Benchmarking the MinION: Evaluating long reads for microbial profiling

Abstract

Nanopore based DNA-sequencing delivers long reads, thereby simplifying the decipherment of bacterial communities. Since its commercial appearance, this technology has been assigned several attributes, such as its error proneness, comparatively low cost, ease-of-use, and, most notably, aforementioned long reads. The technology as a whole is under continued development. As such, benchmarks are required to conceive, test and improve analysis protocols, including those related to the understanding of the composition of microbial communities. Here we present a dataset composed of twelve different prokaryotic species split into four samples differing by nucleic acid quantification technique to assess the specificity and sensitivity of the MinION nanopore sequencer in a blind study design. Taxonomic classification was performed by standard taxonomic sequence classification tools, namely Kraken, Kraken2 and Centrifuge directly on reads. This allowed taxonomic assignments of up to 99.27% on genus level and 92.78% on species level, enabling true-positive classification of strains down to 25,000 genomes per sample. Full genomic coverage is achieved for strains abundant as low as 250,000 genomes per sample under our experimental settings. In summary, we present an evaluation of nanopore sequence processing analysis with respect to microbial community composition. It provides an open protocol and the data may serve as basis for the development and benchmarking of future data processing pipelines.

Assembly methods for nanopore-based metagenomic sequencing: a comparative study

“…The mock communities were composed by the same ten microorganisms, but in different proportions (Table 1). With the aim of reducing the computational resources needed for the first screening of the selected assemblers, the GridION and Prome-thION datasets were subsampled to obtain an output comparable with recent genomic or metagenomic studies based on MinION (approximately 3 Gbp and 6 Gbp) 9,[34][35][36][37][38][39] . In general, mean read length remained the same in the subsampled datasets in comparison to the original sequencing data 15 .…”

“…These platforms follow the same sequencing principles as MinION, but they have a significantly higher output. For that reason, the datasets were subsampled in order to adapt their output to the current yield offered by MinION (3-6 Gbp) 9,[34][35][36][37][38][39] , then extending the study to higher yields comparable with other recent works 46 . Despite the relatively low complexity of the mock communities analyzed in this evaluation study, our results show that there is a huge variation in assembly results depending on the software chosen to perform the analysis.…”

“…In order to reduce the computational effort, we performed an initial subsampling of this data. In particular, GridION and PromethION datasets were subsampled at two different sequencing depths (3 Gbp and 6 Gbp) to recreate MinION runs with different outputs, and the yield matched the output described in recent shotgun sequencing experiments based on MinION 9, [34][35][36][37][38][39] . Subsampling was performed by selecting the top lines of the FASTQ files.…”

Nanopore sequencing and its application to the study of microbial communities

“…Yet, most of the widely adopted metagenomic classification tools or pipelines often rely on algorithms built on short reads which, by default, do not scale well with long-read datasets – ranging from 13 kb up to 2 Mb – and/or do not account for the higher error profile of nanopore reads. The inclusion of long-read datasets in benchmarking studies and bioinformatic software updates have provided some guidance for metagenomic tool suitability and performance with nanopore reads [53] . Furthermore, long-read specific tools are being continuously developed [54] , including error correction methods [55] and hybrid approaches to overcome read error-related issues [56] , [57] .…”

Is Oxford Nanopore sequencing ready for analyzing complex microbiomes?

“…Leidenfrost et al . ( 2020 ) utilized a cocktail of 12 different bacterial genomic DNAs that were sequenced and analyzed by Kraken, Kraken 2 and Centrifuge. The number of reads assigned to each genome correlated well with the abundance in the mixture depending on the DNA quantitation method ( r 2 = 0.81 for quantitation by ddPCR and r 2 = 0.97 for quantitation by Qubit; Table S5 in citation).…”