Taxonomic classification and abundance estimation using 16S and WGS—A comparison using controlled reference samples
• WGS allows for better taxonomic annotation of microbiomes in comparison to 16S. • 16S based analysis suffers from database- and tool biases. • Distinct taxonomy assignment algorithms produce similar results when using WGS data. • Using 16S data for metagenomic investigations can lead to conclusion...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Khachatryan, Lusine [verfasserIn] |
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| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Modelling the impact of targeted interventions on the HCV epidemic in Pakistan: the road to HCV elimination - Lim, A.G. ELSEVIER, 2017, an international journal dedicated to the applications of genetics in the administration of justice, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:46 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.fsigen.2020.102257 |
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ELV049823086 |
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| 520 | |a • WGS allows for better taxonomic annotation of microbiomes in comparison to 16S. • 16S based analysis suffers from database- and tool biases. • Distinct taxonomy assignment algorithms produce similar results when using WGS data. • Using 16S data for metagenomic investigations can lead to conclusions that are incorrect. | ||
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• WGS allows for better taxonomic annotation of microbiomes in comparison to 16S. • 16S based analysis suffers from database- and tool biases. • Distinct taxonomy assignment algorithms produce similar results when using WGS data. • Using 16S data for metagenomic investigations can lead to conclusions that are incorrect. |
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