Ingenuity pathway analysis software ipa

Manufactured by Qiagen

Sourced in United States, Germany

Ingenuity Pathway Analysis (IPA) software is a bioinformatics tool designed to analyze and interpret complex biological and chemical systems. It provides a comprehensive database and analytical platform for understanding the significance of genes, proteins, chemicals, and their interactions within biological systems.

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Lab products found in correlation

Genespring gx11, by Agilent Technologies (2 mentions) Agilent 2100 bioanalyzer, by Agilent Technologies (2 mentions) Rneasy mini kit, by Qiagen (2 mentions) Hiseq 2500, by Illumina (2 mentions) Agilent 2200 tapestation, by Agilent Technologies (2 mentions) Qubit 2.0 fluorometer, by Thermo Fisher Scientific (2 mentions) Qubit dsdna broad range assay kit, by Thermo Fisher Scientific (2 mentions) Rneasy kit, by Qiagen (2 mentions) Mouse clariom d array, by Thermo Fisher Scientific (2 mentions) Rneasy plus mini kit, by Qiagen (1 mentions) Genespring gx 9, by Agilent Technologies (1 mentions) Genespring gx 12, by Agilent Technologies (1 mentions) Ncounter digital analyzer, by NanoString (1 mentions) Ncounter rcc collector, by NanoString (1 mentions) Genespring gx software, by Agilent Technologies (1 mentions) Sas v9, by SAS Institute (1 mentions) Genomestudio software, by Illumina (1 mentions) Proteome discoverer 2, by Thermo Fisher Scientific (1 mentions) Nanodrop 1000, by Thermo Fisher Scientific (1 mentions) Genechips, by Thermo Fisher Scientific (1 mentions)

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Ingenuity Pathway Analysis (1628 citations) Ingenuity Pathway Analysis (IPA, (1378 citations) IPA software (351 citations) Ingenuity Pathways Analysis (107 citations) Ingenuity Pathways Analysis (IPA; (47 citations) Ingenuity software (28 citations) Ingenuity Pathways Analysis software (IPA; (25 citations) Ingenuity IPA Software (9 citations) Ingenuity Pathway software (5 citations)

159 protocols using ingenuity pathway analysis software ipa

Differential Gene Expression Analysis via IPA

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To integrate our results into a more general model, differentially expressed transcripts classified according to Gene Ontology (GO) and functional associations were investigated using the Ingenuity Pathway Analysis software (IPA, Ingenuity Systems, Redwood City, CA). As indicated, only genes with a fold change >2 (at least in one of the probe sets where the gene was identified) were included. Moreover, Osteopontin (SPP1) with a fold change of 1.47 was also included in the Ingenuity analysis because previous studies had shown its specific role in porcine fertilization [31 (link)].

López-Úbeda R., García-Vázquez F.A., Romar R., Gadea J., Muñoz M., Hunter R.H, & Coy P. (2015). Oviductal Transcriptome Is Modified after Insemination during Spontaneous Ovulation in the Sow. PLoS ONE, 10(6), e0130128.

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Pathway Analysis of Differentially Expressed miRNAs

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The functions of differentially expressed miRNAs were further analyzed using Ingenuity Pathway Analysis software (IPA, Ingenuity Systems, USA). First, the target genes of differentially expressed miRNAs were predicted by IPA. Next, the intersection of the predicted target genes of differentially expressed miRNAs and the DEGs (p-value < 0.05, absolute fold change > 2) of FMT water extract treatment group was identified by IPA. The target mRNAs changed in the opposite direction of the corresponding miRNAs were selected for analysis by IPA Systems. Lastly, the biological and molecular functions of these genes were explored in IPA, such as relevant diseases and disorders, top canonical pathways, and top toxic lists. The canonical pathways of the DEGs were also performed by IPA as well as the network of the specific pathway.

Zheng J., Ji C., Lu X., Tong W., Fan X, & Gao Y. (2015). Integrated expression profiles of mRNA and microRNA in the liver of Fructus Meliae Toosendan water extract injured mice. Frontiers in Pharmacology, 6, 236.

