Human genome u133 plus 2

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The Human Genome U133 Plus 2.0 is a microarray platform designed for the comprehensive analysis of the human genome. It provides a high-density oligonucleotide array with over 54,000 probe sets, covering more than 47,000 transcripts and variants from over 38,500 well-characterized human genes.

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U133 Plus 2.0 (67 citations) GeneChip Human Genome U133 Plus 2.0 (41 citations) Human Genome U133 Plus 2.0 GeneChips (38 citations) Affymetrix Human Genome U133 Plus 2.0 Array platform (16 citations) Human Genome U133 Plus 2.0 GeneChip arrays (13 citations) Human Genome U133 Plus 2.0 Array chips (7 citations) PLUSTM (3 citations) Human Genome U133 2.0 Plus GeneChip Set (2 citations) Human Genome U133 plus 2.0 GeneChip oligonucleotide microarrays (2 citations) GeneChip® Human Genome U133 (HG-U133) plus 2.0 Array (2 citations)

111 protocols using human genome u133 plus 2

Batch Normalization of Affymetrix Gene Expression Data

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MCP datasets and tumor datasets from Affymetrix Human Genome U133 Plus 2.0, Human Genome 133A, and HuGene 1.0 ST arrays were normalized using the frozen robust multiarray average (fRMA) method, implemented in the fRMA R package (version 1.18.0). Unlike RMA, fRMA uses fixed estimates of probe-specific effects and variances, allowing a consistent normalization of gene expression profiles (GEP) from different series, provided that they were obtained on the same gene expression platform.
GEP obtained with the Affymetrix Human Genome U133 Plus 2.0, Human Genome 133A, and HuGene 1.0 ST array platforms were thus normalized using the frma function of the frma Bioconductor R package using the preprocessing input vectors provided by the Bioconductor R packages frmahgu133plus2frmavecs version 1.3.0, frma133afrmavecs version 1.3.0, and hugene.1.0.st.v1frmavecs version 1.0.0, respectively. The frma method was called on batches of CEL files corresponding to individual series.

Becht E., Giraldo N.A., Lacroix L., Buttard B., Elarouci N., Petitprez F., Selves J., Laurent-Puig P., Sautès-Fridman C., Fridman W.H, & de Reyniès A. (2016). Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biology, 17, 218.

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COPD Transcriptional Profiling in Airways

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Three datasets (GSE11906, GSE11784, and GSE37147) were used to assess association of COPD status with ABC transporter gene expression profile. GSE11906 and GSE11784 collected epithelial cells from small airways (10^th-12^th generation) while GSE37147 collected from medium airways (6^th-8^th generation), with the two different sample types also analyzed on different microarray platforms (Affymetrix Human Genome U133 Plus 2 and Affymetrix Human Gene 1 ST, respectively).
In GSE11906, 20 independent samples of epithelial cells were isolated from individuals with >38 pack years smoking history with reported COPD and were compared to 54 independent samples of epithelial cells isolated from individuals with >25 pack years smoking history with no reported COPD^{18 (link)}. In GSE11784, 36 independent samples of epithelial cells were isolated from individuals with >34 pack years smoking history with reported COPD and were compared to 72 independent samples of epithelial cells isolated from individuals with >25 pack years smoking history with no reported COPD^{17 (link)}. In GSE37147, 87 independent samples of epithelial cells were isolated from individuals with >51 pack years smoking history with reported COPD and were compared to 151 independent samples of epithelial cells isolated from individuals with >47 pack years smoking history with no reported COPD^{19 (link)}.

Aguiar J.A., Tamminga A., Lobb B., Huff R.D., Nguyen J.P., Kim Y., Dvorkin-Gheva A., Stampfli M.R., Doxey A.C, & Hirota J.A. (2019). The impact of cigarette smoke exposure, COPD, or asthma status on ABC transporter gene expression in human airway epithelial cells. Scientific Reports, 9, 153.

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Cigarette Smoke Impact on ABC Transporter Expression

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Three datasets (GSE11906, GSE11784, and GSE4498), which were all generated from the Affymetrix Human Genome U133 Plus 2 microarray platform, were used to assess the impact of cigarette smoke exposure on ABC transporter gene expression^{16 (link)–18 (link)}. All three datasets are comprised of small airway (10^th-12^th generation) epithelial cell transcript expression patterns in healthy subjects and those with a history of smoking without a diagnosis of COPD. In GSE11906, 54 independent samples of epithelial cells were isolated from individuals with >25 pack years smoking history with no reported COPD^{18 (link)}. In GSE11784, 72 independent samples of epithelial cells were isolated from individuals with >25 pack years smoking history with no reported COPD^{17 (link)}. In GSE4498, 10 independent samples of epithelial cells were isolated from individuals with >25 pack years smoking history with no reported COPD^{16 (link)}. We independently curated these three GSE datasets to ensure that no samples were repeated across the analyses.

