RNA samples were labeled with the Agilent low input labeling kit in single-color mode. The resulting cRNA was then hybridized overnight to Agilent 4x44K Mouse Whole Genome v2 arrays. The arrays were scanned with an Agilent scanner and the resulting images processed with Agilent's Feature Extraction program (v10.10.1.1). The gNetSignal data values were then quantile-normalized (R package limma, normalizeBetweenArrays function) and statistical significance of differential expression assessed with the SAMR package.
Feature extraction program
Manufactured by Agilent Technologies
Sourced in United States
The Feature Extraction program is a software tool developed by Agilent Technologies to analyze and extract relevant data from complex experimental data. The program's core function is to identify and quantify specific features or characteristics within the data, providing users with a streamlined and efficient way to extract meaningful information.
Automatically generated - may contain errors
Lab products found in correlation
Nimblegen ms 200 microarray scanner, by Roche (3 mentions)
Qiamp dna blood kit, by Qiagen (1 mentions)
Genespring 7, by Agilent Technologies (1 mentions)
Agilent one color microarray based gene expression analysis protocol, by Agilent Technologies (1 mentions)
Genespring gx v12, by Agilent Technologies (1 mentions)
9 protocols using feature extraction program
1
Agilent Microarray RNA Expression Analysis
2
Functional Gene Analysis of Coral Microbiome
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The new generation of functional gene array, GeoChip 5.0, was used to analyze the functional potential of coral-associated microbial communities. The purified DNA (500 ng) was labelled with Cy 3 as described previously24 (link). The labeled DNA was then re-suspended in hybridization solution [42 μl; 1 × HI-RPM hybridization buffer, 1 × Acgh blocking, 0.05 μg/μl Cot-1 DNA, 10 pM universal standard DNA and 10% formamide (final concentrations)]. GeoChip hybridization was carried out at 67 °C in an Agilent hybridization oven for 24 h. After hybridization, the slides were washed with Agilent Wash Buffers I and II for 5 min and 1 min, respectively. The arrays were then scanned with a NimbleGen MS200 Microarray Scanner (Roche NimbleGen, Inc., Madison, WI, USA). The images were extracted by the Agilent Feature Extraction program.
The raw microarray data was submitted to the GeoChip Microarray Data Manager pipeline (http://ieg.ou.edu/microarray/ ), and processed as previously described24 (link). Poor quality spots or those with a signal-to-noise ratio of less than 2.0 were removed; the positive signals were normalized within each sample and across all samples; and then any spots only detected in one sample were removed. The processed data was then used for further analysis.
The raw microarray data was submitted to the GeoChip Microarray Data Manager pipeline (
3
Differential circRNA Profiling in CRC
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CircRNAs were collected from the CRC and corresponding adjacent non-tumoral tissues of patients for microarray analysis. Sample preparation and microarray hybridization were performed according to Arraystar standard protocols.45 (link)
The Agilent feature extraction program was used to examine the acquired array images. Quantile normalization and further data processing were performed using the R limma package. Statistical criteria for the selection of differentially expressed circRNAs were set with |fold changes| ≥1.5 with P values <0.05. A volcano plot was used to visualize and screen significantly differentially expressed circRNAs between the two groups. Distinct circRNA expression patterns among the samples were displayed using hierarchical clustering.
The Agilent feature extraction program was used to examine the acquired array images. Quantile normalization and further data processing were performed using the R limma package. Statistical criteria for the selection of differentially expressed circRNAs were set with |fold changes| ≥1.5 with P values <0.05. A volcano plot was used to visualize and screen significantly differentially expressed circRNAs between the two groups. Distinct circRNA expression patterns among the samples were displayed using hierarchical clustering.
4
miRNA Microarray Analysis of CRC Cells
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For miRNA microarray analysis, total RNAs were extracted from the CRC cells transfected with WT IDH1 plasmid and IDH1 K224R plasmid. Sample preparation and microarray hybridization were performed according to Arraystar standard protocols. The Agilent feature extraction program was used to examine the acquired array images.
