Abstract

BackgroundThe human microbiota are complex systems with important roles in our physiological activities and diseases. Sequencing the microbial genomes in the microbiota can help in our interpretation of their activities. The vast majority of the microbes in the microbiota cannot be isolated for individual sequencing. Current metagenomics practices use short-read sequencing to simultaneously sequence a mixture of microbial genomes. However, these results are in ambiguity during genome assembly, leading to unsatisfactory microbial genome completeness and contig continuity. Linked-read sequencing is able to remove some of these ambiguities by attaching the same barcode to the reads from a long DNA fragment (10–100 kb), thus improving metagenome assembly. However, it is not clear how the choices for several parameters in the use of linked-read sequencing affect the assembly quality.ResultsWe first examined the effects of read depth (C) on metagenome assembly from linked-reads in simulated data and a mock community. The results showed that C positively correlated with the length of assembled sequences but had little effect on their qualities. The latter observation was corroborated by tests using real data from the human gut microbiome, where C demonstrated minor impact on the sequence quality as well as on the proportion of bins annotated as draft genomes. On the other hand, metagenome assembly quality was susceptible to read depth per fragment (CR) and DNA fragment physical depth (CF). For the same C, deeper CR resulted in more draft genomes while deeper CF improved the quality of the draft genomes. We also found that average fragment length (μFL) had marginal effect on assemblies, while fragments per partition (NF/P) impacted the off-target reads involved in local assembly, namely, lower NF/P values would lead to better assemblies by reducing the ambiguities of the off-target reads. In general, the use of linked-reads improved the assembly for contig N50 when compared to Illumina short-reads, but not when compared to PacBio CCS (circular consensus sequencing) long-reads.ConclusionsWe investigated the influence of linked-read sequencing parameters on metagenome assembly comprehensively. While the quality of genome assembly from linked-reads cannot rival that from PacBio CCS long-reads, the case for using linked-read sequencing remains persuasive due to its low cost and high base-quality. Our study revealed that the probable best practice in using linked-reads for metagenome assembly was to merge the linked-reads from multiple libraries, where each had sufficient CR but a smaller amount of input DNA.7PaeKL-RUCqQkRQg5_3dd9Video

Highlights

  • The human microbiota are complex systems with important roles in our physiological activities and diseases

  • We investigated the influence of linked-read sequencing parameters on metagenome assembly comprehensively

  • Our study revealed that the probable best practice in using linked-reads for metagenome assembly was to merge the linked-reads from multiple libraries, where each had sufficient Average depth of short reads per fragment (CR) but a smaller amount of input DNA

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Summary

Introduction

The human microbiota are complex systems with important roles in our physiological activities and diseases. Current metagenomics practices use short-read sequencing to simultaneously sequence a mixture of microbial genomes These results are in ambiguity during genome assembly, leading to unsatisfactory microbial genome completeness and contig continuity. Linked-read sequencing is able to remove some of these ambiguities by attaching the same barcode to the reads from a long DNA fragment (10–100 kb), improving metagenome assembly. There have been attempts to use computational means to reconstruct the microbial genomes from a mixture of shortreads sequenced from them. Such metagenome assembly faces the difficulties of having repetitive sequences of both intra- and inter-species origin, horizontal gene transfers, and mobilization events [1], complicated by uneven abundance of microbes in the sample

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