Metaviz: interactive statistical and visual analysis of metagenomic data

Abstract Large studies profiling microbial communities and their association with healthy or disease phenotypes are now commonplace. Processed data from many of these studies are publicly available but significant effort is required for users to effectively organize, explore and integrate it, limiting the utility of these rich data resources. Effective integrative and interactive visual and statistical tools to analyze many metagenomic samples can greatly increase the value of these data for researchers. We present Metaviz, a tool for interactive exploratory data analysis of annotated microbiome taxonomic community profiles derived from marker gene or whole metagenome shotgun sequencing. Metaviz is uniquely designed to address the challenge of browsing the hierarchical structure of metagenomic data features while rendering visualizations of data values that are dynamically updated in response to user navigation. We use Metaviz to provide the UMD Metagenome Browser web service, allowing users to browse and explore data for more than 7000 microbiomes from published studies. Users can also deploy Metaviz as a web service, or use it to analyze data through the metavizr package to interoperate with state-of-the-art analysis tools available through Bioconductor. Metaviz is free and open source with the code, documentation and tutorials publicly accessible.

[1]  M. Pop,et al.  Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli and subsequent ciprofloxacin treatment , 2016, BMC Genomics.

[2]  Florin Chelaru,et al.  Epiviz: a view inside the design of an integrated visual analysis software for genomics , 2015, BMC Bioinformatics.

[3]  A. Murat Eren,et al.  VAMPS: a website for visualization and analysis of microbial population structures , 2014, BMC Bioinformatics.

[4]  Luis Pedro Coelho,et al.  Structure and function of the global ocean microbiome , 2015, Science.

[5]  Allyson L. Byrd,et al.  Biogeography and individuality shape function in the human skin metagenome , 2014, Nature.

[6]  Shawn Rynearson,et al.  Taxonomer: an interactive metagenomics analysis portal for universal pathogen detection and host mRNA expression profiling , 2016, Genome Biology.

[7]  Adam M. Phillippy,et al.  Interactive metagenomic visualization in a Web browser , 2011, BMC Bioinformatics.

[8]  Jeffrey Heer,et al.  D³ Data-Driven Documents , 2011, IEEE Transactions on Visualization and Computer Graphics.

[9]  Paolo Manghi,et al.  Accessible, curated metagenomic data through ExperimentHub , 2017, Nature Methods.

[10]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[11]  Guido V. Bloemberg,et al.  Recognition of Potentially Novel Human Disease-Associated Pathogens by Implementation of Systematic 16S rRNA Gene Sequencing in the Diagnostic Laboratory , 2010, Journal of Clinical Microbiology.

[12]  Héctor Corrada Bravo,et al.  Epiviz: interactive visual analytics for functional genomics data , 2014, Nature Methods.

[13]  Tom O. Delmont,et al.  Anvi’o: an advanced analysis and visualization platform for ‘omics data , 2015, PeerJ.

[14]  Cristina Aurrecoechea,et al.  MicrobiomeDB: a systems biology platform for integrating, mining and analyzing microbiome experiments , 2017, bioRxiv.

[15]  M. Pop,et al.  Robust methods for differential abundance analysis in marker gene surveys , 2013, Nature Methods.

[16]  Tom H. Pringle,et al.  The human genome browser at UCSC. , 2002, Genome research.

[17]  Georgios A. Pavlopoulos,et al.  Uncovering Earth’s virome , 2016, Nature.

[18]  C. Huttenhower,et al.  Metagenomic microbial community profiling using unique clade-specific marker genes , 2012, Nature Methods.

[19]  Intawat Nookaew,et al.  PanViz: interactive visualization of the structure of functionally annotated pangenomes , 2016, Bioinform..

[20]  Mihai Pop,et al.  Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition , 2014, Genome Biology.

[21]  J. Doré,et al.  Structural robustness of the gut mucosal microbiota is associated with Crohn's disease remission after surgery , 2015, Gut.

[22]  Raimund Dachselt,et al.  FacetZoom: a continuous multi-scale widget for navigating hierarchical metadata , 2008, CHI.

[23]  J. Gaudart,et al.  Tropheryma whipplei associated with diarrhoea in young children. , 2016, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[24]  Nayoung Kim,et al.  Analysis of Gastric Body Microbiota by Pyrosequencing: Possible Role of Bacteria Other Than Helicobacter pylori in the Gastric Carcinogenesis , 2017, Journal of cancer prevention.