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Analysing Microbial Community Composition through Amplicon Sequencing: From Sampling to Hypothesis Testing
KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Karolinska Institutet, Sweden.
KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.ORCID-id: 0000-0002-3627-6899
2017 (engelsk)Inngår i: Frontiers in Microbiology, ISSN 1664-302X, E-ISSN 1664-302X, Vol. 8, artikkel-id 1561Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

Microbial ecology as a scientific field is fundamentally driven by technological advance. The past decade's revolution in DNA sequencing cost and throughput has made it possible for most research groups to map microbial community composition in environments of interest. However, the computational and statistical methodology required to analyse this kind of data is often not part of the biologist training. In this review, we give a historical perspective on the use of sequencing data inmicrobial ecology and restate the current need for this method; but also highlight the major caveats with standard practices for handling these data, from sample collection and library preparation to statistical analysis. Further, we outline the main new analytical tools that have been developed in the past few years to bypass these caveats, as well as highlight the major requirements of common statistical practices and the extent to which they are applicable to microbial data. Besides delving into the meaning of select alpha- and beta-diversity measures, we give special consideration to techniques for finding the main drivers of community dissimilarity and for interaction network construction. While every project design has specific needs, this review should serve as a starting point for considering what options are available.

sted, utgiver, år, opplag, sider
FRONTIERS MEDIA SA , 2017. Vol. 8, artikkel-id 1561
Emneord [en]
bioinformatics, biostatistics, amplicon sequencing, microbiome, NGS, 16S rRNA, microbial ecology
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-215454DOI: 10.3389/fmicb.2017.01561ISI: 000409344400001PubMedID: 28928718Scopus ID: 2-s2.0-85028756314OAI: oai:DiVA.org:kth-215454DiVA, id: diva2:1150582
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QC 20171019

Tilgjengelig fra: 2017-10-19 Laget: 2017-10-19 Sist oppdatert: 2017-11-29bibliografisk kontrollert

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