Membrane gene ontology bias in sequencing and microarray obtained by housekeeping-gene analysis

Zhang, Yijuan; Akintola, Oluwafemi; Liu, Ken J.A.; Sun, Bingyun

doi:10.1016/j.gene.2015.09.041

Cited by 10 publications

(16 citation statements)

References 63 publications

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“…Thus, mRNA of genes related to the extracellular matrix is expressed at a higher level compared to nucleus related genes. These findings contradict to some extent the conclusions of Zhang et al [49], who found that microarrays are more sensitive to genes coding for membrane proteins, and sequencing – for genes in the nucleus. However, these authors used a completely different paradigm: they considered housekeeping genes, while we performed the analysis based on differentially expressed ones.…”

Section: Discussioncontrasting

confidence: 88%

RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples

et al. 2017

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BackgroundRNA sequencing (RNA-seq) and microarrays are two transcriptomics techniques aimed at the quantification of transcribed genes and their isoforms. Here we compare the latest Affymetrix HTA 2.0 microarray with Illumina 2000 RNA-seq for the analysis of patient samples - normal lung epithelium tissue and squamous cell carcinoma lung tumours. Protein coding mRNAs and long non-coding RNAs (lncRNAs) were included in the study.ResultsBoth platforms performed equally well for protein-coding RNAs, however the stochastic variability was higher for the sequencing data than for microarrays. This reduced the number of differentially expressed genes and genes with predictive potential for RNA-seq compared to microarray data. Analysis of this variability revealed a lack of reads for short and low abundant genes; lncRNAs, being shorter and less abundant RNAs, were found especially susceptible to this issue. A major difference between the two platforms was uncovered by analysis of alternatively spliced genes. Investigation of differential exon abundance showed insufficient reads for many exons and exon junctions in RNA-seq while the detection on the array platform was more stable. Nevertheless, we identified 207 genes which undergo alternative splicing and were consistently detected by both techniques.ConclusionsDespite the fact that the results of gene expression analysis were highly consistent between Human Transcriptome Arrays and RNA-seq platforms, the analysis of alternative splicing produced discordant results. We concluded that modern microarrays can still outperform sequencing for standard analysis of gene expression in terms of reproducibility and cost.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3819-y) contains supplementary material, which is available to authorized users.

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Section: Discussioncontrasting

confidence: 88%

RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples

et al. 2017

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“…RNA microarrays and RNA-Seq are the two main types of high-throughput technologies used to assess gene expression (26). Although RNA-Seq is considered to be more sensitive and has a broader dynamic range than RNA microarrays (27), in large comparative studies these two methods have produced comparable results in terms of gene expression profiling (27–29). Microarrays remain an accurate tool for measuring the levels of gene expression (29) and continue to provide useful data-mining resources.…”

Section: Discussionmentioning

confidence: 99%

“…Although RNA-Seq is considered to be more sensitive and has a broader dynamic range than RNA microarrays (27), in large comparative studies these two methods have produced comparable results in terms of gene expression profiling (27–29). Microarrays remain an accurate tool for measuring the levels of gene expression (29) and continue to provide useful data-mining resources. The approach of the present study takes advantage of the large number of previous transcriptomic studies that were performed with microarray technology and stored in publicly available databases, and also of the results provided in the form of a list of genes and the corresponding expression values.…”

Section: Discussionmentioning

confidence: 99%

“…This was recognized by Kwon et al (46), who selected low variability as the leading parameter, as have other studies, whereas the approach of the present study was aimed at finding genes with the best combination of criteria, considering that a high expression value may be advantageous in practice for the usability of a reference gene. Finally, it should be noted that other studies have frequently used very different and original approaches to the problem, using computational classifiers (47), the controlled vocabulary of Medical Subject Headings (48) and Gene Ontology classifications (29). …”

Section: Discussionmentioning

confidence: 99%

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Systematic identification of human housekeeping genes possibly useful as references in gene expression studies

