Bioinformatic analysis was performed to analyze differentiable expressed hepatic proteins. Briefly, t‐test differences from proteomic analysis were uploaded into Qiagen's Ingenuity Pathway Analysis (IPA) system for core analysis and overlaid with the Ingenuity pathway knowledge base. IPA was performed to identify canonical pathways, and putative upstream regulators that were the most significant in global molecular networks.[33 ] These results were ranked based on their p value (p ≤ 0.05) or activation score (z‐score) of pathway activation/inhibition.
Ingenuity pathway analysis system
Manufactured by Qiagen
Sourced in United States
Ingenuity Pathway Analysis (IPA) system is a software tool designed to facilitate the analysis and interpretation of biological and chemical systems. The core function of IPA is to provide a comprehensive platform for the examination of complex datasets, such as those generated from genomic, transcriptomic, and metabolomic studies.
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Lab products found in correlation
Human genome u133 plus 2.0 array, by Thermo Fisher Scientific (1 mentions)
Genechip scanner 7g, by Agilent Technologies (1 mentions)
Human genechip primeview array, by Thermo Fisher Scientific (1 mentions)
Trizol reagent, by Thermo Fisher Scientific (1 mentions)
Genechip wt sense target labeling and control reagents, by Thermo Fisher Scientific (1 mentions)
Genechip human gene 1.0 st array, by Thermo Fisher Scientific (1 mentions)
Fluidics station 450, by Thermo Fisher Scientific (1 mentions)
12 protocols using ingenuity pathway analysis system
1
Hepatic Protein Differential Expression
2
Pathway Analysis of Key Cancer Genes
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The identified key cancer genes were first uploaded onto Qiagen’s Ingenuity Pathway Analysis (IPA) system for core analysis and then overlaid with the global molecular network in the ingenuity pathway knowledge base. IPA was performed to identify canonical pathways, diseases and functions, and gene networks that are most significant to key cancer genes and to categorize differentially expressed genes in specific molecular and cellular functions. Thresholds of two-fold or greater in changes in expression and a p-value of 0.05 or less for significance were used to filter the findings from the analysis with IPA software.
3
Transcriptional Profiling of SCA2 and SCA3 iPSC-Derived Neurons
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Two μg of total RNA from CTRL-1, CTRL-2, CTRL-3, SCA2-1, SCA2- 2, SCA3-1, and SCA3-2 iPSC-derived neurons at day 100–120 of neural differentiation were used to generate biotin-labeled cRNA probes, which were hybridized to the Affymetrix Human Genome U133 plus 2.0 array (Affymetrix, Santa Clara, CA, USA) by the Affymetrix gene expression service laboratory at Academia Sinica (GRC, Academia Sinica, Taipei, Taiwan). Chips were scanned with an Affymetrix GeneChip Scanner 7G, and data were analyzed with GeneSpring X software (Agilent, Santa Clara, CA, USA). Raw data were normalized independently for each cell line using the Robust Multichip Average method. By comparing the gene expression from the data set of control and SCA2 or SCA3 iPSC lines, expression changes with a fold change >2 and an adjusted P < 0.05 were considered to be significantly different, and analyzed further for their biological function in the canonical networks with the Ingenuity Pathway Analysis (IPA) system (Qiagen; www.ingenuity.com ). For all analyses, Fisher’s exact test was used to calculate a P-value determining the probability that the association between the significantly different genes in the data set and the canonical pathway was due to chance alone. The microarray data have been deposited in the Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo , Accession No. GSE96826).
4
Integrative Genomic Analysis of ALS
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Copy number variation (CNV) data were collected from 4 published studies [33 (link)–36 (link)]. A total of 338 genes associated with ALS were included in the analysis. Additionally, genes linked to ALS were collected from The Huge Navigator (an integrated knowledge base of human genome epidemiology). We specifically searched the Phenopedia page for ALS related association studies and reported genes. Using the exact match of term “ALS” or “motoneuron disease,” we retrieved 143 genes annotated in the HuGE database (2011). From those, 128 genes were finally included in the analysis (14 genes were excluded from the analysis because there were not associated with the disease upon revision) (Additional file 1 : Table S1). We uploaded the selected CNV and HuGE genes into the Ingenuity Pathway Analysis (IPA) system (Qiagen), which contains protein/protein interaction (PPI) and expression datasets. We ran a “core analysis” approach and obtained 241 genes in 12 ALS-associated subnetworks (Additional file 2 : Table S2).
