| Autor: |
Yunxi Liu, R. A. Leo Elworth, Michael D. Jochum, Kjersti M. Aagaard, Todd J. Treangen |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 13, Iss 1, Pp 1-14 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-022-34409-z |
| Popis: |
Contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Here the authors describe Squeegee, a computational approach designed to detect microbial contamination within low microbial biomass microbiomes and identify microbial contaminants in publicly available datasets that lack negative controls. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
Full text from SpringerLink