Frequentmers - a novel way to look at metagenomic next generation sequencing data and an application in detecting liver cirrhosis

• Published in: BMC genomics Vol. 24; no. 1; p. 768
• Main Authors: Mouratidis, Ioannis, Chantzi, Nikol, Khan, Umair, Konnaris, Maxwell A, Chan, Candace S Y, Mareboina, Manvita, Moeckel, Camille, Georgakopoulos-Soares, Ilias
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 12-12-2023; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Biological markers; Biomarkers; Care and treatment; Cirrhosis; detection; Diagnosis; Disease; DNA sequencing; Genomes; Genomics; k-mers; Liver; Liver cirrhosis; liver lirrhosis; Machine learning; Metagenomics; Microorganisms; mNGS; Molecular mechanics; Next-generation sequencing; Nucleotide sequencing; Patients; Risk factors; United States; k-mers; detection; liver lirrhosis; mNGS
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-023-09861-w

Early detection of human disease is associated with improved clinical outcomes. However, many diseases are often detected at an advanced, symptomatic stage where patients are past efficacious treatment periods and can result in less favorable outcomes. Therefore, methods that can accurately detect human disease at a presymptomatic stage are urgently needed. Here, we introduce "frequentmers"; short sequences that are specific and recurrently observed in either patient or healthy control samples, but not in both. We showcase the utility of frequentmers for the detection of liver cirrhosis using metagenomic Next Generation Sequencing data from stool samples of patients and controls. We develop classification models for the detection of liver cirrhosis and achieve an AUC score of 0.91 using ten-fold cross-validation. A small subset of 200 frequentmers can achieve comparable results in detecting liver cirrhosis. Finally, we identify the microbial organisms in liver cirrhosis samples, which are associated with the most predictive frequentmer biomarkers.