Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?

Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of food DNA sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer's d...

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Published in:	Molecular ecology Vol. 28; no. 2; pp. 391 - 406
Main Authors:	Deagle, Bruce E., Thomas, Austen C., McInnes, Julie C., Clarke, Laurence J., Vesterinen, Eero J., Clare, Elizabeth L., Kartzinel, Tyler R., Eveson, J. Paige
Format:	Journal Article
Language:	English
Published:	England Blackwell Publishing Ltd 01.01.2019 John Wiley and Sons Inc
Subjects:	Computer Simulation Contaminants Counting data collection Data recovery Datasets Deoxyribonucleic acid Diet DNA DNA barcoding DNA Barcoding, Taxonomic - methods DNA sequencing Feces - chemistry Food Food Chain High-Throughput Nucleotide Sequencing Nucleotide sequence nucleotide sequences Special Issue: Species Interactions, Ecological Networks and Community Dynamics Summaries Trophic relationships
ISSN:	0962-1083, 1365-294X, 1365-294X
Online Access:	Get full text
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Abstract	Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of food DNA sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer's diet should we work strictly with data sets summarizing frequency of occurrence of different food taxa, or is it possible to use relative number of sequences? Both approaches are applied to obtain semi‐quantitative diet summaries, but occurrence data are often promoted as a more conservative and reliable option due to taxa‐specific biases in recovery of sequences. We explore representative dietary metabarcoding data sets and point out that diet summaries based on occurrence data often overestimate the importance of food consumed in small quantities (potentially including low‐level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that using relative read abundance (RRA) information often provides a more accurate view of population‐level diet even with moderate recovery biases incorporated; however, RRA summaries are sensitive to recovery biases impacting common diet taxa. Both approaches are more accurate when the mean number of food taxa in samples is small. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue addressing methodological challenges and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research.
AbstractList	Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of food DNA sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer's diet should we work strictly with data sets summarizing frequency of occurrence of different food taxa, or is it possible to use relative number of sequences? Both approaches are applied to obtain semi-quantitative diet summaries, but occurrence data are often promoted as a more conservative and reliable option due to taxa-specific biases in recovery of sequences. We explore representative dietary metabarcoding data sets and point out that diet summaries based on occurrence data often overestimate the importance of food consumed in small quantities (potentially including low-level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that using relative read abundance (RRA) information often provides a more accurate view of population-level diet even with moderate recovery biases incorporated; however, RRA summaries are sensitive to recovery biases impacting common diet taxa. Both approaches are more accurate when the mean number of food taxa in samples is small. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue addressing methodological challenges and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research.Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of food DNA sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer's diet should we work strictly with data sets summarizing frequency of occurrence of different food taxa, or is it possible to use relative number of sequences? Both approaches are applied to obtain semi-quantitative diet summaries, but occurrence data are often promoted as a more conservative and reliable option due to taxa-specific biases in recovery of sequences. We explore representative dietary metabarcoding data sets and point out that diet summaries based on occurrence data often overestimate the importance of food consumed in small quantities (potentially including low-level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that using relative read abundance (RRA) information often provides a more accurate view of population-level diet even with moderate recovery biases incorporated; however, RRA summaries are sensitive to recovery biases impacting common diet taxa. Both approaches are more accurate when the mean number of food taxa in samples is small. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue addressing methodological challenges and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research. Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of food DNA sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer's diet should we work strictly with data sets summarizing frequency of occurrence of different food taxa, or is it possible to use relative number of sequences? Both approaches are applied to obtain semi‐quantitative diet summaries, but occurrence data are often promoted as a more conservative and reliable option due to taxa‐specific biases in recovery of sequences. We explore representative dietary metabarcoding data sets and point out that diet summaries based on occurrence data often overestimate the importance of food consumed in small quantities (potentially including low‐level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that using relative read abundance (RRA) information often provides a more accurate view of population‐level diet even with moderate recovery biases incorporated; however, RRA summaries are sensitive to recovery biases impacting common diet taxa. Both approaches are more accurate when the mean number of food taxa in samples is small. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue addressing methodological challenges and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research. Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of food DNA sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer's diet should we work strictly with data sets summarizing frequency of occurrence of different food taxa, or is it possible to use relative number of sequences? Both approaches are applied to obtain semi‐quantitative diet summaries, but occurrence data are often promoted as a more conservative and reliable option due to taxa‐specific biases in recovery of sequences. We explore representative dietary metabarcoding data sets and point out that diet summaries based on occurrence data often overestimate the importance of food consumed in small quantities (potentially including low‐level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that using relative read abundance ( RRA ) information often provides a more accurate view of population‐level diet even with moderate recovery biases incorporated; however, RRA summaries are sensitive to recovery biases impacting common diet taxa. Both approaches are more accurate when the mean number of food taxa in samples is small. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue addressing methodological challenges and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research.
