Professional Education


  • Magister, Universitat Wien (2009)
  • Doctor of Philosophy, Universitat Wien (2015)

Stanford Advisors


All Publications


  • Diversity analysis of sulfite- and sulfate-reducing microorganisms by multiplex dsrA and dsrB amplicon sequencing using new primers and mock community-optimized bioinformatics ENVIRONMENTAL MICROBIOLOGY Pelikan, C., Herbold, C. W., Hausmann, B., Mueller, A. L., Pester, M., Loy, A. 2016; 18 (9): 2994-3009

    Abstract

    Genes encoding dissimilatory sulfite reductase (DsrAB) are commonly used as diagnostic markers in ecological studies of sulfite- and sulfate-reducing microorganisms. Here, we developed new high-coverage primer sets for generation of reductive bacterial-type dsrA and dsrB polymerase chain reaction (PCR) products for highly parallel amplicon sequencing and a bioinformatics workflow for processing and taxonomic classification of short dsrA and dsrB reads. We employed two diverse mock communities that consisted of 45 or 90 known dsrAB sequences derived from environmental clones to precisely evaluate the performance of individual steps of our amplicon sequencing approach on the Illumina MiSeq platform. Although PCR cycle number, gene-specific primer mismatches and stringent filtering for high-quality sequences had notable effects on the observed dsrA and dsrB community structures, recovery of most mock community sequences was generally proportional to their relative input abundances. Successful dsrA and dsrB diversity analysis in selected environmental samples further proved that the multiplex amplicon sequencing approach is adequate for monitoring spatial distribution and temporal abundance dynamics of dsrAB-containing microorganisms. Although tested for reductive bacterial-type dsrAB, this method is readily applicable for oxidative-type dsrAB of sulfur-oxidizing bacteria and also provides guidance for processing short amplicon reads of other functional genes.

    View details for DOI 10.1111/1462-2920.13139

    View details for Web of Science ID 000387550700019