scholarly journals An integrated approach in gene-expression landscape profiling to identify housekeeping and tissue-specific genes in cattle

2021 ◽  
Vol 61 (16) ◽  
pp. 1643
Author(s):  
Peng Li ◽  
Yun Zhu ◽  
Xiaolong Kang ◽  
Xingang Dan ◽  
Yun Ma ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Large Scale ◽  
Gene Expression Analysis ◽  
Housekeeping Genes ◽  
Analysis Tool ◽  
Expression Data ◽  
Rna Seq ◽  
Web Based ◽  
Tissue Specific

Context High-throughput transcriptome sequencing (RNA-Seq) has been widely applied in cattle studies. Public databases such as the National Center for Biotechnology Information (NCBI) contain large collections of gene expression data from various cattle tissues that can be used in gene expression analysis research Aims This study was conducted to investigate patterns of transcriptome variation across tissues of cattle through large-scale identification of housekeeping genes (i.e. those crucial to maintaining basic cellular activity) and tissue-specific genes in cattle tissues. Methods Using data available in the NCBI Sequence Read Archive database, we analysed 1377 transcriptome data sequences from 60 bovine tissue types, identified tissue-specific and housekeeping genes, and set up a web-based bovine gene expression analysis tool. Key results We found 101 genes widely expressed in almost all tissue and screened out five housekeeping genes: RPL35A, eIF4A2, GAPDH, IPO5 and PAK2. Focusing on 12 major organs, we found 861 genes specifically expressing in these tissues. Furthermore, 187 significantly differentially expressed genes were found among six types of muscle tissues. All expression data were made available at our new website http://cattleExp.org, which can be freely accessed for future gene expression analyses. Conclusions The housekeeping genes and tissue-specific genes identified will provide more information for researchers studying gene expression in cattle. Implications The web-based cattle gene expression analysis tool will make it easy for researchers to access large public datasets. Users can easily access all publicly available RNA data and upload their own RNA-Seq data.

Analyzing Large Gene Expression Data Sets

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Clustering Methods ◽  
Biologically Relevant ◽  
Large Gene ◽  
Functional Coherence

The most interesting and challenging gene expression data sets to analyze are large multidimensional data sets that contain expression values for many genes across multiple conditions. In these data sets the use of scientific text can be particularly useful, since there are a myriad of genes examined under vastly different conditions, each of which may induce or repress expression of the same gene for different reasons. There is an enormous complexity to the data that we are examining—each gene is associated with dozens if not hundreds of expression values as well as multiple documents built up from vocabularies consisting of thousands of words. In Section 2.4 we reviewed common gene expression strategies, most of which revolve around defining groups of genes based on common profiles. A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present computational methods that leverage the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in gene expression data analysis offers an opportunity to incorporate background functional information about the genes when defining expression clusters. In Chapter 5 we saw how literature- based approaches could help in the analysis of single condition experiments. Here we will apply the strategies introduced in Chapter 6 to assess the coherence of groups of genes to enhance gene expression analysis approaches. The methods proposed here could, in fact, be applied to any multivariate genomics data type. The key concepts discussed in this chapter are listed in the frame box. We begin with a discussion of gene groups and their role in expression analysis; we briefly discuss strategies to assign keywords to groups and strategies to assess their functional coherence. We apply functional coherence measures to gene expression analysis; for examples we focus on a yeast expression data set. We first demonstrate how functional coherence can be used to focus in on the key biologically relevant gene groups derived by clustering methods such as self-organizing maps and k-means clustering.

175 Nobiletin enhances the quality of in vitro-matured bovine oocytes and blastocysts by altering the transcription of key developmental genes

2019 ◽  
Vol 31 (1) ◽  
pp. 212
Author(s):  
Y. N. Cajas ◽  
K. Cañón-Beltrán ◽  
M. E. González ◽  
P. Ramos-Ibeas ◽  
A. Gutierrez-Adán ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Statistical Significance ◽  
Housekeeping Genes ◽  
Cumulus Cells ◽  
Cell Junction ◽  
In Vitro Production

