Evidence map›Paper›PMID 26047335›Full record

ArticlePloS one2015

Bioinformatic Amplicon Read Processing Strategies Strongly Affect Eukaryotic Diversity and the Taxonomic Composition of Communities.

Markus Majaneva, Kirsi Hyytiäinen, Sirkka Liisa Varvio, Satoshi Nagai, Jaanika Blomster

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Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

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    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Markus MajanevaDepartment of Environmental Sciences, University of Helsinki, Helsinki, Finland; Tvärminne Zoological Station, University of Helsinki, Hanko, Finland.
Kirsi HyytiäinenDepartment of Environmental Sciences, University of Helsinki, Helsinki, Finland; Tvärminne Zoological Station, University of Helsinki, Hanko, Finland.
Sirkka Liisa VarvioDepartment of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
Satoshi NagaiResearch Center for Aquatic Genomics, National Research Institute of Fisheries Science, Yokohama, Japan.
Jaanika BlomsterDepartment of Environmental Sciences, University of Helsinki, Helsinki, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amplicon read sequencing has revolutionized the field of microbial diversity studies. The technique has been developed for bacterial assemblages and has undergone rigorous testing with mock communities. However, due to the great complexity of eukaryotes and the numbers of different rDNA copies, analyzing eukaryotic diversity is more demanding than analyzing bacterial or mock communities, so studies are needed that test the methods of analyses on taxonomically diverse natural communities. In this study, we used 20 samples collected from the Baltic Sea ice, slush and under-ice water to investigate three program packages (UPARSE, mothur and QIIME) and 18 different bioinformatic strategies implemented in them. Our aim was to assess the impact of the initial steps of bioinformatic strategies on the results when analyzing natural eukaryotic communities. We found significant differences among the strategies in resulting read length, number of OTUs and estimates of diversity as well as clear differences in the taxonomic composition of communities. The differences arose mainly because of the variable number of chimeric reads that passed the pre-processing steps. Singleton removal and denoising substantially lowered the number of errors. Our study showed that the initial steps of the bioinformatic amplicon read processing strategies require careful consideration before applying them to eukaryotic communities.

Indexed as

Baltic StatesBiodiversityComputational BiologyDNA, RibosomalEcosystemEukaryotaEukaryotic CellsGenetic VariationHigh-Throughput Nucleotide SequencingIceIce CoverPolymerase Chain ReactionRNA, Ribosomal, 18SSeawaterDNA, RibosomalIceRNA, Ribosomal, 18S

Identifiers

PMID26047335
PMCPMC4457843

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.