bioRxivMicrobiologyPreprint
Long read metagenomics, the next step?
Abstract
BackgroundThird-generation sequencing has penetrated little in metagenomics due to the high error rate and dependence for assembly on short-read designed bioinformatics. However, 2nd generation sequencing metagenomics (mostly Illumina) suffers from limitations, particularly in allowing assembly of microbes with high microdiversity or retrieving the flexible (adaptive) compartment of prokaryotic genomes. ResultsHere we have used different 3rd generation techniques to study the metagenome of a well-known marine sample from the mixed epipelagic water column of the winter Mediterranean. We have compared Oxford Nanopore and PacBio last generation technologies with the classical approach using Illumina short reads followed by assembly. PacBio Sequel II CCS appears particularly suitable for cellular metagenomics due to its low error rate. Long reads allow efficient direct retrieval of complete genes (473M/Tb) and operons before assembly, facilitating annotation and compensates the limitations of short reads or short-read assemblies. MetaSPAdes was the most appropriate assembly program when used in combination with short reads. The assemblies of the long reads allow also the reconstruction of much more complete metagenome-assembled genomes, even from microbes with high microdiversity. The flexible genome of reconstructed MAGs is much more complete and allows rescuing more adaptive genes. ConclusionsFor most applications of metagenomics, from community structure analysis to ecosystem functioning, long-reads should be applied whenever possible. Particularly for in-silico screening of biotechnologically useful genes, or population genomics, long-read metagenomics appears presently as a very fruitful approach and can be used from raw reads, before a computing-demanding (and potentially artefactual) assembly step.
- assay: metagenomics
- research focus: clustering
- research focus: gene regulatory network inference
- research focus: variant calling
- research focus: deconvolution
- approach: deep learning
- approach: statistical model
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