene prediction algorithms.After genome sequencing, genome annotation is critical to denote and demarcate the functional elements in the genome (structural annotation) and to link these genomic elements to biological function (functional annotation). Structural annotation of newly sequenced genomes begins during the final stages of genome assembly with electronic prediction of open reading frames (ORFs) [1-3]. Sequencing consortiums typically release these predicted genes and their translated products into public databases, where they account for the majority of data for the newly sequenced species [4,5] and are critical for high-throughput wet lab functional genomics (microarray and proteomics) experiments [4,6]. The NCBI N
Re-Annotation Is an Essential Step in Systems Biology Modeling of Functional Genomics Data
Bart H. J. van den Berg,Fiona M. McCarthy,Susan J. Lamont,Shane C. Burgess
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0010642
Abstract: One motivation of systems biology research is to understand gene functions and interactions from functional genomics data such as that derived from microarrays. Up-to-date structural and functional annotations of genes are an essential foundation of systems biology modeling. We propose that the first essential step in any systems biology modeling of functional genomics data, especially for species with recently sequenced genomes, is gene structural and functional re-annotation. To demonstrate the impact of such re-annotation, we structurally and functionally re-annotated a microarray developed, and previously used, as a tool for disease research. We quantified the impact of this re-annotation on the array based on the total numbers of structural- and functional-annotations, the Gene Annotation Quality (GAQ) score, and canonical pathway coverage. We next quantified the impact of re-annotation on systems biology modeling using a previously published experiment that used this microarray. We show that re-annotation improves the quantity and quality of structural- and functional-annotations, allows a more comprehensive Gene Ontology based modeling, and improves pathway coverage for both the whole array and a differentially expressed mRNA subset. Our results also demonstrate that re-annotation can result in a different knowledge outcome derived from previous published research findings. We propose that, because of this, re-annotation should be considered to be an essential first step for deriving value from functional genomics data.
ArrayIDer: automated structural re-annotation pipeline for DNA microarrays
Bart HJ van den Berg, Jay H Konieczka, Fiona M McCarthy, Shane C Burgess
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-30
Abstract: We utilized the Fred Hutchinson Cancer Research Centre 13K chicken cDNA array – a widely-used non-commercial chicken microarray – to demonstrate the principle that ArrayIDer could markedly improve annotation. We structurally re-annotated 55% of the entire array. Moreover, we decreased non-chicken functional annotations by 2 fold. One beneficial consequence of our re-annotation was to identify 290 pseudogenes, of which 66 were previously incorrectly annotated.ArrayIDer allows rapid automated structural re-annotation of entire arrays and provides multiple accession types for use in subsequent functional analysis. This information is especially valuable for systems biology modeling in the non-traditional biomedical model organisms.Microarrays have become a standard tool for functional genomics allowing analysis of thousands of mRNA transcripts simultaneously and they are widely used for a diverse range of species [1-4]. Microarrays have been applied to species regardless of whether or not their whole genome sequence is available. However, understanding the biological meaning represented by microarray data is hindered by lack of structural – and functional annotation (i.e. identifying the genes represented on arrays and linking these to functional information, respectively). Despite the prevalence of EST sequences represented on microarrays for most species, existing tools for expression data analyses and array functional annotation [5-11] do not accept EST clone names or accessions as input. Therefore, researchers are often hindered to first convert EST clone names or accession numbers to identifiers compatible with these functional analyses tools.Although 10 software packages have been developed to map between popular database identifiers [5,9,10,12-18], these gene cross-reference tools are not compatible with EST clone name input, focus only on widely-used commercially-available arrays or only incorporate limited organisms. Moreover, functional information (such as t
The path to fracture in granular flows: dynamics of contact networks
Mark Herrera,Shane McCarthy,Steven Slotterback,Emmanuel Cephas,Wolfgang Losert,Michelle Girvan
Physics , 2011, DOI: 10.1103/PhysRevE.83.061303
Abstract: Capturing the dynamics of granular flows at intermediate length scales can often be difficult. We propose studying the dynamics of contact networks as a new tool to study fracture at intermediate scales. Using experimental three-dimensional flow fields with particle-scale resolution, we calculate the time evolving broken-links network and find that a giant component of this network is formed as shear is applied to this system. We implement a model of link breakages where the probability of a link breaking is proportional to the average rate of longitudinal strain (elongation) in the direction of the edge and find that the model demonstrates qualitative agreement with the data when studying the onset of the giant component. We note, however, that the broken-links network formed in the model is less clustered than our experimental observations, indicating that the model reflects less localized breakage events and does not fully capture the dynamics of the granular flow.
