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. Thesbv IMPROVER metagenomics diagnosis for inflammatory bowel disease challenge investigatedcomputational metagenomics methods for discriminating IBD and nonIBD subjects. Participants inthis challenge were given independent training and test metagenomics data from IBD and nonIBDsubjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed TaxonomyandFunction-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions werereceived between September 2019 and March 2020. Most participants' predictions performed betterthan 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 remainschallenging, with the classification quality similar to the set of random predictions. We analyzed theclass prediction accuracy, the metagenomics features by the teams, and computational methodsused. These results will be openly shared with the scientific community to help advance IBD researchand illustrate the application of a range of computational methodologies for effective metagenomicclassification

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

Granata I.;Giordano M.;Maddalena L;Piccirillo M;Manipur I;Guarracino M. R.;
2023

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. Thesbv IMPROVER metagenomics diagnosis for inflammatory bowel disease challenge investigatedcomputational metagenomics methods for discriminating IBD and nonIBD subjects. Participants inthis challenge were given independent training and test metagenomics data from IBD and nonIBDsubjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed TaxonomyandFunction-based profiles (sub-challenge 2, SC2). A total of 81 anonymized submissions werereceived between September 2019 and March 2020. Most participants' predictions performed betterthan 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 remainschallenging, with the classification quality similar to the set of random predictions. We analyzed theclass prediction accuracy, the metagenomics features by the teams, and computational methodsused. These results will be openly shared with the scientific community to help advance IBD researchand illustrate the application of a range of computational methodologies for effective metagenomicclassification
2023
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
metagenomics diagnostics
data science
machine learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/459933
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