Ultraplexing: increasing the efficiency of long-read sequencing for hybrid assembly with k-mer-based multiplexing

Dilthey, Alexander; Meyer, Sebastian; Kaasch, Achim J.

doi:10.1186/s13059-020-01974-9

Cited by 7 publications

(6 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Dilthey et al postulated a new method that allows the multiplexing step to be skipped by pooling together multiple non-barcoded samples in the same ONT flow cell [ 17 ]. This method relies on the availability of Illumina data and inter-sample genetic differences to in silico assign ONT reads to particular isolates.…”

Section: Discussionmentioning

confidence: 99%

“…There are different strategies to decrease the price of completing a genome with ONT sequencing [ 16 , 17 ]. Recently, Lipworth et al [ 18 ] showed that use of wash-kits coupled with shorter sequencing times can be optimized to obtain 36 complete genomes per single flow cell, achieving a reduction in long-read sequencing costs of 27%.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A high-throughput multiplexing and selection strategy to complete bacterial genomes

et al. 2021

View full text Add to dashboard Cite

Background Bacterial whole-genome sequencing based on short-read technologies often results in a draft assembly formed by contiguous sequences. The introduction of long-read sequencing technologies permits those contiguous sequences to be unambiguously bridged into complete genomes. However, the elevated costs associated with long-read sequencing frequently limit the number of bacterial isolates that can be long-read sequenced. Here we evaluated the recently released 96 barcoding kit from Oxford Nanopore Technologies (ONT) to generate complete genomes on a high-throughput basis. In addition, we propose an isolate selection strategy that optimizes a representative selection of isolates for long-read sequencing considering as input large-scale bacterial collections. Results Despite an uneven distribution of long reads per barcode, near-complete chromosomal sequences (assembly contiguity = 0.89) were generated for 96 Escherichia coli isolates with associated short-read sequencing data. The assembly contiguity of the plasmid replicons was even higher (0.98), which indicated the suitability of the multiplexing strategy for studies focused on resolving plasmid sequences. We benchmarked hybrid and ONT-only assemblies and showed that the combination of ONT sequencing data with short-read sequencing data is still highly desirable (i) to perform an unbiased selection of isolates for long-read sequencing, (ii) to achieve an optimal genome accuracy and completeness, and (iii) to include small plasmids underrepresented in the ONT library. Conclusions The proposed long-read isolate selection ensures the completion of bacterial genomes that span the genome diversity inherent in large collections of bacterial isolates. We show the potential of using this multiplexing approach to close bacterial genomes on a high-throughput basis.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A high-throughput multiplexing and selection strategy to complete bacterial genomes

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Evaluation of minimap2 was performed analogously to HiLive2's, selecting the representative alignment if a chimeric match was found. In addition to the simulated data prepared as explained above, we also used two real SRA datasets: a SARS-CoV-2 isolate (SRR11140745, collected on 14 Feb 2020) and a clinical S. aureus sample (SRR8776887 [40]).…”

Section: Real Illumina Runsmentioning

confidence: 99%

Deep learning-based real-time detection of novel pathogens during sequencing

Bartoszewicz

Genske

Renard

2021

Briefings in Bioinformatics

View full text Add to dashboard Cite

Novel pathogens evolve quickly and may emerge rapidly, causing dangerous outbreaks or even global pandemics. Next-generation sequencing is the state of the art in open-view pathogen detection, and one of the few methods available at the earliest stages of an epidemic, even when the biological threat is unknown. Analyzing the samples as the sequencer is running can greatly reduce the turnaround time, but existing tools rely on close matches to lists of known pathogens and perform poorly on novel species. Machine learning approaches can predict if single reads originate from more distant, unknown pathogens but require relatively long input sequences and processed data from a finished sequencing run. Incomplete sequences contain less information, leading to a trade-off between sequencing time and detection accuracy. Using a workflow for real-time pathogenic potential prediction, we investigate which subsequences already allow accurate inference. We train deep neural networks to classify Illumina and Nanopore reads and integrate the models with HiLive2, a real-time Illumina mapper. This approach outperforms alternatives based on machine learning and sequence alignment on simulated and real data, including SARS-CoV-2 sequencing runs. After just 50 Illumina cycles, we observe an 80-fold sensitivity increase compared to real-time mapping. The first 250 bp of Nanopore reads, corresponding to 0.5 s of sequencing time, are enough to yield predictions more accurate than mapping the finished long reads. The approach could also be used for screening synthetic sequences against biosecurity threats.

show abstract

“…Even with ultra-short run times of 12 h (1/6th of the total run time that is currently standard in our laboratory and others) we were able to circularize the vast majority of plasmids (and most chromosomes). Combining washing steps with a recently published chemistry-free approach to demultiplexing may provide additional savings and would seem worth exploring in future work [18]. Whilst we had access to a large university computer cluster, this will not be the case for all investigators, though we estimate that if one were to use cloud computing (e.g.…”

Section: Discussionmentioning

confidence: 99%

Optimized use of Oxford Nanopore flowcells for hybrid assemblies

et al. 2020

View full text Add to dashboard Cite

Hybrid assemblies are highly valuable for studies of Enterobacteriaceae due to their ability to fully resolve the structure of mobile genetic elements, such as plasmids, which are involved in the carriage of clinically important genes (e.g. those involved in antimicrobial resistance/virulence). The widespread application of this technique is currently primarily limited by cost. Recent data have suggested that non-inferior, and even superior, hybrid assemblies can be produced using a fraction of the total output from a multiplexed nanopore [Oxford Nanopore Technologies (ONT)] flowcell run. In this study we sought to determine the optimal minimal running time for flowcells when acquiring reads for hybrid assembly. We then evaluated whether the ONT wash kit might allow users to exploit shorter running times by sequencing multiple libraries per flowcell. After 24 h of sequencing, most chromosomes and plasmids had circularized and there was no benefit associated with longer running times. Quality was similar at 12 h, suggesting that shorter running times are likely to be acceptable for certain applications (e.g. plasmid genomics). The ONT wash kit was highly effective in removing DNA between libraries. Contamination between libraries did not appear to affect subsequent hybrid assemblies, even when the same barcodes were used successively on a single flowcell. Utilizing shorter run times in combination with between-library nuclease washes allows at least 36 Enterobacteriaceae isolates to be sequenced per flowcell, significantly reducing the per-isolate sequencing cost. Ultimately this will facilitate large-scale studies utilizing hybrid assembly, advancing our understanding of the genomics of key human pathogens.

show abstract

Ultraplexing: increasing the efficiency of long-read sequencing for hybrid assembly with k-mer-based multiplexing

Cited by 7 publications

References 47 publications

A high-throughput multiplexing and selection strategy to complete bacterial genomes

A high-throughput multiplexing and selection strategy to complete bacterial genomes

Deep learning-based real-time detection of novel pathogens during sequencing

Optimized use of Oxford Nanopore flowcells for hybrid assemblies

Contact Info

Product

Resources

About