DeepSimulator: a deep simulator for Nanopore sequencing

Li, Mo; Ren, Han; Bi, Chongwei; Wang, Sheng; Gao, Xin

doi:10.1101/238683

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…Initially, raw nanopore signal was translated to nucleotides using a Hidden Markov Model (HMM) 12,13 , but recently, deep learning was found to perform the task better and it is now used to translate a raw nanopore signal into a nucleotide sequence 14,15 . Deep learning is also used to perform tasks such as predicting DNA methylation 16 and simulating a raw signal based on a reference genome 17 . These ndings reinforce our suggestion to use deep learning in order to classify reads based on their raw signal.…”

Section: Deep Learningmentioning

confidence: 99%

Real-time selective sequencing using nanopores and deep learning

Danilevsky¹,

Polsky²,

Shomron³

2021

Preprint

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Nanopore sequencing is an emerging technology that utilizes a unique method of reading nucleic acid sequences and, at the same time, it detects various chemical modifications. Deep learning has increased in popularity as a useful technique to solve many complex computational tasks. Selective sequencing has been widely used in genomic research; although it introduces several caveats to the process of sequencing, its advantages supersede them. In this study we demonstrate an alternative method of software-based selective sequencing that is performed in real time by combining nanopore sequencing and deep learning. Our results show the feasibility of using deep learning for classifying signals from only the first 200 nucleotides in a raw nanopore sequencing signal format. Using custom deep learning models and a script utilizing "Read-Until" framework to target mitochondrial molecules in real time from a human cell line sample, we achieved a significant separation and enrichment ability of more than 2-fold. In a series of very short sequencing runs (10, 30, and 120 minutes), we identified genomic and mitochondrial reads with accuracy above 90%, although mitochondrial DNA comprises only 0.1% of the total input material. We believe that our results will lay the foundation for rapid and selective sequencing using nanopore technology and will pave the way for future clinical applications using nanopore sequencing data.

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Section: Deep Learningmentioning

confidence: 99%

Real-time selective sequencing using nanopores and deep learning

Danilevsky¹,

Polsky²,

Shomron³

2021

Preprint

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“…The copyright holder for this preprint (which this version posted July 29, 2021. ; https://doi.org/10.1101/2021.07. 29.454031 doi: bioRxiv preprint Illumina (MiSeq and iSeq instruments) and Oxford Nanopore Technologies (R9 and FLG flowcells) sequencing platforms 5,6 . META is openly available and can be accessed at the following github URLs: https://github.com/JHUAPL/meta-system, and https://github.com/JHUAPL/meta-simulator.…”

Section: Validated Deployable Bioinformaticsmentioning

confidence: 99%

The META tool optimizes metagenomic analyses across sequencing platforms and classifiers

Player

Aguinaldo

Merritt

et al. 2021

Preprint

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A major challenge in the field of metagenomics is the selection of the correct combination of sequencing platform and downstream metagenomic analysis algorithm, or classifier. Here, we present the Metagenomic Evaluation Tool Analyzer (META), which produces simulated data and facilitates platform and algorithm selection for any given metagenomic use case. META-generated in silico read data are modular, scalable, and reflect user-defined community profiles, while the downstream analysis is done using a variety of metagenomic classifiers. Reported results include information on resource utilization, time-to-answer, and performance. Real-world data can also be analyzed using selected classifiers and results benchmarked against simulations. To test the utility of the META software, simulated data was compared to real-world viral and bacterial metagenomic samples run on four different sequencers and analyzed using 12 metagenomic classifiers. Lastly, we introduce META Score: a unified, quantitative value which rates an analytic classifiers' ability to both identify and count taxa in a representative sample.

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TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering

Banu

Singh

Alser

et al. 2022

Preprint

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Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, i.e., reads. State-of-the-art basecallers employ complex deep learning models to achieve high basecalling accuracy. This makes basecalling computationally-inefficient and memory-hungry; bottlenecking the entire genome analysis pipeline. However, for many applications, the majority of reads do no match the reference genome of interest (i.e., target reference) and thus are discarded in later steps in the genomics pipeline, wasting the basecalling computation. To overcome this issue, we propose TargetCall, the first fast and widely-applicable pre-basecalling filter to eliminate the wasted computation in basecalling. TargetCall's key idea is to discard reads that will not match the target reference (i.e., off-target reads) prior to basecalling. TargetCall consists of two main components: (1) LightCall, a lightweight neural network basecaller that produces noisy reads; and (2) Similarity Check, which labels each of these noisy reads as on-target or off-target by matching them to the target reference. TargetCall filters out all off-target reads before basecalling; and the highly-accurate but slow basecalling is performed only on the raw signals whose noisy reads are labeled as on-target. Our thorough experimental evaluations using both real and simulated data show that TargetCall 1) improves the end-to-end basecalling performance of the state-of-the-art basecaller by 3.31x while maintaining high (98.88%) sensitivity in keeping on-target reads, 2) maintains high accuracy in downstream analysis, 3) precisely filters out up to 94.71% of off-target reads, and 4) achieves better performance, sensitivity, and generality compared to prior works. We freely open-source TargetCall to aid future research in pre-basecalling filtering at https://github.com/CMU-SAFARI/TargetCall.

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DeepSimulator: a deep simulator for Nanopore sequencing

Cited by 5 publications

References 42 publications

Real-time selective sequencing using nanopores and deep learning

Real-time selective sequencing using nanopores and deep learning

The META tool optimizes metagenomic analyses across sequencing platforms and classifiers

TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering

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