Abstract
BackgroundRecent 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.ResultsWe 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.ConclusionsThe 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/.
Highlights
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
The user supplies a set of National Center for Biotechnology Information (NCBI) taxonomy identification numbers for organisms to be included in the library (Figure 2)
As PathoLib extracts the reference library, the NCBI GeneInfo number is linked to the taxonomy identification (taxID), and the taxID and organism name are appended to the sequence headers to further link sequences in downstream analyses
Summary
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. With the steadily increasing number of microbial genomes available in public data repositories, metagenomic characterization using high-throughput sequencing techniques can be used to catalogue microbes co-habituating in human systems [1] and to rapidly identify pathogens responsible for infectious disease outbreaks [2,3,4]. Assemblybased methods [12,13,14,15] have recently gained in popularity due to their increased sensitivity for strain identification These approaches can suffer from issues of specificity, efficiency, and typically do not include a complete metagenomic analysis framework with reference library generation, read quality control, and reporting
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
