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Journal of Biomedicine and Biotechnology
Volume 2011 (2011), Article ID 495849, 11 pages
Research Article

Discovering the Unknown: Improving Detection of Novel Species and Genera from Short Reads

1Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA
2Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ 08028, USA
3Spoken Language Systems Laboratory, Instituto Superior Técnico, 1049-001 Lisbon, Portugal
4School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA 19104, USA

Received 20 September 2010; Revised 23 December 2010; Accepted 25 January 2011

Academic Editor: Chiu-Chung Young

Copyright © 2011 Gail L. Rosen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


High-throughput sequencing technologies enable metagenome profiling, simultaneous sequencing of multiple microbial species present within an environmental sample. Since metagenomic data includes sequence fragments (“reads”) from organisms that are absent from any database, new algorithms must be developed for the identification and annotation of novel sequence fragments. Homology-based techniques have been modified to detect novel species and genera, but, composition-based methods, have not been adapted. We develop a detection technique that can discriminate between “known” and “unknown” taxa, which can be used with composition-based methods, as well as a hybrid method. Unlike previous studies, we rigorously evaluate all algorithms for their ability to detect novel taxa. First, we show that the integration of a detector with a composition-based method performs significantly better than homology-based methods for the detection of novel species and genera, with best performance at finer taxonomic resolutions. Most importantly, we evaluate all the algorithms by introducing an “unknown” class and show that the modified version of PhymmBL has similar or better overall classification performance than the other modified algorithms, especially for the species-level and ultrashort reads. Finally, we evaluate theperformance of several algorithms on a real acid mine drainage dataset.