Data · dataset · 2021
Data from: Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count?
Listed in Borealis and Agri-environmental Research Data Dataverse — shown once because both records carry DOI 10.5683/sp2/kazyic
Description
Abstract
A goal of many environmental DNA barcoding studies is to infer quantitative information about relative abundances of different taxa based on sequence read proportions generated by high-throughput sequencing. However, potential biases associated with this approach are only beginning to be examined. We sequenced DNA amplified from faeces (scats) of captive harbour seals (Phoca vitulina) to investigate whether sequence counts could be used to quantify the seals’ diet.
Read the rest (5 more)
Seals were fed fish in fixed proportions, a chordate-specific mitochondrial 16S marker was amplified from scat DNA and amplicons sequenced using an Ion Torrent PGM™. For a given set of bioinformatic parameters, there was generally low variability between scat samples in proportions of prey species sequences recovered. However, proportions varied substantially depending on sequencing direction, level of quality filtering (due to differences in sequence quality between species) and minimum read length considered.
Short primer tags used to identify individual samples also influenced species proportions. In addition, there were complex interactions between factors; for example, the effect of quality filtering was influenced by the primer tag and sequencing direction. Resequencing of a subset of samples revealed some, but not all, biases were consistent between runs.
Less stringent data filtering (based on quality scores or read length) generally produced more consistent proportional data, but overall proportions of sequences were very different than dietary mass proportions, indicating additional technical or biological biases are present. Our findings highlight that quantitative interpretations of sequence proportions generated via high-throughput sequencing will require careful experimental design and thoughtful data analysis.
Usage notes
Ion Torrent diet data FASTQ sequence files and additional data described in the ReadMe file 1 MER paper functions Functions needed to run R code 2 MER R code database R code 3 MER R code summary R code
Links
Where it is published
- Dataverse dataset page borealisdata.ca/dataset.xhtml?persistentId=doi%3A10.5683%2FSP2%2FKAZYIC ↗
landing page · from borealisdata ca
- DOI doi.org/10.5683/sp2/kazyic ↗
DOI / persistent id · from borealisdata ca
Catalogue records · 1
- Dataverse API borealisdata.ca/api/datasets/:persistentId/?persistentId=doi%3A10.5683%2FSP2%2… ↗
metadata API · from borealisdata ca
Topics
- From keywords
- Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Mathematics & Statistics · Medicine & Health · Ocean & Atmospheric Science · Social Science · Social Science
- Inferred from text
- Bioinformatics and computational biology 72% · Sequencing 75%
Provenance · 2 source records, 22 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Borealis | doi:10.5683/SP2/KAZYIC | 9 d ago | JSON v1 |
| Agri-environmental Research Data Dataverse | doi:10.5683/SP2/KAZYIC | 7 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3102 | enrichment · borealisdata ca | taxonomy-embedding@1.0.0 | title+keywords+description (72%) |
| concepts[field].dataverse_subject:other | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[field].dataverse_subject:other | source · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:chemistry | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:economics-finance | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:engineering | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:humanities | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:mathematics-statistics | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:medicine-health | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:ocean-atmospheric | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:social-science | mapping · borealisdata ca dataverse | connector:borealisdata_ca_dataverse@1.0.0 | /subjects |
| concepts[field].local:field:social-science | mapping · borealisdata ca | connector:borealisdata_ca@1.0.0 | /subjects |
| concepts[modality].local:modality:sequencing | enrichment · borealisdata ca | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| description | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /description |
| publication_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| title | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | /name |
| updated_date | source · borealisdata ca | connector:borealisdata_ca@1.0.0 | |
| version_label | source · borealisdata ca | connector:borealisdata_ca@1.0.0 |