High accuracy base calls in nanopore sequencing

Philippe Faucon; Robert Trevino; Parithi Balachandran; Kylie Standage-Beier; Xiao Wang

doi:10.1145/3121138.3121186

High accuracy base calls in nanopore sequencing

Philippe Faucon, Robert Trevino, Parithi Balachandran, Kylie Standage-Beier, Xiao Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Nanopore sequencing has introduced the ability to sequence long stretches of DNA, enabling the resolution of repeating segments, or paired SNPs across long stretches of DNA. Unfortunately, significant error rates >15%, introduced through systematic and random noise inhibit downstream analysis. We propose a novel method, using unsupervised learning, to correct biologically amplified reads before downstream analysis proceeds. We also demonstrate that our method has performance comparable to existing techniques without limiting the detection of repeats, or the length of the input sequence.

Original language	English (US)
Title of host publication	Proceedings of 2017 6th International Conference on Bioinformatics and Biomedical Science, ICBBS 2017
Publisher	Association for Computing Machinery
Pages	12-16
Number of pages	5
ISBN (Electronic)	9781450352222
DOIs	https://doi.org/10.1145/3121138.3121186
State	Published - Jun 22 2017
Event	6th International Conference on Bioinformatics and Biomedical Science, ICBBS 2017 - Singapore, Singapore Duration: Jun 22 2017 → Jun 24 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F130950

Other

Other	6th International Conference on Bioinformatics and Biomedical Science, ICBBS 2017
Country/Territory	Singapore
City	Singapore
Period	6/22/17 → 6/24/17

Keywords

Accuracy improvement
DNA thumbprint
Nanopore sequencing

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/3121138.3121186

Cite this

Faucon, P., Trevino, R., Balachandran, P., Standage-Beier, K., & Wang, X. (2017). High accuracy base calls in nanopore sequencing. In Proceedings of 2017 6th International Conference on Bioinformatics and Biomedical Science, ICBBS 2017 (pp. 12-16). (ACM International Conference Proceeding Series; Vol. Part F130950). Association for Computing Machinery. https://doi.org/10.1145/3121138.3121186

High accuracy base calls in nanopore sequencing. / Faucon, Philippe; Trevino, Robert; Balachandran, Parithi et al.
Proceedings of 2017 6th International Conference on Bioinformatics and Biomedical Science, ICBBS 2017. Association for Computing Machinery, 2017. p. 12-16 (ACM International Conference Proceeding Series; Vol. Part F130950).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Faucon, P, Trevino, R, Balachandran, P, Standage-Beier, K & Wang, X 2017, High accuracy base calls in nanopore sequencing. in Proceedings of 2017 6th International Conference on Bioinformatics and Biomedical Science, ICBBS 2017. ACM International Conference Proceeding Series, vol. Part F130950, Association for Computing Machinery, pp. 12-16, 6th International Conference on Bioinformatics and Biomedical Science, ICBBS 2017, Singapore, Singapore, 6/22/17. https://doi.org/10.1145/3121138.3121186

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N2 - Nanopore sequencing has introduced the ability to sequence long stretches of DNA, enabling the resolution of repeating segments, or paired SNPs across long stretches of DNA. Unfortunately, significant error rates >15%, introduced through systematic and random noise inhibit downstream analysis. We propose a novel method, using unsupervised learning, to correct biologically amplified reads before downstream analysis proceeds. We also demonstrate that our method has performance comparable to existing techniques without limiting the detection of repeats, or the length of the input sequence.

AB - Nanopore sequencing has introduced the ability to sequence long stretches of DNA, enabling the resolution of repeating segments, or paired SNPs across long stretches of DNA. Unfortunately, significant error rates >15%, introduced through systematic and random noise inhibit downstream analysis. We propose a novel method, using unsupervised learning, to correct biologically amplified reads before downstream analysis proceeds. We also demonstrate that our method has performance comparable to existing techniques without limiting the detection of repeats, or the length of the input sequence.

KW - Accuracy improvement

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High accuracy base calls in nanopore sequencing

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this