Skip to main content

Computational Approaches for Metagenomic Datasets

  • Living reference work entry
  • Latest version View entry history
  • First Online:
Encyclopedia of Metagenomics

Synonyms

Bioinformatic analysis; Metagenome data analysis

Definition

The process of gaining information about a metagenomic community from sequence data using a variety of interdisciplinary techniques and approaches.

Introduction

The history of computational approaches to metagenomic data analysis is brief given the rapid development of the field. In 1998, a visionary paper described techniques for investigation of the molecular diversity of environmental communities and coined the term metagenome (Handelsman et al. 1998). Focus was placed on screening clone libraries for interesting biological activities, a mainly laboratory-based endeavor which has been continually successful at identifying relevant novel genes with novel functionality. Other researchers took a more technology-driven approach by randomly sequencing metagenomic DNA from an acid mine biofilm and the well-known Sargasso Sea projects. These sequence-based approaches required considerable computational capacity for...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  • Davenport CF, Tümmler B. Advances in computational analysis of metagenome sequences. Environ Microbiol. 2012. doi:10.1111/j.1462-2920.2012.02843.x.

    Google Scholar 

  • Dinsdale EA, Edwards RA, Hall D, et al. Functional metagenomic profiling of nine biomes. Nature. 2008;452:629–32.

    Article  CAS  PubMed  Google Scholar 

  • Field D, Garrity G, Gray T, et al. The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol. 2008;26:541–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Flicek P, Birney E. Sense from sequence reads: methods for alignment and assembly. Nat Methods. 2009;6:S6–12.

    Article  CAS  PubMed  Google Scholar 

  • Frey UH, Bachmann HS, Peters J, Siffert W. PCR-amplification of GC-rich regions: slowdown PCR. Nat Protoc. 2008;3:1312–7.

    Article  CAS  PubMed  Google Scholar 

  • Hamady M, Lozupone C, Knight R. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 2010;4:17–27.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Handelsman J, Rondon MR, Brady SF, et al. Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. Chem Biol. 1998;5:R245–9.

    Article  CAS  PubMed  Google Scholar 

  • Hess M, Sczyrba A, Egan R, et al. Metagenomic discovery of biomass-degrading genes and genomes from cow rumen. Science. 2011;331:463–7.

    Article  CAS  PubMed  Google Scholar 

  • Kembel SW, Wu M, Eisen JA, et al. Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Comput Biol. 2012. doi:10.1371/journal.pcbi.1002743.

    Google Scholar 

  • Luo C, Tsementzi D, Kyrpides NC, et al. Individual genome assembly from complex community short-read metagenomic datasets. ISME J. 2012;6:898–901.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Mrázek J. Phylogenetic signals in DNA composition: limitations and prospects. Mol Biol Evol. 2009;26:1163–9.

    Article  PubMed  Google Scholar 

  • Pinto AJ, Raskin L. PCR biases distort bacterial and archaeal community structure in pyrosequencing datasets. PLoS One. 2012;7:e43093.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Temperton B, Giovannoni SJ. Metagenomics: microbial diversity through a scratched lens. Curr Opin Microbiol. 2012;15:605–12.

    Article  CAS  PubMed  Google Scholar 

  • van den Oord EJCG, Sullivan PF. False discoveries and models for gene discovery. Trends Genet. 2003;19:537–42.

    Article  PubMed  Google Scholar 

  • Wendl MC, Kota K, Weinstock GM, et al. Coverage theories for metagenomic DNA sequencing based on a generalization of Stevens theorem. J Math Biol. 2012. doi:10.1007/s00285-012-0586-x.

    PubMed Central  PubMed  Google Scholar 

  • Willner D, Thurber RV, Rohwer F. Metagenomic signatures of 86 microbial and viral metagenomes. Environ Microbiol. 2009;11:1752–66.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Philippe Chouvarine Dr. .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this entry

Cite this entry

Chouvarine, P., Tümmler, B., Davenport, C. (2015). Computational Approaches for Metagenomic Datasets. In: Nelson, K. (eds) Encyclopedia of Metagenomics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6418-1_739-2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-6418-1_739-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-6418-1

  • eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences

Publish with us

Policies and ethics

Chapter history

  1. Latest

    Computational Approaches for Metagenomic Datasets
    Published:
    27 May 2015

    DOI: https://doi.org/10.1007/978-1-4614-6418-1_739-2

  2. Original

    Computational Approaches for Metagenomic Datasets
    Published:
    04 April 2014

    DOI: https://doi.org/10.1007/978-1-4614-6418-1_739-1