Evaluating the potential of 18S rDNA clone libraries to complement pyrosequencing data of marine protists with near full-length sequence information

• Published in: Marine Biology Research
• Main Authors: Wolf, Christian, Kilias, Estelle Silvia, Metfies, Katja
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: TAYLOR & FRANCIS AS 2014
• Subjects: Arctic; Southern Ocean
• Online Access: https://epic.awi.de/id/eprint/34189/; https://epic.awi.de/id/eprint/34189/1/Wolf_et_al_2014_Clone_vs_454.pdf; https://hdl.handle.net/10013/epic.43597; https://hdl.handle.net/10013/epic.43597.d001

Sequencing of 18S rDNA clone libraries and 454-pyrosequencing are valuable methods used to describe microbial diversity. The massively parallel 454-pyrosequencing generates vast amounts of ribosomal sequence data and has the potential to uncover more organisms, even rare species. However, the relatively short sequence lengths of ~500 bp are suboptimal for taxonomic annotation and phylogenetic analyses. In this study, we assessed the potential of 18S ribosomal clone libraries to complement corresponding 454-pyrosequencing data with near full-length sequence information. This involved a comparison of protist community compositions in five polar samples suggested by 18S rDNA clone libraries with the corresponding community compositions suggested by 454-pyrosequencing. The study was conducted with four Arctic water samples, focusing on the eukaryotic picoplankton (0.4-3 µm), and with one sample collected in the Southern Ocean, examining the whole size spectrum (>0.4 µm). For all individual samples, the protist community compositions suggested by the two different approaches showed significant similarities. Around 70% of the sequences detected by sequencing of clone libraries were also present in the 454-pyrosequencing data set. However, the clone library sequences reflected only ~20% of the abundant biosphere identified by 454-pyrosequencing and identified ribosomal sequences, that were not detected in the 454-pyrosequencing data sets.