Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge

Autor: Lusine Khachatryan, Yang Xiang, Artem Ivanov, Enrico Glaab, Garrett Graham, Ilaria Granata, Maurizio Giordano, Lucia Maddalena, Marina Piccirillo, Ichcha Manipur, Giacomo Baruzzo, Marco Cappellato, Batiste Avot, Adrian Stan, James Battey, Giuseppe Lo Sasso, Stephanie Boue, Nikolai V. Ivanov, Manuel C. Peitsch, Julia Hoeng, Laurent Falquet, Barbara Di Camillo, Mario R. Guarracino, Vladimir Ulyantsev, Nicolas Sierro, Carine Poussin
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Reports, Vol 13, Iss 1, Pp 1-19 (2023)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-023-33050-0
Popis: Abstract A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER metagenomics diagnosis for inflammatory bowel disease challenge investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed Taxonomy- and Function-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions were received between September 2019 and March 2020. Most participants’ predictions performed better than random predictions in classifying IBD versus nonIBD, Ulcerative Colitis (UC) versus nonIBD, and Crohn’s Disease (CD) versus nonIBD. However, discrimination between UC and CD remains challenging, with the classification quality similar to the set of random predictions. We analyzed the class prediction accuracy, the metagenomics features by the teams, and computational methods used. These results will be openly shared with the scientific community to help advance IBD research and illustrate the application of a range of computational methodologies for effective metagenomic classification.
Databáze: Directory of Open Access Journals
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