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Gene Expression Patterns in Human Airways

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Gene expression patterns of ABCF1 in human airway epithelial cells was determined relative to markers for immune cells (CD34), ABC transporters of known function in airway epithelial cells (ABCC4, ABCC7), and junctions (CDH1) in a dataset containing samples from trachea, large airways (generation 2^nd-3^rd), and small airways (generation 10^th-12^th) from healthy subjects (GSE11906, Affymetrix Human Genome U133 Plus 2 microarray platform) (Raman et al., 2009 (link)). The following probesets were used to extract gene expression data: ABCF1 (200045_at), ABCC4 (203196_at), ABCC7 (CFTR; 205043_at), CDH1 (201131_s_at), and CD34 (209543_s_at). In cases where more than one probe corresponded to a given gene, the following hierarchy was used to select an individual probe for further use: perfect, unique matches (probes ending in _at or _a_at) were preferred over mismatch or non-unique probes (ending in _s_at or _x_at). GSE11906 included 17 trachea (age−42 +/– 7), 21 large airway (age−42 +/– 9), and 35 small airway samples.

Cao Q.T., Aguiar J.A., Tremblay B.J., Abbas N., Tiessen N., Revill S., Makhdami N., Ayoub A., Cox G., Ask K., Doxey A.C, & Hirota J.A. (2020). ABCF1 Regulates dsDNA-induced Immune Responses in Human Airway Epithelial Cells. Frontiers in Cellular and Infection Microbiology, 10, 487.

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Comparative Analysis of Intracranial Aneurysm and Periodontitis Gene Expression

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First we show the flow chart of this study (Fig. 1). The expression data of IAs and PDs were obtained from the GEO data base (https://www.ncbi.nlm.nih.gov/geo). The search strategy for this study included: (1) subject searches for “intracranial aneurysms” and “periodontitis”, respectively; (2) Study type option selection “Expression profiling by array”; (3) samples were obtained from Homo sapiens; (4) the dataset contained normal control group samples. The mRNA sequencing of the data set GSE54083 was based on GPL4133 Agilent-014850 Whole Human Genome Microarray 4 × 44 K G4112F (Feature Number version). The mRNA sequencing of GSE10334 samples was based on GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0. The former includes 8 ruptured intracranial aneurysms samples, 5 unruptured aneurysm samples and 10 Superficial temporal artery samples, but the ruptured intracranial aneurysms samples will be excluded in this study. The latter contains 183 PD-affected gingival tissue samples and 64 unaffected gingival tissue samples.
Fig. 1

The flow diagram for the whole study

Chen Y., Huang J.H., Kang Y.B., Yao Z.J, & Song J.H. (2024). Bioinformatics analysis revealed the potential crosstalk genes and molecular mechanisms between intracranial aneurysms and periodontitis. BMC Medical Genomics, 17, 114.

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Preprocessing Microarray Data for CRC Analysis

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Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. The GSE110224 and GSE25070 microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 May 2021)). The GSE110224 dataset was based on GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array with 34 samples (17 patients with CRC and 17 adjacent samples). The GSE25070 dataset, based on the Illumina HumanRef-8 v3.0 expression bead chip, included 26 CRC samples and 26 adjacent non-tumor colorectal tissue samples. The raw data were corrected and quantile-normalized with the affy package of R 3.4.1 in Bioconductor [8 (link),9 (link)]. The annotation file published by Affymetrix was applied to assign probes to gene IDs and symbols. Data of probe IDs that could not be converted were excluded. Then, the average expression data of identifiers were obtained for each sample. The heatmap was plotted using the pheatmap R package for the CTLA-4 gene in two different CRC datasets.

Derakhshani A., Hashemzadeh S., Asadzadeh Z., Shadbad M.A., Rasibonab F., Safarpour H., Jafarlou V., Solimando A.G., Racanelli V., Singh P.K., Najafi S., Javadrashid D., Brunetti O., Silvestris N, & Baradaran B. (2021). Cytotoxic T-Lymphocyte Antigen-4 in Colorectal Cancer: Another Therapeutic Side of Capecitabine. Cancers, 13(10), 2414.

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Standardized Genome Profiling Analysis

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Gene profiling was measure by GeneSpring 12.6 using Affymetrix Human Genome U133 plus2.0. All sample files were preprocessed using “justRMA” and standardized as mean = 0 and SD = 1.

