DRAGoM: Classification and Quantification of Noncoding RNA in Metagenomic Data

Cuncong Zhong

Abstract: Noncoding RNA (ncRNA) plays important regulatory and functional roles in microorganisms, such as regulation of gene expression, signaling, protein synthesis, and RNA processing. Hence, the classification and quantification of them is a central task towards the understanding of the function of the microbial community. However, the majority of the current metagenomic sequencing technologies generate short reads, which may only contain partial secondary structure that complicates ncRNA homology detection. Meanwhile, de novo assembly of the metagenomic sequencing data remains challenging for complex communities. To tackle these challenges, we developed a novel algorithm called DRAGoM (Detection of RNA using Assembly Graph from Metagenomic data). DRAGoM first constructs a hybrid graph by merging an assembly string graph and an assembly de Bruijn graph. Then, it classifies paths in the hybrid graph and their constituent reads into different ncRNA families based on both sequence and structural homology. Our benchmark experiments show that DRAGoM can improve the performance and robustness over the traditional approaches on the classification and quantification of a wide class of ncRNA families.