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Ingenuity software

Manufactured by Qiagen
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Ingenuity software is a bioinformatics tool designed to analyze and interpret complex biological and chemical data. It provides a comprehensive database and analytical capabilities to support researchers in understanding the interactions and relationships between genes, proteins, chemicals, and diseases.

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Lab products found in correlation

Trizol, by Thermo Fisher Scientific (3 mentions) Mouse genome 430 2.0 genechip arrays, by Thermo Fisher Scientific (2 mentions) Scanner 3000, by Thermo Fisher Scientific (2 mentions) Genechip expression console software, by Thermo Fisher Scientific (2 mentions) Q exactive quadrupole orbitrap, by Thermo Fisher Scientific (2 mentions) Trizol reagent, by Thermo Fisher Scientific (2 mentions) Direct zol rna miniprep plus, by Zymo Research (2 mentions) Ingenuity pathway analysis ipa, by Qiagen (1 mentions) Rneasy rna extraction kit, by Qiagen (1 mentions) Beadarray platform, by Illumina (1 mentions) Tissuelyser magna lyser, by Roche (1 mentions) Iscript cdna synthesis kit, by Bio-Rad (1 mentions) Rneasy mini kit, by Qiagen (1 mentions) Ingenuity pathway analysis software, by Qiagen (1 mentions) Nextseq500v2 instrument, by Illumina (1 mentions) Genomebrowse, by Golden Helix (1 mentions) Matlab, by MathWorks (1 mentions) Mascot daemon, by Matrix Science (1 mentions) Trusight myeloid sequencing panel, by Illumina (1 mentions) Qubit fluorometric quantitation, by Thermo Fisher Scientific (1 mentions)

28 protocols using ingenuity software

1

Pathway Analysis of Organ Graft Rejection

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Pathway analysis (Ingenuity Pathway Analysis, IPA, Qiagen) was used to obtain further insight into potential cellular pathways that might be modified as a result of protein changes identified in present experiments. IPA automatically generated networks of gene, protein, small molecule, drug, and disease associations on the basis of “hand-curated” data held in a proprietary database. The identifiers (GI mouse identification number) of DEPs were uploaded as an Excel spreadsheet file onto the Ingenuity software (Ingenuity Systems, Redwood City, CA, USA). Each GI mouse identification number was mapped to its corresponding molecule in the Ingenuity Pathway Knowledge Base. The biological functions assigned to each network were ranked according to the significance of that biological function to the network. Networks of these proteins were algorithmically generated based on their connectivity and assigned a score. The score was used to rank networks according to how relevant they were to the proteins in the input dataset. The network identified was then presented as a graph indicating the molecular relationship between proteins. Finally, we compared the proteins differentially expressed between rejecting and non-rejecting grafts varying more than 20% between groups and time points
Oltean M., Bagge J., Dindelegan G., Kenny D., Molinaro A., Hellström M., Nilsson O., Sihlbom C., Casselbrant A., Davila M, & Olausson M. (2021). The Proteomic Signature of Intestinal Acute Rejection in the Mouse. Metabolites, 12(1), 23.
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2

Comparing Pathway Analysis Tools

Cited 1 time
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Two different tools were used to analyze candidate genes from the methylation array to obtain associated cellular pathways for comparison. The first one is the Ingenuity Pathway Analysis (IPA) system, which automatically generates networks of gene, protein, small-molecule, and disease associations on the basis of data held in a proprietary database. Data were uploaded as an Excel file into the Ingenuity software (Ingenuity Systems, Redwood City, CA, USA) [39 (link)]. The second one is the Database for Annotation, Visualization and Integrated Discovery (DAVID) web tool, which is a high-throughput and integrated data-mining environment for analyzing the gene lists acquired from high-throughput genomic experiments [40 (link)]. Data were uploaded as a text file into the DAVID web tool. The pathway map was obtained by KEGG [41 (link)].
Husni R.E., Shiba-Ishii A., Nakagawa T., Dai T., Kim Y., Hong J., Sakashita S., Sakamoto N., Sato Y, & Noguchi M. (2019). DNA hypomethylation-related overexpression of SFN, GORASP2 and ZYG11A is a novel prognostic biomarker for early stage lung adenocarcinoma. Oncotarget, 10(17), 1625-1636.
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3