YFitter: Maximum likelihood assignment of Y chromosome haplogroups from low-coverage sequence data
Luke Jostins,Yali Xu,Shane McCarthy,Qasim Ayub,Richard Durbin,Jeff Barrett,Chris Tyler-Smith
Quantitative Biology , 2014,
Abstract: Low-coverage short-read resequencing experiments have the potential to expand our understanding of Y chromosome haplogroups. However, the uncertainty associated with these experiments mean that haplogroups must be assigned probabilistically to avoid false inferences. We propose an efficient dynamic programming algorithm that can assign haplogroups by maximum likelihood, and represent the uncertainty in assignment. We apply this to both genotype and low-coverage sequencing data, and show that it can assign haplogroups accurately and with high resolution. The method is implemented as the program YFitter, which can be downloaded from http://sourceforge.net/projects/yfitter/
Transcriptome-Based Differentiation of Closely-Related Miscanthus Lines
Philippe Chouvarine, Amanda M. Cooksey, Fiona M. McCarthy, David A. Ray, Brian S. Baldwin, Shane C. Burgess, Daniel G. Peterson
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0029850
Abstract: Background Distinguishing between individuals is critical to those conducting animal/plant breeding, food safety/quality research, diagnostic and clinical testing, and evolutionary biology studies. Classical genetic identification studies are based on marker polymorphisms, but polymorphism-based techniques are time and labor intensive and often cannot distinguish between closely related individuals. Illumina sequencing technologies provide the detailed sequence data required for rapid and efficient differentiation of related species, lines/cultivars, and individuals in a cost-effective manner. Here we describe the use of Illumina high-throughput exome sequencing, coupled with SNP mapping, as a rapid means of distinguishing between related cultivars of the lignocellulosic bioenergy crop giant miscanthus (Miscanthus × giganteus). We provide the first exome sequence database for Miscanthus species complete with Gene Ontology (GO) functional annotations. Results A SNP comparative analysis of rhizome-derived cDNA sequences was successfully utilized to distinguish three Miscanthus × giganteus cultivars from each other and from other Miscanthus species. Moreover, the resulting phylogenetic tree generated from SNP frequency data parallels the known breeding history of the plants examined. Some of the giant miscanthus plants exhibit considerable sequence divergence. Conclusions Here we describe an analysis of Miscanthus in which high-throughput exome sequencing was utilized to differentiate between closely related genotypes despite the current lack of a reference genome sequence. We functionally annotated the exome sequences and provide resources to support Miscanthus systems biology. In addition, we demonstrate the use of the commercial high-performance cloud computing to do computational GO annotation.
MAOA haplotypes associated with thrombocyte-MAO activity
M?rten Jansson, Shane McCarthy, Patrick F Sullivan, Paul Dickman, Bj?rn Andersson, Lars Oreland, Martin Schalling, Nancy L Pedersen
BMC Genetics , 2005, DOI: 10.1186/1471-2156-6-46
Abstract: Our results reveal a profound SNP desert in the MAOB gene. Both the MAOA and MAOB genes segregate as two distinct LD blocks. We found a significant association between two MAOA gene haplotypes and reduced trbc-MAO activity, but no association with depressed state.The MAO locus seems to have an effect on trbc-MAO activity in the study population. The findings suggest incomplete X-chromosome inactivation at this locus. It is plausible that a gene-dosage effect can provide some insight into the greater prevalence of depressed state in females than males.Monoamine oxidase A (MAOA) and B (MAOB) are enzymes that deaminate monoamines such as serotonin, dopamine and noradrenaline. The genes encoding MAOA and B are located on the X chromosome in a tail-to-tail orientation and separated by approximately 20 kilobases (kb) [1,2]. Although MAOA and MAOB span 65 kb and 116 kb, respectively, both genes display a high degree of homology and most certainly have a common ancestry [3]. The frequencies of confirmed polymorphisms in the two genes vary widely among different ethnic groups [4-6]. Only two common haplotype variants of the MAOA locus were found among individuals of northern European ancestry [5].Both enzymes are localized in the outer mitochondrial membrane [7]. They are also present in glial cells [8], although MAOA is less expressed than MAOB [9]. The enzymes differ in their expression patterns not only peripherally in the body but also in the central nervous system (CNS) [10]. MAOB is the only form that is expressed in human blood cells. MAOA is primarily expressed in catecholaminergic neurons in the human brain [10,11], whereas MAOB is expressed in serotonergic [10] and histaminergic neurons [8]. The two MAO-enzymes also differ on substrate preferences; MAOA preferentially metabolizes serotonin and norepinephrine while MAOB has a much higher affinity for phenylethylamine [12,13] and benzylamine [14].Thrombocyte-MAO activity (Trbc-MAO) has been associated with cerebrospi
The Proteogenomic Mapping Tool
William S Sanders, Nan Wang, Susan M Bridges, Brandon M Malone, Yoginder S Dandass, Fiona M McCarthy, Bindu Nanduri, Mark L Lawrence, Shane C Burgess
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-115
Abstract: The Proteogenomic Mapping Tool includes a Java implementation of the Aho-Corasick string searching algorithm which takes as input standardized file types and rapidly searches experimentally observed peptides against a given genome translated in all 6 reading frames for exact matches. The Java implementation allows the application to scale well with larger eukaryotic genomes while providing cross-platform functionality.The Proteogenomic Mapping Tool provides a standalone application for mapping peptides back to their source genome on a number of operating system platforms with standard desktop computer hardware and executes very rapidly for a variety of datasets. Allowing the selection of different genetic codes for different organisms allows researchers to easily customize the tool to their own research interests and is recommended for anyone working to structurally annotate genomes using MS derived proteomics data.Expressed proteins provide experimental evidence that genes in the genome are being transcribed and translated to produce a protein product. Recently, a new structural genome annotation method, proteogenomic mapping, has been developed that uses identified peptides from experimentally derived proteomics data to identify functional elements in genomes and to improve genome annotation [1,2]. Initially used for the structural annotation of prokaryotic genomes, proteogenomic mapping is rapidly gaining traction in eukaryotic genome annotation projects with larger genomes as a complementary method [3,4].Proteogenomic mapping can identify potential new genes or corrections to the boundaries of predicted genes by using peptide matches against the genome that do not match against the predicted proteome to generate expressed Protein Sequence Tags (ePSTs) [2]. When aligned with the genome and combined with the published structural annotation, these ePSTs are indicative of translation throughout the genome and can serve to supplement traditional structural genome ann
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