One of the problems associated with in vitro production of embryos in bovine is the increase in reactive oxygen species (ROS), which leads to cell alterations and death. Nobiletin is a polymethoxyflavone isolated from citrus fruits with various beneficial effects on cell cycle regulation and inhibition of ROS production. In a preliminary study, we demonstrated that supplementation of 25 or 50 µM nobiletin to the in vitro maturation (IVM) medium reduces oxidative stress and improves oocyte nuclear and cytoplasmic maturation and embryo development. Thus, in this study, we aimed to evaluate the antioxidant activity of nobiletin during IVM on bovine matured oocytes, their cumulus cells (CC), and blastocysts by quantitative changes of gene expression. Immature cumulus oocytes complexes (COC) were aspirated from ovaries of slaughtered heifers. Selected COC underwent IVM in TCM-199+10% FCS and 10ng mL−1 epidermal growth factor (EGF; Control) supplemented with 25 µM (Nob25) or 50 µM (Nob50) nobiletin (MedChemExpress, Monmouth Junction, NJ, USA) or 0.001% dimethyl sulfoxide (DMSO control), a vehicle for nobiletin dilution, in 5% CO2 in air at 38.5°C. After 24h, 50 matured oocytes/group and their CC were snap-frozen in LN2 for gene expression analysis. The remaining oocytes were fertilized (Day 0) and cultured in vitro. Blastocysts (Day 7; n=50/group) were snap-frozen in LN2 for gene expression analysis (5 replicates). The mRNA abundance of candidate genes related with oxidative stress (SOD2, CYP51); apoptosis (BAX); quality (BMP15, BMP7, CLIC1, MAPK1, ABCB1); and cell junction (GJA1) was measured by quantitative PCR; H2AFZ and 18S rRNA were used as housekeeping genes. Statistical significance was assessed by one-way ANOVA. Supplementation of IVM medium with Nob25 or Nob50 produced changes in the expression levels of genes related to oxidative stress and apoptosis during IVM compared with controls. SOD2 and CYP51 were down-regulated in oocytes and CC (P<0.05) but not in blastocysts, whereas BAX was down-regulated only in CC (P<0.05). Nobiletin supplementation in IVM increased the expression of MAPK1 in oocytes and blastocysts (P<0.05); however, no differences were observed in CC. BMP15 for oocytes and their CC and GJA1 for CC were up-regulated in Nob25 and Nob50 groups compared with controls (P<0.05). The relative abundance of CLIC1 decreased in blastocysts from both nobiletin groups compared with controls (P<0.05). No significant differences in the expression in ABCB1 and BMP7 were detected. In conclusion, our results suggest that supplementation of 25 or 50 µM nobiletin to the IVM medium reduces oxidative stress in oocytes and CC, decreases CC apoptosis, and provokes positive changes in the expression of genes related to oocyte and embryo quality. This research was supported by Spanish MINECO (AGL2015-70140-R and AGL2015-66145-R). Y. N. Cajas was supported by a grant from SENESCYT-Ecuador.

scholarly journals Gepoclu: a software tool for identifying and analyzing gene positional clusters in large-scale gene expression analysis

2011 ◽  
Vol 12 (1) ◽  
pp. 34 ◽  
Author(s):  
Tania Dottorini ◽  
Nicola Senin ◽  
Giorgio Mazzoleni ◽  
Kalle Magnusson ◽  
Andrea Crisanti
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Large Scale ◽  
Gene Expression Analysis ◽  
Software Tool

NGS-based targeted RNA sequencing for expression analysis of patients with triple-negative breast cancer using a modulized, 96-gene biomarker panel.

2012 ◽  
Vol 30 (30_suppl) ◽  
pp. 56-56
Author(s):  
Byung-In Lee ◽  
Kahuku Oades ◽  
Lien Vo ◽  
Jerry Lee ◽  
Mark Landers ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Sequence Analysis ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Triple Negative ◽  
Dynamic Range ◽  
Rna Seq ◽  
Single Tube ◽  
Qrt Pcr

56 Background: Gene expression profiling has been shown to be effective in analyzing postoperative tumor samples in various cancers. However, in analyzing small specimens such as core biopsies, the limited amount of available material makes multi-gene analyses difficult or impossible. Microarray-based analyses also provide limited dynamic range. We describe the development of targeted RNA-sequencing methodology which combines the power of a universal RNA amplification with NGS for an ultra-deep expression analysis of multiple target genes, enabling <100 ng of sample input for multi-gene analysis in a single tube format. Methods: The gene expression patterns of triple-negative breast cancer FFPE samples were analyzed using a 96-gene breast cancer biomarker panel across three different platforms: Affymetrix Human Gene ST 1.0 microarrays, a pre-developed OncoScore qRT-PCR panel, and targeted RNA-seq. For targeted RNA-seq analysis, the 96-gene panel was amplified using a universal, single-tube “XP-PCR” amplification strategy followed by sequence analysis using the Ion-Torrent Personal Genome Machine. Results: Targeted RNA-seq provided the most sensitivity in terms of detection rates with <100 ng FFPE RNA input and provides unlimited dynamic range with increased sequencing depth. Expression ratio compression issues typically associated with a high number of pre-amplification cycles in standard multiplex-primed methods were not observed here. Low expressing genes, undetectable by qRT-PCR analysis from 1,000 ng input FFPE RNA, were detected and eligible for expression analysis with a significant number of sequencing reads. Alternative transcription/splicing analysis is also possible from sequence analysis of the target transcripts using targeted RNA-seq. Conclusions: By combining universally primed pre-amplification and NGS in multi-gene expression analysis, targeted RNA-seq provides the most sensitive gene expression analysis methodology.

Large-scale gene expression analysis of human skeletal myoblast differentiation

2004 ◽  
Vol 14 (8-9) ◽  
pp. 507-518 ◽  
Author(s):  
Ellen Sterrenburg ◽  
Rolf Turk ◽  
Peter A.C. 't Hoen ◽  
Judith C.T. van Deutekom ◽  
Judith M. Boer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Large Scale ◽  
Gene Expression Analysis ◽  
Myoblast Differentiation ◽  
Skeletal Myoblast
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