scholarly journals Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset

BMC Genomics ◽  
2013 ◽  
Vol 14 (S2) ◽  
Author(s):  
Koki Tsuyuzaki ◽  
Daisuke Tominaga ◽  
Yeondae Kwon ◽  
Satoru Miyazaki
Keyword(s):  
Differentially Expressed Genes ◽  
Large Scale ◽  
Differentially Expressed

scholarly journals Large-Scale Metadata Analysis of Ovary Based Multi-Omics Datasets for Understanding the Genes Regulating Litter Size

2020 ◽  
Author(s):  
Ayyappa Kumar Sista Kameshwar ◽  
Julang Li
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Litter Size ◽  
Large Scale ◽  
Expression Patterns ◽  
Genetic Selection ◽  
Follicular Development ◽  
Oocyte Quality ◽  
Differentially Expressed ◽  
Metadata Analysis

Abstract Background : Litter size is a very important production index in the livestock industry, which is controlled by various complex physiological processes. To understand and reveal the common gene expression patterns involved in controlling prolificacy, we have performed a large-scale metadata analysis of five genome-wide transcriptome datasets of pig and sheep ovary samples obtained from high and low litter groups, respectively. We analyzed separately each transcriptome dataset using GeneSpring v14.8 software by implementing standard, generic analysis pipelines and further compared the list of most significant and differentially expressed genes obtained from each dataset to identify genes that are found to be common and significant across all the studies. Results : We have observed a total of 62 differentially expressed genes common among more than two gene expression datasets. The KEGG pathway analysis of most significant genes has shown that they are involved in metabolism, the biosynthesis of lipids, cholesterol and steroid hormones, immune system, cell growth and death, cancer-related pathways and signal transduction pathways. Of these 62 genes, we further narrowed the list to the 25 most significant genes by focusing on the ones with fold change >1.5 and p<0.05. These genes are CYP11A1, HSD17B2, STAR, SCARB1, IGSF8, MSMB, SERPINA1 , FAM46C, HEXA, PTTG1, TIMP1, FAM167B, CCNG1, FAXDC2, HMGCS1, L2HGDH, Lipin1, MME, MSMO1, PARM1, PTGFR, SLC22A4, SLC35F5, CCNA2, CENPU, CEP55, RASSF2, and SLC16A3 . Conclusions : Interestingly, comparing the list of genes with the list of genes obtained from our literature search analysis, we found only three genes in common. These genes are HEXA, PTTG1, and TIMP1. Our finding points to the potential of a few genes that may be important for ovarian follicular development and oocyte quality. Future studies revealing the function of these genes will further our understanding of how litter size is controlled in the ovary while also providing insight on genetic selection of high litter gilts.

scholarly journals Evaluation of Combining Several Statistical Methods with a Flexible Cutoff for Identifying Differentially Expressed Genes in Pairwise Comparison of EST Sets

2008 ◽  
Vol 2 ◽  
pp. BBI.S431 ◽  
Author(s):  
Angelica Lindlöf ◽  
Marcus Bräutigam ◽  
Aakash Chawade ◽  
Olof Olsson ◽  
Björn Olsson
Keyword(s):  
Statistical Methods ◽  
Differentially Expressed Genes ◽  
Relative Frequency ◽  
Large Scale ◽  
Pairwise Comparison ◽  
Differentially Expressed ◽  
Biological Processes ◽  
Exact Test ◽  
The Difference ◽  
Derived Data

The detection of differentially expressed genes from EST data is of importance for the discovery of potential biological or pharmaceutical targets, especially when studying biological processes in less characterized organisms and where large-scale microarrays are not an option. We present a comparison of five different statistical methods for identifying up-regulated genes through pairwise comparison of EST sets, where one of the sets is generated from a treatment and the other one serves as a control. In addition, we specifically address situations where the sets are relatively small (~2,000– 10,000 ESTs) and may differ in size. The methods were tested on both simulated and experimentally derived data, and compared to a collection of cold stress induced genes identified by microarrays. We found that combining the method proposed by Audic and Claverie with Fisher's exact test and a method based on calculating the difference in relative frequency was the best combination for maximizing the detection of up-regulated genes. We also introduced the use of a flexible cutoff, which takes the size of the EST sets into consideration. This could be considered as an alternative to a static cutoff. Finally, the detected genes showed a low overlap with those identified by microarrays, which indicates, as in previous studies, low overall concordance between the two platforms.

scholarly journals UGM: a more stable procedure for large-scale multiple testing problems, new solutions to identify oncogene

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Chengyou Liu ◽  
Leilei Zhou ◽  
Yuhe Wang ◽  
Shuchang Tian ◽  
Junlin Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
High Throughput ◽  
Multiple Testing ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Type I Error ◽  
Differentially Expressed ◽  
Type I ◽  
Brca1 Brca2

