Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas

  • Jesper Lund
  • Qihua Tan
  • Jan Baumbach
Part of the Methods in Molecular Biology book series (MIMB, volume 1807)


In recent decades, the accumulation of data on 16s ribosomal RNA genes has yielded free and public databases such as SILVA, GreenGenes, The Ribosomal Database Project, and IMG, handling massive amounts of raw data and meta information. 16s rRNA gene contains hypervariable regions with great classification power. As a result, numerous classification tools have emerged including state-of-the-art tools such as Mothur, Qiime, and the 16s classifier. However, there is a gap between the sequence databases, the taxonomy profiling tools and available meta information such as geo/body-location information. Here, we present BioAtlas, and interactive web tool for searching, exploring, and analyzing prokaryotic distributions by integration of various resources of metagenomics databases. In the following section we show how to use BioAtlas to (1) search and explore prokaryote occurrences across the geospatial map of the world, (2) investigate and hunt for occurrences across generic user-generated surface-specific maps, with an example map of a human female, with data from Bouslimani et al., and (3) classify a user-given sequences dataset through our online platform for visual exploration of the spatial abundances of the identified microbes.

Key words

Taxonomic classification 16s gene Ribosomal RNA Distributional analysis Microbiology Online tool Maps Data mining Integration Metadata 



JBL is grateful for financial support from his VILLUM Young Investigator Grant. Funding for open access charge: VILLUM Young Investigator Grant of Jan Baumbach (Young Investigator Grant nr. 13154).

JBL is grateful for financial support from his Velux Foundation research grant (Research grant nr. 000121540), supporting his Ph.D. project.

Conflict of Interest Statement

None declared.


  1. 1.
    Berney C, Ciuprina A, Bender S, Brodie J, Edgcomb V, Kim E, Rajan J, Parfrey LW, Adl S, Audic S et al (2017) Unieuk: time to speak a common language in protistology! J Eukaryot Microbiol 64(3):407–411Google Scholar
  2. 2.
    Bouslimani A, Porto C, Rath CM, Wang M, Guo Y, Gonzalez A, Berg-Lyon D, Ackermann G, Christensen GJM, Nakatsuji T et al (2015) Molecular cartography of the human skin surface in 3d. Proc Natl Acad Sci 112(17):E2120–E2129Google Scholar
  3. 3.
    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI et al (2010) Qiime allows analysis of high-throughput community sequencing data. Nat Methods 7(5):335–336Google Scholar
  4. 4.
    Chaudhary N, Sharma AK, Agarwal P, Gupta A, Sharma VK (2015) 16s classifier: a tool for fast and accurate taxonomic classification of 16s rRNA hypervariable regions in metagenomic datasets. PLoS One 10(2):e0116106Google Scholar
  5. 5.
    Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM (2013) Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 42(D1):D633–D642Google Scholar
  6. 6.
    de Queiroz K (1997) The linnaean hierarchy and the evolutionization of taxonomy, with emphasis on the problem of nomenclature. Aliso J Syst Evol Bot 15(2):125–144Google Scholar
  7. 7.
    DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL (2006) Greengenes, achimera-checked 16s rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72(7):5069–5072Google Scholar
  8. 8.
    Giovannoni SJ, Britschgi TB, Moyer CL, Field KG (1990) Genetic diversity in sargasso sea bacterioplankton. Nature 345(6270): 60–63Google Scholar
  9. 9.
    Lund JB, List M, Baumbach J (2017) Interactive microbial distribution analysis using bioatlas. Nucleic Acids Res.
  10. 10.
    Markowitz VM, Chen IMA, Palaniappan K, Chu K, Szeto E, Grechkin Y, Ratner A, Jacob B, Huang J, Williams P et al (2011) Img: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res 40(D1):D115–D122CrossRefPubMedCentralGoogle Scholar
  11. 11.
    Mitchell A, Bucchini F, Cochrane G, Denise H, Hoopen Pt, Fraser M, Pesseat S, Potter S, Scheremetjew M, Sterk P et al (2015) Ebi metagenomics in 2016-an expanding and evolving resource for the analysis and archiving of metagenomic data. Nucleic Acids Res 44(D1):D595–D603CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Mizrahi-Man O, Davenport ER, Gilad Y (2013) Taxonomic classification of bacterial 16s rRNA genes using short sequencing reads: evaluation of effective study designs. PloS One 8(1):e53608CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Pagani I, Liolios K, Jansson J, Chen IMA, Smirnova T, Nosrat B, Markowitz VM, Kyrpides NC (2011) The genomes online database (gold) v. 4: status of genomic and metagenomic projects and their associated metadata. Nucleic Acids Res 40(D1):D571–D579CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2012) The silva ribosomal rna gene database project: improved data processing and web-based tools. Nucleic Acids Res 41(D1):D590–D596CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ et al (2009) Introducing mothur: open-source,platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75(23):7537–7541CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W, Schleifer KH, Whitman WB, Euzéby J, Amann R, Rosselló-Móra R (2014) Uniting the classification of cultured and uncultured bacteria and archaea using 16s rrna gene sequences. Nat Rev Microbiol 12(9):635CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jesper Lund
    • 1
  • Qihua Tan
    • 2
  • Jan Baumbach
    • 3
    • 4
  1. 1.Epidemiology and Biostatistics, Department of Public HealthUniversity of Southern DenmarkOdenseDenmark
  2. 2.Epidemiology and Biostatistics, Department of Public HealthUniversity of Southern DenmarkOdenseDenmark
  3. 3.University of Southern DenmarkOdenseDenmark
  4. 4.Technical University of MunichMunichGermany

Personalised recommendations