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Group 8: Challenge: Event detection in nanopore data

Group 8: Challenge: Event detection in nanopore data
Group 8: Challenge: Event detection in nanopore data
Translocation of DNA molecules through special membrane nanopores- under the application of electric current- results in change of the nanopore current [1,2]. The produced signals have characteristic patterns that depend on the type of event that takes place, i.e. the configuration of the DNA molecule that passes through the pore. The focus of this challenge is the development of methods for: 1. detecting the events 2. classifying the events based on the signal pattern 3. finding an appropriate measure for quantifying the success of the model The provided data for this challenge consist of nine simulation sets produced by tuning the signal-to-noise ratio (SNR) at 100, 50, 10, 5, 4, 3, 2, 1.5, and 1. Each set (same SNR level) consists of five simulations of electric current time series, resulting in a total of forty-five simulations. An example plot of the data with the maximum SNR of 100 is shown in Figure 1, where the spikes in the data correspond to events. Zooming in on the graph at each of these spikes, it is possible to identify a range of three patterns corresponding to the different classes of event. As the SNR decreases, the true signals become more difficult to distinguish from the natural variation resulting from the noise. Therefore, devising a method to identify events when the SNR is low is a particularly important challenge.
AI3SD, Machine Learning, Summer School
6
University of Southampton
Adewole, Wole
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Aysel, Halil Ibrahim
9db69eca-47c7-4443-86a1-33504e172d60
Gow, Stephen
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Jiang, Zheng
d42bc017-e3fd-46bf-9db5-e05ea72dd78c
Stamatis, Dimitrios
2df20642-1e56-4079-90d1-7435c230e960
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Adewole, Wole
e59df248-9096-4ec8-a553-00fc9e52e732
Aysel, Halil Ibrahim
9db69eca-47c7-4443-86a1-33504e172d60
Gow, Stephen
922171a1-6d31-4969-9e2e-8443daff9c0c
Jiang, Zheng
d42bc017-e3fd-46bf-9db5-e05ea72dd78c
Stamatis, Dimitrios
2df20642-1e56-4079-90d1-7435c230e960
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420

Adewole, Wole, Aysel, Halil Ibrahim, Gow, Stephen, Jiang, Zheng and Stamatis, Dimitrios , Frey, Jeremy G., Niranjan, Mahesan and Kanza, Samantha (eds.) (2022) Group 8: Challenge: Event detection in nanopore data (AI4SD-Machine-Learning-Summer-School, 6) University of Southampton 13pp. (doi:10.5258/SOTON/AI3SD0249).

Record type: Monograph (Project Report)

Abstract

Translocation of DNA molecules through special membrane nanopores- under the application of electric current- results in change of the nanopore current [1,2]. The produced signals have characteristic patterns that depend on the type of event that takes place, i.e. the configuration of the DNA molecule that passes through the pore. The focus of this challenge is the development of methods for: 1. detecting the events 2. classifying the events based on the signal pattern 3. finding an appropriate measure for quantifying the success of the model The provided data for this challenge consist of nine simulation sets produced by tuning the signal-to-noise ratio (SNR) at 100, 50, 10, 5, 4, 3, 2, 1.5, and 1. Each set (same SNR level) consists of five simulations of electric current time series, resulting in a total of forty-five simulations. An example plot of the data with the maximum SNR of 100 is shown in Figure 1, where the spikes in the data correspond to events. Zooming in on the graph at each of these spikes, it is possible to identify a range of three patterns corresponding to the different classes of event. As the SNR decreases, the true signals become more difficult to distinguish from the natural variation resulting from the noise. Therefore, devising a method to identify events when the SNR is low is a particularly important challenge.

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Published date: 1 July 2022
Venue - Dates: AI4SD Machine Learning Summer School, University of Southampton, Southampton, United Kingdom, 2022-06-20 - 2022-06-24
Keywords: AI3SD, Machine Learning, Summer School

Identifiers

Local EPrints ID: 470702
URI: http://eprints.soton.ac.uk/id/eprint/470702
PURE UUID: 9df82f0e-8122-4d2a-b626-abf1897aa44d
ORCID for Halil Ibrahim Aysel: ORCID iD orcid.org/0000-0002-4981-0827
ORCID for Stephen Gow: ORCID iD orcid.org/0000-0003-0121-1697
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489

Catalogue record

Date deposited: 18 Oct 2022 16:42
Last modified: 19 Oct 2022 02:01

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Contributors

Author: Wole Adewole
Author: Halil Ibrahim Aysel ORCID iD
Author: Stephen Gow ORCID iD
Author: Zheng Jiang
Author: Dimitrios Stamatis
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan ORCID iD
Editor: Samantha Kanza ORCID iD

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