The COMPARE Data Hubs

Amid, Clara; Pakseresht, Nima; Silvester, Nicole C.; Jayathilaka, Suran; Lund, Ole; Pataki, Bálint Ármin; Visontai, Dávid; Cotten, Matthew; Xavier, Basil Britto; Alako, Blaise T. F.; Belka, Ariane; Cisneros, Jessica; Haringhuizen, George B.; Harrison, Peter W.; Hoeper, Dirk; Holt, Sam; Hundahl, C.; Hussein, Abdulrahman; Kaas, Rolf Sommer; Malhotra-Kumar, Surbhi; Leinonen, Rasko; Nieuwenhuijse, David F.; Rahman, Nadim; Ribeiro, Carolina dos S.; Skiby, Jeffrey Edward; Stéger, József; Szalai-Gindl, János M.; Thomsen, Martin Christen Frølund; Csabai, István; Aarestrup, Frank Møller; Cochrane, Guy

doi:10.1101/555938

Cited by 14 publications

(18 citation statements)

References 12 publications

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“…However, in order to fully capitalize on the potential added value of WGS for public health decision making, systems for combined analysis of data are needed that are in agreement with general data protection rules. We previously developed a model for collaborative exploration of WGS and metadata in a protected sharing environment 28…”

Section: Discussionmentioning

confidence: 99%

Rapid SARS-CoV-2 whole genome sequencing for informed public health decision making in the Netherlands

Munnink

Nieuwenhuijse

Stein

et al. 2020

Preprint

Self Cite

105

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SARS-CoV-2 is a novel coronavirus that has rapidly spread across the globe. In the Netherlands, the first case of SARS-CoV-2 has been notified on the 27 th of February. Here, we describe the first three weeks of the SARS-CoV-2 outbreak in the Netherlands, which started with several different introductory events from Italy, Austria, Germany and France followed by local amplification in, and later also, outside the South of the Netherlands. The timely generation of whole genome sequences combined with epidemiological investigations facilitated early decision making in an attempt to control local transmission of SARS-CoV-2 in the Netherlands.

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Section: Discussionmentioning

confidence: 99%

Rapid SARS-CoV-2 whole genome sequencing for informed public health decision making in the Netherlands

Munnink

Nieuwenhuijse

Stein

et al. 2020

Preprint

Self Cite

105

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“…Sequences. Raw reads were shared in a private datahub called dcc_Vivaldi 8 (https://www.ebi.ac.uk/ena/pathogens/login) set up for the purpose and hosted at the European Nucleotide Archive (ENA). An accession number was linked to the single sequences upon uploading to ENA.…”

Section: Methodsmentioning

confidence: 99%

“…Flow chart of data generation, data management and quality control. dcc_vivaldi 8 : a private datahub (https://www.ebi.ac.uk/ena/pathogens/login) set up for data sharing and hosted at the European Nucleotide Archive (ENA). Computerome: a local server used by DTU Food (https://www.computerome.dk).…”

Section: Figmentioning

confidence: 99%

Four European Salmonella Typhimurium datasets collected to develop WGS-based source attribution methods

et al. 2020

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Zoonotic Salmonella causes millions of human salmonellosis infections worldwide each year. Information about the source of the bacteria guides risk managers on control and preventive strategies. Source attribution is the effort to quantify the number of sporadic human cases of a specific illness to specific sources and animal reservoirs. Source attribution methods for Salmonella have so far been based on traditional wet-lab typing methods. With the change to whole genome sequencing there is a need to develop new methods for source attribution based on sequencing data. Four European datasets collected in Denmark (DK), Germany (DE), the United Kingdom (UK) and France (FR) are presented in this descriptor. The datasets contain sequenced samples of Salmonella Typhimurium and its monophasic variants isolated from human, food, animal and the environment. The objective of the datasets was either to attribute the human salmonellosis cases to animal reservoirs or to investigate contamination of the environment by attributing the environmental isolates to different animal reservoirs.

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“…ENA is an open repository for sequence and related data and a member of the International Nucleotide Sequence Database Collaboration (INSDC; http://www.insdc.org/) [21]. A full description of the COMPARE Data Hub system is provided in a preprint version of Amid et al [22]. First, consensus sequences derived from a preliminary analysis were compared and one overarching consensus sequence was determined for each gene segment for each virus.…”

Section: Experimental Designmentioning

confidence: 99%

“…In addition, data sets are ultimately made publicly and through the INSDC network globally available and accessible in real-time as required without further upload to a different repository. Full details of the COMPARE Data Hub system are available in a submitted manuscript [22]. In summary, this process was suitable for quick data sharing in an outbreak scenario.…”

Section: Data Sharingmentioning

confidence: 99%

Comparison of sequencing methods and data processing pipelines for whole genome sequencing and minority single nucleotide variant (mSNV) analysis during an influenza A/H5N8 outbreak

et al. 2020

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As high-throughput sequencing technologies are becoming more widely adopted for analysing pathogens in disease outbreaks there needs to be assurance that the different sequencing technologies and approaches to data analysis will yield reliable and comparable results. Conversely, understanding where agreement cannot be achieved provides insight into the limitations of these approaches and also allows efforts to be focused on areas of the process that need improvement. This manuscript describes the next-generation sequencing of three closely related viruses, each analysed using different sequencing strategies, sequencing instruments and data processing pipelines. In order to determine the comparability of consensus sequences and minority (sub-consensus) single nucleotide variant (mSNV) identification, the biological samples, the sequence data from 3 sequencing platforms and the *.bam quality-trimmed alignment files of raw data of 3 influenza A/H5N8 viruses were shared. This analysis demonstrated that variation in the final result could be attributed to all stages in the process, but the most critical were the well-known homopolymer errors introduced by 454 sequencing, and the alignment processes in the different data processing pipelines which affected the consistency of mSNV detection. However, homopolymer errors aside, there was generally a good agreement between consensus sequences that were obtained for all combinations of sequencing platforms and data processing pipelines. Nevertheless, minority variant analysis will need a different level of careful standardization and awareness about the possible limitations, as shown in this study.

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The COMPARE Data Hubs

Cited by 14 publications

References 12 publications

Rapid SARS-CoV-2 whole genome sequencing for informed public health decision making in the Netherlands

Rapid SARS-CoV-2 whole genome sequencing for informed public health decision making in the Netherlands

Four European Salmonella Typhimurium datasets collected to develop WGS-based source attribution methods

Comparison of sequencing methods and data processing pipelines for whole genome sequencing and minority single nucleotide variant (mSNV) analysis during an influenza A/H5N8 outbreak

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