scholarly journals RefEx, a reference gene expression dataset as a web tool for the functional analysis of genes

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Hiromasa Ono ◽  
Osamu Ogasawara ◽  
Kosaku Okubo ◽  
Hidemasa Bono
Keyword(s):  
Gene Expression ◽  
Functional Analysis ◽  
Reference Gene ◽  
Gene Expression Dataset ◽  
Web Tool ◽  
Reference Gene Expression

scholarly journals Selecting Suitable Reference Genes for qPCR Normalization: A Comprehensive Analysis in MCF-7 Breast Cancer Cell Line

2020 ◽  
Author(s):  
Nityanand Jain ◽  
Dina Nitisa ◽  
Valdis Pirsko ◽  
Inese Cakstina
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Reference Gene ◽  
Reference Genes ◽  
Cancer Cell Line ◽  
Internal Controls ◽  
Reference Gene Expression ◽  
Qpcr Normalization ◽  
Suitable Reference ◽  
Mcf 7

Abstract BackgroundMCF-7 breast cancer cell line is undoubtedly amongst the most extensively studied patient-derived research models, providing pivotal results that have over the decades translated to constantly improving patient care. Many research groups, have previously identified suitable reference genes for qPCR normalization in MCF-7 cell line. However, over the course of identification of suitable reference genes, a comparative analysis comprising these genes together in a single study have not been reported. Furthermore, the expression dynamics of these reference genes within sub-clones cultured over multiple passages (p) has attracted limited attention from research groups. Therefore, we investigated the expression dynamics of 12 previously suggested reference genes within two sub-clones (culture A1 and A2) cultured identically over multiple passages. Additionally, the effect of nutrient stress on reference gene expression was examined to devise an evidence-based recommendation of the least variable reference genes that could be employed in future gene expression studies.ResultsThe analysis revealed the presence of differential reference gene expression within the sub-clones of MCF-7. In culture A1, GAPDH-CCSER2 were identified as the least variable reference gene pair while for culture A2, GAPDH-RNA28S was identified. However, upon validation using genes of interest, both these pairs were found to be unsuitable control pairs. Normalization of AURKA and KRT19 with triplet pair GAPDH-PCBP1-CCSER2 yielded successful results. The triplet also proved its capability to handle variations arising from nutrient stress.ConclusionsThe variance in expression behavior amongst sub-clones highlights the potential need for exercising caution while selecting reference genes for MCF-7. GAPDH-PCBP1-CCSER2 triplet offers a reliable alternative to otherwise traditionally used internal controls for optimizing intra- and inter-assay gene expression differences. Furthermore, we suggest avoiding the use of ACTB, GAPDH and PGK1 as single internal controls.

Multiplex qPCR assay for assessment of reference gene expression stability in rat tissues/samples

2020 ◽  
Vol 53 ◽  
pp. 101611 ◽  
Author(s):  
Alexander P. Schwarz ◽  
Daria A. Malygina ◽  
Anna A. Kovalenko ◽  
Alexander N. Trofimov ◽  
Aleksey V. Zaitsev
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Qpcr Assay ◽  
Rat Tissues ◽  
Expression Stability ◽  
Gene Expression Stability ◽  
Multiplex Qpcr ◽  
Reference Gene Expression

scholarly journals Selection of Reference Gene Expression in a Schizophrenia Brain Cohort

2010 ◽  
Vol 44 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Cynthia Shannon Weickert ◽  
Donna Sheedy ◽  
Debora A. Rothmond ◽  
Irina Dedova ◽  
Samantha Fung ◽  
...  
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Reference Gene Expression ◽  
Selection Of

Impact of Thawing on Reference Gene Expression Stability in Renal Cell Carcinoma Samples

2012 ◽  
Vol 21 (3) ◽  
pp. 157-163 ◽  
Author(s):  
Yi Ma ◽  
HuiLi Dai ◽  
XianMing Kong ◽  
LiMin Wang
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Reference Gene ◽  
Renal Cell ◽  
Expression Stability ◽  
Gene Expression Stability ◽  
Reference Gene Expression

