Document Type
Journal Article
Publication Date
10-1-2013
Journal
Genome Research
Volume
23
Issue
10
DOI
10.1101/gr.150151.112
Keywords
Algorithms; Bacillus anthracis; Bacteria; Bayes Theorem; Bioterrorism; Burkholderia mallei; Burkholderia pseudomallei; Clostridium botulinum; Computational Biology; Databases, Genetic; Escherichia coli; Escherichia coli Infections; Europe; Francisella tularensis; Genome, Bacterial; Genomics; High-Throughput Nucleotide Sequencing; Humans; Sequence Analysis, DNA; Software; Species Specificity; Yersinia pestis
Abstract
Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of target genomes. Importantly, our approach also incorporates the possibility that multiple species can be present in the sample and considers cases when the sample species/strain is not in the reference database. Furthermore, our approach can accurately discriminate between very closely related strains of the same species with very little coverage of the genome and without the need for multiple alignment steps, extensive homology searches, or genome assembly--which are time-consuming and labor-intensive steps. We demonstrate the utility of our approach on genomic data from purified and in silico "environmental" samples from known bacterial agents impacting human health for accuracy assessment and comparison with other approaches.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 License
APA Citation
Francis, O., Bendall, M., Manimaran, S., Hong, C., Clement, N., Castro-Nallar, E., Snell, Q., Schaalje, G., Clement, M., Crandall, K., & Johnson, W. (2013). Pathoscope: species identification and strain attribution with unassembled sequencing data.. Genome Research, 23 (10). http://dx.doi.org/10.1101/gr.150151.112
Peer Reviewed
1
Open Access
1
Included in
Computational Biology Commons, Research Methods in Life Sciences Commons, Structural Biology Commons
Comments
Reproduced with permission of Cold Spring Harbor Laboratory Press. Genome Research