Deconvolute individual genomes from metagenome sequences through short read clustering

Abstract

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

Hybrid Clustering of Long and Short-read for Improved Metagenome Assembly

“…LPA is capable of resolving genomes with shared reads and has near linear computational performance. SpaRC can be run at two different modes: "local mode" only cluster reads based on their overlap, while "global mode" further clusters the results from local mode based on multiple sample statistics (Li et al, 2020).…”

“…We previously developed a scalable metagenome clustering tool called SpaRC (Shi et al, 2018;Li et al, 2020) based on Apache Spark. SpaRC can form pure and complete clusters with long-read sequencing technologies.…”