Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Analysis of Genomic Copy Number Variation in Equine Recurrent Airway Obstruction

ABSTRACT: Sixteen severly RAO (Recurrent Airway Obstruction) affected horses were studied. All RAO affected male horses were hybridized with GSM1332974 (Thoroughbred male 1, male reference), and the female horses were with GSM1332975 (Thoroughbred female 2, female reference). Finally results are compared with GSE55266 and two other control horses (SPA-H1-3 and SPA-H1-5) and relatively novel RAO CNVs were reported.

ORGANISM(S): Equus caballus

PROVIDER: GSE63033 | GEO | 2016/03/07

SECONDARY ACCESSION(S): PRJNA266527

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Comparison of gene expression profile of bronchoalveolar lavage fluid (BAL) from RAO- and IAD- affected horses with respect to control animals

Project description:An equine immuno-specific oligo microarray platform was designed to evidence differences in gene expression profiles in BAL fluid samples from eight (8) RAO-affected horses, ten (10) IAD-affected horses and seven (7) control animals. An unpaired t test was performed using the software Significant Analysis of Microarrays (SAM). 1763 and 379 genes were found differentially expressed between RAO and IAD horses respectively vs. controls.

2021-12-30 | GSE155084 | GEO

Genome-wide detection by array CGH of copy number variations among diverse horse breeds

Project description:Investigating genome-wide characteristics of CNVs in 6 horses representing 6 distinct breeds by using the aCGH method and performed GO and KEGG analysis for the CNVs genes.This result is an important complement to the mapping of horse whole-genome CNVs and helpful to study plateau horsesM-bM-^@M-^Y adaption to the plateauM-bM-^@M-^Ys environment. Comparison Mongolia horse , Abaga horse, Hequ horse, Kazakh horse, Debao pony, Thoroughbred with Thoroughbred

2014-02-26 | E-GEOD-52504 | biostudies-arrayexpress

Equine gluteal muscle LC-MSMS: Thoroughbred

Project description:Recurrent exertional rhabdomyolysis (RER) develops in 5-10% of Thoroughbred racehorses. High-stress environments, nervous temperament, and diet influence the presentation of RER. RER-susceptibility is associated with alterations in intramuscular Ca2+ regulation with detrimental effects on mitochondria. Our study aims to determine underlying molecular drivers influencing RER-susceptibility by comparing the muscle proteome of control, RER-susceptible, and RER horses treated with dantrolene. Animals used in this study were Thoroughbred mares in race training between episodes of RER.

2021-01-07 | PXD020100 | Pride

Project description:rHuEPO micro-dosing Thoroughbred Horses

| PRJNA769732 | ENA

Analysis of Unannotated Equine Transcripts Identified by mRNA Sequencing

Project description:Horse-specific genes are not readily identified from available equine EST/cDNA resources due to relatively limited coverage. In addition, equine gene sets predicted in silico by Ensembl and NCBI will not identify horse specific genes since they rely on homology-based projection of gene structure annotation from other species. In this study, RNA-seq of 8 equine RNA samples representing 6 distinct tissues was performed and used to improve and refine equine gene structure annotation. The samples and RNA were collected as part of the related study E-GEOD-21925 and are described in Coleman et al 2010. Anim Genet 41 Suppl 2: 121-30 (PMID: 21070285). The RNA from these samples was re-sequenced in this experiment. The tissues were i). the articular cartilage and synovial membrane samples from a 3-year-old male pony. The left carpal joints received four LPS injections (0.5 ng) over 8 days, while the right carpal joints received control injections of PBS. ii) A cerebellum sample was collected from a 2-year-old female thoroughbred. iii) A testis sample from a 4-year-old thoroughbred. iv) A placental villous sample collected immediately post-partum from a full-term female thoroughbred foal. v) A whole embryo sample was obtained from a 34-day-old male thoroughbred conceptus. The embryo, cerebellum, testis and placental samples were of apparent normal gross morphology.

