A scored human protein-protein interaction network to catalyze genomic interpretation

Li, Taibo; Wernersson, Rasmus; Hansen, Rasmus Borup; Horn, Heiko; Mercer, Johnathan; Slodkowicz, Greg; Workman, Christopher; Regina, Olga; Rapacki, Kristoffer; Stærfeldt, Hans‐Henrik; Brunak, Søren; Jensen, Thomas Skøt; Lage, Kasper

doi:10.1101/064535

Cited by 97 publications

(136 citation statements)

References 56 publications

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“…Protein–protein interaction (PPI) data were downloaded from STRING v10.0 and InBioMap . Finally, a non‐redundant PPI network containing 7638 proteins and 160 420 interactions was obtained for further analysis.…”

Section: Methodsmentioning

confidence: 99%

Identification of genes with universally upregulated or downregulated expressions in colorectal cancer

Song

Liu

et al. 2018

J of Gastro and Hepatol

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Background and Aim Differentially expressed (DE) genes detected at the population‐level through case–control comparison provide no information on the dysregulation frequencies of DE genes in a cancer. In this work, we aimed to identify the genes with universally upregulated or downregulated expressions in colorectal cancer (CRC). Methods We firstly clarified that DE genes in an individual cancer tissue should be the disease‐relevant genes, which are dysregulated in the cancer tissue in comparison with its own previously normal state. Then, we identified DE genes at the individual level for 2233 CRC samples collected from multiple data sources using the RankComp algorithm. Results We found 26 genes that were upregulated or downregulated in almost all the 2233 CRC samples and validated the results using 124 CRC tissues with paired adjacent normal tissues. Especially, we found that two genes (AJUBA and EGFL6), upregulated universally in CRC tissues, were extremely lowly expressed in normal colorectal tissues, which were considered to be oncogenes in CRC oncogenesis and development. Oppositely, we found that one gene (LPAR1), downregulated universally in CRC tissues, was silenced in CRC tissues but highly expressed in normal colorectal tissues, which were considered to be tumor suppressor genes in CRC. Functional evidences revealed that these three genes may induce CRC through deregulating pathways for ribosome biogenesis, cell proliferation, and cell cycle. Conclusions In conclusion, the individual‐level DE genes analysis can help us find genes dysregulated universally in CRC tissues, which may be important diagnostic biomarkers and therapy targets.

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Section: Methodsmentioning

confidence: 99%

Identification of genes with universally upregulated or downregulated expressions in colorectal cancer

Song

Liu

et al. 2018

J of Gastro and Hepatol

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“…This data-type had deep transcriptomic coverage, while it retained cell specificity. We built the cDC1-specific interactome by sub-setting the physical PPI database InWeb_IM [2], keeping only proteins with genes expressed in cDC1 cells (n=8,569). InWeb_IM is an aggregation of experimentally generated PPI datasets and databases from human, mice and other eukaryotes.…”

Section: Construction and Visualization Of The Cdc1 Interactomementioning

confidence: 99%

“…InWeb_IM is an aggregation of experimentally generated PPI datasets and databases from human, mice and other eukaryotes. Each InWeb_IM interaction has been quality benchmarked and scored accordingly [2].…”

Section: Construction and Visualization Of The Cdc1 Interactomementioning

confidence: 99%

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Bow-tie motifs enable protein multifunctionality by connecting cellular processes in the interactome

Niss

Gómez‐Casado

et al. 2018

Preprint

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/ 175 words)Cell type-specific protein interactomes are promising resources for the understanding of cell functionality and genetic perturbations in cellular states. However, while many low-and top-level aspects of interactome topology have been mapped, we still lack an understanding of the topological motifs at the intermediate level, where connectivity between cellular processes takes place. We combine conventional dendritic cell lineage 1 (cDC1)-specific expression data with a high-quality protein interactome, subsequently creating an information-rich cDC1-specifc interactome matrix that displays topological information on an unprecedented 36.7 million protein pairs at once. By dissecting the interactome-matrix, we reveal that the bow-tie motif is a major connector of cellular processes and an extremely widespread topology in the protein interactome.In aggregation, the bow-tie motifs form a scaffold within the interactome that hierarchically link cellular processes together. We further demonstrate that the bow-tie motif's design can enable protein multifunction. Our generic approach allows a topological characterization and visualization of any large network, facilitating a more effective usage of community-produced interaction data, and is applicable to any cell type's interactome.

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“…We used the inBioMap database 13 The resulting network contained 23,886 nodes and 17,108,116 edges. The knowledge graph's purpose is to utilize the "guilt by association" rule: although many related medical concepts may not directly co-occur in any medical records, they may indirectly share neighbors in the knowledge graph through the protein-protein, SNP-symptom, and drug-protein edges.…”

Section: Knowledge Graph Constructionmentioning

confidence: 99%

VisAGE: Integrating external knowledge into electronic medical record visualization

2017

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In this paper, we present VisAGE, a method that visualizes electronic medical records (EMRs) in a low-dimensional space. Effective visualization of new patients allows doctors to view similar, previously treated patients and to identify the new patients' disease subtypes, reducing the chance of misdiagnosis. However, EMRs are typically incomplete or fragmented, resulting in patients who are missing many available features being placed near unrelated patients in the visualized space. VisAGE integrates several external data sources to enrich EMR databases to solve this issue. We evaluated VisAGE on a dataset of Parkinson's disease patients. We qualitatively and quantitatively show that VisAGE can more effectively cluster patients, which allows doctors to better discover patient subtypes and thus improve patient care.

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A scored human protein-protein interaction network to catalyze genomic interpretation

Cited by 97 publications

References 56 publications

Identification of genes with universally upregulated or downregulated expressions in colorectal cancer

Identification of genes with universally upregulated or downregulated expressions in colorectal cancer

Bow-tie motifs enable protein multifunctionality by connecting cellular processes in the interactome

VisAGE: Integrating external knowledge into electronic medical record visualization

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