Literature DB >> 31293086

SPIKEPIPE: A metagenomic pipeline for the accurate quantification of eukaryotic species occurrences and intraspecific abundance change using DNA barcodes or mitogenomes.

Yinqiu Ji1, Tea Huotari2, Tomas Roslin2,3, Niels Martin Schmidt4,5, Jiaxin Wang1, Douglas W Yu1,6,7, Otso Ovaskainen8,9.   

Abstract

The accurate quantification of eukaryotic species abundances from bulk samples remains a key challenge for community ecology and environmental biomonitoring. We resolve this challenge by combining shotgun sequencing, mapping to reference DNA barcodes or to mitogenomes, and three correction factors: (a) a percent-coverage threshold to filter out false positives, (b) an internal-standard DNA spike-in to correct for stochasticity during sequencing, and (c) technical replicates to correct for stochasticity across sequencing runs. The SPIKEPIPE pipeline achieves a strikingly high accuracy of intraspecific abundance estimates (in terms of DNA mass) from samples of known composition (mapping to barcodes R2  = .93, mitogenomes R2  = .95) and a high repeatability across environmental-sample replicates (barcodes R2  = .94, mitogenomes R2  = .93). As proof of concept, we sequence arthropod samples from the High Arctic, systematically collected over 17 years, detecting changes in species richness, species-specific abundances, and phenology. SPIKEPIPE provides cost-efficient and reliable quantification of eukaryotic communities.
© 2019 John Wiley & Sons Ltd.

Keywords:  Araneae; Arthropoda; COI internal standard; DNA barcoding; biomonitoring; community composition; insecta; metagenomics; mitogenomes; mitogenomics

Mesh:

Substances:

Year:  2019        PMID: 31293086     DOI: 10.1111/1755-0998.13057

Source DB:  PubMed          Journal:  Mol Ecol Resour        ISSN: 1755-098X            Impact factor:   7.090


  10 in total

1.  Nonlinear trends in abundance and diversity and complex responses to climate change in Arctic arthropods.

Authors:  Toke T Høye; Sarah Loboda; Amanda M Koltz; Mark A K Gillespie; Joseph J Bowden; Niels M Schmidt
Journal:  Proc Natl Acad Sci U S A       Date:  2021-01-12       Impact factor: 11.205

2.  Measuring the absolute abundance of the microbiome by adding yeast containing 16S rRNA gene from a hyperthermophile.

Authors:  Ju Yeong Kim; Myung-Hee Yi; Myungjun Kim; Seogwon Lee; Hye Su Moon; Dongeun Yong; Tai-Soon Yong
Journal:  Microbiologyopen       Date:  2021-08       Impact factor: 3.139

3.  Comparison of traditional and DNA metabarcoding samples for monitoring tropical soil arthropods (Formicidae, Collembola and Isoptera).

Authors:  Yves Basset; Mehrdad Hajibabaei; Michael T G Wright; Anakena M Castillo; David A Donoso; Simon T Segar; Daniel Souto-Vilarós; Dina Y Soliman; Tomas Roslin; M Alex Smith; Greg P A Lamarre; Luis F De León; Thibaud Decaëns; José G Palacios-Vargas; Gabriela Castaño-Meneses; Rudolf H Scheffrahn; Marleny Rivera; Filonila Perez; Ricardo Bobadilla; Yacksecari Lopez; José Alejandro Ramirez Silva; Maira Montejo Cruz; Angela Arango Galván; Héctor Barrios
Journal:  Sci Rep       Date:  2022-06-24       Impact factor: 4.996

Review 4.  Prospects and challenges of implementing DNA metabarcoding for high-throughput insect surveillance.

Authors:  Alexander M Piper; Jana Batovska; Noel O I Cogan; John Weiss; John Paul Cunningham; Brendan C Rodoni; Mark J Blacket
Journal:  Gigascience       Date:  2019-08-01       Impact factor: 6.524

5.  Developing a non-destructive metabarcoding protocol for detection of pest insects in bulk trap catches.

Authors:  Jana Batovska; Alexander M Piper; Isabel Valenzuela; John Paul Cunningham; Mark J Blacket
Journal:  Sci Rep       Date:  2021-04-12       Impact factor: 4.996

6.  Metabarcoding reveals massive species diversity of Diptera in a subtropical ecosystem.

Authors:  Junhao Huang; Xiaoqian Miao; Qingyun Wang; Frank Menzel; Pu Tang; Ding Yang; Hong Wu; Alfried P Vogler
Journal:  Ecol Evol       Date:  2022-01-23       Impact factor: 2.912

7.  Development and evaluation of a meat mitochondrial metagenomic (3MG) method for composition determination of meat from fifteen mammalian and avian species.

Authors:  Mei Jiang; Shu-Fei Xu; Tai-Shan Tang; Li Miao; Bao-Zheng Luo; Yang Ni; Fan-De Kong; Chang Liu
Journal:  BMC Genomics       Date:  2022-01-07       Impact factor: 3.969

8.  Climate-induced forest dieback drives compositional changes in insect communities that are more pronounced for rare species.

Authors:  Lucas Sire; Paul Schmidt Yáñez; Cai Wang; Annie Bézier; Béatrice Courtial; Jérémy Cours; Diego Fontaneto; Laurent Larrieu; Christophe Bouget; Simon Thorn; Jörg Müller; Douglas W Yu; Michael T Monaghan; Elisabeth A Herniou; Carlos Lopez-Vaamonde
Journal:  Commun Biol       Date:  2022-01-18

9.  Metabarcoding versus mapping unassembled shotgun reads for identification of prey consumed by arthropod epigeal predators.

Authors:  Débora Pires Paula; Suellen Karina Albertoni Barros; Rafael Major Pitta; Marliton Rocha Barreto; Roberto Coiti Togawa; David A Andow
Journal:  Gigascience       Date:  2022-03-24       Impact factor: 6.524

10.  Parasitoids indicate major climate-induced shifts in arctic communities.

Authors:  Tuomas Kankaanpää; Eero Vesterinen; Bess Hardwick; Niels M Schmidt; Tommi Andersson; Paul E Aspholm; Isabel C Barrio; Niklas Beckers; Joël Bêty; Tone Birkemoe; Melissa DeSiervo; Katherine H I Drotos; Dorothee Ehrich; Olivier Gilg; Vladimir Gilg; Nils Hein; Toke T Høye; Kristian M Jakobsen; Camille Jodouin; Jesse Jorna; Mikhail V Kozlov; Jean-Claude Kresse; Don-Jean Leandri-Breton; Nicolas Lecomte; Maarten Loonen; Philipp Marr; Spencer K Monckton; Maia Olsen; Josée-Anne Otis; Michelle Pyle; Ruben E Roos; Katrine Raundrup; Daria Rozhkova; Brigitte Sabard; Aleksandr Sokolov; Natalia Sokolova; Anna M Solecki; Christine Urbanowicz; Catherine Villeneuve; Evgenya Vyguzova; Vitali Zverev; Tomas Roslin
Journal:  Glob Chang Biol       Date:  2020-09-11       Impact factor: 13.211

  10 in total

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