ricME: Long-Read Based Mobile Element Variant Detection Using Sequence Realignment and Identity Calculation

Huidong Ma; Cheng Zhong; Hui Sun; Danyang Chen; HaiXiang Lin

doi:10.1007/978-981-99-7074-2_13

ricME: Long-Read Based Mobile Element Variant Detection Using Sequence Realignment and Identity Calculation

Huidong Ma, Cheng Zhong^*, Hui Sun, Danyang Chen, HaiXiang Lin

^*Corresponding author for this work

Mathematical Physics

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

The mobile element variant is a very important structural variant, accounting for a quarter of structural variants, and it is closely related to many issues such as genetic diseases and species diversity. However, few detection algorithms of mobile element variants have been developed on third-generation sequencing data. We propose an algorithm ricME that combines sequence realignment and identity calculation for detecting mobile element variants. The ricME first performs an initial detection to obtain the positions of insertions and deletions, and extracts the variant sequences; then applies sequence realignment and identity calculation to obtain the transposon classes related to the variant sequences; finally, adopts a multi-level judgment rule to achieve accurate detection of mobile element variants based on the transposon classes and identities. Compared with a representative long-read based mobile element variant detection algorithm rMETL, the ricME improves the F1-score by 11.5 and 21.7% on simulated datasets and real datasets, respectively.

Original language	English
Title of host publication	Bioinformatics Research and Applications - 19th International Symposium, ISBRA 2023, Proceedings
Editors	Xuan Guo, Serghei Mangul, Murray Patterson, Alexander Zelikovsky
Place of Publication	Singapore
Publisher	Springer
Pages	165-177
Number of pages	13
ISBN (Electronic)	978-981-99-7074-2
ISBN (Print)	978-981-99-7073-5
DOIs	https://doi.org/10.1007/978-981-99-7074-2_13
Publication status	Published - 2023
Event	19th International Symposium on Bioinformatics Research and Applications, ISBRA 2023 - Wroclaw, Poland Duration: 9 Oct 2023 → 12 Oct 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14248 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Symposium on Bioinformatics Research and Applications, ISBRA 2023
Country/Territory	Poland
City	Wroclaw
Period	9/10/23 → 12/10/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

identity calculation
mobile element variants
sequence realignment
third-generation sequencing data

Access to Document

10.1007/978-981-99-7074-2_13

978-981-99-7074-2_13 (1)Final published version, 518 KB

Cite this

Ma, H., Zhong, C., Sun, H., Chen, D., & Lin, H. (2023). ricME: Long-Read Based Mobile Element Variant Detection Using Sequence Realignment and Identity Calculation. In X. Guo, S. Mangul, M. Patterson, & A. Zelikovsky (Eds.), Bioinformatics Research and Applications - 19th International Symposium, ISBRA 2023, Proceedings (pp. 165-177). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14248 LNBI). Springer. https://doi.org/10.1007/978-981-99-7074-2_13

Ma, Huidong ; Zhong, Cheng ; Sun, Hui et al. / ricME : Long-Read Based Mobile Element Variant Detection Using Sequence Realignment and Identity Calculation. Bioinformatics Research and Applications - 19th International Symposium, ISBRA 2023, Proceedings. editor / Xuan Guo ; Serghei Mangul ; Murray Patterson ; Alexander Zelikovsky. Singapore : Springer, 2023. pp. 165-177 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "ricME: Long-Read Based Mobile Element Variant Detection Using Sequence Realignment and Identity Calculation",

abstract = "The mobile element variant is a very important structural variant, accounting for a quarter of structural variants, and it is closely related to many issues such as genetic diseases and species diversity. However, few detection algorithms of mobile element variants have been developed on third-generation sequencing data. We propose an algorithm ricME that combines sequence realignment and identity calculation for detecting mobile element variants. The ricME first performs an initial detection to obtain the positions of insertions and deletions, and extracts the variant sequences; then applies sequence realignment and identity calculation to obtain the transposon classes related to the variant sequences; finally, adopts a multi-level judgment rule to achieve accurate detection of mobile element variants based on the transposon classes and identities. Compared with a representative long-read based mobile element variant detection algorithm rMETL, the ricME improves the F1-score by 11.5 and 21.7% on simulated datasets and real datasets, respectively.",

keywords = "identity calculation, mobile element variants, sequence realignment, third-generation sequencing data",

author = "Huidong Ma and Cheng Zhong and Hui Sun and Danyang Chen and HaiXiang Lin",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 19th International Symposium on Bioinformatics Research and Applications, ISBRA 2023 ; Conference date: 09-10-2023 Through 12-10-2023",

