MEGAN-LR: new algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs

Abstract

BackgroundThere are numerous computational tools for taxonomic or functional analysis of microbiome samples, optimized to run on hundreds of millions of short, high quality sequencing reads. Programs such as MEGAN allow the user to interactively navigate these large datasets. Long read sequencing technologies continue to improve and produce increasing numbers of longer reads (of varying lengths in the range of 10k-1M bps, say), but of low quality. There is an increasing interest in using long reads in microbiome sequencing, and there is a need to adapt short read tools to long read datasets.MethodsWe describe a new LCA-based algorithm for taxonomic binning, and an interval-tree based algorithm for functional binning, that are explicitly designed for long reads and assembled contigs. We provide a new interactive tool for investigating the alignment of long reads against reference sequences. For taxonomic and functional binning, we propose to use LAST to compare long reads against the NCBI-nr protein reference database so as to obtain frame-shift aware alignments, and then to process the results using our new methods.ResultsAll presented methods are implemented in the open source edition of MEGAN, and we refer to this new extension as MEGAN-LR (MEGAN long read). We evaluate the LAST+MEGAN-LR approach in a simulation study, and on a number of mock community datasets consisting of Nanopore reads, PacBio reads and assembled PacBio reads. We also illustrate the practical application on a Nanopore dataset that we sequenced from an anammox bio-rector community.ReviewersThis article was reviewed by Nicola Segata together with Moreno Zolfo, Pete James Lockhart and Serghei Mangul.ConclusionThis work extends the applicability of the widely-used metagenomic analysis software MEGAN to long reads. Our study suggests that the presented LAST+MEGAN-LR pipeline is sufficiently fast and accurate.

Ultra-deep, long-read nanopore sequencing of mock microbial community standards

“…A notable alternative mock community sample is from the Human Microbiome Project (HMP) and consists of 20 microbial samples (available from BEI Resources). This mock community have been sequenced as part of other studies, although the datasets are much smaller than the ones presented here [9,22]. Bertrand et al presented a synthetic mock community of their own construction to demonstrate hybrid nanopore-Illumina metagenome assemblies [12].…”

“…The most notable alternative mock community sample is from the Human Microbiome Project (HMP) and consists of 20 microbial samples (available from BEI Resources). Nanopore metagenomic datasets for the HMP mock community have been sequenced as part of other studies, although the datasets are much smaller than the ones presented here [9,12,21].…”

CAMITAX: Taxon labels for microbial genomes

“…Inferring genome taxonomy from a set of gene-level assignments is not trivial, and-inspired by procedures implemented in anvi'o [27] and dRep [33]-CAMITAX places the query genome on the lowest taxonomic node with at least 50% support in gene assignments (which corresponds to the interval-union LCA algorithm [28]) for nucleotide and protein searches.…”

“…However, with the advent of genomics and-more recently-culture-independent methods, this definition was found to be impractical and difficult to implement [22]. Today, 16S rRNA gene similarity, average nucleotide identity (ANI), genome phylogeny, or gene-centric voting schemes are used for taxonomic assignments [23][24][25][26][27][28]. These approaches all have their merits (see below), but, to the best of our knowledge, no unifying workflow implementation existed.…”

metaVaR: introducing metavariant species models for reference-free metagenomic-based population genomics

“…These approaches were successfully applied on prokaryotes (Sedlar et al, 2016;Parks et al, 2017;Stewart et al, 2019) but the main limit of the alignment-based approach still relies on the availability, completeness and quality of reference genomes that can be constructed from metagenomic data (Parks et al, 2017). The approach took recently advantages of long-read sequencing (Huson et al, 2018;Somerville et al, 2019). However, due to the large genome size of eukaryotes and the difficulties to obtain high molecular weight DNA, yet, such approaches did not produce results on eukaryotes.…”