Data on the optimization of an archaea-specific probe-based qPCR assay

Malin Bomberg (Corresponding Author), Hanna Miettinen

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)


Estimation of archaeal numbers by use of fluorescent DNA binding dyes is challenging, because primers targeting the archaeal 16SrRNA genes readily also bind to bacterial 16S rRNA gene sequences, especially when the relative abundance of bacteria is greater than that of archaea. In order to increase specificity, we optimized a fluorescent probe-based assay using previously published archaeal primers and probe. The assay was tested on genomic DNA of pure bacterial and archaeal cultures and optimized using PCR amplicons of the archaeal pure cultures. The used bacterial strains showed slight amplification using the fluorescent dye assay, whereas all archaeal strains could be amplified with the archaea primers used. Due to differences in genome size and number of 16S rRNA gene copies between the tested archaeal strains, the amplification level varied greatly between the strains. Therefore, we also tested the amplification using PCR amplified fragments of the archaeal 16S rRNA genes. The tests with the archaeal 16S rRNA gene amplicons showed good amplification, although the amplification efficiency still varied between archaeal strains. The qPCR assay was used to estimate the archaeal numbers in process water of a multi-metal mine's metallurgical plant [1] and will be used in similar future microbiological analysis included in the H2020 ITERAMS project (Grant agreement# 730480).
Original languageEnglish
Article number106610
Number of pages9
JournalData in Brief
Publication statusPublished - Dec 2020
MoE publication typeA1 Journal article-refereed


This work was funded by the European Union's Horizon 2020 Research and Innovation program under Grant Agreement no. 730480, ITERAMS project (Integrated mineral technologies for more sustainable raw material supply).


  • Microorganisms in mine processing water
  • Microbial metabolism
  • High throughput sequencing
  • Bioinformatics
  • Bacteria


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