Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Comparative study of Latvian and Ukrainian breeds of sheep by SNP markers involved in the formation of economically useful traits

ABSTRACT: The aim of this project is to develop and evaluate the genetic tool for predicting economically important traits in different sheep breeds. Methodology is based on the analysis of single nucleotide polymorphisms in the myostatin (MSTN) gene. The ultimate goal is to create an effective method for animal selection based on genotype.

INSTRUMENT(S): -

ORGANISM(S): Ovis Aries

SUBMITTER: EBI

PROVIDER: PRJEB98010 | EVA | 2025-09-25

REPOSITORIES: EVA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	mstn_intron1_ucm_sheep.accessioned.vcf.gz	Vcf
	mstn_intron1_ucm_sheep.accessioned.vcf.gz.csi	Vcf
	mstn_intron1_ucm_sheep.vcf.csi	Other
	mstn_intron1_ucm_sheep.vcf.gz	Vcf

Items per page:

1 - 4 of 4

Similar Datasets

Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept

Project description:Alefacept treatment is highly effective in a select group patients with moderate-to-severe psoriasis, and is an ideal candidate to develop systems to predict who will respond to therapy. A clinical trial of 22 patients with moderate to severe psoriasis treated with alefacept (7.5mg weekly i.v. x12 weeks) was conducted in 2002-2003, as a mechanism of action study. Patients were classified as responders or non-responders to alefacept based on histological criteria. Microarray data from PBMCs of 16 of these patients was analyzed to generate a treatment response classifier. We used a discriminant analysis method that performs sample classification from gene expression data, via nearest shrunken centroid method''. A disease response classifier using 23 genes was created to accurately predict response to alefacept (12.3% error rate in favour of responders). This preliminary study may provide a useful tool to predict response of psoriatic patients with alefacept. Keywords: class prediction Microarray data from 16 patients was analyzed to generate a treatment response classifier. We used a discriminant analysis method that performs sample classification from gene expression data, via nearest shrunken centroid method.

2010-10-02 | E-GEOD-18948 | biostudies-arrayexpress

Identification of the human DPR core promoter element using machine learning

Project description:The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to the initiation of DNA transcription, but the downstream core promoter in humans has been difficult to understand. Here we analyse the human Pol II core promoter and use machine learning to generate predictive models for the downstream core promoter region (DPR) and the TATA box. We developed a method termed HARPE (high-throughput analysis of randomized promoter elements) to create hundreds of thousands of DPR (or TATA box) variants, each with known transcriptional strength. We then analysed the HARPE data by support vector regression (SVR) to provide comprehensive models for the sequence motifs, and found that the SVR-based approach is more effective than a consensus-based method for predicting transcriptional activity. These results show that the DPR is a functionally important core promoter element that is widely used in human promoters. Notably, there appears to be a duality between the DPR and the TATA box, as many promoters contain one or the other element. More broadly, these findings show that functional DNA motifs can be identified by machine learning analysis of a comprehensive set of sequence variants.

2020-06-04 | GSE139635 | GEO

Microarray-based comparative genomic profiling of reference strains of Actinobacillus pleuropneumoniae

Project description:Microarray-based comparative genomic hybridizations were used to estimate whole genomic diversity of representative Actinobacillus pleuropneumoniae strains. Our goal was to identify conserved genes, especially those predicted to encode outer membrane proteins and lipoproteins because of their potential for the development of more effective vaccines. Keywords: Comparative Genomic Hybridisation

2009-02-03 | GSE11921 | GEO

Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept

2009-12-01 | GSE18948 | GEO

A spatial transcriptomics based Label-free Method for Assessment of Human Stem Cell Distribution and Effects in a Mouse Model of Lung Fibrosis

Project description:Recently, cell therapy has emerged as a promising treatment option for various disorders. Given the intricate mechanisms of action (MOA) and heterogenous distribution in target tissues inherent to cell therapy, it is necessary to develop more sophisticated, unbiased approaches to evaluate the distribution of administered cells and the molecular changes at a microscopic level. This study introduces a label-free approach for assessing the tissue distribution of administered human mesenchymal stem cells (hMSCs) and their MOA, leveraging spatially resolved transcriptomics (ST) analysis. The hMSCs were introduced into a mouse model with lung fibrosis, followed by the manipulation of ST to visualize the spatial distribution of hMSCs within the tissue. This was achieved by capitalizing on interspecies transcript differences between human and mouse. Furthermore, the method allowed for the examination of molecular changes associated with the spatial distribution of hMSCs. Therefore, our method has the potential to serve as an effective tool for various cell-based therapeutic agents.

2024-05-01 | GSE253378 | GEO

Microarray-based comparative genomic profiling of reference strains of Actinobacillus pleuropneumoniae

2009-02-02 | E-GEOD-11921 | biostudies-arrayexpress

Low-cost, low-bias and low-input RNA-seq with High Experimental Verifiability based on Semiconductor Sequencing

Project description:We developed a new method on sequencing low-input RNA. This method shows much low-bias with the advantage of semiconductor while competing with smart-seq2. In order to analyze the low-input RNA datasets sensitively, we also develop FANSe2splice with high experimental verification rate as the analysis tool in our method.

2017-06-12 | GSE87660 | GEO

Homo sapiens

Project description:RED-ML: a novel, effective RNA editing detection method based on machine learning

| PRJNA373807 | ENA

Computational frameworks for predicting protein interactions via single-cell proximity sequencing

Project description:Proximity sequencing (Prox-seq) is a method that can simultaneously measure gene expression, protein abundance and protein complex information in single cells. By making use of proximity information from proteins, Prox-seq can infer surface protein complexes at the single-cell level. Previously, we proposed a statistical method for predicting protein complex abundance in single cells from Prox-seq data. However, it is difficult to experimentally validate the prediction results. Here, we first introduces a physical model which enables simulation of Prox-seq data. The simulated data is then used to validate the statistical method that we proposed previously, and to understand how protein abundance can influence protein complex prediction. Second, we propose a modification to Prox-seq, and use the new measurements from the modification to develop a second, independent method for predicting protein complexes. We demonstrate that on experimental data, the two methods generally agree with each other, with the second method generally producing more accurate protein complex abundance results. Thus, we present a simple way to investigate the behavior of Prox-seq data under various biological conditions, and independent methods for predicting and quantifying protein complexes.

2022-02-07 | GSE196130 | GEO

GPTransformer: A transformer-based deep learning method for predicting Fusarium related traits in barley.

Project description:GPTransformer: A transformer-based deep learning method for predicting Fusarium related traits in barley.

| PRJNA780317 | ENA