• Web-based Tools for the Analysis of DNA Microarrays

      Geeleher, P.; Golden, A.; Hinde, J.; Morris, Dermot G. (Teagasc, 2008-01-01)
      DNA microarrays are widely used for gene expression profiling. Raw data resulting from microarray experiments, however, tends to be very noisy and there are many sources of technical variation and bias. This raw data needs to be quality assessed and interactively preprocessed to minimise variation before statistical analysis in order to achieve meaningful result. Therefore microarray analysis requires a combination of visualisation and statistical tools, which vary depending on what microarray platform or experimental design is used.Bioconductor is an existing open source software project that attempts to facilitate analysis of genomic data. It is a collection of packages for the statistical programming language R. Bioconductor is particularly useful in analyzing microarray experiments. The problem is that the R programming language’s command line interface is intimidating to many users who do not have a strong background in computing. This often leads to a situation where biologists will resort to using commercial software which often uses antiquated and much less effective statistical techniques, as well as being expensively priced. This project aims to bridge this gap by providing a user friendly web-based interface to the cutting edge statistical techniques of Bioconductor.
    • Whole blood gene expression profiling of neonates with confirmed bacterial sepsis

      Dickinson, Paul; Smith, Claire L.; Forster, Thorsten; Craigon, Marie; Ross, Alan J.; Khondoker, Mizan R; Ivens, Alasdair; Lynn, David J.; Orme, Judith; Jackson, Allan; et al. (ElsevierDickinson, P., Smith, C., Forster, T., Craigon, M., Ross, A., Khondoker, M., Ivens, A., Lynn, D., Orme, J., Jackson, A., Lacaze, P., Flanagan, K., Stenson, B. and Ghazal, P. Whole blood gene expression profiling of neonates with confirmed bacterial sepsis. Genomics Data, [online] 3, pp.41-48. Available at: https://dx.doi.org/10.1016/j.gdata.2014.11.003 [Accessed 1 Aug. 2019]., 2014-11-15)
      Neonatal infection remains a primary cause of infant morbidity and mortality worldwide and yet our understanding of how human neonates respond to infection remains incomplete. Changes in host gene expression in response to infection may occur in any part of the body, with the continuous interaction between blood and tissues allowing blood cells to act as biosensors for the changes. In this study we have used whole blood transcriptome profiling to systematically identify signatures and the pathway biology underlying the pathogenesis of neonatal infection. Blood samples were collected from neonates at the first clinical signs of suspected sepsis alongside age matched healthy control subjects. Here we report a detailed description of the study design, including clinical data collected, experimental methods used and data analysis workflows and which correspond with data in Gene Expression Omnibus (GEO) data sets (GSE25504). Our data set has allowed identification of a patient invariant 52-gene classifier that predicts bacterial infection with high accuracy and lays the foundation for advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.
    • Whole blood gene expression profiling of neonates with confirmed bacterial sepsis

      Dickinson, Paul; Smith, Claire L.; Forster, Thorsten; Craigon, Marie; Ross, Alan J.; Khondoker, Mizan R.; Ivens, Alasdair; Lynn, David J.; Orme, Judith; Jackson, Allan; et al. (Elsevier BV, 2014-11-15)
      Neonatal infection remains a primary cause of infant morbidity and mortality worldwide and yet our understanding of how human neonates respond to infection remains incomplete. Changes in host gene expression in response to infection may occur in any part of the body, with the continuous interaction between blood and tissues allowing blood cells to act as biosensors for the changes. In this study we have used whole blood transcriptome profiling to systematically identify signatures and the pathway biology underlying the pathogenesis of neonatal infection. Blood samples were collected from neonates at the first clinical signs of suspected sepsis alongside age matched healthy control subjects. Here we report a detailed description of the study design, including clinical data collected, experimental methods used and data analysis workflows and which correspond with data in Gene Expression Omnibus (GEO) data sets (GSE25504). Our data set has allowed identification of a patient invariant 52-gene classifier that predicts bacterial infection with high accuracy and lays the foundation for advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.
    • Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle

      Doran, Anthony G; Berry, Donagh; Creevey, Christopher J.; Department of Agriculture, Food and the Marine, Ireland; Teagasc Walsh Fellowship Programme; Science Foundation Ireland; Irish Cattle Breeding Federation; RSF-06-0353; 11/S/112; 2009183; et al. (Biomed Central, 2014-10)
      Background Four traits related to carcass performance have been identified as economically important in beef production: carcass weight, carcass fat, carcass conformation of progeny and cull cow carcass weight. Although Holstein-Friesian cattle are primarily utilized for milk production, they are also an important source of meat for beef production and export. Because of this, there is great interest in understanding the underlying genomic structure influencing these traits. Several genome-wide association studies have identified regions of the bovine genome associated with growth or carcass traits, however, little is known about the mechanisms or underlying biological pathways involved. This study aims to detect regions of the bovine genome associated with carcass performance traits (employing a panel of 54,001 SNPs) using measures of genetic merit (as predicted transmitting abilities) for 5,705 Irish Holstein-Friesian animals. Candidate genes and biological pathways were then identified for each trait under investigation. Results Following adjustment for false discovery (q-value < 0.05), 479 quantitative trait loci (QTL) were associated with at least one of the four carcass traits using a single SNP regression approach. Using a Bayesian approach, 46 QTL were associated (posterior probability > 0.5) with at least one of the four traits. In total, 557 unique bovine genes, which mapped to 426 human orthologs, were within 500kbs of QTL found associated with a trait using the Bayesian approach. Using this information, 24 significantly over-represented pathways were identified across all traits. The most significantly over-represented biological pathway was the peroxisome proliferator-activated receptor (PPAR) signaling pathway. Conclusions A large number of genomic regions putatively associated with bovine carcass traits were detected using two different statistical approaches. Notably, several significant associations were detected in close proximity to genes with a known role in animal growth such as glucagon and leptin. Several biological pathways, including PPAR signaling, were shown to be involved in various aspects of bovine carcass performance. These core genes and biological processes may form the foundation for further investigation to identify causative mutations involved in each trait. Results reported here support previous findings suggesting conservation of key biological processes involved in growth and metabolism.
    • Within- and across-breed imputation of high-density genotypes in dairy and beef cattle from medium- and low-density genotypes

      Berry, Donagh; McClure, Matthew C.; Mullen, Michael P. (Wiley, 2013-12-05)
      The objective of this study was to evaluate, using three different genotype density panels, the accuracy of imputation from lower- to higher-density genotypes in dairy and beef cattle. High-density genotypes consisting of 777 962 single-nucleotide polymorphisms (SNP) were available on 3122 animals comprised of 269, 196, 710, 234, 719, 730 and 264 Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental bulls, respectively. Three different genotype densities were generated: low density (LD; 6501 autosomal SNPs), medium density (50K; 47 770 autosomal SNPs) and high density (HD; 735 151 autosomal SNPs). Imputation from lower- to higher-density genotype platforms was undertaken within and across breeds exploiting population-wide linkage disequilibrium. The mean allele concordance rate per breed from LD to HD when undertaken using a single breed or multiple breed reference population varied from 0.956 to 0.974 and from 0.947 to 0.967, respectively. The mean allele concordance rate per breed from 50K to HD when undertaken using a single breed or multiple breed reference population varied from 0.987 to 0.994 and from 0.987 to 0.993, respectively. The accuracy of imputation was generally greater when the reference population was solely comprised of the breed to be imputed compared to when the reference population comprised of multiple breeds, although the impact
    • β-Defensins: Farming the Microbiome for Homeostasis and Health

      Meade, Kieran G; O'Farrelly, Cliona; Department of Agriculture, Food and the Marine; 11/S/104 (Frontiers, 2019-01-15)
      Diverse commensal populations are now regarded as key to physiological homeostasis and protection against disease. Although bacteria are the most abundant component of microbiomes, and the most intensively studied, the microbiome also consists of viral, fungal, archael, and protozoan communities, about which comparatively little is known. Host-defense peptides (HDPs), originally described as antimicrobial, now have renewed significance as curators of the pervasive microbial loads required to maintain homeostasis and manage microbiome diversity. Harnessing HDP biology to transition away from non-selective, antibiotic-mediated treatments for clearance of microbes is a new paradigm, particularly in veterinary medicine. One family of evolutionarily conserved HDPs, β-defensins which are produced in diverse combinations by epithelial and immune cell populations, are multifunctional cationic peptides which manage the cross-talk between host and microbes and maintain a healthy yet dynamic equilibrium across mucosal systems. They are therefore key gatekeepers to the oral, respiratory, reproductive and enteric tissues, preventing pathogen-associated inflammation and disease and maintaining physiological normality. Expansions in the number of genes encoding these natural antibiotics have been described in the genomes of some species, the functional significance of which has only recently being appreciated. β-defensin expression has been documented pre-birth and disruptions in their regulation may play a role in maladaptive neonatal immune programming, thereby contributing to subsequent disease susceptibility. Here we review recent evidence supporting a critical role for β-defensins as farmers of the pervasive and complex prokaryotic ecosystems that occupy all body surfaces and cavities. We also share some new perspectives on the role of β-defensins as sensors of homeostasis and the immune vanguard particularly at sites of immunological privilege where inflammation is attenuated.