SELECTION OF REFERENCE GENE EXPRESSION IN A SCHIZOPHRENIA BRAIN COHORT

2010 ◽  
Vol 117 (2-3) ◽  
pp. 372
Author(s):  
Debora A. Rothmond ◽  
Samantha J. Fung ◽  
Jenny Wong ◽  
Carlotta Duncan ◽  
Shan-Yuan Tsai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Reference Gene Expression ◽  
Selection Of

scholarly journals Reference gene expression in human osteosarcoma cell lines treated by EGB and CTX

2017 ◽  
Vol 41 ◽  
pp. 439-447
Author(s):  
Ming REN ◽  
Qiwei YANG ◽  
Yuanyuan SONG ◽  
Ao WANG ◽  
Qingyu WANG ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Reference Gene ◽  
Osteosarcoma Cell ◽  
Human Osteosarcoma ◽  
Human Osteosarcoma Cell ◽  
Reference Gene Expression

scholarly journals Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells

2019 ◽  
Vol 12 (S8) ◽  
Author(s):  
Yen-Jung Chiu ◽  
Yi-Hsuan Hsieh ◽  
Yen-Hua Huang
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Immune Cells ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Deconvolution Method ◽  
Cell Composition ◽  
Reference Gene Expression ◽  
Cell Fractions

Abstract Background To facilitate the investigation of the pathogenic roles played by various immune cells in complex tissues such as tumors, a few computational methods for deconvoluting bulk gene expression profiles to predict cell composition have been created. However, available methods were usually developed along with a set of reference gene expression profiles consisting of imbalanced replicates across different cell types. Therefore, the objective of this study was to create a new deconvolution method equipped with a new set of reference gene expression profiles that incorporate more microarray replicates of the immune cells that have been frequently implicated in the poor prognosis of cancers, such as T helper cells, regulatory T cells and macrophage M1/M2 cells. Methods Our deconvolution method was developed by choosing ε-support vector regression (ε-SVR) as the core algorithm assigned with a loss function subject to the L1-norm penalty. To construct the reference gene expression signature matrix for regression, a subset of differentially expressed genes were chosen from 148 microarray-based gene expression profiles for 9 types of immune cells by using ANOVA and minimizing condition number. Agreement analyses including mean absolute percentage errors and Bland-Altman plots were carried out to compare the performances of our method and CIBERSORT. Results In silico cell mixtures, simulated bulk tissues, and real human samples with known immune-cell fractions were used as the test datasets for benchmarking. Our method outperformed CIBERSORT in the benchmarks using in silico breast tissue-immune cell mixtures in the proportions of 30:70 and 50:50, and in the benchmark using 164 human PBMC samples. Our results suggest that the performance of our method was at least comparable to that of a state-of-the-art tool, CIBERSORT. Conclusions We developed a new cell composition deconvolution method and the implementation was entirely based on the publicly available R and Python packages. In addition, we compiled a new set of reference gene expression profiles, which might allow for a more robust prediction of the immune cell fractions from the expression profiles of cell mixtures. The source code of our method could be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets.

Characterization of reference gene expression in tung tree (Vernicia fordii)

2013 ◽  
Vol 50 ◽  
pp. 248-255 ◽  
Author(s):  
Heping Cao ◽  
Fangping Cao ◽  
K. Thomas Klasson
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Tung Tree ◽  
Vernicia Fordii ◽  
Reference Gene Expression

scholarly journals Analysis of reference gene expression for real-time PCR based on relative quantitation and dual spike-in strategy in the silkworm Bombyx mori

2012 ◽  
Vol 44 (7) ◽  
pp. 614-622 ◽  
Author(s):  
R. Peng ◽  
Y. Zhai ◽  
H. Ding ◽  
T. Di ◽  
T. Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bombyx Mori ◽  
Real Time ◽  
Reference Gene ◽  
Real Time Pcr ◽  
Relative Quantitation ◽  
Reference Gene Expression ◽  
Silkworm Bombyx Mori
Sign in / Sign up

Export Citation Format

Share Document