Ingenuity Pathway Analysis for Protein Changes

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The Ingenuity Pathway Analysis (IPA) system was used to obtain cellular pathways that might be modified by protein changes identified in these experiments. IPA automatically generated networks of gene, protein, small-molecule, drug, and disease associations on the basis of “hand-curated” data held in a proprietary database. Identifiers (Swiss-Prot identification number) of DEPs were uploaded as an Excel spreadsheet file into the Ingenuity software (Ingenuity Systems, Redwood City, CA, USA). Each human identification number was mapped to its corresponding molecule in the Ingenuity Pathway Knowledge Base. Biological functions assigned to each network were ranked according to significance of that biological function to the network. Protein networks were algorithmically generated based on their connectivity and were assigned a score. The score was used to rank networks according to how relevant they were to the proteins in the input data set. The network was presented as a graph that indicated the molecular relationship between proteins.
Zhan X., Wang X., Long Y, & Desiderio D.M. (2014). Heterogeneity analysis of the proteomes in clinically nonfunctional pituitary adenomas. BMC Medical Genomics, 7, 69.
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4

Ingenuity Software for Functional Analysis

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Analysis to identify physiologic functions or diseases most relevant to sets of differentially expressed genes in RNA-seq and microarray studies was carried out using Ingenuity software (Ingenuity Systems, Redwood City, California), which uses Fisher’s exact test to calculate a p-value that the association between biological functions and a set of genes is due to chance alone.
Laitman B.M., Asp L., Mariani J.N., Zhang J., Liu J., Sawai S., Chapouly C., Horng S., Kramer E.G., Mitiku N., Loo H., Burlant N., Pedre X., Hara Y., Nudelman G., Zaslavsky E., Lee Y.M., Braun D.A., Lu Q.R., Narla G., Raine C.S., Friedman S.L., Casaccia P, & John G.R. (2016). The Transcriptional Activator Krüppel-like Factor-6 Is Required for CNS Myelination. PLoS Biology, 14(5), e1002467.
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5

Transcriptomic Analysis of White Adipose Tissue

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Subcutaneous white adipose tissues were excised from mice and immediately snap-frozen in liquid nitrogen. Total RNA was isolated following tissue homogenization in Trizol (Invitrogen, Carslbad, CA) utilizing a TissueLyser (MagNA Lyser, Roche), then isolated using an RNeasy RNA extraction kit (Qiagen). The quality and quantity of the RNA was determined by absorbance at 260/280 nm cDNA was prepared by reverse transcribing 1 ug of RNA with an iScript cDNA Synthesis Kit (BioRad, Hercules CA). Results were calculated using the threshold cycle method [25] (link), with β-actin utilized for normalization. For RNA deep sequencing, total sWAT RNA (10 ng) was submitted for transcriptome sequencing (RNA-Seq). For Illumina microarray, total cDNA was synthesized from BAT and spotted onto a mouse Illumina BeadArray platform (Illumina, Inc.). Fold-changes and significance were calculated based on three independent replicates. Key gene lists and cluster analyses of the data sets were performed using Ingenuity software (Ingenuity Systems Inc.).
Kusminski C.M., Gallardo-Montejano V.I., Wang Z.V., Hegde V., Bickel P.E., Dhurandhar N.V, & Scherer P.E. (2015). E4orf1 induction in adipose tissue promotes insulin-independent signaling in the adipocyte. Molecular Metabolism, 4(10), 653-664.
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6

Siomycin A Modulates GBM Transcriptome

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RNA was extracted from GBM sphere samples (GBM146, GBM157, and GBM206) treated with 1 μM Siomycin A or control (DMSO) for 24 hours with RNeasy Mini Kit according to the manufactur’s protocol (Qiagen). RNA samples were subjected to cluster (A) and canonical pathway analyses (D) by Ingenuity software (Ingenuity Systems, www.ingenuity.com). The GEO submission number for this microarray is GSE50227.
Minata M., Gu C., Joshi K., Nakano-Okuno M., Hong C., Nguyen C.H., Kornblum H.I., Molla A, & Nakano I. (2014). Multi-Kinase Inhibitor C1 Triggers Mitotic Catastrophe of Glioma Stem Cells Mainly through MELK Kinase Inhibition. PLoS ONE, 9(4), e92546.
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7

