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

• Published in: Gigascience 2019-05, Vol.8 (5)
• Main Authors: Nicholls, Samuel M, Quick, Joshua C, Tang, Shuiquan, Loman, Nicholas J
• Format: Article
• Language: English
• Subjects: Abundance; Assembly; Bioinformatics; Data Note; Datasets; Metagenome; Metagenomics; Metagenomics - methods; Metagenomics - standards; Microbiomes; Microbiota - genetics; Microorganisms; Nanopore Sequencing - methods; Nanopore Sequencing - standards; Reference Standards; Species
• ISSN: 2047-217X; 2047-217X
• DOI: 10.1093/gigascience/giz043

Long sequencing reads are information-rich: aiding de novo assembly and reference mapping, and consequently have great potential for the study of microbial communities. However, the best approaches for analysis of long-read metagenomic data are unknown. Additionally, rigorous evaluation of bioinformatics tools is hindered by a lack of long-read data from validated samples with known composition. We sequenced 2 commercially available mock communities containing 10 microbial species (ZymoBIOMICS Microbial Community Standards) with Oxford Nanopore GridION and PromethION. Both communities and the 10 individual species isolates were also sequenced with Illumina technology. We generated 14 and 16 gigabase pairs from 2 GridION flowcells and 150 and 153 gigabase pairs from 2 PromethION flowcells for the evenly distributed and log-distributed communities, respectively. Read length N50 ranged between 5.3 and 5.4 kilobase pairs over the 4 sequencing runs. Basecalls and corresponding signal data are made available (4.2 TB in total). Alignment to Illumina-sequenced isolates demonstrated the expected microbial species at anticipated abundances, with the limit of detection for the lowest abundance species below 50 cells (GridION). De novo assembly of metagenomes recovered long contiguous sequences without the need for pre-processing techniques such as binning. We present ultra-deep, long-read nanopore datasets from a well-defined mock community. These datasets will be useful for those developing bioinformatics methods for long-read metagenomics and for the validation and comparison of current laboratory and software pipelines.