Literature DB >> 31127124

Exploratory Gene Ontology Analysis with Interactive Visualization.

Junjie Zhu1, Qian Zhao2, Eugene Katsevich2, Chiara Sabatti3,4.   

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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Year:  2019        PMID: 31127124      PMCID: PMC6534545          DOI: 10.1038/s41598-019-42178-x

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  31 in total

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Journal:  Nat Genet       Date:  2000-05       Impact factor: 38.330

2.  Analyzing gene expression data in terms of gene sets: methodological issues.

Authors:  Jelle J Goeman; Peter Bühlmann
Journal:  Bioinformatics       Date:  2007-02-15       Impact factor: 6.937

3.  GREAT improves functional interpretation of cis-regulatory regions.

Authors:  Cory Y McLean; Dave Bristor; Michael Hiller; Shoa L Clarke; Bruce T Schaar; Craig B Lowe; Aaron M Wenger; Gill Bejerano
Journal:  Nat Biotechnol       Date:  2010-05-02       Impact factor: 54.908

4.  Differential analysis of gene regulation at transcript resolution with RNA-seq.

Authors:  Cole Trapnell; David G Hendrickson; Martin Sauvageau; Loyal Goff; John L Rinn; Lior Pachter
Journal:  Nat Biotechnol       Date:  2012-12-09       Impact factor: 54.908

5.  Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations.

Authors:  Aurelie Tomczak; Jonathan M Mortensen; Rainer Winnenburg; Charles Liu; Dominique T Alessi; Varsha Swamy; Francesco Vallania; Shane Lofgren; Winston Haynes; Nigam H Shah; Mark A Musen; Purvesh Khatri
Journal:  Sci Rep       Date:  2018-03-23       Impact factor: 4.379

6.  CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

Authors:  Henry Heberle; Marcelo Falsarella Carazzolle; Guilherme P Telles; Gabriela Vaz Meirelles; Rosane Minghim
Journal:  BMC Bioinformatics       Date:  2017-09-13       Impact factor: 3.169

7.  ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks.

Authors:  Gabriela Bindea; Bernhard Mlecnik; Hubert Hackl; Pornpimol Charoentong; Marie Tosolini; Amos Kirilovsky; Wolf-Herman Fridman; Franck Pagès; Zlatko Trajanoski; Jérôme Galon
Journal:  Bioinformatics       Date:  2009-02-23       Impact factor: 6.937

8.  QuickGO: a web-based tool for Gene Ontology searching.

Authors:  David Binns; Emily Dimmer; Rachael Huntley; Daniel Barrell; Claire O'Donovan; Rolf Apweiler
Journal:  Bioinformatics       Date:  2009-09-10       Impact factor: 6.937

9.  Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data.

Authors:  Anton Valouev; David S Johnson; Andreas Sundquist; Catherine Medina; Elizabeth Anton; Serafim Batzoglou; Richard M Myers; Arend Sidow
Journal:  Nat Methods       Date:  2008-09       Impact factor: 28.547

10.  DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

Authors:  Maxat Kulmanov; Mohammed Asif Khan; Robert Hoehndorf; Jonathan Wren
Journal:  Bioinformatics       Date:  2018-02-15       Impact factor: 6.937

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Journal:  Medicine (Baltimore)       Date:  2021-08-20       Impact factor: 1.817

Review 2.  Mapping the multiscale structure of biological systems.

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3.  RDFizing the biosynthetic pathway of E.coli O-antigen to enable semantic sharing of microbiology data.

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4.  Discovery of Cellular RhoA Functions by the Integrated Application of Gene Set Enrichment Analysis.

Authors:  Kwang-Hoon Chun
Journal:  Biomol Ther (Seoul)       Date:  2022-01-01       Impact factor: 4.634

5.  GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data.

Authors:  Federico Marini; Annekathrin Ludt; Jan Linke; Konstantin Strauch
Journal:  BMC Bioinformatics       Date:  2021-12-23       Impact factor: 3.169

  5 in total

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