title: Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data.
abstract: BACKGROUND
  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. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens.
  
  
  RESULTS
  Here 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.
  
  
  CONCLUSIONS
  Clinical 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/.
authors.forename: Allyson L
authors.initials: AL
authors.lastname: Byrd
authors.forename: Joseph F
authors.initials: JF
authors.lastname: Perez-Rogers
authors.forename: Solaiappan
authors.initials: S
authors.lastname: Manimaran
authors.forename: Eduardo
authors.initials: E
authors.lastname: Castro-Nallar
authors.forename: Ian
authors.initials: I
authors.lastname: Toma
authors.forename: Tim
authors.initials: T
authors.lastname: McCaffrey
authors.forename: Marc
authors.initials: M
authors.lastname: Siegel
authors.forename: Gary
authors.initials: G
authors.lastname: Benson
authors.affiliation: Department of Bioinformatics, Boston University, Boston, MA, USA. kcrandall@gwu.edu.
authors.forename: Keith A
authors.initials: KA
authors.lastname: Crandall
authors.forename: William Evan
authors.initials: WE
authors.lastname: Johnson
mesh_terms: D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D054884:Host-Pathogen Interactions; D006801:Humans; D008828:Microbiological Techniques / Q000379:methods*; D010802:Phylogeny; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*; D013045:Species Specificity; D013997:Time Factors
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