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2015 ; 16
(ä): 416
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English Wikipedia
Pathosphere org: pathogen detection and characterization through a web-based,
open source informatics platform
#MMPMID26714571
Kilianski A
; Carcel P
; Yao S
; Roth P
; Schulte J
; Donarum GB
; Fochler ET
; Hill JM
; Liem AT
; Wiley MR
; Ladner JT
; Pfeffer BP
; Elliot O
; Petrosov A
; Jima DD
; Vallard TG
; Melendrez MC
; Skowronski E
; Quan PL
; Lipkin WI
; Gibbons HS
; Hirschberg DL
; Palacios GF
; Rosenzweig CN
BMC Bioinformatics
2015[Dec]; 16
(ä): 416
PMID26714571
show ga
BACKGROUND: The detection of pathogens in complex sample backgrounds has been
revolutionized by wide access to next-generation sequencing (NGS) platforms.
However, analytical methods to support NGS platforms are not as uniformly
available. Pathosphere (found at Pathosphere.org) is a cloud - based open -
sourced community tool that allows for communication, collaboration and sharing
of NGS analytical tools and data amongst scientists working in academia, industry
and government. The architecture allows for users to upload data and run
available bioinformatics pipelines without the need for onsite processing
hardware or technical support. RESULTS: The pathogen detection capabilities
hosted on Pathosphere were tested by analyzing pathogen-containing samples
sequenced by NGS with both spiked human samples as well as human and zoonotic
host backgrounds. Pathosphere analytical pipelines developed by Edgewood Chemical
Biological Center (ECBC) identified spiked pathogens within a common sample
analyzed by 454, Ion Torrent, and Illumina sequencing platforms. ECBC pipelines
also correctly identified pathogens in human samples containing arenavirus in
addition to animal samples containing flavivirus and coronavirus. These
analytical methods were limited in the detection of sequences with limited
homology to previous annotations within NCBI databases, such as parvovirus.
Utilizing the pipeline-hosting adaptability of Pathosphere, the analytical suite
was supplemented by analytical pipelines designed by the United States Army
Medical Research Insititute of Infectious Diseases and Walter Reed Army Institute
of Research (USAMRIID-WRAIR). These pipelines were implemented and detected
parvovirus sequence in the sample that the ECBC iterative analysis previously
failed to identify. CONCLUSIONS: By accurately detecting pathogens in a variety
of samples, this work demonstrates the utility of Pathosphere and provides a
platform for utilizing, modifying and creating pipelines for a variety of NGS
technologies developed to detect pathogens in complex sample backgrounds. These
results serve as an exhibition for the existing pipelines and web-based interface
of Pathosphere as well as the plug-in adaptability that allows for integration of
newer NGS analytical software as it becomes available.