scholarly journals Systematic Identification of Housekeeping Genes Possibly Used as References in Caenorhabditis elegans by Large-Scale Data Integration

Cells ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 786 ◽  
Author(s):  
Jingxin Tao ◽  
Youjin Hao ◽  
Xudong Li ◽  
Huachun Yin ◽  
Xiner Nie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Caenorhabditis Elegans ◽  
Gene Expression Data ◽  
Reference Genes ◽  
Large Scale ◽  
Gene Array ◽  
Housekeeping Genes ◽  
Functional Enrichment ◽  
Expression Data ◽  
Data Types

For accurate gene expression quantification, normalization of gene expression data against reliable reference genes is required. It is known that the expression levels of commonly used reference genes vary considerably under different experimental conditions, and therefore, their use for data normalization is limited. In this study, an unbiased identification of reference genes in Caenorhabditis elegans was performed based on 145 microarray datasets (2296 gene array samples) covering different developmental stages, different tissues, drug treatments, lifestyle, and various stresses. As a result, thirteen housekeeping genes (rps-23, rps-26, rps-27, rps-16, rps-2, rps-4, rps-17, rpl-24.1, rpl-27, rpl-33, rpl-36, rpl-35, and rpl-15) with enhanced stability were comprehensively identified by using six popular normalization algorithms and RankAggreg method. Functional enrichment analysis revealed that these genes were significantly overrepresented in GO terms or KEGG pathways related to ribosomes. Validation analysis using recently published datasets revealed that the expressions of newly identified candidate reference genes were more stable than the commonly used reference genes. Based on the results, we recommended using rpl-33 and rps-26 as the optimal reference genes for microarray and rps-2 and rps-4 for RNA-sequencing data validation. More importantly, the most stable rps-23 should be a promising reference gene for both data types. This study, for the first time, successfully displays a large-scale microarray data driven genome-wide identification of stable reference genes for normalizing gene expression data and provides a potential guideline on the selection of universal internal reference genes in C. elegans, for quantitative gene expression analysis.

Genome-wide identification of suitable zebrafish Danio rerio reference genes for normalization of gene expression data by RT-qPCR

2016 ◽  
Vol 88 (6) ◽  
pp. 2095-2110 ◽  
Author(s):  
H. Xu ◽  
C. Li ◽  
Q. Zeng ◽  
I. Agrawal ◽  
X. Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Danio Rerio ◽  
Reference Genes ◽  
Expression Data ◽  
Genome Wide

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

GENE DISCOVERY METHODS FROM LARGE-SCALE GENE EXPRESSION DATA

2010 ◽  
Author(s):  
AKIFUMI SHIMIZU ◽  
KENTARO YANO
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Gene Discovery ◽  
Expression Data

Identification of stable reference genes in differentiating human pluripotent stem cells

2015 ◽  
Vol 47 (6) ◽  
pp. 232-239 ◽  
Author(s):  
Gustav Holmgren ◽  
Nidal Ghosheh ◽  
Xianmin Zeng ◽  
Yalda Bogestål ◽  
Peter Sartipy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Gene Expression Data ◽  
Pluripotent Stem Cells ◽  
Reference Genes ◽  
Cell Types ◽  
Human Pluripotent Stem Cells ◽  
Expression Data ◽  
Large Variability ◽  
The Stability

Reference genes, often referred to as housekeeping genes (HKGs), are frequently used to normalize gene expression data based on the assumption that they are expressed at a constant level in the cells. However, several studies have shown that there may be a large variability in the gene expression levels of HKGs in various cell types. In a previous study, employing human embryonic stem cells (hESCs) subjected to spontaneous differentiation, we observed that the expression of commonly used HKG varied to a degree that rendered them inappropriate to use as reference genes under those experimental settings. Here we present a substantially extended study of the HKG signature in human pluripotent stem cells (hPSC), including nine global gene expression datasets from both hESC and human induced pluripotent stem cells, obtained during directed differentiation toward endoderm-, mesoderm-, and ectoderm derivatives. Sets of stably expressed genes were compiled, and a handful of genes (e.g., EID2, ZNF324B, CAPN10, and RABEP2) were identified as generally applicable reference genes in hPSCs across all cell lines and experimental conditions. The stability in gene expression profiles was confirmed by reverse transcription quantitative PCR analysis. Taken together, the current results suggest that differentiating hPSCs have a distinct HKG signature, which in some aspects is different from somatic cell types, and underscore the necessity to validate the stability of reference genes under the actual experimental setup used. In addition, the novel putative HKGs identified in this study can preferentially be used for normalization of gene expression data obtained from differentiating hPSCs.

scholarly journals LSTrAP-Crowd: Prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data

2020 ◽  
Author(s):  
Benedict Hew ◽  
Qiao Wen Tan ◽  
William Goh ◽  
Jonathan Wei Xiong Ng ◽  
Kenny Koh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Rna Sequencing ◽  
Gene Expression Data ◽  
Large Scale ◽  
Bacterial Resistance ◽  
Expression Data ◽  
Sequencing Data ◽  
Novel Proteins ◽  
Novel Antibiotics

AbstractBacterial resistance to antibiotics is a growing problem that is projected to cause more deaths than cancer in 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the bacterial ribosomes, proteins that are involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. The data can be used to identify other vulnerabilities or bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowdsourced.

scholarly journals Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network

2020 ◽  
Vol 16 (12) ◽  
pp. e1007974
Author(s):  
Bánk G. Fenyves ◽  
Gábor S. Szilágyi ◽  
Zsolt Vassy ◽  
Csaba Sőti ◽  
Peter Csermely
Keyword(s):  
Gene Expression ◽  
Caenorhabditis Elegans ◽  
Gene Expression Data ◽  
Receptor Gene ◽  
Directed Network ◽  
Chemical Synapse ◽  
Expression Data ◽  
Chemical Synapses ◽  
Theoretical Analyses ◽  
Receptor Gene Expression

Graph theoretical analyses of nervous systems usually omit the aspect of connection polarity, due to data insufficiency. The chemical synapse network of Caenorhabditis elegans is a well-reconstructed directed network, but the signs of its connections are yet to be elucidated. Here, we present the gene expression-based sign prediction of the ionotropic chemical synapse connectome of C. elegans (3,638 connections and 20,589 synapses total), incorporating available presynaptic neurotransmitter and postsynaptic receptor gene expression data for three major neurotransmitter systems. We made predictions for more than two-thirds of these chemical synapses and observed an excitatory-inhibitory (E:I) ratio close to 4:1 which was found similar to that observed in many real-world networks. Our open source tool (http://EleganSign.linkgroup.hu) is simple but efficient in predicting polarities by integrating neuronal connectome and gene expression data.

scholarly journals Defining transcription modules using large-scale gene expression data

2004 ◽  
Vol 20 (13) ◽  
pp. 1993-2003 ◽  
Author(s):  
J. Ihmels ◽  
S. Bergmann ◽  
N. Barkai
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Expression Data

Processing Large-Scale, High-Dimension Genetic and Gene Expression Data

2009 ◽  
pp. 307-330
Author(s):  
Cliona Molony ◽  
Solveig K. Sieberts ◽  
Eric E. Schadt
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
High Dimension ◽  
Large Scale ◽  
Expression Data

Mining the Gene Expression Matrix: Inferring Gene Relationships from Large Scale Gene Expression Data

1998 ◽  
pp. 203-212 ◽  
Author(s):  
Patrik D’haeseleer ◽  
Xiling Wen ◽  
Stefanie Fuhrman ◽  
Roland Somogyi
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Expression Data ◽  
Gene Expression Matrix ◽  
Expression Matrix
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