PAIPline: pathogen identification in metagenomic and clinical next generation sequencing samples
Author(s) -
Andreas Andrusch,
Piotr Wojciech Dąbrowski,
Jeanette Klenner,
Simon H. Tausch,
Claudia Kohl,
Abdalla A. Osman,
Bernhard Y. Renard,
Andreas Nitsche
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty595
Subject(s) - metagenomics , false positive paradox , preprocessor , computational biology , identification (biology) , computer science , profiling (computer programming) , dna sequencing , software , biology , data mining , artificial intelligence , genetics , ecology , gene , programming language , operating system
Next generation sequencing (NGS) has provided researchers with a powerful tool to characterize metagenomic and clinical samples in research and diagnostic settings. NGS allows an open view into samples useful for pathogen detection in an unbiased fashion and without prior hypothesis about possible causative agents. However, NGS datasets for pathogen detection come with different obstacles, such as a very unfavorable ratio of pathogen to host reads. Alongside often appearing false positives and irrelevant organisms, such as contaminants, tools are often challenged by samples with low pathogen loads and might not report organisms present below a certain threshold. Furthermore, some metagenomic profiling tools are only focused on one particular set of pathogens, for example bacteria.
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