5
Genome-Wide DNA Methylation Profiling
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Sample genomic DNAs (gDNAs) were extracted from the peripheral blood of the subjects using QIAmp DNA Blood Kits (Qiagen, USA). Then methylated DNA immunoprecipitation and differentially methylation region (DMR) screening was performed using Agilent 1 × 244 K DNA methylation Microarray (Agilent, USA) according to the manufacturer's instructions. Images of the slides were acquired using Agilent SureScan Microarray Scanner G2505C and transformed to digital features using Agilent Feature Extraction program (version 10.7.1.1). The feature data were analyzed using Agilent GenomicWorkBench (version 7.0) for quality control and DMR calling. Based on BATMAN algorithm [26] (link), the methylation status of all probes in each samples was calculated and shown with three categorized score: “1” (high, DNA methylation > 60%), “0” (moderate, DNA methylation 40 ∼ 60%) and “-1” (low, DNA methylation < 40%) [26] (link). Probes associated with cellular specificity or aging [27 (link),28 (link)] and probes located on sex chromosomes were excluded.
6
Functional Gene Microarray Analysis
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The GeoChip 5.0_108K (Glomics Inc., Norman, OK, United States), a functional gene microarray, was used to determine the presence and relative abundance of petroleum degradation genes in the October offshore incubation. Using an aliquot of the same original DNA extract used for 16S rRNA amplicon-based microbial community analysis (described above), Glomics, Inc. conducted GeoChip analysis, which included amplification, labeling, hybridization, and data preprocessing (Van Nostrand et al., 2016 ). GeoChips were imaged (NimbleGen MS 200 microarray scanner; Roche NimbleGen Inc., Madison, WI, United States) and the data were extracted using the Agilent Feature Extraction program. Extracted data were then loaded onto the GeoChip data analysis pipeline1 where singletons were removed. Prior to statistical analyses, all signals were converted into relative abundances.
7
Gene Expression Analysis Pipeline
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Scanned images were analyzed by the Feature Extraction program (Agilent Technologies). The average fluorescence intensity for each spot was calculated and local background was subtracted. All data manipulation, selection of fold-changed genes, and statistical Student’s t-test calculations were performed using GeneSpring 7.3.1 (Agilent Technologies). All numerical data were normalized by the intensity-dependent normalization (LOWESS), where the ratio was reduced to the residual of the Lowess fit of the intensity vs. ratio curve using GeneSpring 7.3.1 software. Genes with more than twofold change in the expression level were selected and considered as significant. For the expression pattern analysis, hierarchical clustering analysis was also performed using GeneSpring 7.3.1 software. Functional enrichment/grouping analyses were done using Gene Ontology (GO) functional classification system (www.geneontology.org ) or DAVID (http://david.abcc.ncifcrf.gov/ )
8
One-Color Microarray Gene Expression Analysis
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Sample labeling and array hybridization were performed using the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technologies).47 (link)
The Agilent Feature Extraction program (version 11.0.1.1) was used to evaluate the collected array images. Quantile normalization and further data processing were performed using the GeneSpring GX v12.1 software package (Agilent Technologies). The threshold used to determine upregulated or downregulated mRNAs was |fold change| ≥1.5, with P values <0.05. Significantly differentially expressed genes between the two groups were screened by volcano plot filtering and fold-change filtering. R scripts were used to perform hierarchical clustering. The standard enrichment computation approach was used to perform GO and pathway analysis.
The Agilent Feature Extraction program (version 11.0.1.1) was used to evaluate the collected array images. Quantile normalization and further data processing were performed using the GeneSpring GX v12.1 software package (Agilent Technologies). The threshold used to determine upregulated or downregulated mRNAs was |fold change| ≥1.5, with P values <0.05. Significantly differentially expressed genes between the two groups were screened by volcano plot filtering and fold-change filtering. R scripts were used to perform hierarchical clustering. The standard enrichment computation approach was used to perform GO and pathway analysis.
9
Functional Analysis of Microbial Samples
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The new generation of functional gene array (GeoChip 5.0) was used to analyze the functional potential of microbial samples. The purified DNA (500 ng) was labeled with Cy 3 as described previously (Sun et al., 2014) . Briefly, the labeled DNA was re-suspended in hybridization solution, and then hybridized in an Agilent hybridization oven at 67 °C for 24 h. After hybridization, the slides were washed with buffers to remove unbound DNA. The arrays were scanned with a NimbleGen MS200 Microarray Scanner (Roche NimbleGen, Inc., Madison, WI, USA). The images were extracted by the Agilent Feature Extraction program. Poor quality spots with a signal-to-noise ratio of less than 2.0 were removed before statistical analysis. The positive signals were normalized within each sample and across all samples.
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