Metacore database

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MetaCore is a comprehensive database that provides access to a wide range of molecular interactions, pathways, and disease-related data. It integrates information from various scientific literature sources and offers tools for data analysis and visualization.

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Somascan, by SomaLogic (1 mentions)

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MetaCore (136 citations)

5 protocols using metacore database

Validating miRNA and mRNA Markers for sPTB

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A primer for each selected miR was designed for quantitative (q)RT PCR and tested on the same cDNA samples used for Discovery. Two confirmed miRs were also quantitated across gestation in samples from the same Discovery subjects.
The as yet unconfirmed potential mRNA markers and confirmed miRs were compared to a list of myometrial Preterm Initiator genes that are differentially expressed in sPTB≦32 weeks^{17 (link)}. The Metacore^™ database (Thomson Reuters, release 2010) was then queried to identify whether any selected potential RNA marker was known to interact with Preterm Initiator genes in any reported cell system. The mRNAs so identified were confirmed using qRT-PCR.

Weiner C.P., Dong Y., Zhou H., Cuckle H., Ramsey R., Egerman R., Buhimschi I, & Buhimschi C. (2021). Early Pregnancy Prediction of Spontaneous Preterm Birth Prior to 32 Completed Weeks of Pregnancy Using Plasma RNA: Transcriptome Discovery and Initial Validation of an RNA Panel of Markers. BJOG : an international journal of obstetrics and gynaecology, 128(11), 1870-1880.

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Functional Analysis of Differentially Expressed Proteins in UM-CLL

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Proteins found to have significantly higher or lower levels of expression in UM-CLL samples by iTRAQ-MS (p < 0.05) were subjected to computational functional analysis. Proteins were functionally classified using the Protein Analysis Through Evolutionary Relationships (PANTHER) classification system. Pathway analysis was conducted using the GeneGo pathway maps in the MetaCore database (version 6.14, build 61508; Thomson Reuters, New York, NY). The Pathway Maps tool was used to enrich for pathways, and p values were calculated based on a hypergeometric distribution, with the default database used as the background. Significant pathway enrichment was defined as a false discovery rate-corrected p < 0.05.

Eagle G.L., Zhuang J., Jenkins R.E., Till K.J., Jithesh P.V., Lin K., Johnson G.G., Oates M., Park K., Kitteringham N.R, & Pettitt A.R. (2015). Total Proteome Analysis Identifies Migration Defects as a Major Pathogenetic Factor in Immunoglobulin Heavy Chain Variable Region (IGHV)-unmutated Chronic Lymphocytic Leukemia. Molecular & Cellular Proteomics : MCP, 14(4), 933-945.

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Integrated Omics Pathway Analysis

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The pathway analysis was generated from integrated RNA-seq and ChIP-seq data. Using the RNA-seq and H3K9Ac ChIP-seq data, we generated ranked lists of DMGs (for the ChIP-seq signal in the vicinity of a specific gene; for peaks within the gene body, both exons or introns and within TSS, up to 5-kb upstream of the TSS) and differentially expressed genes (for RNA-seq signal) (ranked from the most significant decrease to the most significant increase). The 2 gene lists were used for gene ontology analysis using the MetaCore database from the Thomson Reuters (version 6.11, build 41105, GeneGo; Thomson Reuters, New York, NY)⁴⁵ pathway analysis tool. This pathway analysis tool was used to perform gene network enrichment analysis from integration of RNA-seq and H3K9Ac ChIP-seq data.

Perez S., Lavi-Itzkovitz A., Gidoni M., Domovitz T., Dabour R., Khurana I., Davidovich A., Tobar A., Livoff A., Solomonov E., Maman Y., El-Osta A., Tsai Y., Yu M.L., Stemmer S.M., Haviv I., Yaari G, & Gal-Tanamy M. (2023). High-Resolution Genomic Profiling of Liver Cancer Links Etiology With Mutation and Epigenetic Signatures. Cellular and Molecular Gastroenterology and Hepatology, 16(1), 63-81.

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Gene Regulatory Network Construction for Differentiating Cell Subpopulations

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In order to build the gene regulatory networks corresponding to each cell subpopulation, single-cell transcriptomics data was analyzed to identify the DEG between different cell subpopulations of a given time point (P < 0.05). Then the derived DE genes were used to reconstruct the interaction network including high quality gene–gene interaction information. For this purpose, we used the DE gene data and compiled the high-confidence interaction networks querying the MetaCore database from Thomson Reuters, which gave us the possibility of building gene regulatory networks of differentiating cell subpopulations (Crespo et al. 2013a (link),b (link); Zickenrott et al. 2016 (link)). After building the gene regulatory networks for the cell subpopulations, we identified the most relevant regulatory motifs, including the gene circuits that could have a more influential effect in the regulation of the gene expression patterns characteristic of each cell subpopulation (Zickenrott et al. 2016 (link)). The comparative analysis of these regulatory circuits allowed us to identify the regulatory genes that may play a role in the stabilization of the subpopulation phenotype and whose perturbation may have a significant effect and trigger transitions between cell subpopulations during neuronal differentiation.

Wang J., Jenjaroenpun P., Bhinge A., Angarica V.E., Del Sol A., Nookaew I., Kuznetsov V.A, & Stanton L.W. (2017). Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons. Genome Research, 27(11), 1783-1794.

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SOMAscan Analysis of Innate Immunity in NMDEs

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SOMAscan (SomaLogic, Boulder, Colo) analysis of NMDEs was performed by using the SOMAscan Assay Cells & Tissue Kit, 1.3k, according to the recommended protocol from the manufacturer, by using 1.7 mg of protein per sample. Three provided kit controls and 1 no-protein buffer control were run in parallel with the samples per plate. Median normalization and calibration of the SOMAscan data were performed according to the standard quality control protocols at SomaLogic. Results from the SOMAscan assay were reported as log 2 -normalized relative fluorescent units. Results from the SOMAscan assay were imported into the MetaCore Database (Thomson Reuters, Cambridge, Mass), and an enrichment analysis was performed, selecting for proteins involved in innate immunity and NOS-related process pathways. Morpheus software (https://software.broadinstitute.org/morpheus/) was used to generate a Spearman correlation matrix according to hierarchical clustering. GeneMANIA software (https://genemania.org/) was used to confirm innate immune networks and functional associations reported by MetaCore.

Nocera A.L., Mueller S.K., Stephan J.R., Hing L., Seifert P., Han X., Lin D.T., Amiji M.M., Libermann T, & Bleier B.S. (2019). Exosome swarms eliminate airway pathogens and provide passive epithelial immunoprotection through nitric oxide. The Journal of allergy and clinical immunology, 143(4).

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