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Scaling up: A guide to high‐throughput genomic approaches for biodiversity analysis

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Porter, Teresita M., Hajibabaei, Mehrdad·2018·Molecular Ecology

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Abstract

The purpose of this review is to present the most common and emerging DNA‐based methods used to generate data for biodiversity and biomonitoring studies. As environmental assessment and monitoring programmes may require biodiversity information at multiple levels, we pay particular attention to the DNA metabarcoding method and discuss a number of bioinformatic tools and considerations for producing DNA‐based indicators using operational taxonomic units (OTU s), taxa at a variety of ranks and community composition. By developing the capacity to harness the advantages provided by the newest technologies, investigators can “scale up” by increasing the number of samples and replicates processed, the frequency of sampling over time and space, and even the depth of sampling such as by sequencing more reads per sample or more markers per sample. The ability to scale up is made possible by the reduced hands‐on time and cost per sample provided by the newest kits, platforms and software tools. Results gleaned from broad‐scale monitoring will provide opportunities to address key scientific questions linked to biodiversity and its dynamics across time and space as well as being more relevant for policymakers, enabling science‐based decision‐making, and provide a greater socio‐economic impact. As genomic approaches are continually evolving, we provide this guide to methods used in biodiversity genomics.

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Inferring Species Membership Using DNA Sequences with Back-Propagation Neural Networks
Zhang, A., Sikes, D. S., Muster, C., & Li, S. Q. (2008) · Systematic Biology · doi:10.1080/10635150802032982 · Cited by 80
Biodiversity assessment: State‐of‐the‐art techniques in phylogenomics and species identification
Kellar, P. R., & Pires, J. C. (2011) · American Journal of Botany · doi:10.3732/ajb.1000296 · Cited by 78
A comparison of random sequence reads versus 16S rDNA sequences for estimating the biodiversity of a metagenomic library
Manichanh, C., Chapple, C. E., Frangeul, L., Gloux, K., Guigó, R., & Dore, J. M. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn496 · Cited by 77
MetaPhyler: Taxonomic profiling for metagenomic sequences
Liu, B., Gibbons, T., Ghodsi, M., & Pop, M. (2010) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2010.5706544 · Cited by 76
Fast phylogenetic DNA barcoding
Munch, K., Boomsma, W., Willerslev, E., & Nielsen, R. (2008) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2008.0169 · Cited by 72
Reevaluation and Reduction of a PCR Bias Caused by Reannealing of Templates
Kurata, S., Kanagawa, T., Magariyama, Y., Takatsu, K., YAMADA, K., Yokomaku, T., & Kamagata, Y. (2004) · Applied and Environmental Microbiology · doi:10.1128/aem.70.12.7545-7549.2004 · Cited by 72
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QPCR: a tool for analysis of mitochondrial and nuclear DNA damage in ecotoxicology
Meyer, J. N. (2010) · Ecotoxicology · doi:10.1007/s10646-009-0457-4 · Cited by 61
Rapid and accurate taxonomic classification of insect (class Insecta) cytochrome c oxidase subunit 1 ( COI ) DNA barcode sequences using a naïve Bayesian classifier
Porter, T. M., Gibson, J. F., Shokralla, S., Baird, D. J., Golding, G. B., & Hajibabaei, M. (2014) · Molecular Ecology Resources · doi:10.1111/1755-0998.12240 · Cited by 61
Self-citation
Ancient pathogen DNA in archaeological samples detected with a Microbial Detection Array
Devault, A. M., McLoughlin, K., Jaing, C. J., Gardner, S. N., Porter, T. M., Enk, J. M., Thissen, J. B., Allen, J., Borucki, M. K., DeWitte, S. N., et al. (2014) · Scientific Reports · doi:10.1038/srep04245 · Cited by 59
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Environmental DNA Barcode Sequence Capture: Targeted, PCR-free Sequence Capture for Biodiversity Analysis from Bulk Environmental SamplesPreprint
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See all 305

Authors: Porter, T. M., & Hajibabaei, M.
Venue: Molecular Ecology
Publisher: Wiley
Published: 2018-01
Volume: 27
Issue: 2
Pages: 313-338
ISSN: 0962-1083 (ISSN-L)
1365-294X
DOI: 10.1111/mec.14478
OpenAlex: W2782451002
Cited by: 305
References: 197
FWCI: 26.25
Type: journal article
Language: en
Open access: hybrid

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Scaling up: A guide to high‐throughput genomic approaches for biodiversity analysis

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