Cheng L.H., Hung K.F., Huang T.F., Hsieh H.P., Wang S.Y., Huang C.Y, & Lo J.F. (2016). Attenuation of cancer-initiating cells stemness properties by abrogating S100A4 calcium binding ability in head and neck cancers. Oncotarget, 7(48), 78946-78957.

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Identifying Critical Genes in Nasopharyngeal Carcinoma

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To identify clinically relevant genes that are critical in the pathogenesis of NPC, we reappraised gene expression profiling datasets for NPC versus non-neoplastic nasopharyngeal tissues from the Gene Expression Omnibus, which contains transcriptome and copy number data (GSE34573) obtained using Affymetrix Human Genome U133 Plus 2.0 and Mapping 250K Nsp SNP Arrays, respectively. The raw CEL files obtained from Affymetrix Human Genome U133 Plus 2.0 and Mapping 250K Nsp SNP Array platforms were imported into Nexus Expression 3 (BioDiscovery, EI Segundo, CA) and Nexus 6 (BioDiscovery) to analyze all probe sets without preselection or filtering, respectively. For the analysis of expression profiling, supervised comparative analysis and functional profiling were performed to identify statistically significant genes that were differentially expressed, with special attention given to the nucleobase, nucleoside, nucleotide, and nucleic acid metabolic process (GO:0006139). Those genes with P < 0.01 and log₂-transformed expression fold change >±0.1 were chosen for validation. For the purpose of exploring the copy number alteration of target genes, Mapping 250K Nsp SNP Arrays was also analyzed.

He H.L., Lee Y.E., Shiue Y.L., Lee S.W., Chen T.J, & Li C.F. (2015). Characterization and Prognostic Significance of Methylthioadenosine Phosphorylase Deficiency in Nasopharyngeal Carcinoma. Medicine, 94(49), e2271.

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Transcriptomic Profiling of Bladder Cancer

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The RNA of 31 PDX and patient tumors samples were hybridized in three batches in Affymetrix Human Genome U133 plus 2.0 Array Plates (Santa Clara, CA) according to Affymetrix standard protocols. Raw CEL files were RMA-normalized (28 (link)) using R statistical software. PCA confirmed that no batch effect was observed. The arrays were mapped to genes with a Brainarray Custom CDF (Human EntrezG version 24) (29 (link)).
Molecular consensus classes were determined with the “consensusMIBC” R package (v1.1.0, https://github.com/cit-bioinfo/consensusMIBC) using the RMA-normalized transcriptomic data. For WISP, we used a previously published dataset (15 (link)), which contains human MIBC samples (n = 85) also hybridized with Affymetrix Human Genome U133 plus 2.0 according to Affymetrix standard protocols. The raw CEL files used here are available from ArrayExpress (http://www.ebi.ac.uk/arrayexpress/) under accession number E-MTAB-1803.
Raw CEL files were RMA-normalized using R statistical software. The arrays were mapped to genes with a Brainarray Custom CDF (Human EntrezG version 23) (29 (link))
In both datasets, we obtained a log2-transformed expression matrix with one value per gene.

Lang H., Béraud C., Cabel L., Fontugne J., Lassalle M., Krucker C., Dufour F., Groeneveld C.S., Dixon V., Meng X., Kamoun A., Chapeaublanc E., De Reynies A., Gamé X., Rischmann P., Bieche I., Masliah-Planchon J., Beaurepere R., Allory Y., Lindner V., Misseri Y., Radvanyi F., Lluel P., Bernard-Pierrot I, & Massfelder T. (2022). Integrated molecular and pharmacological characterization of patient-derived xenografts from bladder and ureteral cancers identifies new potential therapies. Frontiers in Oncology, 12, 930731.

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Spina Bifida Gene Expression Profiles

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The Gene Expression Omnibus (GEO) database¹ was used to obtain gene expression profiles for spina bifida. Exclusion criteria: (1) Profiles based on cell lines and animal models were excluded. Inclusion criteria: (1) Only homo sapiens species samples with spina bifida and healthy controls were included in this study. The GSE4182 dataset was detected on a platform of Affymetrix GeneChip Human Genome U133 Plus 2.0 [HG-U133_Plus_2]. GSE4182 included nine amniotic fluid samples from pregnant women with spina bifida (N = 4) or healthy (N = 5) fetuses. Amniocytes in the amniotic fluid were collected by amniocentesis, from which fetal mRNA was isolated and analyzed. In this study, the expression profile was acquired directly from a public database.

Li Z., Feng J, & Yuan Z. (2020). Key Modules and Hub Genes Identified by Coexpression Network Analysis for Revealing Novel Biomarkers for Spina Bifida. Frontiers in Genetics, 11, 583316.

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