Literature DB >> 20331766

Ultrasequencing of the meiofaunal biosphere: practice, pitfalls and promises.

S Creer1, V G Fonseca, D L Porazinska, R M Giblin-Davis, W Sung, D M Power, M Packer, G R Carvalho, M L Blaxter, P J D Lambshead, W K Thomas.   

Abstract

Biodiversity assessment is the key to understanding the relationship between biodiversity and ecosystem functioning, but there is a well-acknowledged biodiversity identification gap related to eukaryotic meiofaunal organisms. Meiofaunal identification is confounded by the small size of taxa, morphological convergence and intraspecific variation. However, the most important restricting factor in meiofaunal ecological research is the mismatch between diversity and the number of taxonomists that are able to simultaneously identify and catalogue meiofaunal diversity. Accordingly, a molecular operational taxonomic unit (MOTU)-based approach has been advocated for en mass meiofaunal biodiversity assessment, but it has been restricted by the lack of throughput afforded by chain termination sequencing. Contemporary pyrosequencing offers a solution to this problem in the form of environmental metagenetic analyses, but this represents a novel field of biodiversity assessment. Here, we provide an overview of meiofaunal metagenetic analyses, ranging from sample preservation and DNA extraction to PCR, sequencing and the bioinformatic interrogation of multiple, independent samples using 454 Roche sequencing platforms. We report two examples of environmental metagenetic nuclear small subunit 18S (nSSU) analyses of marine and tropical rainforest habitats and provide critical appraisals of the level of putative recombinant DNA molecules (chimeras) in metagenetic data sets. Following stringent quality control measures, environmental metagenetic analyses achieve MOTU formation across the eukaryote domain of life at a fraction of the time and cost of traditional approaches. The effectiveness of Roche 454 sequencing brings substantial advantages to studies aiming to elucidate the molecular genetic richness of not only meiofaunal, but also all complex eukaryotic communities.

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Year:  2010        PMID: 20331766     DOI: 10.1111/j.1365-294X.2009.04473.x

Source DB:  PubMed          Journal:  Mol Ecol        ISSN: 0962-1083            Impact factor:   6.185


  87 in total

1.  Marine Nematode Taxonomy in Africa: Promising Prospects Against Scarcity of Information.

Authors:  Fehmi Boufahja; Federica Semprucci; Hamouda Beyrem; Punyasloke Bhadury
Journal:  J Nematol       Date:  2015-09       Impact factor: 1.402

Review 2.  Phylogeny, phylogeography, phylobetadiversity and the molecular analysis of biological communities.

Authors:  Brent C Emerson; Francesco Cicconardi; Pietro P Fanciulli; Peter J A Shaw
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2011-08-27       Impact factor: 6.237

3.  Utility of Classical α-Taxonomy for Biodiversity of Aquatic Nematodes.

Authors:  Wilfrida Decraemer; Thierry Backeljau
Journal:  J Nematol       Date:  2015-03       Impact factor: 1.402

4.  Tree diversity and species identity effects on soil fungi, protists and animals are context dependent.

Authors:  Leho Tedersoo; Mohammad Bahram; Tomáš Cajthaml; Sergei Põlme; Indrek Hiiesalu; Sten Anslan; Helery Harend; Franz Buegger; Karin Pritsch; Julia Koricheva; Kessy Abarenkov
Journal:  ISME J       Date:  2015-07-14       Impact factor: 10.302

5.  MOTUs, Morphology, and Biodiversity Estimation: A Case Study Using Nematodes of the Suborder Criconematina and a Conserved 18S DNA Barcode.

Authors:  Thomas Powers; Timothy Harris; Rebecca Higgins; Peter Mullin; Lisa Sutton; Kirsten Powers
Journal:  J Nematol       Date:  2011-03       Impact factor: 1.402

6.  Metagenetic community analysis of microbial eukaryotes illuminates biogeographic patterns in deep-sea and shallow water sediments.

Authors:  Holly M Bik; Way Sung; Paul De Ley; James G Baldwin; Jyotsna Sharma; Axayácatl Rocha-Olivares; W Kelley Thomas
Journal:  Mol Ecol       Date:  2011-10-10       Impact factor: 6.185

7.  Current noise-removal methods can create false signals in ecogenomic data.

Authors:  Axel G Rossberg; Tim Rogers; Alan J McKane
Journal:  Proc Biol Sci       Date:  2014-03-26       Impact factor: 5.349

8.  A critique of Rossberg et al.: Noise obscures the genetic signal of meiobiotal ecospecies in ecogenomic datasets.

Authors:  M J Morgan; D Bass; H Bik; C W Birky; M Blaxter; M D Crisp; S Derycke; D Fitch; D Fontaneto; C M Hardy; A J King; K C Kiontke; T Moens; J W Pawlowski; D Porazinska; C Q Tang; W K Thomas; D K Yeates; S Creer
Journal:  Proc Biol Sci       Date:  2014-03-26       Impact factor: 5.349

9.  Simultaneous assessment of the macrobiome and microbiome in a bulk sample of tropical arthropods through DNA metasystematics.

Authors:  Joel Gibson; Shadi Shokralla; Teresita M Porter; Ian King; Steven van Konynenburg; Daniel H Janzen; Winnie Hallwachs; Mehrdad Hajibabaei
Journal:  Proc Natl Acad Sci U S A       Date:  2014-05-07       Impact factor: 11.205

10.  Second-generation environmental sequencing unmasks marine metazoan biodiversity.

Authors:  Vera G Fonseca; Gary R Carvalho; Way Sung; Harriet F Johnson; Deborah M Power; Simon P Neill; Margaret Packer; Mark L Blaxter; P John D Lambshead; W Kelley Thomas; Simon Creer
Journal:  Nat Commun       Date:  2010-10-19       Impact factor: 14.919

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