Andrusch, A. | Search bioRxiv

bioRxiv2018

PathoLive - Real time pathogen identification from metagenomic Illumina datasets

MotivationOver 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. ResultsWe 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. AvailabilityPathoLive is open source and available on GitLab (https://gitlab.com/rkibioinformatics/PathoLive) and BioConda (conda install -c bioconda patholive). ContactBernhard.Renard@hpi.de, NitscheA@rki.de

bioinformatics↗

bioRxiv2018

DREAM-Yara: An implementation of an exact read mapper for very large databases

MotivationMapping-based approaches have become limited in their application to very large sets of references since computing an FM-index for very large databases (e.g. > 10 GB) has become a bottleneck. This affects many analyses that need such index as an essential step for approximate matching of the NGS reads to reference databases. For instance, in typical metagenomics analysis, the size of the reference sequences has become prohibitive to compute a single full-text index on standard machines. Even on large memory machines, computing such index takes about one day of computing time. As a result, updates of indices are rarely performed. Hence, it is desirable to create an alternative way of indexing while preserving fast search times.\n\nResultsTo solve the index construction and update problem we propose the DREAM (Dynamic seaRchablE pArallel coMpressed index) framework and provide an implementation. The main contributions are the introduction of an approximate search distributor directories via a novel use of Bloom filters. We combine several Bloom filters to form an interleaved Bloom filter and use this new data structure to quickly exclude reads for parts of the databases where they cannot match. This allows us to keep the databases in several indices which can be easily rebuilt if parts are updated while maintaining a fast search time. The second main contribution is an implementation of DREAM-Yara a distributed version of a fully sensitive read mapper under the DREAM framework.\n\nContacttemesgen.dadi@fu-berlin.de\n\nAvailabilityhttps://gitlab.com/pirovc/dream_yara/

bioinformatics↗