Literature DB >> 26425990

Mitochondrial capture enriches mito-DNA 100 fold, enabling PCR-free mitogenomics biodiversity analysis.

Shanlin Liu1,2,3, Xin Wang1,2, Lin Xie2, Meihua Tan1,2, Zhenyu Li2, Xu Su1,4, Hao Zhang2, Bernhard Misof5, Karl M Kjer6, Min Tang1,2, Oliver Niehuis5, Hui Jiang2, Xin Zhou1,2.   

Abstract

Biodiversity analyses based on next-generation sequencing (NGS) platforms have developed by leaps and bounds in recent years. A PCR-free strategy, which can alleviate taxonomic bias, was considered as a promising approach to delivering reliable species compositions of targeted environments. The major impediment of such a method is the lack of appropriate mitochondrial DNA enrichment ways. Because mitochondrial genomes (mitogenomes) make up only a small proportion of total DNA, PCR-free methods will inevitably result in a huge excess of data (>99%). Furthermore, the massive volume of sequence data is highly demanding on computing resources. Here, we present a mitogenome enrichment pipeline via a gene capture chip that was designed by virtue of the mitogenome sequences of the 1000 Insect Transcriptome Evolution project (1KITE, www.1kite.org). A mock sample containing 49 species was used to evaluate the efficiency of the mitogenome capture method. We demonstrate that the proportion of mitochondrial DNA can be increased by approximately 100-fold (from the original 0.47% to 42.52%). Variation in phylogenetic distances of target taxa to the probe set could in principle result in bias in abundance. However, the frequencies of input taxa were largely maintained after capture (R(2) = 0.81). We suggest that our mitogenome capture approach coupled with PCR-free shotgun sequencing could provide ecological researchers an efficient NGS method to deliver reliable biodiversity assessment.
© 2015 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  biodiversity; gene capture; microarray; mitochondrial genome

Mesh:

Substances:

Year:  2015        PMID: 26425990     DOI: 10.1111/1755-0998.12472

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


  24 in total

Review 1.  Progress, pitfalls and parallel universes: a history of insect phylogenetics.

Authors:  Karl M Kjer; Chris Simon; Margarita Yavorskaya; Rolf G Beutel
Journal:  J R Soc Interface       Date:  2016-08       Impact factor: 4.118

2.  MitoZ: a toolkit for animal mitochondrial genome assembly, annotation and visualization.

Authors:  Guanliang Meng; Yiyuan Li; Chentao Yang; Shanlin Liu
Journal:  Nucleic Acids Res       Date:  2019-06-20       Impact factor: 16.971

3.  Fecal metagenomics for the simultaneous assessment of diet, parasites, and population genetics of an understudied primate.

Authors:  Amrita Srivathsan; Andie Ang; Alfried P Vogler; Rudolf Meier
Journal:  Front Zool       Date:  2016-04-21       Impact factor: 3.172

Review 4.  A new way to contemplate Darwin's tangled bank: how DNA barcodes are reconnecting biodiversity science and biomonitoring.

Authors:  Mehrdad Hajibabaei; Donald J Baird; Nicole A Fahner; Robert Beiko; G Brian Golding
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2016-09-05       Impact factor: 6.237

Review 5.  Odonata (dragonflies and damselflies) as a bridge between ecology and evolutionary genomics.

Authors:  Seth Bybee; Alex Córdoba-Aguilar; M Catherine Duryea; Ryo Futahashi; Bengt Hansson; M Olalla Lorenzo-Carballa; Ruud Schilder; Robby Stoks; Anton Suvorov; Erik I Svensson; Janne Swaegers; Yuma Takahashi; Phillip C Watts; Maren Wellenreuther
Journal:  Front Zool       Date:  2016-10-10       Impact factor: 3.172

6.  Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity.

Authors:  Iliana Bista; Gary R Carvalho; Kerry Walsh; Mathew Seymour; Mehrdad Hajibabaei; Delphine Lallias; Martin Christmas; Simon Creer
Journal:  Nat Commun       Date:  2017-01-18       Impact factor: 14.919

7.  Presence-absence of marine macrozoobenthos does not generally predict abundance and biomass.

Authors:  Allert I Bijleveld; Tanya J Compton; Lise Klunder; Sander Holthuijsen; Job Ten Horn; Anita Koolhaas; Anne Dekinga; Jaap van der Meer; Henk W van der Veer
Journal:  Sci Rep       Date:  2018-02-14       Impact factor: 4.379

8.  DNA Metabarcoding of Amazonian Ichthyoplankton Swarms.

Authors:  M E Maggia; Y Vigouroux; J F Renno; F Duponchelle; E Desmarais; J Nunez; C García-Dávila; F M Carvajal-Vallejos; E Paradis; J F Martin; C Mariac
Journal:  PLoS One       Date:  2017-01-17       Impact factor: 3.240

Review 9.  Mitochondrial metagenomics: letting the genes out of the bottle.

Authors:  Alex Crampton-Platt; Douglas W Yu; Xin Zhou; Alfried P Vogler
Journal:  Gigascience       Date:  2016-03-22       Impact factor: 6.524

10.  Testing the potential of a ribosomal 16S marker for DNA metabarcoding of insects.

Authors:  Vasco Elbrecht; Pierre Taberlet; Tony Dejean; Alice Valentini; Philippe Usseglio-Polatera; Jean-Nicolas Beisel; Eric Coissac; Frederic Boyer; Florian Leese
Journal:  PeerJ       Date:  2016-04-19       Impact factor: 2.984

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