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Ma, H, Zhong, C, Sun, H, Chen, D & Lin, H 2023, ricME: Long-Read Based Mobile Element Variant Detection Using Sequence Realignment and Identity Calculation. in X Guo, S Mangul, M Patterson & A Zelikovsky (eds), Bioinformatics Research and Applications - 19th International Symposium, ISBRA 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14248 LNBI, Springer, Singapore, pp. 165-177, 19th International Symposium on Bioinformatics Research and Applications, ISBRA 2023, Wroclaw, Poland, 9/10/23. https://doi.org/10.1007/978-981-99-7074-2_13

ricME: Long-Read Based Mobile Element Variant Detection Using Sequence Realignment and Identity Calculation. / Ma, Huidong; Zhong, Cheng; Sun, Hui et al.
Bioinformatics Research and Applications - 19th International Symposium, ISBRA 2023, Proceedings. ed. / Xuan Guo; Serghei Mangul; Murray Patterson; Alexander Zelikovsky. Singapore: Springer, 2023. p. 165-177 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14248 LNBI).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Ma, Huidong

AU - Zhong, Cheng

AU - Sun, Hui

AU - Chen, Danyang

AU - Lin, HaiXiang

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

Y1 - 2023

N2 - The mobile element variant is a very important structural variant, accounting for a quarter of structural variants, and it is closely related to many issues such as genetic diseases and species diversity. However, few detection algorithms of mobile element variants have been developed on third-generation sequencing data. We propose an algorithm ricME that combines sequence realignment and identity calculation for detecting mobile element variants. The ricME first performs an initial detection to obtain the positions of insertions and deletions, and extracts the variant sequences; then applies sequence realignment and identity calculation to obtain the transposon classes related to the variant sequences; finally, adopts a multi-level judgment rule to achieve accurate detection of mobile element variants based on the transposon classes and identities. Compared with a representative long-read based mobile element variant detection algorithm rMETL, the ricME improves the F1-score by 11.5 and 21.7% on simulated datasets and real datasets, respectively.

AB - The mobile element variant is a very important structural variant, accounting for a quarter of structural variants, and it is closely related to many issues such as genetic diseases and species diversity. However, few detection algorithms of mobile element variants have been developed on third-generation sequencing data. We propose an algorithm ricME that combines sequence realignment and identity calculation for detecting mobile element variants. The ricME first performs an initial detection to obtain the positions of insertions and deletions, and extracts the variant sequences; then applies sequence realignment and identity calculation to obtain the transposon classes related to the variant sequences; finally, adopts a multi-level judgment rule to achieve accurate detection of mobile element variants based on the transposon classes and identities. Compared with a representative long-read based mobile element variant detection algorithm rMETL, the ricME improves the F1-score by 11.5 and 21.7% on simulated datasets and real datasets, respectively.

KW - identity calculation

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KW - sequence realignment

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DO - 10.1007/978-981-99-7074-2_13

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SN - 978-981-99-7073-5

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Bioinformatics Research and Applications - 19th International Symposium, ISBRA 2023, Proceedings

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A2 - Mangul, Serghei

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Ma H, Zhong C, Sun H, Chen D, Lin H. ricME: Long-Read Based Mobile Element Variant Detection Using Sequence Realignment and Identity Calculation. In Guo X, Mangul S, Patterson M, Zelikovsky A, editors, Bioinformatics Research and Applications - 19th International Symposium, ISBRA 2023, Proceedings. Singapore: Springer. 2023. p. 165-177. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-7074-2_13

ricME: Long-Read Based Mobile Element Variant Detection Using Sequence Realignment and Identity Calculation

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