Quantum analysis of squiggle data.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2023-10-06 DOI:10.1186/s13040-023-00343-z
Naya Nagy, Matthew Stuart-Edwards, Marius Nagy, Liam Mitchell, Athanasios Zovoilis
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Abstract

Squiggle data is the numerical output of DNA and RNA sequencing by the Nanopore next generation sequencing platform. Nanopore sequencing offers expanded applications compared to previous sequencing techniques but produces a large amount of data in the form of current measurements over time. The analysis of these segments of current measurements require more complex and computationally intensive algorithms than previous sequencing technologies. The purpose of this study is to investigate in principle the potential of using quantum computers to speed up Nanopore data analysis. Quantum circuits are designed to extract major features of squiggle current measurements. The circuits are analyzed theoretically in terms of size and performance. Practical experiments on IBM QX show the limitations of the state of the art quantum computer to tackle real life squiggle data problems. Nevertheless, pre-processing of the squiggle data using the inverse wavelet transform, as experimented and analyzed in this paper as well, reduces the dimensionality of the problem in order to fit a reasonable size quantum computer in the hopefully near future.

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扭曲数据的量子分析。
波形数据是纳米孔下一代测序平台对DNA和RNA测序的数字输出。与以前的测序技术相比,纳米孔测序提供了更广泛的应用,但随着时间的推移,会以当前测量的形式产生大量数据。与以前的测序技术相比,对当前测量的这些片段的分析需要更复杂和计算密集的算法。本研究的目的是从原理上研究使用量子计算机加速纳米孔数据分析的潜力。量子电路设计用于提取波形电流测量的主要特征。从尺寸和性能方面对电路进行了理论分析。在IBM QX上进行的实际实验表明,最先进的量子计算机在解决现实生活中的数据问题方面存在局限性。然而,正如本文所实验和分析的那样,使用小波逆变换对波形数据进行预处理,降低了问题的维数,以便在不久的将来适合一台合理尺寸的量子计算机。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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