AbstractVariations of gene expression levels play an important role in tumors. There are numerous methods to identify differentially expressed genes in high-throughput sequencing. Several algorithms endeavor to identify distinctive genetic patterns susceptable to particular diseases. Although these processes have been proved successful, the probability that the number of non-differentially expressed genes measured by false discovery rate (FDR) has a large standard deviation, and the misidentification rate (type I error) grows rapidly when the number of genes to be detected become larger. In this study we developed a new method, Unit Gamma Measurement (UGM), accounting for multiple hypotheses test statistics distribution, which could reduce the dependency problem. Simulated expression profile data and breast cancer RNA-Seq data were utilized to testify the accuracy of UGM. The results show that the number of non-differentially expressed genes identified by the UGM is very close to the real-evidence data, and the UGM also has a smaller standard error, range, quartile range and RMS error. In addition, the UGM can be used to screen many breast cancer-associated genes, such as BRCA1, BRCA2, PTEN, BRIP1, etc., provides better accuracy, robustness and efficiency, the method of identification differentially expressed genes in high-throughput sequencing.

scholarly journals Transcriptomic Analysis Provides Novel Insights into Heat Stress Responses in Sheep

Animals ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 387 ◽  
Author(s):  
Zengkui Lu ◽  
Mingxing Chu ◽  
Qing Li ◽  
Meilin Jin ◽  
Xiaojuan Fei ◽  
...  
Keyword(s):  
Heat Stress ◽  
Differentially Expressed Genes ◽  
Stress Responses ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Transcriptomic Analysis ◽  
Differentially Expressed ◽  
P Value ◽  
Comprehensive Overview

With the intensified and large-scale development of sheep husbandry and global warming, sheep heat stress has become an increasingly important issue. However, little is known about the molecular mechanisms related to sheep responses to heat stress. In this study, transcriptomic analysis of liver tissues of sheep in the presence and absence of heat stress was conducted, with the goal of identifying genes and pathways related to regulation when under such stress. After a comparison with the sheep reference genome, 440,226,436 clean reads were obtained from eight libraries. A p-value ≤ 0.05 and fold change ≥ 2 were taken as thresholds for categorizing differentially expressed genes, of which 1137 were identified. The accuracy and reliability of the RNA-Seq results were confirmed by qRT-PCR. The identified differentially expressed genes were significantly associated with 419 GO terms and 51 KEGG pathways, which suggested their participation in biological processes such as response to stress, immunoreaction, and fat metabolism. This study’s results provide a comprehensive overview of sheep heat stress-induced transcriptional expression patterns, laying a foundation for further analysis of the molecular mechanisms of sheep heat stress.

Characterization of differentially expressed genes in ovarian cancer by cDNA microarrays

2005 ◽  
Vol 15 (1) ◽  
pp. 50-57
Author(s):  
X. Zhang ◽  
J. Feng ◽  
Y. Cheng ◽  
Y. Yao ◽  
X. Ye ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Ovarian Carcinoma ◽  
Differentially Expressed Genes ◽  
Large Scale ◽  
Ovarian Tissue ◽  
Immunohistochemical Analysis ◽  
Cdna Microarrays ◽  
Suppressor Gene ◽  
Differentially Expressed ◽  
Normal Ovarian Tissue

The molecular events leading to the development and progression of ovarian carcinoma are not completely understood. We performed a large-scale survey for the identification of differentially expressed genes between ovarian carcinoma and normal ovarian tissue by using cDNA microarray analysis. We utilized 512 member human novel putative oncogene and tumor suppressor gene cDNA microarrays to study the differences in gene expression between ovarian carcinoma and normal ovarian tissues. Some differentially expressed genes have been further confirmed by immunohistochemical analysis. A total of 39 differentially expressed genes were identified, of which 16 and 23 were specifically expressed in ovarian cancer and normal ovarian tissue, respectively. The comparison of average signal of differentially expressed genes exhibited at least a twofold difference in expression. The differentially expressed genes may be related to the carcinogenesis and progression of the malignant growth. The use of cDNA microarrays allows simultaneous monitor of the expression of many genes, thereby it speeds up the identification of differentially expressed genes. It is essential for further exploration of the mechanisms of the disease.

scholarly journals Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

2021 ◽  
Author(s):  
Yanzhi Ge ◽  
Zuxiang Chen ◽  
Yanbin Fu ◽  
Li Zhou ◽  
Haipeng Xu ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Specific Gene ◽  
Hub Genes

Abstract Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with partially common phenotypes and genotypes. This study aimed to determine the mechanistic similarities and differences between osteoarthritis and rheumatoid arthritis by analyzing the differentially expressed genes and signaling pathways. Microarray data of osteoarthritis and rheumatoid arthritis were obtained from the Gene Expression Omnibus. By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified in synovial membrane samples from patients and healthy donations. Then, the Gene ontology significant functions annotation, Kyoto Encyclopedia of Genes and Genomes pathways and protein-protein interaction network analysis were conducted. Moreover, CIBERSORT was used to further distinguish OA and RA in immune infiltration. Finally, animal experimentation was conducted and the establishment of model, which was verified using PCR in the mouse. As an overlapping process, we identified 1116 DEGs between OA and RA. It was indicated that specific gene signatures differed significantly between OA and RA connected with the distinct pathways. Of identified DEGs, 9 immune cell types among 22 were identified to distinguish from each other. The qRT-PCR result showed that the eight-tenths expression levels of the hub genes were significantly increased in OA samples (P < 0.05). This large-scale gene expression study provided new insights for disease-associated genes and molecular mechanisms as well as their associated function in osteoarthritis and rheumatoid arthritis, which simultaneously offer a new direction for biomarker development and the distinguishment of gene-level mechanisms between osteoarthritis and rheumatoid arthritis.

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