Models and Information-Theoretic Bounds for Nanopore Sequencing

Recent advances in DNA sequencing technology and DNA storage systems have rekindled the interest in deletion channels. Multiple recent works have looked at variants of sequence reconstruction over a single and over multiple deletion channels, a notoriously difficult problem due to its highly combinatorial nature. Although works in theoretical computer science have provided algorithms which guarantee perfect reconstruction with multiple independent observations from the deletion channel, they are only applicable in the large blocklength regime and more restrictively, when the number of observations is also large. Indeed, with only a few observations, perfect reconstruction of the input sequence may not even be possible in most cases. In such situations, maximum likelihood (ML) and maximum aposteriori (MAP) estimates for the deletion channels are natural questions that arise and these have remained open to the best of our knowledge. In this work, we take steps to answer the two aforementioned questions. Specifically: 1. We show that solving for the ML estimate over the single deletion channel (which can be cast as a discrete optimization problem) is equivalent to solving its relaxation, a continuous optimization problem; 2. We exactly compute the symbolwise posterior distributions (under some assumptions on the priors) for both the single as well as multiple deletion channels. As part of our contributions, we also introduce tools to visualize and analyze error events, which we believe could be useful in other related problems concerning deletion channels.

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“…As seen in[15],[16] there are more complicated effects of the nanopore reader not captured in this simple representation.…”

mentioning

confidence: 99%

On Maximum Likelihood Reconstruction over Multiple Deletion Channels

Srinivasavaradhan¹,

Du²,

Diggavi³

et al. 2018

2018 IEEE International Symposium on Information Theory (ISIT)

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“…This raw current signal is sampled (typically at a frequency of 4 kHz), and is used by the basecaller to infer the most likely base sequence that could have induced the raw signal. shows the various causes of distortions in the nanopore sequencing process from the channel coding perspective [24,25]. Firstly, as the raw signal depends on multiple bases (usually 6 bases) at a time, this causes inter-symbol interference.…”

Section: Nanopore Sequencing and Basecallingmentioning

confidence: 99%

Overcoming High Nanopore Basecaller Error Rates for DNA Storage Via Basecaller-Decoder Integration and Convolutional Codes

Chandak

Neu

Tatwawadi

et al. 2019

Preprint

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As magnetization and semiconductor based storage technologies approach their limits, bio-molecules, such as DNA, have been identified as promising media for future storage systems, due to their high storage density (petabytes/gram) and long-term durability (thousands of years). Furthermore, nanopore DNA sequencing enables high-throughput sequencing using devices as small as a USB thumb drive and thus is ideally suited for DNA storage applications. Due to the high insertion/deletion error rates associated with basecalled nanopore reads, current approaches rely heavily on consensus among multiple reads and thus incur very high reading costs. We propose a novel approach which overcomes the high error rates in basecalled sequences by integrating a Viterbi error correction decoder with the basecaller, enabling the decoder to exploit the soft information available in the deep learning based basecaller pipeline. Using convolutional codes for error correction, we experimentally observed 3x lower reading costs than the state-of-the-art techniques at comparable writing costs.The code, data and Supplementary Material is available at https://github.com/shubhamchandak94/nanopore_ dna_storage.

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“…Informationtheoretic analysis of reference-based DNA shotgun sequencing has been derived in (Mohajer, A. Motahari, and Tse, 2013), and recently refined in (Weinberger and Shomorony, 2023). Information-theoretic methods were also applied to the analysis of nanopore DNA sequencing (Mao, Diggavi, and Kannan, 2018), where the nanopore channel was modeled as an insertion-deletion channel and general bounds on the number of possible decodable sequences were developed. However, tight bounds on the error probability were not developed.…”

Section: Introductionmentioning

confidence: 99%

Design of optimal labeling patterns for optical genome mapping via information theory

Nogin,

Bar-Lev,

Hanania

et al. 2023

Preprint

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Optical genome mapping (OGM) is a technique that extracts partial genomic information from optically imaged and linearized DNA fragments containing fluorescently labeled short sequence patterns. This information can be used for various genomic analyses and applications, such as the detection of structural variations and copy-number variations, epigenomic profiling, and microbial species identification. Currently, the choice of labeled patterns is based on the available bio-chemical methods, and is not necessarily optimized for the application. In this work, we develop a model of OGM based on information theory, which enables the design of optimal labeling patterns for specific applications and target organism genomes. We validated the model through experimental OGM on human DNA and simulations on bacterial DNA. Our model predicts up to 10-fold improved accuracy by optimal choice of labeling patterns, which may guide future development of OGM bio-chemical labeling methods and significantly improve its accuracy and yield for applications such as epigenomic profiling and cultivation-free pathogen identification in clinical samples.

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Models and Information-Theoretic Bounds for Nanopore Sequencing

Cited by 35 publications

References 39 publications

On Maximum Likelihood Reconstruction over Multiple Deletion Channels

On Maximum Likelihood Reconstruction over Multiple Deletion Channels

Overcoming High Nanopore Basecaller Error Rates for DNA Storage Via Basecaller-Decoder Integration and Convolutional Codes

Design of optimal labeling patterns for optical genome mapping via information theory

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