Caracausi

Piovesan

Antonaros

et al. 2017

Molecular Medicine Reports

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The ideal reference, or control, gene for the study of gene expression in a given organism should be expressed at a medium-high level for easy detection, should be expressed at a constant/stable level throughout different cell types and within the same cell type undergoing different treatments, and should maintain these features through as many different tissues of the organism. From a biological point of view, these theoretical requirements of an ideal reference gene appear to be best suited to housekeeping (HK) genes. Recent advancements in the quality and completeness of human expression microarray data and in their statistical analysis may provide new clues toward the quantitative standardization of human gene expression studies in biology and medicine, both cross- and within-tissue. The systematic approach used by the present study is based on the Transcriptome Mapper tool and exploits the automated reassignment of probes to corresponding genes, intra- and inter-sample normalization, elaboration and representation of gene expression values in linear form within an indexed and searchable database with a graphical interface recording quantitative levels of expression, expression variability and cross-tissue width of expression for more than 31,000 transcripts. The present study conducted a meta-analysis of a pool of 646 expression profile data sets from 54 different human tissues and identified actin γ 1 as the HK gene that best fits the combination of all the traditional criteria to be used as a reference gene for general use; two ribosomal protein genes, RPS18 and RPS27, and one aquaporin gene, POM121 transmembrane nucleporin C, were also identified. The present study provided a list of tissue- and organ-specific genes that may be most suited for the following individual tissues/organs: Adipose tissue, bone marrow, brain, heart, kidney, liver, lung, ovary, skeletal muscle and testis; and also provides in these cases a representative, quantitative portrait of the relative, typical gene-expression profile in the form of searchable database tables.

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“…To investigate the roles of lncRNAs in the SCG and SG of T2D rats, GO analysis was conducted to annotate the transcripts under the cellular component, biological process, and molecular function ontologies (Chang et al, ; Morton et al, ; Yang et al, ; Zhang et al, ). The most significant GO processes that were upregulated in diabetes were associated with the immune response, cell migration, defense response, taxis, and chemotaxis (Table ).…”

Section: Resultsmentioning

confidence: 99%

Co‐expression changes of lncRNAs and mRNAs in the cervical sympathetic ganglia in diabetic cardiac autonomic neuropathic rats

Xuan

et al. 2016

J of Neuroscience Research

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Cardiac autonomic neuropathy in Type 2 diabetes (T2D) is often a devastating complication. Long non-coding RNAs (lncRNAs) have important effects on both normal development and disease pathogenesis. In this study, we explored the expression profiles of some lncRNAs involved in inflammation which may be co-expressed with messenger RNA (mRNA) in superior cervical and stellate ganglia after type 2 diabetic injuries. Total RNA isolated from 10 pairs of superior cervical and stellate ganglia in diabetic and normal male rats was hybridized to lncRNA arrays for detections. Pathway analysis indicated that the most significant gene ontology (GO) processes that were upregulated in diabetes were associated with immune response, cell migration, defense response, taxis, and chemotaxis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway revealed that most of the target genes of the lncRNAs were located in cytokine-cytokine receptor interactions, the chemokine signaling pathway and cell adhesion molecules, which were involved in T2D. Gene co-expression network construction showed that the co-expression network in the experimental rats consisted of 268 regulation edges among 105 lncRNAs and 11 mRNAs. Our studies demonstrated the co-expression profile of lncRNAs and mRNAs in diabetic cardiac autonomic ganglia, suggesting possible roles for multiple lncRNAs as potential targets for the development of therapeutic strategies or biomarkers for diabetic cardiac autonomic neuropathy. © 2016 Wiley Periodicals, Inc.

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Membrane gene ontology bias in sequencing and microarray obtained by housekeeping-gene analysis

Cited by 10 publications

References 63 publications

RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples

RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples

Systematic identification of human housekeeping genes possibly useful as references in gene expression studies

Co‐expression changes of lncRNAs and mRNAs in the cervical sympathetic ganglia in diabetic cardiac autonomic neuropathic rats

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