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

• Published in: BioMed research international Vol. 2011; no. 2011; pp. 1 - 11
• Main Authors: Rosen, Gail L., Polikar, Robi, Caseiro, Diamantino A., Essinger, Steven D., Sokhansanj, Bahrad A.
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Puplishing Corporation 01.01.2011; Hindawi Publishing Corporation; Dar al -Nasr -al-Llktruni; John Wiley & Sons, Inc
• Subjects: Accuracy; Acid mine drainage; Algorithms; Bacteria - genetics; Biological and medical sciences; Biomedical research; Computer engineering; Databases, Nucleic Acid; Deoxyribonucleic acid; DNA; DNA Barcoding, Taxonomic - methods; Genome - genetics; Genomes; High-throughput screening (Biochemical assaying); Medical sciences; Metagenomics; Methods; Mining; Open Reading Frames - genetics; Organisms; Pharmacology. Drug treatments; ROC Curve; Sequence Analysis, DNA - methods; Species Specificity; Taxonomy; Waste Disposal, Fluid; United States; Portugal; Detection; Species; Diagnosis
• ISSN: 1110-7243; 2314-6133; 1110-7251; 2314-6141; 1110-7251; 2314-6141
• DOI: 10.1155/2011/495849

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.