Publication | Open Access
Paleo-diatom composition from Santa Barbara Basin deep-sea sediments: a comparison of <i>18S-V9</i> and <i>diat-rbcL</i> metabarcoding vs shotgun metagenomics
33
Citations
50
References
2021
Year
Sedimentary RecordEngineeringGeneticsSedimentary GeologyGenomicsDna BarcodingEarth ScienceVs Shotgun MetagenomicsPhylogeneticsMolecular EcologyMarine GenomicsMarine GeologySequence AnalysisSedimentary Ancient DnaAmplification BiasesSedimentologyBiologyPaleo-diatom CompositionNatural SciencesEvolutionary BiologyAncient DnaGeochemistryPaleoecologySequence Assembly
Sedimentary ancient DNA (sedaDNA) analyses are increasingly used to reconstruct marine ecosystems. The majority of marine sedaDNA studies use a metabarcoding approach (extraction and analysis of specific DNA fragments of a defined length), targeting short taxonomic marker genes. Promising examples are 18S-V9 rRNA (~121-130 base pairs, bp) and diat-rbcL (76 bp), targeting eukaryotes and diatoms, respectively. However, it remains unknown how 18S-V9 and diat-rbcL derived compositional profiles compare to metagenomic shotgun data, the preferred method for ancient DNA analyses as amplification biases are minimised. We extracted DNA from five Santa Barbara Basin sediment samples (up to ~11 000 years old) and applied both a metabarcoding (18S-V9 rRNA, diat-rbcL) and a metagenomic shotgun approach to (i) compare eukaryote, especially diatom, composition, and (ii) assess sequence length and database related biases. Eukaryote composition differed considerably between shotgun and metabarcoding data, which was related to differences in read lengths (~112 and ~161 bp, respectively), and overamplification of short reads in metabarcoding data. Diatom composition was influenced by reference bias that was exacerbated in metabarcoding data and characterised by increased representation of Chaetoceros, Thalassiosira and Pseudo-nitzschia. Our results are relevant to sedaDNA studies aiming to accurately characterise paleo-ecosystems from either metabarcoding or metagenomic data.
| Year | Citations | |
|---|---|---|
Page 1
Page 1