37: A Computational Framework for Enhancing 16S Sequencing Using Long-Read Phylogenetic Reference Data

Music City Center 

16S sequencing is a widely used approach for studying microbial communities, but its resolution limits its ability to provide comprehensive functional and mutational insights. We introduce a computational framework that integrates 16S sequencing data with a phylogenetic reference tree constructed through ancestral state reconstruction, utilizing the most up-to-date reference databases from long-read sequencing. By leveraging this long-read-informed phylogenetic framework, our method enhances functional predictions and mutation assessments, addressing key limitations of 16S sequencing and enabling more precise insights into microbial functional and genetic diversity. Importantly, this framework empowers researchers to extract richer information from 16S data, even in the absence of direct access to long-read sequencing technologies. Furthermore, we generalize this framework to accommodate shotgun sequencing data, broadening its applicability and utility across diverse microbiome research applications.

16S sequencing

Long-read sequencing

Phylogenetic reference tree

Ancestral state reconstruction

Microbiome analysis 

Section on Statistical Learning and Data Science