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Enrichment Analysis of Differentially Expressed miRNAs in HCC

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Enrichment analysis was performed using Ingenuity Pathway Analysis software (IPA, Ingenuity Systems Inc., CA). The unique or common significantly differentially expressed miRNAs in Spontaneous or GBE-induced HCC samples as compared to the normal controls were uploaded to IPA. The IPA microRNA Target Filter module was then applied to identify the messenger RNA target genes for these microRNAs. This microRNA Target Filter module derives miRNA targets from TargetScan, TarBase, miRecords and the Ingenuity Knowledge base. The identified miRNA-targeted mRNA genes were then associated to the corresponding mRNA expression data from previous studies (Hoenerhoff et al. 2013 (link); Kovi et al. 2019 (link)). miRNA-targeted genes with adjusted p-value < 0.05 and an absolute fold change of 2 were considered as statistically significant and used as input datasets for IPA analysis to further investigate enriched canonical signaling pathways. The Benjamini and Hochberg corrected p-value < 0.05 was applied to define the significantly enriched pathways (Benjamini and Hochberg 1995 ).

Yamashita H., Surapureddi S., Kovi R.C., Bhusari S., Vu Ton T., Li J.L., Shockley K.R., Peddada S.D., Gerrish K.E., Rider C.V., Hoenerhoff M.J., Sills R.C, & Pandiri A.R. (2020). Unique microRNA Alterations in Hepatocellular Carcinomas Arising Either Spontaneously or due to Chronic Exposure to Ginkgo biloba Extract (GBE) in B6C3F1/N Mice. Archives of toxicology, 94(7), 2523-2541.

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Bioinformatic Analysis of Differentially Expressed Genes

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Differentially expressed genes were utilized for canonical pathway and upstream regulator identification with Ingenuity Pathway Analysis software (IPA, Ingenuity Systems, Inc., Redwood City, CA, USA; http://www.ingenuity.com). Associations of gene signatures derived from RNAseq data using MDA-MB-231-ERβ cell lines and human tumors with publically available databases were performed using Gene Set Enrichment Analysis (GSEA)^{76 (link),77 (link)}.

Aspros K.G., Carter J.M., Hoskin T.L., Suman V.J., Subramaniam M., Emch M.J., Ye Z., Sun Z., Sinnwell J.P., Thompson K.J., Tang X., Rodman E.P., Wang X., Nelson A.W., Chernukhin I., Hamdan F.H., Bruinsma E.S., Carroll J.S., Fernandez-Zapico M.E., Johnsen S.A., Kalari K.R., Huang H., Leon-Ferre R.A., Couch F.J., Ingle J.N., Goetz M.P, & Hawse J.R. (2022). Estrogen receptor beta repurposes EZH2 to suppress oncogenic NFκB/p65 signaling in triple negative breast cancer. NPJ Breast Cancer, 8, 20.

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Ingenuity Pathway Analysis of Research Data

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Ingenuity Pathway core analysis was performed using Ingenuity Pathway Analysis software (IPA, Ingenuity Systems).

Kennedy D.E., Okoreeh M.K., Maienschein-Cline M., Ai J., Veselits M., McLean K.C., Dhugana Y., Wang H., Peng J., Chi H., Mandal M, & Clark M.R. (2020). Novel specialized cell state and spatial compartments within the germinal center. Nature immunology, 21(6), 660-670.

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Bioinformatic Analysis of Sexual Dimorphism in MOG-EAE

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To obtain bioinformatic insight into the gene networks influencing the sexual dimorphism of MOG-EAE in B6-PWD consomics, we ran analyses on male-specific, female-specific, and non-sex-specific lists of genes generated from S2 Table using the core analysis tool of Ingenuity Pathway Analysis Software (IPA) (Ingenuity Systems, http://www.ingenuity.com). The Ingenuity Knowledge Base was used as a reference, with default settings. The genes and their interactors residing in the top networks were imported into IPA’s pathway designer tool, thereby producing the male-specific, female-specific, and non-sex-specific modules. The interactions between the genes within the non-sex-specific and the male or female modules were identified using the connect tool. We then again used the connect tool to identify those genes that have known associations with androgens and/or estrogens.

Bearoff F., Case L.K., Krementsov D.N., Wall E.H., Saligrama N., Blankenhorn E.P, & Teuscher C. (2015). Identification of Genetic Determinants of the Sexual Dimorphism in CNS Autoimmunity. PLoS ONE, 10(2), e0117993.

