Public Health Surveillance using Decentralized Technologies

This article describes how blockchain technologies can be used in the context of Public Health Surveillance through decentralized sharing of genomic data. A brief analysis of why blockchain technologies are needed in public health is presented together with a distinction between public and private blockchains. Finally, a proposal for a network of blockchains, using the Cosmos framework, together with decentralized storage systems like IPFS and BigchainDB, is included to address the issues of interoperability in the health sector.&#x0D; Keywords: Blockchain, Cosmos Framework, Decentralized Technology, PublicHealth Surveillance</jats:p

A Bacterial Analysis Platform: An Integrated System for Analysing Bacterial Whole Genome Sequencing Data for Clinical Diagnostics and Surveillance

Recent advances in whole genome sequencing have made the technology available for routine use in microbiological laboratories. However, a major obstacle for using this technology is the availability of simple and automatic bioinformatics tools. Based on previously published and already available web-based tools we developed a single pipeline for batch uploading of whole genome sequencing data from multiple bacterial isolates. The pipeline will automatically identify the bacterial species and, if applicable, assemble the genome, identify the multilocus sequence type, plasmids, virulence genes and antimicrobial resistance genes. A short printable report for each sample will be provided and an Excel spreadsheet containing all the metadata and a summary of the results for all submitted samples can be downloaded. The pipeline was benchmarked using datasets previously used to test the individual services. The reported results enable a rapid overview of the major results, and comparing that to the previously found results showed that the platform is reliable and able to correctly predict the species and find most of the expected genes automatically. In conclusion, a combined bioinformatics platform was developed and made publicly available, providing easy-to-use automated analysis of bacterial whole genome sequencing data. The platform may be of immediate relevance as a guide for investigators using whole genome sequencing for clinical diagnostics and surveillance. The platform is freely available at: https://cge.cbs.dtu.dk/services/CGEpipeline-1.1 and it is the intention that it will continue to be expanded with new features as these become available

Virulence gene prediction by the VirulenceFinder.

<p>Virulence gene prediction by the VirulenceFinder.</p

Sequence type prediction by the MLST algorithm.

<p>Sequence type prediction by the MLST algorithm.</p

KmerFinder top results for sample <i>S</i>. <i>aureus</i> F38.

<p>The result shows that there are more than just significant hits for <i>S</i>. <i>aureus</i>. This indicates that the sample was contaminated and thus not a single isolate.</p

Flowchart depicting the workflow of the BAP.

<p>The input from the user is assembled if needed, and the bacterial species is identified through the KmerFinder algorithm. When ready, the assembled contigs are submitted to the ContigAnalyzer and ResFinder for annotation of contig metrics and identification of resistance genes. If the bacterial species is identified, the contigs are further, if applicable, submitted to MLST, PlasmidFinder and VirulenceFinder to identify the sequence type, known plasmids (and, if applicable, their plasmid sequence type), and known virulence genes. When all services are done, the BAP produces a summary report of the services result (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157718#pone.0157718.g002" target="_blank">Fig 2</a>).</p

Plasmid sequence type prediction by the pMLST algorithm.

<p>Plasmid sequence type prediction by the pMLST algorithm.</p

List of the seven mandatory metadata fields from the CGE metadata template.

<p>List of the seven mandatory metadata fields from the CGE metadata template.</p