2013-07-30 | E-GEOD-46858 | biostudies-arrayexpress

Whole genome sequencing horses

Project description:Whole genome sequencing of Thoroughbred horses

| PRJEB60529 | ENA

Characterization of trotter horses urine metabolome by means of proton nuclear magnetic resonance spectroscopy

Project description:BACKGROUND: Metabolomics has been recognized as a powerful approach for disease screening. In order to highlight potential health issues in subjects, a key factor is the possibility to compare quantitatively the metabolome of their biofluids with reference values from healthy individuals. Such efforts towards the systematic characterization of the metabolome of biofluids in perfect health conditions, far from concluded for humans, have barely begun on horses. </br> OBJECTIVES: The present work attempts, for the first time, to give reference quantitative values for the molecules mostly represented in the urine metabolome of horses at rest and under light training, as observable by 1H-NMR. </br> METHODS: The metabolome of ten trotter horses, four male and six female, ranging from 3 to 8 years of age, has been observed by 1H-NMR spectroscopy before and after three training sessions. </br> RESULTS: We could characterize and quantify 54 molecules in trotter horse urine, originated from diet, protein digestion, energy generation or gut-microbial co-metabolism. </br> CONCLUSION: We were able to describe how gender, age and exercise affected their concentration, by means of a two steps protocol based on univariate and robust principal component analysis.

2019-02-12 | MTBLS675 | MetaboLights

Watanabe2018_Simple markov model

Project description:Simple Markov model. There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: 0.01; Healthy to Sick: 0.2 for Male and 0.1 for Female; Sick to Healthy: 0.1; Sick to Dead: 0.3. The transition probability now depends on the cohort (Male or Female) and can be expressed as a function of a Boolean covariate Male. Initial conditions: Healthy = (50 Male, 50 Female), Sick = (0,0) and Dead = (0,0). Output: Number of men and women in each disease state for years 1-10.

2019-01-30 | MODEL1803120004 | BioModels

Next Generation Sequencing Transcriptomic Analysis (RNA-seq) of Thoroughbred horse at rest, after exercise and after training

Project description:A single bout of exercise induces changes in gene expression in skeletal muscle. Regular exercise results in an adaptive response involving changes in muscle architecture and biochemistry, and is an effective way to manage and prevent common human diseases such as obesity, cardiovascular disorders and type II diabetes. Our study is a transcriptome-wide analysis of skeletal muscle tissue in a large cohort of untrained Thoroughbred horses before and after a bout of high-intensity exercise and again after an extended period of training. We hypothesized that regular high-intensity exercise training primes the transcriptome for the demands of high-intensity exercise.

2018-08-07 | E-MTAB-5447 | biostudies-arrayexpress

Watanabe2018_State Transition Model with Treatment and Costs

Project description:Model with functions depending on Age, Male, BP (Blood Pressure). There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: Age/1000; Healthy to Sick: According to function F1 depending on Age and Male and BP; Sick to Healthy: 0.1; Sick to Dead: according to function F2 depending on Age and Male. Pre-Transition Rules: Age increased by 1 and BP by Age/10 each simulation cycle. Post-Transition Rules: Treatment = BP>140 , becomes 1 when BP crosses 140 threshold; BP =BP-Treatment*10 , meaning a drop of 10 once treatment is applied; CostThisYear = Age + \Treatment*10 , cost depends on age and if treatment was taken; Cost= Cost + CostThisYear , it accumulates cost over time. Initial conditions: Healthy = (50 Male, 50 Female with Age =1,2,...,50 for each individual), BP =120, Sick = (0,0) and Dead = (0,0). Output: Number of men and women in each disease state for years 1-10 and their ages and costs in each state. A stratified report by male and female and young – up to age 30 and old above age 30 is produced.

2019-01-30 | MODEL1803120006 | BioModels

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data