Document Type
Journal Article
Publication Date
1-1-2014
Journal
Microbiome
Volume
2
DOI
10.1186/2049-2618-2-33
Abstract
BACKGROUND: Recent innovations in sequencing technologies have provided researchers with the ability to rapidly characterize the microbial content of an environmental or clinical sample with unprecedented resolution. These approaches are producing a wealth of information that is providing novel insights into the microbial ecology of the environment and human health. However, these sequencing-based approaches produce large and complex datasets that require efficient and sensitive computational analysis workflows. Many recent tools for analyzing metagenomic-sequencing data have emerged, however, these approaches often suffer from issues of specificity, efficiency, and typically do not include a complete metagenomic analysis framework.
RESULTS: We present PathoScope 2.0, a complete bioinformatics framework for rapidly and accurately quantifying the proportions of reads from individual microbial strains present in metagenomic sequencing data from environmental or clinical samples. The pipeline performs all necessary computational analysis steps; including reference genome library extraction and indexing, read quality control and alignment, strain identification, and summarization and annotation of results. We rigorously evaluated PathoScope 2.0 using simulated data and data from the 2011 outbreak of Shiga-toxigenic Escherichia coli O104:H4.
CONCLUSIONS: The results show that PathoScope 2.0 is a complete, highly sensitive, and efficient approach for metagenomic analysis that outperforms alternative approaches in scope, speed, and accuracy. The PathoScope 2.0 pipeline software is freely available for download at: http://sourceforge.net/projects/pathoscope/.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
APA Citation
Hong, C., Manimaran, S., Shen, Y., Perez-Rogers, J., Byrd, A., Castro-Nallar, E., Crandall, K., & Johnson, W. (2014). PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples.. Microbiome, 2 (). http://dx.doi.org/10.1186/2049-2618-2-33
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 BioMed Central Ltd. Microbiome