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Human 6 v2.0 expression beadchip

Manufactured by Thermo Fisher Scientific
Sourced in Austria

The Human-6 v2.0 expression beadchip is a microarray-based gene expression analysis tool designed for the quantitative measurement of RNA expression levels of human genes. The beadchip contains probes targeting over 47,000 human transcripts, providing comprehensive coverage of the human transcriptome. This product is intended for use in research applications requiring high-throughput gene expression profiling.

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3 protocols using human 6 v2.0 expression beadchip

1

RNA-Seq and Microarray Analysis of FOXM1 and MKI67

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Gene expression analyses of FOXM1 and MKI67 are based on paired-end RNA-Seq (101 + 7 + 101 bp) analysis on an on an Illumina HiSeq 2000. Data were normalized as described before and log2 transformed. Cluster assignments were performed by ConsensusClusterPlus using the programming software R (version 3.2.2, R Foundation for Statistical Computing, Vienna, Austria) (Hedegaard et al. 2016 (link)).
Expression data of the Chungbuk and Lund cohort were based on Illumina human-6 v2.0 expression beadchip and Affymetrix Human Gene 1.0 ST Array analyses, respectively. Processed gene expression data, as used in the respective studies, were downloaded from the Gene Expression Omnibus database (Kim et al. 2010 (link); Sjödahl et al. 2012 (link)).
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2

Differential mRNA Expression in AID PBMCs

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To identify differentially expressed mRNAs in AID PBMCs, we used investigative analysis of array datasets that were retrieved from the Gene Expression Omnibus (GEO) database: for IBD, GEO: GSE3365 (Platform: GPL96; Affymetrix Human Genome U133A Array; 127 samples); for MS, GEO: GSE21942 (Platform: GPL570; Affymetrix Human Genome U133 Plus 2.0 Array; 29 samples); for RA, GEO: GSE15573 (Platform: GPL6102; Illumina Human-6 v2.0 expression beadchip; 33 samples), for SS, GEO: GSE84844 (Platform: GPL570; Affymetrix Human Genome U133 Plus 2.0 Array; 60 samples), for SLE, GEO: GSE10325 (Platform: GPL96; Affymetrix Human Genome U133A Array; 67 samples). These were utilized for identification of differentially expressed mRNAs with the GEO2R analyzer between patient samples and controls. We considered differentially expressed genes with adjusted p values < 0.05 and absolute log fold changes > 1. Next, each protein-coding gene’s proximity to lncRNAs was checked via Python programming language in order to select adjusted lncRNAs (a similar code as that described in the immediately preceding section).
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3

Transcriptomic Profiling of Acute Myocardial Infarction

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In our research, we searched the datasets, which contained patients AMI and normal controls. The microarray expression datasets (GSE48060, GSE60993, GSE66360, GSE61144, GSE34198, and GSE97320) were downloaded from the GEO database1 (11 (link)–13 (link)). The gene expression profiling dataset GSE48060 contained 49 AMIs and 50 controls collected from blood, while GSE66360 included 31 AMIs and 21 controls collected from circulating endothelial cells, which were both using the GPL570 platform of Affymetrix Human Genome U133 Plus 2.0 Array. In addition, the GSE60993 dataset included 7 AMIs and 7 controls collected from peripheral blood, which is based on the GPL6884 platform of Illumina HumanWG-6 v3.0 Expression BeadChip. In addition, the GSE61144 dataset was used as the validation set, which collects 7 AMIs and 10 control samples based on the GPL6106 platform of Sentrix Human-6 v2 Expression BeadChip, the dataset GSE34198 collects 49 AMIs and 48 controls based on GPL6102 platform of Illumina Human-6 v2.0 Expression BeadChip, and GSE97320 collects 3 AMIs and 3 controls using the GPL570 platform of Affymetrix Human Genome U133 Plus 2.0 Array.
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