Using Gap Visualization to Navigate Multivariate Metabarcode Data, Select Primer Pairs, and Enhance Reference Data Quality
ABSTRACT The success of amplicon sequencing studies of environmental DNA (eDNA) depends on the choice of primer pairs used to select taxa‐specific regions of DNA for amplification then sequencing to characterize sample composition. This paper presents practical methods to visualize the extent to whi...
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | Environmental DNA |
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| Online Access: | https://doi.org/10.1002/edn3.597 |
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| author | Xin‐Yi Chua Louise Ord Stephen J. Bent David Lovell Annette McGrath |
| author_facet | Xin‐Yi Chua Louise Ord Stephen J. Bent David Lovell Annette McGrath |
| author_sort | Xin‐Yi Chua |
| collection | DOAJ |
| description | ABSTRACT The success of amplicon sequencing studies of environmental DNA (eDNA) depends on the choice of primer pairs used to select taxa‐specific regions of DNA for amplification then sequencing to characterize sample composition. This paper presents practical methods to visualize the extent to which different primer pairs can differentiate taxonomic groups, enabling researchers to assess which primers might be best suited for a study or environment of interest. These methods can also be used to review taxonomic annotations in genomic reference sequence databases. We apply the concept of DNA barcoding gaps to metabarcoding of multiple species in environmental DNA to leverage reference data on the amplicon sequences of previously characterized specimens. Since reference data sets are large and complex, we provide a simple and intuitive method to navigate subsets of reference sequence data containing conflicting or ambiguous relationships between genomic information and taxonomic classification. We demonstrate how to use gap visualization and taxonomic segmentation in comparing how well different primer pairs discriminate species of interest, and in detecting anomalies in reference sequence annotation. We show how these visualization methods can enable amplicon survey study design and make fundamental molecular resources more accessible to a wider research audience beyond bioinformaticians and data scientists. |
| format | Article |
| id | doaj-art-7914405bec2547329c157140cf8bd1c1 |
| institution | Kabale University |
| issn | 2637-4943 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Environmental DNA |
| spelling | doaj-art-7914405bec2547329c157140cf8bd1c12025-01-29T05:11:50ZengWileyEnvironmental DNA2637-49432024-11-0166n/an/a10.1002/edn3.597Using Gap Visualization to Navigate Multivariate Metabarcode Data, Select Primer Pairs, and Enhance Reference Data QualityXin‐Yi Chua0Louise Ord1Stephen J. Bent2David Lovell3Annette McGrath4Science and Engineering Faculty Queensland University of Technology Brisbane Queensland AustraliaData Analytics and Visualisation, Scientific Computing, Information Management & Technology, CSIRO Eveleigh New South Wales AustraliaNational Collections & Marine Infrastructure, CSIRO Brisbane Queensland AustraliaScience and Engineering Faculty Queensland University of Technology Brisbane Queensland AustraliaScience and Engineering Faculty Queensland University of Technology Brisbane Queensland AustraliaABSTRACT The success of amplicon sequencing studies of environmental DNA (eDNA) depends on the choice of primer pairs used to select taxa‐specific regions of DNA for amplification then sequencing to characterize sample composition. This paper presents practical methods to visualize the extent to which different primer pairs can differentiate taxonomic groups, enabling researchers to assess which primers might be best suited for a study or environment of interest. These methods can also be used to review taxonomic annotations in genomic reference sequence databases. We apply the concept of DNA barcoding gaps to metabarcoding of multiple species in environmental DNA to leverage reference data on the amplicon sequences of previously characterized specimens. Since reference data sets are large and complex, we provide a simple and intuitive method to navigate subsets of reference sequence data containing conflicting or ambiguous relationships between genomic information and taxonomic classification. We demonstrate how to use gap visualization and taxonomic segmentation in comparing how well different primer pairs discriminate species of interest, and in detecting anomalies in reference sequence annotation. We show how these visualization methods can enable amplicon survey study design and make fundamental molecular resources more accessible to a wider research audience beyond bioinformaticians and data scientists.https://doi.org/10.1002/edn3.597amplicon sequencingmetabarcodingmetabarcoding gapstaxonomic resolutionvisualization |
| spellingShingle | Xin‐Yi Chua Louise Ord Stephen J. Bent David Lovell Annette McGrath Using Gap Visualization to Navigate Multivariate Metabarcode Data, Select Primer Pairs, and Enhance Reference Data Quality Environmental DNA amplicon sequencing metabarcoding metabarcoding gaps taxonomic resolution visualization |
| title | Using Gap Visualization to Navigate Multivariate Metabarcode Data, Select Primer Pairs, and Enhance Reference Data Quality |
| title_full | Using Gap Visualization to Navigate Multivariate Metabarcode Data, Select Primer Pairs, and Enhance Reference Data Quality |
| title_fullStr | Using Gap Visualization to Navigate Multivariate Metabarcode Data, Select Primer Pairs, and Enhance Reference Data Quality |
| title_full_unstemmed | Using Gap Visualization to Navigate Multivariate Metabarcode Data, Select Primer Pairs, and Enhance Reference Data Quality |
| title_short | Using Gap Visualization to Navigate Multivariate Metabarcode Data, Select Primer Pairs, and Enhance Reference Data Quality |
| title_sort | using gap visualization to navigate multivariate metabarcode data select primer pairs and enhance reference data quality |
| topic | amplicon sequencing metabarcoding metabarcoding gaps taxonomic resolution visualization |
| url | https://doi.org/10.1002/edn3.597 |
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