The Gene Expression Barcode 3.0: improved data processing and mining tools

McCall, Matthew N.; Jaffee, Harris A.; Zelisko, Susan; Sinha, Neeraj; Hooiveld, Guido; Irizarry, Rafael A.; Zilliox, Michael J.

doi:10.1093/nar/gkt1204

Cited by 71 publications

(101 citation statements)

References 13 publications

(19 reference statements)

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“…To investigate absolute expression profiles of NLRC3 and IQGAPQ1 in human cells we screened an additional database: The Barcode 3.0(63). We explored the absolute expression levels of each of these transcripts in primary immune cells and tissues to determine if transcripts were silenced or expressed (63).…”

Section: Resultsmentioning

confidence: 99%

“…We explored the absolute expression levels of each of these transcripts in primary immune cells and tissues to determine if transcripts were silenced or expressed (63). In Figure 2D and E, gene expression barcoding for immune tissues and cells is illustrated as a function of Z-score.…”

Section: Resultsmentioning

confidence: 99%

“…In Figure 2D and E, gene expression barcoding for immune tissues and cells is illustrated as a function of Z-score. A Z-score above 5 suggests a high likelihood that a gene is expressed in that cell or tissue (63). This barcoding demonstrates that IQGAP1 and NLRC3 are expressed in the spleen and thymus (Figure 2D and E) consistent with our expression data in Figure 2A.…”

Section: Resultsmentioning

confidence: 99%

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The Scaffolding Protein IQGAP1 Interacts with NLRC3 and Inhibits Type I IFN Production

Tocker

Durocher

Jacob

et al. 2017

The Journal of Immunology

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Sensing of cytosolic nucleotides is a critical initial step in the elaboration of type I interferon. One of several upstream receptor cGAS (cyclic-GMP-AMP synthase) binds to cytosolic DNA and generates di-cyclic nucleotides that act as secondary messengers. These secondary messengers bind directly to Stimulator of Interferon Genes (STING). STING recruits TANK binding kinase 1 (TBK1) which acts as a critical node that allows for efficient activation of interferon regulatory factors (IRFs) to drive the anti-viral transcriptome. NLRC3 is a recently characterized nucleotide-binding domain, leucine rich repeat containing protein (NLR) that negatively regulates the type I interferon pathway by inhibiting subcellular redistribution and effective signaling of STING, thus blunting the transcription of type I interferons. NLRC3 is predominantly expressed in lymphoid and myeloid cells. IQGAP1 was identified as a putative interacting partner of NLRC3 through yeast two hybrid screening. Here we show that IQGAP1 associates with NLRC3 and can disrupt the NLRC3:STING interaction in the cytosol of human epithelial cells. Furthermore, knock down of IQGAP1 in THP1 and HeLa cells causes significantly more interferon-β production in response to cytosolic nucleic acids. This result phenocopies NLRC3 deficient macrophages and fibroblasts and shRNA knock down of NLRC3 in THP1 cells. Our findings suggest IQGAP1 is a novel regulator of type I interferon production possibly via interacting with NLRC3 in human monocytic and epithelial cells.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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The Scaffolding Protein IQGAP1 Interacts with NLRC3 and Inhibits Type I IFN Production

Tocker

Durocher

Jacob

et al. 2017

The Journal of Immunology

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“…The microarray data were downloaded from the Gene Expression Barcode 3.0 (http://barcode.luhs.org; McCall et al., 2014) or from the GEO database (GEO: GSE30652; Nazor et al., 2012). …”

Section: Methodsmentioning

confidence: 99%

KeyGenes, a Tool to Probe Tissue Differentiation Using a Human Fetal Transcriptional Atlas

Roost¹,

Iperen²,

Ariyürek³

et al. 2015

Stem Cell Reports

118

129

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SummaryDifferentiated derivatives of human pluripotent stem cells in culture are generally phenotypically immature compared to their adult counterparts. Their identity is often difficult to determine with certainty because little is known about their human fetal equivalents in vivo. Cellular identity and signaling pathways directing differentiation are usually determined by extrapolating information from either human adult tissue or model organisms, assuming conservation with humans. To resolve this, we generated a collection of human fetal transcriptional profiles at different developmental stages. Moreover, we developed an algorithm, KeyGenes, which uses this dataset to quantify the extent to which next-generation sequencing or microarray data resemble specific cell or tissue types in the human fetus. Using KeyGenes combined with the human fetal atlas, we identified multiple cell and tissue samples unambiguously on a limited set of features. We thus provide a flexible and expandable platform to monitor and evaluate the efficiency of differentiation in vitro.

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“…34 After the normalization step, an expression probability threshold was calculated for each sample to distinguish the expressed probe sets from the nonexpressed ones. The Gene Expression Barcode algorithm 35 has been designed to estimate this value taking into account a huge amount of publicly available experiments from different Affymetrix platforms, including the Affymetrix Human Genome U133 Plus 2.0 Array. The output of the algorithm is the z score value under the unexpressed normal distribution for each probe set.…”

Section: Analysis Of Transcriptomics Datamentioning

confidence: 99%

Proteogenomics Dashboard for the Human Proteome Project

et al. 2015

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dasHPPboard is a novel proteomics-based dashboard that collects and reports the experiments produced by the Spanish Human Proteome Project consortium (SpHPP) and aims to help HPP to map the entire human proteome. We have followed the strategy of analog genomics projects like the Encyclopedia of DNA Elements (ENCODE), which provides a vast amount of data on human cell lines experiments. The dashboard includes results of shotgun and selected reaction monitoring proteomics experiments, post-translational modifications information, as well as proteogenomics studies. We have also processed the transcriptomics data from the ENCODE and Human Body Map (HBM) projects for the identification of specific gene expression patterns in different cell lines and tissues, taking special interest in those genes having little proteomic evidence available (missing proteins). Peptide databases have been built using single nucleotide variants and novel junctions derived from RNA-Seq data that can be used in search engines for sample-specific protein identifications on the same cell lines or tissues. The dasHPPboard has been designed as a tool that can be used to share and visualize a combination of proteomic and transcriptomic data, providing at the same time easy access to resources for proteogenomics analyses. The dasHPPboard can be freely accessed at: http://sphppdashboard.cnb.csic.es.

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The Gene Expression Barcode 3.0: improved data processing and mining tools

Cited by 71 publications

References 13 publications

The Scaffolding Protein IQGAP1 Interacts with NLRC3 and Inhibits Type I IFN Production

The Scaffolding Protein IQGAP1 Interacts with NLRC3 and Inhibits Type I IFN Production

KeyGenes, a Tool to Probe Tissue Differentiation Using a Human Fetal Transcriptional Atlas

Proteogenomics Dashboard for the Human Proteome Project

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