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Methods in molecular exercise physiology
Published in Adam P. Sharples, James P. Morton, Henning Wackerhage, Molecular Exercise Physiology, 2022
Adam P. Sharples, Daniel C. Turner, Stephen Roth, Robert A. Seaborne, Brendan Egan, Mark Viggars, Jonathan C. Jarvis, Daniel J. Owens, Jatin G. Burniston, Piotr P. Gorski, Claire E. Stewart
As discussed in Chapter 1, NGS is now becoming much more common, yet thus far, is not as widely used in exercise genetics due to the cost and the requirement of large sample sizes. Indeed, NGS is perhaps being more frequently used for the analysis of RNA/gene expression analysis e.g. RNA-sequencing (RNA-seq) where lower sample number is feasible, as discussed below in this chapter. Therefore, in exercise genetics, researchers have been typically focused on a smaller number of specific alleles of interest, perhaps numbering in the thousands. Researchers also often rely on microarray technology to determine a large number of alleles in a single individual at one time. This is often seen in genome-wide association studies (GWAS) in which researchers use SNP chips (microarrays) that measure hundreds of thousands of SNPs all over the human genome and look for associations with the trait of interest (6). Whether using a targeted microarray of many alleles specifically chosen by researchers or using commercially available chips with thousands of alleles across the genome to allow more unbiased exploratory analyses, the microarray technique allows for a small amount of gDNA that provides a considerable amount of information in a single assay. The greater task with microarray analysis (or sequencing) is the bioinformatics and statistical procedures required later to properly analyse the data. DNA microarrays are also covered in more detail below in sections for DNA methylation/methylome analysis and RNA/gene expression/transcriptome analysis.
High-Dimensional Data Analysis
Published in Atanu Bhattacharjee, Bayesian Approaches in Oncology Using R and OpenBUGS, 2020
Data science is an emerging field in oncology. Due to the availability of huge gene data, it can not be avoided. Always analyst faces difficulties to deal with high-dimensional data where the feature dimension p grows exponentially. The sample size is fixed at n, but for some α ∈ (0,1/2). Currently, modern technology, like microarray analysis and next-generation sequencing data is available to generate genomics data. Large amounts of data containing more than thousands of variables are available. It is common to collect gene expressions from p > 20,000 genomics studies. These genomics data are having a large sample size and a limited size p parameter. The large size gene data analysis is a challenge. Consideration of not relevant features in the statistical literature indeed may provide undesirable computing challenges. The challenges are handled by variable screening and selection techniques. Among all methodological development, the penalized approach is favored by researchers with K-fold cross-validation.
Disease Prediction and Drug Development
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
Microarray analysis helps in the gene-expression analysis. However, most diseases are caused by aberrations in metabolic or signaling pathways. The variations are caused by insertion (or deletion) of genes, a change in reaction-rates due to variations in gene-expressions and mutations within the genes. The cluster analysis of gene-expression data derives the set of genes that are co-expressed during the disease-state. The information about how these genes are involved in an affected signaling pathway cannot be determined in a straightforward manner by simple gene-expression analysis. The relationship between the expressed genes in a signaling pathway involves the study of a sequence of protein–protein interactions and timing behaviors.
A pilot study of differential gene expressions in patients with cough variant asthma and classic bronchial asthma
Published in Journal of Asthma, 2022
Guanghong Zhou, Qingcui Zeng, Wei Wei, Hong Teng, Chuntao Liu, Zhongwei Zhou, Binmiao Liang, Huaicong Long
Asthma is caused by a complex interaction between genes and the environment, and gene expression is regulated by epigenetic modifications through gene and environment interactions. Are there differences in gene expression between CVA and classical asthma? Comparative studies between classical asthma and CVA may be helpful for further understanding the pathogenesis of asthma. Whole-genome microarray analysis is an important technique in the field of genetic research, and it possesses many advantages of which the greatest is the ability to improve traditional experiments by allowing the observation of differences in one or multiple genes in a single test (8). Here, we first performed a pilot study on gene expression differences among CVA patients, asthmatic patients and healthy adults by the whole-genome microarray technique. Then, we screened candidate genes by enrichment analysis and validated them by real-time PCR.
Bioinformatics analysis reveals the landscape of immune cell infiltration and immune-related pathways participating in the progression of carotid atherosclerotic plaques
Published in Artificial Cells, Nanomedicine, and Biotechnology, 2021
Liao Tan, Qian Xu, Ruizheng Shi, Guogang Zhang
In recent years, advances in gene chip technology have enabled the analysis of changes in mRNA levels between different samples, which has helped identify novel and important genes when studying disease mechanisms. However, traditional microarray analysis is often restricted to differentially expressed genes (DEGs). Since carotid artery disease is closely associated with immune cells and the inflammatory system, a more comprehensive bioinformatics analysis of immune-related pathways should be applied. In this study, two microarray datasets downloaded from the Gene Expression Omnibus (GEO) were used to screen for enrichment in immune-related pathways and differently infiltrated immune cells. Gene Set Enrichment Analysis (GSEA) was used to analyse enriched gene sets, Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways, and immunological signatures account for all expression data rather than DEGs alone. Finally, CIBERSORT was used to reconstruct the state of immune cell infiltration in carotid atherosclerotic plaques at different stages.
Potential therapeutic drugs for ischemic stroke based on bioinformatics analysis
Published in International Journal of Neuroscience, 2019
Lingyan Zhou, Yuan Wang, Kun Wang, Jing Wang, Aijun Ma, Xudong Pan
With the rapid development of microarray technology, more and more genetic analyses of IS have been reported. There was much evidence that many genetic factors were involved in the acute phase of IS in animal models [6,7]. In Zhang et al.'s study [8] of cerebral artery occlusion, MMP11, E2F, INI1 and PAK1 were identified as biomarkers. Ramos-Cejudo et al. [9] study showed that at 24 h and 3 d the expression of many neurovascular unit development genes was altered in animals, such as LINGO1, HES2, NOGO-A and OLIG2. However, much less microarray analysis of IS has been carried out in humans. In the early 2000s, Moore et al. [10] reported for the first time on the microarray analysis of the whole genome in human blood related to IS, who found 22 differentially expressed genes were obtained. Through the study of Tang et al. [11], many inflammatory protein markers related to post-stroke have been found, including MMP9, coagulation factor V and many others. In the present study, gene chip was downloaded and analyzed to explore the molecular mechanism in the progress of IS, identify possible gene targets and relevant drug molecules in the pathogenesis of IS and provide new fields for the treatment of IS in the future .