Characterization and Simulation of Metagenomic Nanopore Sequencing Data with Meta-NanoSim

Background: Nanopore sequencing is crucial to metagenomic studies as its kilobase-long reads can contribute to resolving genomic structural differences among microbes. However, sequencing platform-specific challenges, including high base-call error rate, non-uniform read lengths, and the presence of chimeric artifacts, necessitate specifically designed analytical tools, such as microbial abundance estimation and metagenome assembly algorithms. When developing and testing bioinformatics tools and pipelines, the use of simulated datasets with characteristics that are true to the sequencing platform under evaluation is a cost-effective way to provide a ground truth and assess the performance in a controlled environment. Results: Here, we present Meta-NanoSim, a fast and versatile utility that characterizes and simulates the unique properties of nanopore metagenomic reads. It improves upon state-of-the-art methods on microbial abundance estimation through a base-level quantification algorithm. Meta-NanoSim can simulate complex microbial communities composed of both linear and circular genomes, and can stream reference genomes from online servers directly. Simulated datasets showed high congruence with experimental data in terms of read length, error profiles, and abundance levels. We demonstrate that Meta-NanoSim simulated data can facilitate the development of metagenomic algorithms and guide experimental design through a metagenome assembly benchmarking task. Conclusions: The Meta-NanoSim characterization module investigates read features including chimeric information and abundance levels, while the simulation module simulates large and complex multi-sample microbial communities with different abundance profiles. All trained models and the software are freely accessible at Github: https://github.com/bcgsc/NanoSim .

BinChecker: a new algorithm for quality assessment of microbial draft genomes

In the reconstruction of microbial genomes from metagenomic sequence data, the estimation of the final completeness and possible contamination is crucial for quality control. In metagenomics candidate genomes are usually obtained from a metagenome assembly and a subsequent binning of the assembled contigs. BinChecker provides a novel approach to quality assessment that is based on a fast protein domain search and a clustering approach for identification of marker domain (“feature”) sets. The feature sets that are used for estimation are not pre-computed for a given database of reference genomes, but are individually found for each bin by adaptive clustering and feature selection. In particular, the adaptivity facilitates the creation and extension of the underlying database, which just requires to add protein feature profiles of reference genomes. Tests with simulated bins indicate that the prediction accuracy of BinChecker meets the current state of the art while providing significant advantages in terms of speed and flexibility.

Metagenomic analysis of ancient dental calculus reveals unexplored diversity of oral archaeal Methanobrevibacter

Background Dental calculus (mineralised dental plaque) preserves many types of microfossils and biomolecules, including microbial and host DNA, and ancient calculus are thus an important source of information regarding our ancestral human oral microbiome. In this study, we taxonomically characterised the dental calculus microbiome from 20 ancient human skeletal remains originating from Trentino-South Tyrol, Italy, dating from the Neolithic (6000–3500 BCE) to the Early Middle Ages (400–1000 CE). Results We found a high abundance of the archaeal genus Methanobrevibacter in the calculus. However, only a fraction of the sequences showed high similarity to Methanobrevibacter oralis, the only described Methanobrevibacter species in the human oral microbiome so far. To further investigate the diversity of this genus, we used de novo metagenome assembly to reconstruct 11 Methanobrevibacter genomes from the ancient calculus samples. Besides the presence of M. oralis in one of the samples, our phylogenetic analysis revealed two hitherto uncharacterised and unnamed oral Methanobrevibacter species that are prevalent in ancient calculus samples sampled from a broad range of geographical locations and time periods. Conclusions We have shown the potential of using de novo metagenomic assembly on ancient samples to explore microbial diversity and evolution. Our study suggests that there has been a possible shift in the human oral microbiome member Methanobrevibacter over the last millennia.

Metagenome Assembly and Metagenome-Assembled Genome of “ Candidatus Aramenus sulfurataquae” from Thermal Sediments from the Los Azufres Volcanic Complex

A plethora of hot springs are found at the Los Azufres volcanic complex in Mexico, and studies are needed to determine their microbial genomic diversity. Here, we report a metagenome of hot spring sediments and a metagenome-assembled genome of “ Candidatus Aramenus sulfurataquae.” This study reveals novel genomic sequences of Sulfolobales archaea.

MetaPlatanus: a metagenome assembler that combines long-range sequence links and species-specific features

De novo metagenome assembly is effective in assembling multiple draft genomes, including those of uncultured organisms. However, heterogeneity in the metagenome hinders assembly and introduces interspecies misassembly deleterious for downstream analysis. For this purpose, we developed a hybrid metagenome assembler, MetaPlatanus. First, as a characteristic function, it assembles the basic contigs from accurate short reads and then iteratively utilizes long-range sequence links, species-specific sequence compositions, and coverage depth. The binning information was also used to improve contiguity. Benchmarking using mock datasets consisting of known bacteria with long reads or mate pairs revealed the high contiguity MetaPlatanus with a few interspecies misassemblies. For published human gut data with nanopore reads from potable sequencers, MetaPlatanus assembled many biologically important elements, such as coding genes, gene clusters, viral sequences, and over-half bacterial genomes. In the benchmark with published human saliva data with high-throughput nanopore reads, the superiority of MetaPlatanus was considerably more evident. We found that some high-abundance bacterial genomes were assembled only by MetaPlatanus as near-complete. Furthermore, MetaPlatanus can circumvent the limitations of highly fragmented assemblies and frequent interspecies misassembles obtained by the other tools. Overall, the study demonstrates that MetaPlatanus could be an effective approach for exploring large-scale structures in metagenomes.

nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install - all dependencies are provided within containers -, portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All code is hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

Strainberry: automated strain separation in low-complexity metagenomes using long reads

High-throughput short-read metagenomics has enabled large-scale species-level analysis and functional characterization of microbial communities. Microbiomes often contain multiple strains of the same species, and different strains have been shown to have important differences in their functional roles. Recent advances on long-read based methods enabled accurate assembly of bacterial genomes from complex microbiomes and an as-yet-unrealized opportunity to resolve strains. Here we present Strainberry, a metagenome assembly pipeline that performs strain separation in single-sample low-complexity metagenomes and that relies uniquely on long-read data. We benchmarked Strainberry on mock communities for which it produces strain-resolved assemblies with near-complete reference coverage and 99.9% base accuracy. We also applied Strainberry on real datasets for which it improved assemblies generating 20-118% additional genomic material than conventional metagenome assemblies on individual strain genomes. We show that Strainberry is also able to refine microbial diversity in a complex microbiome, with complete separation of strain genomes. We anticipate this work to be a starting point for further methodological improvements on strain-resolved metagenome assembly in environments of higher complexities.

Critical Assessment of Metagenome Interpretation - the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from ~1,700 novel and known microbial genomes, as well as ~600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results. Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.