An integrated strain-level analytic pipeline utilizing longitudinal metagenomic data

Abstract

Background With the development of sequencing technology and analytic tools, studying within-species variations enhances the understanding of microbial biological processes. However, most existing methods for strain-level analysis do not allow for the simultaneous interrogation of strain proportions and genome-wide variants in longitudinal metagenomic samples. Results In this study, we introduce LongStrain, an integrated pipeline for the analysis of large-scale metagenomic data from individuals with longitudinal or repeated samples. In LongStrain, we first utilize two efficient tools, Kraken2 and Bowtie2, for the taxonomic classification and alignment of sequencing reads respectively. Then, we propose to jointly model strain proportions and shared haplotypes across samples within individuals, which greatly improves the efficiency and accuracy of strain identification. With extensive simulation studies of a microbial community and single species, we show that LongStrain is superior to three popular reference genome-based methods in variant calling and strain-proportion estimation. Furthermore, we illustrate the potential applications of LongStrain in the real data analysis of The Environmental Determinants of Diabetes in the Young study and a gastric intestinal metaplasia microbiome study. Conclusions The proposed analytic pipeline demonstrates marked statistical efficiency over same type of methods and has great potential in understanding the genomic variants and dynamic changes at strain level.