ToppGene Suite for gene list enrichment analysis and candidate gene prioritization

By J. Chen, Eric E. Bardes, Bruce J. Aronow, Anil G. Jegga. Nucleic Acids Res., 2009

2,865 citations recorded by the source corpus.

ToppGene Suite (http://toppgene.cchmc.org; this web site is free and open to all users and does not require a login to access) is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis. A P-value of each annotation of a test gene is derived by random sampling of the whole genome. The protein–protein interaction network (PPIN)-based disease candidate gene prioritization uses social and Web networks analysis algorithms (extended versions of the PageRank and HITS algorithms, and the K-Step Markov method). We demonstrate the utility of ToppGene Suite using 20 recently reported GWAS-based gene–disease associations (including novel disease genes) representing five diseases. ToppGene ranked 19 of 20 (95%) candidate genes within the top 20%, while ToppNet ranked 12 of 16 (75%) candidate genes among the top 20%.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d82409e27ef3db6bfe8fe6e50f385e910257621c

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Citations in the corpus

SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis (2015, methodology): “In the demonstration, we used ToppGene Suite [52] (http://toppgene.cchmc.org), DAVID Bioinformatics Resources [1,53] (http://david.abcc. ncifcrf.gov), MSigDB [54] (http://www.broadinstitute.org/gsea/msigdb), and Genecards (http://www.genecards.org) for gene sets functional enrichment analysis.”
Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, methodology): “Many of these approaches apply NLP methods to extract known disease-gene relations from the literature, which are then used to predict novel disease-gene relations [22–26].”
TSGene 2.0: an updated literature-based knowledgebase for tumor suppressor genes (2015, methodology): “We used a gene ranking tool, ToppGene (36), to prioritize the tumor suppressor miRNAs and found 24 miRNAs that were significantly associated with the 13 TSGs (Supplementary Table S5, ranking P-values < 0.”
Prioritizing candidate disease genes by network-based boosting of genome-wide association data. (2011, background): “GBA in such networks has been shown to correctly identify disease and phenotype-linked genes based on their network connections to previously known genes (e.g., Fraser and Plotkin 2007; Lage et al. 2007; McGary et al. 2007; Lee et al. 2008, 2010a; Chen et al. 2009b; Huttenhower et al. 2009; Linghu e”
Inductive matrix completion for predicting gene–disease associations (2014, methodology): “(2008)], worm [Caenorhabditis elegans fromWormBaseChen et al. (2005) and Green et al. (2011)], fruit fly [Drosophila melanogaster from FlyBase Tweedie et al. (2009)], mouse [Mus musculus from MGD Eppig et al. (2007)], yeast [Saccharomyces cerevisiae from Dwight et al. (2002), Saito et al. (2004), Mc”
Computational tools for prioritizing candidate genes: boosting disease gene discovery (2012, methodology): “A first possibility is to rely on methods that do not use any prior knowledge about disease phenotype and that perform a priori prioritization using sequence features 80,81 or topological network features only 69 .”
FMRP Modulates Neural Differentiation through m6A-Dependent mRNA Nuclear Export (2019, positive): “Gene ontology analysis was performed using the TOPPFUN module of TOPPGENE (Chen et al., 2009).”
Canonical Genetic Signatures of the Adult Human Brain (2015, positive): “The ToppGene facility (https://topgene.cchmc.org) 24 was used for access to annotation databases as discussed above.”
PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites (2012, methodology): “The significantly enriched GO terms of the predicted caspase-3 substrates according to three major categories (Molecular Function, Biological Process and Cellular Component) were obtained using the gene list enrichment analysis tool, ToppFun [86].”