GeneMANIA update 2018

Franz, Max; Rodriguez, Harold; Lopes, Christian; Zuberi, Khalid; Montojo, Jason; Bader, Gary D.; Morris, Quaid

doi:10.1093/nar/gky311

Cited by 865 publications

(693 citation statements)

References 15 publications

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“…To enable training, each bait-prey association was assigned one of three preliminary labels (false positive interaction, background protein, or specific interactor). All preys corresponding to decoy protein sequences were labeled as false positives; preys whose interactions were confirmed in STRING (Szklarczyk et al, 2017) or GeneMANIA (Franz et al, 2018) were labeled as true positives; all others were labeled as background. In addition, because their levels are strongly enriched by design following affinity purification, baits within each IP were also labeled as "true interactors" for modeling purposes.…”

Section: Identification Of High Confidence Interacting Proteinsmentioning

confidence: 99%

Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome

Huttlin

Bruckner

Navarrete‐Perea

et al. 2020

Preprint

161

233

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SUMMARYThousands of interactions assemble proteins into modules that impart spatial and functional organization to the cellular proteome. Through affinity-purification mass spectrometry, we have created two proteome-scale, cell-line-specific interaction networks. The first, BioPlex 3.0, results from affinity purification of 10,128 human proteins – half the proteome – in 293T cells and includes 118,162 interactions among 14,586 proteins; the second results from 5,522 immunoprecipitations in HCT116 cells. These networks model the interactome at unprecedented scale, encoding protein function, localization, and complex membership. Their comparison validates thousands of interactions and reveals extensive customization of each network. While shared interactions reside in core complexes and involve essential proteins, cell-specific interactions bridge conserved complexes, likely ‘rewiring’ each cell’s interactome. Interactions are gained and lost in tandem among proteins of shared function as the proteome remodels to produce each cell’s phenotype. Viewable interactively online through BioPlexExplorer, these networks define principles of proteome organization and enable unknown protein characterization.

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Section: Identification Of High Confidence Interacting Proteinsmentioning

confidence: 99%

Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome

Huttlin

Bruckner

Navarrete‐Perea

et al. 2020

Preprint

161

233

View full text Add to dashboard Cite

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“…It expands the query list with functionally similar genes that recognizes by using available genomics and proteomics data. The high accuracy of the GeneMANIA prediction algorithm and large database make the GeneMANIA an useful tool for any biologist (49,50) . It is available at (http://genemania.org/ ).…”

Section: Genemaniamentioning

confidence: 99%

The Association between SLC25A15 Gene Polymorphisms and Hyperornithinemia-hyperammonemia-homocitrullinuria Syndrome: Using In Silico Analysis

Mahmoud

Ahmed

Mohammed

et al. 2019

Preprint

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Background: Hyperornithinemia-hyperammonemia-homocitrullinuria (HHH) syndrome is an autosomal recessive inborn error of the urea cycle. It is caused by mutations in the SLC25A15 gene that codes the mitochondrial ornithine transporter. The aim of this study is to detect and identify the pathogenic SNPs in SLC25A15 gene through a combination set of bioinformatics tools and their effect on the structure and function of the protein. Methods:The deleterious SNPs in SLC25A15 are detected by various bioinformatics tools, with addition to identifying their effects on the structure and function of this gene.Results: 20 deleterious SNPs out 287of were found to have their own damaging effects on the structure and function of the SLC25A15 gene. Conclusion:This study is the first in silico analysis of SLC25A15 using a selection of bioinformatics tools to detect functional and structural effects of deleterious SNPs. Finding the pathogenic SNPs is a promising start to innovate new, useful SNP diagnostic markers for medical testing and for safer novel therapies specifically targeting mutant SLC25A15.

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“…Gene function prediction and inference of GRN are in the focal point of bioinformatics, and have been the subject of numerous DREAM challenges, which invite participants to compete in providing the most accurate and complete predictions [30,81]. The conclusions from these challenges and other studies is that integration of multiple data types and inference methods typically improves performance of the bioinformatical predictions [1,[82][83][84].…”

Section: Discussionmentioning

confidence: 99%

Inferring biosynthetic and gene regulatory networks from Artemisia annua RNA sequencing data on a credit card-sized ARM computer

Tan

Mutwil

2019

Preprint

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Highlights • Processing of large scale transcriptomic data with affordable single-board computers • Transcription factors can be found in the same network as their targets • Co-expression of transcription factors and genes in secondary cell wall biosynthesis • Co-expression of transcription factors and genes involved in artemisinin biosynthesis ABSTRACTPrediction of gene function and gene regulatory networks is one of the most active topics in bioinformatics. The accumulation of publicly available gene expression data for hundreds of plant species, together with advances in bioinformatical methods and affordable computing, sets ingenuity as the major bottleneck in understanding gene function and regulation. Here, we show how a credit card-sized computer retailing for less than 50 USD can be used to rapidly predict gene function and infer regulatory networks from RNA sequencing data. To achieve this, we constructed a bioinformatical pipeline that downloads and allows quality-control of RNA sequencing data; and generates a gene co-expression network that can reveal enzymes and transcription factors participating and controlling a given biosynthetic pathway. We exemplify this by first identifying genes and transcription factors involved in the biosynthesis of secondary cell wall in the plant Artemisia annua, the main natural source of the anti-malarial drug artemisinin. Networks were then used to dissect the artemisinin biosynthesis pathway, which suggest potential transcription factors regulating artemisinin biosynthesis. We provide the source code of our pipeline and envision that the ubiquity of affordable computing, availability of biological data and increased bioinformatical training of biologists will transform the field of bioinformatics. 0.

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GeneMANIA update 2018

Cited by 865 publications

References 15 publications

Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome

Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome

The Association between SLC25A15 Gene Polymorphisms and Hyperornithinemia-hyperammonemia-homocitrullinuria Syndrome: Using In Silico Analysis

Inferring biosynthetic and gene regulatory networks from Artemisia annua RNA sequencing data on a credit card-sized ARM computer

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