Open Access Method

A tool kit for quantifying eukaryotic rRNA gene sequences from human microbiome samples

Serena Dollive1, Gregory L Peterfreund1, Scott Sherrill-Mix1, Kyle Bittinger1, Rohini Sinha1, Christian Hoffmann1, Christopher S Nabel1, David A Hill123, David Artis123, Michael A Bachman45, Rebecca Custers-Allen1, Stephanie Grunberg1, Gary D Wu6, James D Lewis7 and Frederic D Bushman1*

Author affiliations

1 Department of Microbiology, Perelman School of Medicine at the University of Pennsylvania, 3610 Hamilton Walk, Philadelphia, PA 19104, USA

2 Institute for Immunology, Perelman School of Medicine at the University of Pennsylvania, 421 Curie Boulevard, Philadelphia, PA 19104, USA

3 Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, 421 Curie Boulevard, Philadelphia, PA 19104, USA

4 Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, Philadelphia, 19104, USA

5 Department of Pathology, University of Michigan, 1301 Catherine St, Ann Arbor, MI 48109, USA

6 Division of Gastroenterology, Perelman School of Medicine at the University of Pennsylvania, 415 Curie Boulevard, Philadelphia, PA 19104, USA

7 Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA

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Citation and License

Genome Biology 2012, 13:R60  doi:10.1186/gb-2012-13-7-r60

Published: 3 July 2012

Abstract

Eukaryotic microorganisms are important but understudied components of the human microbiome. Here we present a pipeline for analysis of deep sequencing data on single cell eukaryotes. We designed a new 18S rRNA gene-specific PCR primer set and compared a published rRNA gene internal transcribed spacer (ITS) gene primer set. Amplicons were tested against 24 specimens from defined eukaryotes and eight well-characterized human stool samples. A software pipeline https://sourceforge.net/projects/brocc/ webcite was developed for taxonomic attribution, validated against simulated data, and tested on pyrosequence data. This study provides a well-characterized tool kit for sequence-based enumeration of eukaryotic organisms in human microbiome samples.