TY - JOUR T1 - PathoLive – Real-time pathogen identification from metagenomic Illumina datasets JF - bioRxiv DO - 10.1101/402370 SP - 402370 AU - Simon H. Tausch AU - Tobias P. Loka AU - Jakob M. Schulze AU - Andreas Andrusch AU - Jeanette Klenner AU - Piotr W. Dabrowski AU - Martin S. Lindner AU - Andreas Nitsche AU - Bernhard Y. Renard Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/04/14/402370.abstract N2 - Motivation Over the past years, NGS has become a crucial workhorse for open-view pathogen diagnostics. Yet, long turnaround times result from using massively parallel high-throughput technologies as the analysis can only be performed after sequencing has finished. The interpretation of results can further be challenged by contaminations, clinically irrelevant sequences, and the sheer amount and complexity of the data.Results We implemented PathoLive, a real-time diagnostics pipeline for the detection of pathogens from clinical samples hours before sequencing has finished. Based on real-time alignment with HiL-ive2, mappings are scored with respect to common contaminations, low-entropy areas, and sequences of widespread, non-pathogenic organisms. The results are visualized using an interactive taxonomic tree that provides an easily interpretable overview of the relevance of hits. For a human plasma sample that was spiked in vitro with six pathogenic viruses, all agents were clearly detected after only 40 of 200 sequencing cycles. For a real-world sample from Sudan the results correctly indicated the presence of Crimean-Congo hemorrhagic Fever Virus. In a second real-world dataset from the 2019 SARS-CoV-2 outbreak in Wuhan, we found the presence of a SARS Coronavirus as the most relevant hit without the novel virus reference genome being included in the database. For all samples, clinically irrelevant hits were correctly de-emphasized. Our approach is valuable to obtain fast and accurate NGS-based pathogen identifications and correctly prioritize and visualize them based on their clinical significance.Availability PathoLive is open source and available on GitLab (https://gitlab.com/rkibioinformatics/PathoLive) and BioConda (conda install –c bioconda patholive).Contact Bernhard.Renard{at}hpi.de, NitscheA{at}rki.deCompeting Interest StatementThe authors have declared no competing interest. ER -