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Metaviz: interactive statistical and visual analysis of metagenomic data

Justin Wagner, Florin Chelaru, Jayaram Kancherla, Joseph N. Paulson, Victor Felix, Anup Mahurkar, Héctor Corrada Bravo
doi: https://doi.org/10.1101/105205
Justin Wagner
1Department of Computer Science, University of Maryland, College Park
2Center for Bioinformatics and Computational Biology, University of Maryland, College Park
3University of Maryland Institute for Advanced Computer Studies
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Florin Chelaru
1Department of Computer Science, University of Maryland, College Park
2Center for Bioinformatics and Computational Biology, University of Maryland, College Park
3University of Maryland Institute for Advanced Computer Studies
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Jayaram Kancherla
2Center for Bioinformatics and Computational Biology, University of Maryland, College Park
3University of Maryland Institute for Advanced Computer Studies
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Joseph N. Paulson
4Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute
5Department of Biostatistics, Harvard TH Chan School of Public Health
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Victor Felix
6Institute for Genome Sciences, University of Maryland, Baltimore
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Anup Mahurkar
6Institute for Genome Sciences, University of Maryland, Baltimore
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Héctor Corrada Bravo
1Department of Computer Science, University of Maryland, College Park
2Center for Bioinformatics and Computational Biology, University of Maryland, College Park
3University of Maryland Institute for Advanced Computer Studies
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  • For correspondence: hcorrada@umiacs.umd.edu
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Abstract

Along with the survey techniques of 16S rRNA amplicon and whole-metagenome shotgun sequencing, an array of tools exists for clustering, taxonomic annotation, normalization, and statistical analysis of microbiome sequencing results. Integrative and interactive visualization that enables researchers to perform exploratory analysis in this feature rich hierarchical data is an area of need. In this work, we present Metaviz, a web browser-based tool for interactive exploratory metagenomic data analysis. Metaviz can visualize abundance data served from an R session or a Python web service that queries a graph database. As metagenomic sequencing features have a hierarchy, we designed a novel navigation mechanism to explore this feature space. We visualize abundance counts with heatmaps and stacked bar plots that are dynamically updated as a user selects taxonomic features to inspect. Metaviz also supports common data exploration techniques, including PCA scatter plots to interpret variability in the dataset and alpha diversity boxplots for examining ecological community composition. The Metaviz application and documentation is hosted at http://www.metaviz.org.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 06, 2017.
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Metaviz: interactive statistical and visual analysis of metagenomic data
Justin Wagner, Florin Chelaru, Jayaram Kancherla, Joseph N. Paulson, Victor Felix, Anup Mahurkar, Héctor Corrada Bravo
bioRxiv 105205; doi: https://doi.org/10.1101/105205
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Metaviz: interactive statistical and visual analysis of metagenomic data
Justin Wagner, Florin Chelaru, Jayaram Kancherla, Joseph N. Paulson, Victor Felix, Anup Mahurkar, Héctor Corrada Bravo
bioRxiv 105205; doi: https://doi.org/10.1101/105205

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