Literature DB >> 33468706

Microbiome Search Engine 2: a Platform for Taxonomic and Functional Search of Global Microbiomes on the Whole-Microbiome Level.

Gongchao Jing1,2, Lu Liu1,2, Zengbin Wang1,2, Yufeng Zhang3,1,2, Li Qian4, Chunxiao Gao5, Meng Zhang1,2, Min Li1,2, Zhenkun Zhang6, Xiaohan Liu6, Jian Xu7,2, Xiaoquan Su8,1,2.   

Abstract

Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (<0.5 s per query) searches against the entire database for best-matched microbiomes using overall taxonomic or functional profiles, and (iii) a Web-based graphical user interface for user-friendly searching, data browsing, and tutoring. MSE 2 is freely accessible via http://mse.ac.cn For standalone searches of customized microbiome databases, the kernel of the MSE 2 search engine is provided at GitHub (https://github.com/qibebt-bioinfo/meta-storms).IMPORTANCE A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird's-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile. Key improvements include database extension, data compatibility, a search engine kernel, and a user interface. The new ability to search the microbiome space via functional similarity greatly expands the scope of search-based mining of the microbiome big data.
Copyright © 2021 Jing et al.

Entities:  

Keywords:  amplicon; metagenome; microbiome; online service; search engine

Year:  2021        PMID: 33468706      PMCID: PMC7820668          DOI: 10.1128/mSystems.00943-20

Source DB:  PubMed          Journal:  mSystems        ISSN: 2379-5077            Impact factor:   6.496


  35 in total

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Journal:  Mol Syst Biol       Date:  2014-11-28       Impact factor: 11.429

3.  The MG-RAST metagenomics database and portal in 2015.

Authors:  Andreas Wilke; Jared Bischof; Wolfgang Gerlach; Elizabeth Glass; Travis Harrison; Kevin P Keegan; Tobias Paczian; William L Trimble; Saurabh Bagchi; Ananth Grama; Somali Chaterji; Folker Meyer
Journal:  Nucleic Acids Res       Date:  2015-12-09       Impact factor: 16.971

4.  A communal catalogue reveals Earth's multiscale microbial diversity.

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Journal:  Nature       Date:  2017-11-01       Impact factor: 49.962

5.  Gut microbiome structure and metabolic activity in inflammatory bowel disease.

Authors:  Eric A Franzosa; Alexandra Sirota-Madi; Julian Avila-Pacheco; Nadine Fornelos; Henry J Haiser; Stefan Reinker; Tommi Vatanen; A Brantley Hall; Himel Mallick; Lauren J McIver; Jenny S Sauk; Robin G Wilson; Betsy W Stevens; Justin M Scott; Kerry Pierce; Amy A Deik; Kevin Bullock; Floris Imhann; Jeffrey A Porter; Alexandra Zhernakova; Jingyuan Fu; Rinse K Weersma; Cisca Wijmenga; Clary B Clish; Hera Vlamakis; Curtis Huttenhower; Ramnik J Xavier
Journal:  Nat Microbiol       Date:  2018-12-10       Impact factor: 17.745

6.  Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences.

Authors:  Morgan G I Langille; Jesse Zaneveld; J Gregory Caporaso; Daniel McDonald; Dan Knights; Joshua A Reyes; Jose C Clemente; Deron E Burkepile; Rebecca L Vega Thurber; Rob Knight; Robert G Beiko; Curtis Huttenhower
Journal:  Nat Biotechnol       Date:  2013-08-25       Impact factor: 54.908

7.  Processing faecal samples: a step forward for standards in microbial community analysis.

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8.  EBI Metagenomics in 2017: enriching the analysis of microbial communities, from sequence reads to assemblies.

Authors:  Alex L Mitchell; Maxim Scheremetjew; Hubert Denise; Simon Potter; Aleksandra Tarkowska; Matloob Qureshi; Gustavo A Salazar; Sebastien Pesseat; Miguel A Boland; Fiona M I Hunter; Petra Ten Hoopen; Blaise Alako; Clara Amid; Darren J Wilkinson; Thomas P Curtis; Guy Cochrane; Robert D Finn
Journal:  Nucleic Acids Res       Date:  2018-01-04       Impact factor: 16.971

9.  Qiita: rapid, web-enabled microbiome meta-analysis.

Authors:  Antonio Gonzalez; Jose A Navas-Molina; Tomasz Kosciolek; Daniel McDonald; Yoshiki Vázquez-Baeza; Gail Ackermann; Jeff DeReus; Stefan Janssen; Austin D Swafford; Stephanie B Orchanian; Jon G Sanders; Joshua Shorenstein; Hannes Holste; Semar Petrus; Adam Robbins-Pianka; Colin J Brislawn; Mingxun Wang; Jai Ram Rideout; Evan Bolyen; Matthew Dillon; J Gregory Caporaso; Pieter C Dorrestein; Rob Knight
Journal:  Nat Methods       Date:  2018-10-01       Impact factor: 28.547

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Review 3.  Towards multi-label classification: Next step of machine learning for microbiome research.

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4.  A Scale-Free, Fully Connected Global Transition Network Underlies Known Microbiome Diversity.

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Journal:  mSystems       Date:  2021-07-13       Impact factor: 6.496

  4 in total

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