Ingenuity Pathway Analysis of GBM Proteome

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Pathway analysis was performed using Ingenuity® software (Ingenuity Systems, USA; http://analysis.ingenuity.com) to assess functional associations (biological and canonical pathways) of differentially abundant proteins (p ≤ 0.05) by performing core expression analyses using default criteria. TRiC subunit proteins (TCP1, CCT2, CCT3, CCT4, CCT5, CCT6A, CCT6B, CCT7, CCT8) and interacting partners in the GBM vs HC dataset were explored using the grow and overlay functions in the pathway designer. KEGG and Reactome Pathways were annotated for the TRiC interactome of 42 proteins using The Database for Annotation, Visualisation, and Integrated Discovery (DAVID). Listed pathways had a fold-enrichment >2 and p-value < 0.05.
Hallal S.M., Tűzesi Á., Sida L.A., Xian E., Madani D., Muralidharan K., Shivalingam B., Buckland M.E., Satgunaseelan L, & Alexander K.L. (2024). Glioblastoma biomarkers in urinary extracellular vesicles reveal the potential for a ‘liquid gold’ biopsy. British Journal of Cancer, 130(5), 836-851.
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8

Differential Gene Expression Analysis

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Data were analyzed using Ingenuity software (Ingenuity Systems, http://www.ingenuity.com). Differentially expressed genes (with corresponding fold changes and P values) were incorporated in canonical pathways and bio-functions and were used to generate biological networks as described previously (Butovsky et al., 2014 (link)). Briefly, uploaded data set for analysis was filtered using the following cutoff definitions: fivefold change, P < 0.01.
Krasemann S., Madore C., Cialic R., Baufeld C., Calcagno N., El Fatimy R., Beckers L., O’Loughlin E., Xu Y., Fanek Z., Greco D.J., Smith S.T., Tweet G., Humulock Z., Zrzavy T., Conde-Sanroman P., Gacias M., Weng Z., Chen H., Tjon E., Mazaheri F., Hartmann K., Madi A., Ulrich J., Glatzel M., Worthmann A., Heeren J., Budnik B., Lemere C., Ikezu T., Heppner F.L., Litvak V., Holtzman D.M., Lassmann H., Weiner H.L., Ochando J., Haass C, & Butovsky O. (2017). The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity, 47(3), 566-581.e9.
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9

Pathway Analysis of Differential Genes

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Data were analyzed using Ingenuity software (Ingenuity Systems, http://www.ingenuity.com). Differentially expressed genes (with corresponding n-fold changes and P-values) were incorporated in canonical pathways and bio-functions and were used for generating biological networks as described previously (Butovsky et al., 2014 (link)). Uploaded dataset for analysis was filtered using the following cutoff definitions: fivefold change, P < 0.01.
Margeta M.A., Yin Z., Madore C., Pitts K.M., Letcher S.M., Tang J., Jiang S., Gauthier C.D., Silveira S.R., Schroeder C.M., Lad E.M., Proia A.D., Tanzi R.E., Holtzman D.M., Krasemann S., Chen D.F, & Butovsky O. (2022). Apolipoprotein E4 impairs the response of neurodegenerative retinal microglia and prevents neuronal loss in glaucoma. Immunity, 55(9), 1627-1644.e7.
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10

Affymetrix Microarray Analysis of Liver Transcriptome

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Total RNA was extracted from liver tissue using TRIzol (Invitrogen). Microarray analysis was performed by NIEHS molecular genomics core laboratory using the Affymetrix Mouse Genome 430 2.0 GeneChip arrays (Affymetrix). Arrays were scanned in an Affymetrix Scanner 3000 and data was obtained using the GeneChip Expression Console Software (Ver. 1.2) using the MAS5 algorithm to generate CHP files. The resulting data were processed using OmicSoft Array Studio (Ver. 6.0) software. In order to identify differentially expressed probes, ANOVA was used to determine if there was a statistical difference between the means of groups. The analysis was performed with setting parameters of at least 2-fold difference and significance at p < 0.01. Pathway analysis was performed using the Ingenuity software (Qiagen).
Hart-Unger S., Arao Y., Hamilton K.J., Lierz S.L., Malarkey D.E., Hewitt S.C., Freemark M, & Korach K.S. (2017). Hormone signaling and fatty liver in females: analysis of estrogen receptor α mutant mice. International journal of obesity (2005), 41(6), 945-954.
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