Pan-genomic Matching Statistics for Targeted Nanopore Sequencing

Ahmed, Omar; Rossi, Massimiliano; Kovaka, Sam; Schatz, Michael C.; Gagie, Travis; Boucher, Christina; Langmead, Ben

doi:10.1101/2021.03.23.436610

Cited by 11 publications

(20 citation statements)

References 17 publications

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“…This aspect of the nanopore sequencing approach helps to limit potential sequencing biases related to complex or repetitive genomic regions where short-read sequencing-by-synthesis methods (e.g., Illumina) typically struggle [12][13][14]. Although the per-base accuracy of ONT sequencing platforms is less than that of other next-generation platforms, steady advances in sequencing chemistry and basecalling have yielded continued and substantial improvements in reliability [3,4,15].…”

Section: Introductionmentioning

confidence: 99%

Enabling metagenomic surveillance for bacterial tick-borne pathogens using nanopore sequencing with adaptive sampling

et al. 2021

Preprint

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Technological and computational advancements in the fields of genomics and bioinformatics are providing exciting new opportunities for pathogen discovery and surveillance. In particular, single-molecule nucleotide sequence data originating from Oxford Nanopore Technologies (ONT) sequencing platforms can be bioinformatically leveraged, in real-time, for enhanced biosurveillance of a vast array of zoonoses. The recently released nanopore adaptive sampling (NAS) pipeline facilitates immediate mapping of individual nucleotide molecules (i.e., DNA, cDNA, and RNA) to a given reference as each molecule is sequenced. User-defined thresholds then allow for the retention or rejection of specific molecules, informed by the real-time reference mapping results, as they are physically passing through a given sequencing nanopore. Here, we show how NAS can be used to selectively sequence entire genomes of bacterial tick-borne pathogens circulating in wild populations of the blacklegged tick vector, Ixodes scapularis. The NAS method provided a two-fold increase in targeted pathogen sequences, successfully enriching for Borrelia (Borreliella) burgdorferi s.s.; Borrelia (Borrelia) miyamotoi; Anaplasma phagocytophilum; and Ehrlichia muris eauclairensis genomic DNA within our I. scapularis samples. Our results indicate that NAS has strong potential for real-time sequence-based pathogen surveillance.

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Section: Introductionmentioning

confidence: 99%

Enabling metagenomic surveillance for bacterial tick-borne pathogens using nanopore sequencing with adaptive sampling

et al. 2021

Preprint

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“…Therefore, we designed experiments that specifically focus on the evaluation of this approach when applied to both adaptive sampling strategies, depletion and targeted sequencing. In a first step, we compare ReadBouncer to minimap2 (11), which is used for classification by Readfish, and the pan-genomics matching tool SPUMONI (22), which is proposed as an alternative to minimap2 in targeted nanopore sequencing pipelines. Here, we assess all three tools on simulated and real reads from a recently published microbial mock community (23).…”

Section: Resultsmentioning

confidence: 99%

ReadBouncer: Precise and Scalable Adaptive Sampling for Nanopore Sequencing

Ulrich

Lutfi

Rutzen

et al. 2022

Preprint

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Nanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast GPU base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications. Here we present a new approach for nanopore adaptive sampling that combines fast CPU and GPU base calling with read classification based on Interleaved Bloom Filters (IBF). Read-Bouncer improves the potential enrichment of low abundance sequences by its high read classification sensitivity and specificity, outperforming existing tools in the field. It robustly removes even reads belonging to large reference sequences while running on commodity hardware without graphical processing units (GPUs), making adaptive sampling accessible for in-field researchers. Readbouncer also provides a user-friendly interface and installer files for end-users without a bioinformatics background.AvailabilityThe C++ source code of ReadBouncer is available at (https://gitlab.com/dacs-hpi/readbouncer).

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“…Hence, learning a precise decision boundary between them can be omitted. Human reads can be ignored, as they can be relatively easily filtered out with traditional methods based on read mapping or k-mers [24,114,115].…”

Section: Multi-class Evaluationmentioning

confidence: 99%

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

Bartoszewicz

Nasri

Nowicka

et al. 2021

Preprint

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BackgroundEmerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant, curated data remains comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone.ResultsWe present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that the resulting database can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and show that the human pathogens are separable from non-human pathogens. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats.ConclusionsThe presented data collection enables accurate detection of novel pathogens from sequencing data. It is also a comprehensive resource that can find use beyond this particular task. This can include possible applications in proteomics and genomics, employing both machine learning and direct sequence comparison.AvailabilityThe database and models are hosted at https://zenodo.org/record/5711852 and https://zenodo.org/record/5711877. Source code is available at https://gitlab.com/dacs-hpi/deepac.

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Pan-genomic Matching Statistics for Targeted Nanopore Sequencing

Cited by 11 publications

References 17 publications

Enabling metagenomic surveillance for bacterial tick-borne pathogens using nanopore sequencing with adaptive sampling

Enabling metagenomic surveillance for bacterial tick-borne pathogens using nanopore sequencing with adaptive sampling

ReadBouncer: Precise and Scalable Adaptive Sampling for Nanopore Sequencing

Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection

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