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Gene Co-Expression Network Analysis

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The PCIT algorithm was used to calculate weighted gene co-expression networks, through the implementation of first-order partial correlations coefficients combined with information theory approach, in order to identify principal interactions between genes^{32 (link),74 (link)}. Only the significant interactions between genes were considered for further steps. Networks were represented with CentiScaPe Cytoscape plug-in^{75 (link)}.
Ingenuity Pathway Analysis software (IPA; Ingenuity Systems) and the Core Analysis function was used to perform functional analysis of genes mapped in the different intervals and for data interpretation in the context of biological processes, pathways and networks. In addition, the iRegulon v1.3. Cytoscape plug-in⁷⁶ was used to identify transcription factor (TF) binding sites in silico. ClueGO plug-in^{77 (link)} was used to integrate and cluster the genes regarding their Gene Ontology and KEGG pathway. Finally, GeneMANIA⁷⁸ and String⁷⁹ were used to evaluate the functional interaction and networks among genes proteins, respectively.

Criado-Mesas L., Ballester M., Crespo-Piazuelo D., Castelló A., Fernández A.I, & Folch J.M. (2020). Identification of eQTLs associated with lipid metabolism in Longissimus dorsi muscle of pigs with different genetic backgrounds. Scientific Reports, 10, 9845.

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Rictor Knockdown Impacts U87-EGFRvIII Transcriptome

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U87-EGFRvIII cells were treated with siRNA against Scramble sequence or Rictor for 48 h (n = 2 for each cell line). Total RNA was isolated by RNeasy Plus Mini Kit (QIAGEN) and submitted to Eurofins Genomics (Kanagawa, Japan) for library preparation and sequencing. Gene expression data was analyzed by Chemicals Evaluation and Research Institute (CERI) (Tokyo, Japan), using Ingenuity Pathway Analysis software (IPA; Ingenuity Systems, Redwood City, CA).

Harachi M., Masui K., Shimizu E., Murakami K., Onizuka H., Muragaki Y., Kawamata T., Nakayama H., Miyata M., Komori T., Cavenee W.K., Mischel P.S., Kurata A, & Shibata N. (2024). DNA hypomethylator phenotype reprograms glutamatergic network in receptor tyrosine kinase gene-mutated glioblastoma. Acta Neuropathologica Communications, 12, 40.

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Bioinformatic Analysis of BPA-Induced Genes

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Lists of genes significantly induced or repressed after exposure to BPA were uploaded into Ingenuity Pathway Analysis Software (IPA, Ingenuity Systems, www.ingenuity.com) for biological analysis by comparison with the Ingenuity Knowledge Database. These lists of altered genes were then processed to investigate the functional distribution of these genes, as defined by Gene Ontology. Datasets and known canonical pathways associations were measured by IPA by using a ratio (R) of the number of genes from a dataset that map to a specific pathway divided by the total number of genes that map to this canonical pathway. A Fisher's exact test was used to determine a p-value representing the significance of these associations.

Ali S., Steinmetz G., Montillet G., Perrard M.H., Loundou A., Durand P., Guichaoua M.R, & Prat O. (2014). Exposure to Low-Dose Bisphenol A Impairs Meiosis in the Rat Seminiferous Tubule Culture Model: A Physiotoxicogenomic Approach. PLoS ONE, 9(9), e106245.

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Differential Gene Expression Analysis

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Host differential expression visualization and biological pathways were done using Ingenuity Pathway Analysis software (IPA, Ingenuity Systems, QIAGEN, Redwood City, CA, USA) [80 (link)]. Significantly different signaling pathways were determined using IPA with a two-tailed Fisher’s exact test (p ≤ 0.05) and canonical pathway enrichment.
Visualization of L. monocytogenes differential expression data was performed using BioCyc SmartTables (Pathway Tools version 21.0, SRI International, Menlo Park). Gene set enrichment analysis was done using a two-tailed Fisher’s exact test (p ≤ 0.05) with the Pathway Tools L. monocytogenes strain EGD-e database version 21.0 [81 (link)] as described by Rivals et al. [82 (link)].

Chen P., Reiter T., Huang B., Kong N, & Weimer B.C. (2017). Prebiotic Oligosaccharides Potentiate Host Protective Responses against L. Monocytogenes Infection. Pathogens, 6(4), 68.

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