Enhancing Long-Read-Based Strain-Aware Metagenome Assembly

Abstract

Microbial communities are usually highly diverse and often involve multiple strains from the participating species due to the rapid evolution of microorganisms. In such a complex microecosystem, different strains may show different biological functions. While reconstruction of individual genomes at the strain level is vital for accurately deciphering the composition of microbial communities, the problem has largely remained unresolved so far. Next-generation sequencing has been routinely used in metagenome assembly but there have been struggles to generate strain-specific genome sequences due to the short-read length. This explains why long-read sequencing technologies have recently provided unprecedented opportunities to carry out haplotype- or strain-resolved genome assembly. Here, we propose MetaBooster and MetaBooster-HiFi, as two pipelines for strain-aware metagenome assembly from PacBio CLR and Oxford Nanopore long-read sequencing data. Benchmarking experiments on both simulated and real sequencing data demonstrate that either the MetaBooster or the MetaBooster-HiFi pipeline drastically outperforms the state-of-the-art de novo metagenome assemblers, in terms of all relevant metagenome assembly criteria, involving genome fraction, contig length, and error rates.

Exploring Long-Read Metagenomics for Full Characterization of Shiga Toxin-Producing Escherichia coli in Presence of Commensal E. coli

“…Indeed, when the two strains reached the required level for full characterization but were present at an equivalent ratio (Mix3 O26:O2 ratio below 10:1), the assembler failed to distinguish the STEC from the commensal strain. Current long-read assemblers (even those allowing metagenomics assemblies) are not able to differentiate different strains from the same species (less than 5% ANI divergence) since they share many genomic regions [ 38 , 39 ]. Although different strains will behave differently during the enrichment step, we showed that to be characterized, the amount of STEC data should be at least 10 times in excess compared to the commensal strain.…”

Eco-evolutionary implications of helminth microbiomes

“…The development of long-range sequencing technologies such as Nanopore and PacBio has promoted and simplified full-length 16S rRNA gene sequencing (Callahan et al , 2019; Johnson et al , 2019). These longer DNA fragments provide improved resolution for bacterial taxonomic classification down to lineage levels, opening possibilities such as identifying lineages on the lower side of the divergence and abundance scale and undertaking phylogenetic assessments of more closely related lineages (Frank et al , 2016; Johnson et al , 2019; Brealey et al , 2022; Luo et al , 2022). However, microbiomes are composed of a number of non-bacterial organisms that are excluded by 16S amplicon-based technologies.…”

Repeat and haplotype aware error correction in nanopore sequencing reads with DeChat

“…Error self-correction is typically the initial and crucial step in the analysis of long-read sequencing data, particularly in genome assembly. It has become a standard procedure in haplotype-aware genome assembly (15; 16; 17; 18; 19; 20; 21) and strain-aware metagenome assembly (22; 23; 24).…”