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Millions of reads, thousands of taxa: microbial community structure and associations analyzed via marker genes
Author(s) -
Miklós Bálint,
Mohammad Bahram,
A. Murat Eren,
Karoline Faust,
Jed A. Fuhrman,
Björn D. Lindahl,
Robert B. O’Hara,
Maarja Öpik,
Mitchell L. Sogin,
Martin Unterseher,
Leho Tedersoo
Publication year - 2016
Publication title -
fems microbiology reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.91
H-Index - 212
eISSN - 1574-6976
pISSN - 0168-6445
DOI - 10.1093/femsre/fuw017
Subject(s) - biology , computational biology , taxon , inference , ecology , statistical inference , data science , evolutionary biology , computer science , artificial intelligence , statistics , mathematics
With high-throughput sequencing (HTS), we are able to explore the hidden world of microscopic organisms to an unpre-cedented level. The fast development of molecular technology and statistical methods means that microbial ecologists must keep their toolkits updated. Here, we review and evaluate some of the more widely adopted and emerging techniques for analysis of diversity and community composition, and the inference of species interactions from co-occurrence data generated by HTS of marker genes. We emphasize the importance of observational biases and statistical properties of the data and methods. The aim of the review is to critically discuss the advantages and disadvantages of established and emerging statistical methods, and to contribute to the integration of HTS-based marker gene data into community ecology.

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