Abstract
BackgroundThe use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens.ResultsHere we present Clinical PathoScope, a pipeline to rapidly and accurately remove host contamination, isolate microbial reads, and identify potential disease-causing pathogens. We have accomplished three essential tasks in the development of Clinical PathoScope. First, we developed an optimized framework for pathogen identification using a computational subtraction methodology in concordance with read trimming and ambiguous read reassignment. Second, we have demonstrated the ability of our approach to identify multiple pathogens in a single clinical sample, accurately identify pathogens at the subspecies level, and determine the nearest phylogenetic neighbor of novel or highly mutated pathogens using real clinical sequencing data. Finally, we have shown that Clinical PathoScope outperforms previously published pathogen identification methods with regard to computational speed, sensitivity, and specificity.ConclusionsClinical PathoScope is the only pathogen identification method currently available that can identify multiple pathogens from mixed samples and distinguish between very closely related species and strains in samples with very few reads per pathogen. Furthermore, Clinical PathoScope does not rely on genome assembly and thus can more rapidly complete the analysis of a clinical sample when compared with current assembly-based methods. Clinical PathoScope is freely available at: http://sourceforge.net/projects/pathoscope/.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-262) contains supplementary material, which is available to authorized users.
Highlights
The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment
We developed the optimized Clinical PathoScope algorithm using a set of simulated clinical samples and later validated our method and compared our results against existing approaches using multiple clinical datasets, some of which are original to this publication
Reference genome library curation and processing One of the most important steps for the accurate identification of benign and pathogenic genomes is to build a comprehensive genome library containing all species and strains likely to be present in the sample. This is a critical step as Clinical PathoScope can only identify organisms or their nearest neighbors if they are present in the library
Summary
The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. Current clinical diagnostic tests for identifying infection-causing pathogens utilize limited technologies such as polymerase chain reactions (PCR), Sanger sequencing, or cell culture These methods typically focus on identifying only a single pathogen at a time. Recent commercial efforts have reduced this time to a few hours or days, projecting within the few years sequencing runs of less than an hour with a cost of under one hundred dollars [15] Once these technologies become widely accessible, the use of sequencing as a diagnostic tool in the clinic will have great potential for more personalized medical applications. DNA from host genomes or commensal species will often dominate clinical samples and sequencing error can swamp out diagnostic signal These challenges highlight the need for the development of highly sensitive algorithms that can distinguish among closely related pathogenic strains in a computationally efficient manner
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