Data

Taxonomy summaries of the metagenomics data sequenced as part of the investigation into the Panzi outbreak.

Flinders University
Edwards, Robert
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25451/flinders.29879330.v3&rft.title=Taxonomy summaries of the metagenomics data sequenced as part of the investigation into the Panzi outbreak.&rft.identifier=10.25451/flinders.29879330.v3&rft.publisher=Flinders University&rft.description=These tables contain the taxonomic summaries of the sequences generated as part of the investigation into the disease outbreak in Panzi in 2024. Tony Wawina-Bokalanga and colleagues generated the sequences, and the taxonomic summaries were generated by comparing those sequences to the UniRef50 database using MMSeqs2 using the atavide-lite pipeline.For each taxonomic level (kingdom, phylum, class, order, family, genus, species) there are two files, raw for the raw data which is the number of reads that mapped at that taxonomic level, and norm for the normalised number of reads that mapped, which is given by the number of reads that mapped at that level, dividided by the total number of reads that mapped, multiplied by 1,000,000.The tables are tab-separated values that can be easily read by R, Python, Pandas, Excel, OpenOffice, or any other software.&rft.creator=Edwards, Robert &rft.date=2025&rft.edition=3&rft_rights= https://creativecommons.org/publicdomain/zero/1.0/&rft_subject=Medical bacteriology&rft_subject=Medical infection agents (incl. prions)&rft_subject=Medical virology&rft_subject=Medical parasitology&rft_subject=metagneomics&rft_subject=panzi&rft_subject=DRC&rft_subject=outbreak&rft_subject=plasmodium&rft.type=dataset&rft.language=English Access the data
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These tables contain the taxonomic summaries of the sequences generated as part of the investigation into the disease outbreak in Panzi in 2024. Tony Wawina-Bokalanga and colleagues generated the sequences, and the taxonomic summaries were generated by comparing those sequences to the UniRef50 database using MMSeqs2 using the atavide-lite pipeline.

For each taxonomic level (kingdom, phylum, class, order, family, genus, species) there are two files, raw for the raw data which is the number of reads that mapped at that taxonomic level, and norm for the normalised number of reads that mapped, which is given by the number of reads that mapped at that level, dividided by the total number of reads that mapped, multiplied by 1,000,000.

The tables are tab-separated values that can be easily read by R, Python, Pandas, Excel, OpenOffice, or any other software.


Issued: 05 11 2025

Created: 05 11 2025

Modified: 05 11 2025

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DRC | Medical bacteriology | Medical infection agents (incl. prions) | Medical parasitology | Medical virology | metagneomics | outbreak | panzi | plasmodium |

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