ArticleScientific reports2019
Exploratory Gene Ontology Analysis with Interactive Visualization.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 28 citations in OpenAlex.
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- Iterative clustering algorithm G-DESC-E and pan-cancer key gene analysis based on single-cell sequencing data.Briefings in bioinformatics · 2025Article
- Bioinformatic Multi-Strategy Profiling of Congenital Heart Defects for Molecular Mechanism Recognition.International journal of molecular sciences · 2024Article
- PRRGO: A Tool for Visualizing and Mapping Globally Expressed Genes in Public Gene Expression Omnibus RNA-Sequencing Studies to PageRank-scored Gene Ontology Terms.bioRxiv : the preprint server for biology · 2024Article
- Discovery of Cellular RhoA Functions by the Integrated Application of Gene Set Enrichment Analysis.Biomolecules & therapeutics · 2022Article
- GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data.BMC bioinformatics · 2021Article
- RDFizing the biosynthetic pathway of E.coli O-antigen to enable semantic sharing of microbiology data.BMC microbiology · 2021Article
- Mapping the multiscale structure of biological systems.Cell systems · 2021Review
- Systems Biology Approaches Reveal Potential Phenotype-Modifier Genes in Neurofibromatosis Type 1.Cancers · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. At the same time, the sheer number of concepts (>30,000) and relationships (>70,000) presents a challenge: it can be difficult to draw a comprehensive picture of how certain concepts of interest might relate with the rest of the ontology structure. Here we present new visualization strategies to facilitate the exploration and use of the information in the GO. We rely on novel graphical display and software architecture that allow significant interaction. To illustrate the potential of our strategies, we provide examples from high-throughput genomic analyses, including chromatin immunoprecipitation experiments and genome-wide association studies. The scientist can also use our visualizations to identify gene sets that likely experience coordinated changes in their expression and use them to simulate biologically-grounded single cell RNA sequencing data, or conduct power studies for differential gene expression studies using our built-in pipeline. Our software and documentation are available at http://aegis.stanford.edu .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.