Quantum analysis of squiggle data.

Naya Nagy, Matthew Stuart-Edwards, Marius Nagy, Liam Mitchell, Athanasios Zovoilis

Abstract read

In one paragraph

Article in BioData mining, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Naya Nagy *College of Computer Science and Information Technology, Department of Networks and Communications, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. nmnagy@iau.edu.sa.ORCID http://orcid.org/0000-0001-5325-0781

Matthew Stuart-Edwards *Department of Chemistry and Biochemistry, University of Lethbridge, T1K3M4, Lethbridge, Alberta, Canada.

Marius NagyCollege of Computer Engineering and Science, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia.

Liam MitchellDepartment of Chemistry and Biochemistry, University of Lethbridge, T1K3M4, Lethbridge, Alberta, Canada.

Athanasios ZovoilisDepartment of Chemistry and Biochemistry, University of Lethbridge, T1K3M4, Lethbridge, Alberta, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID37803453

DOI10.1186/s13040-023-00343-z

PMCPMC10557310

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.