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Open Access Highly Accessed Research article

Artificial and natural duplicates in pyrosequencing reads of metagenomic data

Beifang Niu1, Limin Fu1, Shulei Sun2 and Weizhong Li12*

Author Affiliations

1 California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, California 92093, USA

2 Center for Research in Biological Systems, University of California San Diego, La Jolla, California 92093, USA

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BMC Bioinformatics 2010, 11:187  doi:10.1186/1471-2105-11-187

Published: 13 April 2010



Artificial duplicates from pyrosequencing reads may lead to incorrect interpretation of the abundance of species and genes in metagenomic studies. Duplicated reads were filtered out in many metagenomic projects. However, since the duplicated reads observed in a pyrosequencing run also include natural (non-artificial) duplicates, simply removing all duplicates may also cause underestimation of abundance associated with natural duplicates.


We implemented a method for identification of exact and nearly identical duplicates from pyrosequencing reads. This method performs an all-against-all sequence comparison and clusters the duplicates into groups using an algorithm modified from our previous sequence clustering method cd-hit. This method can process a typical dataset in ~10 minutes; it also provides a consensus sequence for each group of duplicates. We applied this method to the underlying raw reads of 39 genomic projects and 10 metagenomic projects that utilized pyrosequencing technique. We compared the occurrences of the duplicates identified by our method and the natural duplicates made by independent simulations. We observed that the duplicates, including both artificial and natural duplicates, make up 4-44% of reads. The number of natural duplicates highly correlates with the samples' read density (number of reads divided by genome size). For high-complexity metagenomic samples lacking dominant species, natural duplicates only make up <1% of all duplicates. But for some other samples like transcriptomic samples, majority of the observed duplicates might be natural duplicates.


Our method is available from webcite as a downloadable program and a web server. It is important not only to identify the duplicates from metagenomic datasets but also to distinguish whether they are artificial or natural duplicates. We provide a tool to estimate the number of natural duplicates according to user-defined sample types, so users can decide whether to retain or remove duplicates in their projects.