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

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 dev...

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Bibliographic Details
Published in:BioMed research international 2011-01, Vol.2011 (2011), p.1-11
Main Authors: Rosen, Gail L., Polikar, Robi, Caseiro, Diamantino A., Essinger, Steven D., Sokhansanj, Bahrad A.
Format: Article
Language:English
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
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Summary: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.
ISSN:1110-7243
2314-6133
1110-7251
2314-6141
DOI:10.1155/2011/495849