Author	Deagle, Bruce E. Eveson, J. Paige Vesterinen, Eero J. Clarke, Laurence J. Kartzinel, Tyler R. Clare, Elizabeth L. Thomas, Austen C. McInnes, Julie C.
AuthorAffiliation	1 Australian Antarctic Division Channel Highway Kingston TAS Australia 2 Science Department Smith‐Root Inc. Vancouver Washington 4 Biodiversity Unit and Department of Biology University of Turku Turku Finland 5 Department of Agricultural Sciences University of Helsinki Helsinki Finland 7 Department of Ecology and Evolutionary Biology Brown University Providence Rhode Island 8 CSIRO Oceans and Atmosphere Hobart TAS Australia 3 Antarctic Climate & Ecosystems Cooperative Research Centre University of Tasmania Hobart TAS Australia 6 School of Biological and Chemical Sciences Queen Mary University of London London UK
AuthorAffiliation_xml	– name: 6 School of Biological and Chemical Sciences Queen Mary University of London London UK – name: 3 Antarctic Climate & Ecosystems Cooperative Research Centre University of Tasmania Hobart TAS Australia – name: 7 Department of Ecology and Evolutionary Biology Brown University Providence Rhode Island – name: 8 CSIRO Oceans and Atmosphere Hobart TAS Australia – name: 2 Science Department Smith‐Root Inc. Vancouver Washington – name: 1 Australian Antarctic Division Channel Highway Kingston TAS Australia – name: 4 Biodiversity Unit and Department of Biology University of Turku Turku Finland – name: 5 Department of Agricultural Sciences University of Helsinki Helsinki Finland
Author_xml	– sequence: 1 givenname: Bruce E. orcidid: 0000-0001-7651-3687 surname: Deagle fullname: Deagle, Bruce E. email: Bruce.Deagle@aad.gov.au organization: Channel Highway – sequence: 2 givenname: Austen C. surname: Thomas fullname: Thomas, Austen C. organization: Smith‐Root Inc – sequence: 3 givenname: Julie C. orcidid: 0000-0001-8902-5199 surname: McInnes fullname: McInnes, Julie C. organization: Channel Highway – sequence: 4 givenname: Laurence J. orcidid: 0000-0002-0844-4453 surname: Clarke fullname: Clarke, Laurence J. organization: University of Tasmania – sequence: 5 givenname: Eero J. orcidid: 0000-0003-3665-5802 surname: Vesterinen fullname: Vesterinen, Eero J. organization: University of Helsinki – sequence: 6 givenname: Elizabeth L. orcidid: 0000-0002-6563-3365 surname: Clare fullname: Clare, Elizabeth L. organization: Queen Mary University of London – sequence: 7 givenname: Tyler R. surname: Kartzinel fullname: Kartzinel, Tyler R. organization: Brown University – sequence: 8 givenname: J. Paige surname: Eveson fullname: Eveson, J. Paige organization: CSIRO Oceans and Atmosphere
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29858539$$D View this record in MEDLINE/PubMed
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Snippet	Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of... Advances in DNA sequencing technology have revolutionized the field of molecular analysis of trophic interactions, and it is now possible to recover counts of...
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SubjectTerms	Computer Simulation Contaminants Counting data collection Data recovery Datasets Deoxyribonucleic acid Diet DNA DNA barcoding DNA Barcoding, Taxonomic - methods DNA sequencing Feces - chemistry Food Food Chain High-Throughput Nucleotide Sequencing Nucleotide sequence nucleotide sequences Special Issue: Species Interactions, Ecological Networks and Community Dynamics Summaries Trophic relationships
Title	Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?
URI	https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fmec.14734 https://www.ncbi.nlm.nih.gov/pubmed/29858539 https://www.proquest.com/docview/2176283452 https://www.proquest.com/docview/2049560391 https://www.proquest.com/docview/2221020006 https://pubmed.ncbi.nlm.nih.gov/PMC6905394
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