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metaXplor: an interactive viral and microbial metagenomic data manager

Abstract : Background: Efficiently managing large, heterogeneous data in a structured yet flexible way is a challenge to research laboratories working with genomic data. Specifically regarding both shotgun- and metabarcoding-based metagenomics, while online reference databases and user-friendly tools exist for running various types of analyses (e.g., Qiime, Mothur, Megan, IMG/VR, Anvi'o, Qiita, MetaVir), scientists lack comprehensive software for easily building scalable, searchable, online data repositories on which they can rely during their ongoing research. Results: metaXplor is a scalable, distributable, fully web-interfaced application for managing, sharing, and exploring metagenomic data. Being based on a flexible NoSQL data model, it has few constraints regarding dataset contents and thus proves useful for handling outputs from both shotgun and metabarcoding techniques. By supporting incremental data feeding and providing means to combine filters on all imported fields, it allows for exhaustive content browsing, as well as rapid narrowing to find specific records. The application also features various interactive data visualization tools, ways to query contents by BLASTing external sequences, and an integrated pipeline to enrich assignments with phylogenetic placements. The project home page provides the URL of a live instance allowing users to test the system on public data. Conclusion: metaXplor allows efficient management and exploration of metagenomic data. Its availability as a set of Docker containers, making it easy to deploy on academic servers, on the cloud, or even on personal computers, will facilitate its adoption.
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Contributor : Christine Riou <>
Submitted on : Thursday, May 6, 2021 - 10:17:30 AM
Last modification on : Friday, May 7, 2021 - 8:55:56 AM


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Guilhem Sempéré, Adrien Pétel, Magsen Abbé, Pierre Lefeuvre, Philippe Roumagnac, et al.. metaXplor: an interactive viral and microbial metagenomic data manager. GigaScience, BioMed Central, 2021, 10, pp.1-8. ⟨10.1093/gigascience/giab001⟩. ⟨hal-03219063⟩



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