PathoLive – Real-time pathogen identification from metagenomic Illumina datasets

Tausch, Simon H.; Loka, Tobias P.; Jm, Schulze; Andrusch, Andreas; Klenner, Jeanette; Dabrowski, Piotr Wojtek; Ms, Lindner; Nitsche, Andreas; By, Renard

doi:10.1101/402370

Cited by 5 publications

(5 citation statements)

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“…In our experience, smaller databases that include only subsets of eukaryotic genomes increase the false-positive classification rate immensely. The resulting species list is filtered for human, animal or plant pathogens as well as for select CDC or USDA agents using the risk group database from the American Biological Safety Association (ABSA) as it was shown before for viruses in clinical samples (Tausch et al, 2018). For selected pathogens of interest, classified reads are extracted from the metagenomic dataset in order to (i) verify the classification with BLASTn and the nucleotide database from NCBI, (ii) estimate the closest distance to a published reference genome with Mash for subspecies identification and (iii) identify virulence factors using SRST2 using the Virulence Factor Database (VFDB).…”

Section: Resultsmentioning

confidence: 99%

Fishing in the Soup – Pathogen Detection in Food Safety Using Metabarcoding and Metagenomic Sequencing

et al. 2019

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In food safety the detection of food contaminations with pathogenic microorganisms is a race against time and often outpaced by error-prone epidemiological approaches. For evidence-based outbreak investigations fast and reliable techniques and procedures are required to identify the source of infection. Metagenomics has the potential to become a powerful tool in the field of modern food safety, since it allows the detection, identification and characterization of a broad range of pathogens in a single experiment without pre-cultivation within a couple of days. Nevertheless, sample handling, sequencing and data analysis are challenging and can introduce errors and biases into the analysis. In order to evaluate the potential of metagenomics in food safety, we generated a mock community containing DNA of foodborne bacteria. Herewith, we compare the aptitude of the two prevalent approaches – 16S rDNA amplicon sequencing and whole genome shotgun sequencing – for the detection of foodborne bacteria using different parameters during sample preparation, sequencing and data analysis. 16S rDNA sequencing did not only result in high deviations from the expected sample composition on genus and species level, but more importantly lacked the detection of several pathogenic species. While shotgun sequencing is more suitable for species detection, abundance estimation, genome assembly and species characterization, the performance can vary depending on the library preparation kit, which was confirmed for a naturally Francisella tularensis contaminated game meat sample. The application of the Nextera XT DNA Library Preparation Kit for shotgun sequencing did not only result in lower reference genome recovery and coverage, but also in distortions of the mock community composition. For data analysis, we propose a publicly available workflow for pathogen detection and characterization and demonstrate its benefits on the usability of metagenomic sequencing in food safety by analyzing an authentic metagenomic sample.

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Section: Resultsmentioning

confidence: 99%

Fishing in the Soup – Pathogen Detection in Food Safety Using Metabarcoding and Metagenomic Sequencing

et al. 2019

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“…DeePaC-Live could also form a part of more complex real-time pathogen detection workflows like PathoLive (Tausch et al, 2018b). For example, PAIPline (Andrusch et al, 2018) identifies pathogens in metagenomic and clinical samples via mapping and a BLAST follow-up analysis, but can only start the analysis after the sequencing is finished.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based real-time detection of novel pathogens during sequencing

Bartoszewicz

Genske

Renard

2021

Preprint

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MotivationNovel pathogens evolve quickly and may emerge rapidly, causing dangerous outbreaks or even global pandemics. Next-generation sequencing is the state-of-the art in open-view pathogen detection, and one of the few methods available at the earliest stages of an epidemic, even when the biological threat is unknown. Analyzing the samples as the sequencer is running can greatly reduce the turnaround time, but existing tools rely on close matches to lists of known pathogens and perform poorly on novel species. Machine learning approaches can predict if single reads originate from more distant, unknown pathogens, but require relatively long input sequences and processed data from a finished sequencing run.ResultsWe present DeePaC-Live, a Python package for real-time pathogenic potential prediction directly from incomplete sequencing reads. We train deep neural networks to classify Illumina and Nanopore reads and integrate our models with HiLive2, a real-time Illumina mapper. DeePaC-Live outperforms alternatives based on machine learning and sequence alignment on simulated and real data, including SARS-CoV-2 sequencing runs. After just 50 Illumina cycles, we increase the true positive rate 80-fold compared to the live-mapping approach. The first 250bp of Nanopore reads, corresponding to 0.5s of sequencing time, are enough to yield predictions more accurate than mapping the finished long reads. Our approach could also be used for screening synthetic sequences against biosecurity threats.AvailabilityThe code is available at: https://gitlab.com/dacs-hpi/deepac-live and https://gitlab.com/dacs-hpi/deepac. The package can be installed with Bioconda, Docker or pip.ContactJakub.Bartoszewicz@hpi.de, Bernhard.Renard@hpi.deSupplementary informationSupplementary data are available online.

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“…Since the analysis of HTS data for virus diagnostics requires bioinformatics as well as virological knowledge, collaboration between the two disciplines has been emphasized (32). Furthermore, automated pipelines for HTS-based virus diagnostics with unbiased evaluation of the pathogenicity and relevance of the pathogen detected have been implemented; these can help harmonize the analysis and interpretation of HTS sequence results (33).…”

Section: Discussionmentioning

confidence: 99%

Proficiency Testing of Virus Diagnostics Based on Bioinformatics Analysis of Simulated In Silico High-Throughput Sequencing Data Sets

et al. 2019

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Quality management and independent assessment of high-throughput sequencing-based virus diagnostics have not yet been established as a mandatory approach for ensuring comparable results. The sensitivity and specificity of viral high-throughput sequence data analysis are highly affected by bioinformatics processing using publicly available and custom tools and databases and thus differ widely between individuals and institutions. Here we present the results of the COMPARE [Collaborative Management Platform for Detection and Analyses of (Re-)emerging and Foodborne Outbreaks in Europe] in silico virus proficiency test. An artificial, simulated in silico data set of Illumina HiSeq sequences was provided to 13 different European institutes for bioinformatics analysis to identify viral pathogens in high-throughput sequence data. Comparison of the participants’ analyses shows that the use of different tools, programs, and databases for bioinformatics analyses can impact the correct identification of viral sequences from a simple data set. The identification of slightly mutated and highly divergent virus genomes has been shown to be most challenging. Furthermore, the interpretation of the results, together with a fictitious case report, by the participants showed that in addition to the bioinformatics analysis, the virological evaluation of the results can be important in clinical settings. External quality assessment and proficiency testing should become an important part of validating high-throughput sequencing-based virus diagnostics and could improve the harmonization, comparability, and reproducibility of results. There is a need for the establishment of international proficiency testing, like that established for conventional laboratory tests such as PCR, for bioinformatics pipelines and the interpretation of such results.

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PathoLive – Real-time pathogen identification from metagenomic Illumina datasets

Cited by 5 publications

References 85 publications

Fishing in the Soup – Pathogen Detection in Food Safety Using Metabarcoding and Metagenomic Sequencing

Fishing in the Soup – Pathogen Detection in Food Safety Using Metabarcoding and Metagenomic Sequencing

Deep learning-based real-time detection of novel pathogens during sequencing

Proficiency Testing of Virus Diagnostics Based on Bioinformatics Analysis of Simulated In Silico High-Throughput Sequencing Data Sets

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