5
Somatic Mutation and Gene Expression Analysis
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For somatic mutation analysis, an oncoplot was generated based on the maftools [18 (link)] package to visualize non-silent somatic mutations in DNA repair genes. For gene expression analysis, genes that were unexpressed or lowly expressed in all samples (no sample with counts per million mapped reads (CPM) > 1) were excluded from analysis. The differential expression analysis of the carboplatin-resistant versus carboplatin-sensitive groups was performed using the edgeR package [19 (link)]. Significantly differentially expressed genes were identified based on a threshold of false discovery rate (FDR) < 5% and annotated for gene ontology terms. A volcano plot was generated for results visualization. All these analyses were performed using R 4.0.3. The pathway enrichment and network analysis were performed using Qiagen’s Ingenuity Pathway Analysis (IPA) system for the core analysis of the RNA sequencing data and overlaid with the Global Molecular Network Overlay in the IPA knowledge base.
6
Comparing Gastric Cancer Molecular Profiles
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This study is a retrospective cohort comparative study of intestinal-type gastric cancer (IGC, well and moderately differentiated cell types) and diffuse-type gastric cancer (DGC, SRC type). We collected 8 total surgical samples: 5 IGC samples and 3 diffuse-type gastric cancer DGC samples. We compared expression profiles between clinically comparable samples using transcriptome resequencing data. We performed variant analysis and network analysis via the Ingenuity Pathway Analysis system (Qiagen).11 (link)
7
Analyzing Protein Pathways in MCF-7 Cells
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Proteins in MCF‐7 cells were further analyzed using the Ingenuity Pathway Analysis system (Qiagen) to demonstrate their intracellular signal pathways and functions in the cells (10 November 2019 to 30 March 2020). On 23 February 2020, we used publicly available resources NOD, Nucleolar Localization Sequence Detector: http://www.compbio.dundee.ac.uk/www‐nod/ , to assess corresponding sequence in human CK2.
8
Microarray Analysis of URB1 Regulation
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Microarray assay was carried out to identify DEGs between RKO cells transfected with sh1 (sh‐URB1#1, n = 3) and nc (sh‐NC, n = 3) lentiviral vectors. This experiment was implemented by Shanghai GeneChem. In brief, total RNA was extracted from URB1 shRNA‐ and control shRNA‐transfected RKO cells. Complementary DNA was synthesized, labeled, and hybridized to the human GeneChip primeview array (Affymetrix). GeneChip Scanner 3000 and GeneChip GCOS 1.4 software were used to scan and analyze the data. The Ingenuity Pathway Analysis (IPA) system (Ingenuity Systems) was used to identify the DEGs and predict the underlying regulatory mechanisms. The JASPAR database (http://jaspar.genereg.net/ ) was consulted to predict the XBP1 binding sites within the promoter region of ATF4.
9
Ingenuity Pathway Analysis of HD-Altered Gene Expression
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To study the HD-changed gene expression data in the context of biological networks, the gene expression data of TRF-treated Q175 and untreated Q175 control samples were analyzed with the Ingenuity Pathway Analysis (IPA) system (Ingenuity Systems). Datasets containing gene identifiers and corresponding expression values were uploaded in the application. Each gene identifier was mapped to its corresponding gene object in Ingenuity Pathways Knowledge. A cutoff of corrected p value (i.e., q value = 0.005) was set to identify genes whose expression was significantly different as a result of the treatment. These genes were overlaid onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base. Functional analysis using the IPA program identified the biological functions that were most significant to the dataset (uncorrected Fisher’s exact test p < 0.05).
10
Microarray Analysis of miR-126-3p Transfection
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TPC-1 and FTC-133 cells were transfected with miR-126-3p and miR-NC. Three days post-transfection, cells were harvested and total RNA was extracted from cells using TRIzol reagent (Invitrogen, USA). One-hundred fifty nanograms of total RNA were used to perform cDNA reverse transcription, synthesis, amplification, fragmentation, and terminal labeling using GeneChip WT Sense Target Labeling and Control Reagents (Affymetrix, Santa Clara, CA). Approximately 25 ng/μL of cDNA was hybridized to an Affymetrix Human Gene 1.0 ST Array GeneChip. The arrays were washed and stained using the fluidics protocol FS450_0007 procedure on an Affymetrix Fluidics Station 450. The probe intensities were scanned with the GeneChip Scanner 3000. The raw data were normalized and analyzed using Partek Genomic Suite software (Partek, Inc., St. Louis, MO). Variance analysis was used to determine the probe sets that were significantly different between the two groups. The gene list was filtered with a fold-change cutoff of 1.3 and adjusted p < 0.05. The Ingenuity Pathway Analysis (IPA) system (Ingenuity Systems, Redwood City, CA) was used for pathway analysis.
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