SUBTRACTIVE ASSEMBLY APPROACHES FOR INFERRING DISEASE-ASSOCIATED MICROBIAL GENES AND PATHWAYS FROM MICROBIOME SEQUENCING DATA

SummarySuccessful translational applications of microbiome research rely on computational toolsthat can effectively detect microbial markers that are associated with diseases, andprovide explanations to the associations. We propose to develop subtractive assemblyapproaches to microbiome sequencing data analysis, aiming to identity microbial genesand pathways that are associated with diseases. The advantages of using subtractiveassembly approaches include: 1) they significantly reduce the complexity of the fragmentassembly problem by focusing only on the potential difference (genes and genomes) atthe initial (instead of the final) stage of the comparative analysis pipeline, and 2) theyimprove the assemblies of differential genes, which are important inputs for buildingpredictive models for disease diagnosis and characterization of treatment efficacy. Wewill apply our new tools to analyzing disease-associated microbiomes including thoseassociated with type II diabetes, liver cancer, inflammatory bowel disease (IBD) andthose known to be related to the efficacy of cancer immunotherapy.