TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites

Chung En Ni; Duy Phuong Doan; Yen Jung Chiu; Yen Hua Huang

doi:10.1109/BIBE55377.2022.00054

TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites

Chung En Ni
, Duy Phuong Doan
, Yen Jung Chiu
, Yen Hua Huang

National Yang Ming Chiao Tung University

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

Abstract

Several computational methods have been developed to perform promoter classification in prokaryotic genomes. However, those methods were mostly designed to classify promoters only under the condition that the TSS must be located at a predefined anchor position in each input sequence. Hence, this study aims at developing a new method that can classify promoters without assuming a fixed location of TSS. We draw an analogy between TSS identification and object detection, which is a well-known task in image analysis. Thus, TSSNet, a deep neural network model, is developed in this study and it can scan for the TSSs by taking windowed regions in a prokaryotic genome. The benchmark reveals that TSSNet has a recall rate higher than 80% in TSS identification when analyzing the genomic sequences. Our results suggest that an object detection model can be applied to the analysis of genomic sequences for finding the regulatory elements and key points that are important in functional genomics.

Original language	English
Title of host publication	Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	221-224
Number of pages	4
ISBN (Electronic)	9781665484879
DOIs	https://doi.org/10.1109/BIBE55377.2022.00054
State	Published - 2022
Externally published	Yes
Event	22nd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2022 - Virtual, Online, Taiwan Duration: 07 11 2022 → 09 11 2022

Publication series

Name	Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022

Conference

Conference	22nd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2022
Country/Territory	Taiwan
City	Virtual, Online
Period	07/11/22 → 09/11/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

deep neural network
object detection
promoter classification
transcription start sites

Access to Document

10.1109/BIBE55377.2022.00054

Cite this

Ni, C. E., Doan, D. P., Chiu, Y. J., & Huang, Y. H. (2022). TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites. In Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022 (pp. 221-224). (Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBE55377.2022.00054

Ni, Chung En ; Doan, Duy Phuong ; Chiu, Yen Jung et al. / TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites. Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 221-224 (Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022).

@inproceedings{780dd05815034784866b217efbf57ead,

title = "TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites",

abstract = "Several computational methods have been developed to perform promoter classification in prokaryotic genomes. However, those methods were mostly designed to classify promoters only under the condition that the TSS must be located at a predefined anchor position in each input sequence. Hence, this study aims at developing a new method that can classify promoters without assuming a fixed location of TSS. We draw an analogy between TSS identification and object detection, which is a well-known task in image analysis. Thus, TSSNet, a deep neural network model, is developed in this study and it can scan for the TSSs by taking windowed regions in a prokaryotic genome. The benchmark reveals that TSSNet has a recall rate higher than 80\% in TSS identification when analyzing the genomic sequences. Our results suggest that an object detection model can be applied to the analysis of genomic sequences for finding the regulatory elements and key points that are important in functional genomics.",

keywords = "deep neural network, object detection, promoter classification, transcription start sites",

author = "Ni, \{Chung En\} and Doan, \{Duy Phuong\} and Chiu, \{Yen Jung\} and Huang, \{Yen Hua\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2022 ; Conference date: 07-11-2022 Through 09-11-2022",

year = "2022",

doi = "10.1109/BIBE55377.2022.00054",

language = "英语",

series = "Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022",

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Ni, CE, Doan, DP, Chiu, YJ & Huang, YH 2022, TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites. in Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022. Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022, Institute of Electrical and Electronics Engineers Inc., pp. 221-224, 22nd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2022, Virtual, Online, Taiwan, 07/11/22. https://doi.org/10.1109/BIBE55377.2022.00054

TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites. / Ni, Chung En; Doan, Duy Phuong; Chiu, Yen Jung et al.
Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 221-224 (Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022).

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

TY - GEN

T1 - TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites

AU - Ni, Chung En

AU - Doan, Duy Phuong

AU - Chiu, Yen Jung

AU - Huang, Yen Hua

PY - 2022

Y1 - 2022

N2 - Several computational methods have been developed to perform promoter classification in prokaryotic genomes. However, those methods were mostly designed to classify promoters only under the condition that the TSS must be located at a predefined anchor position in each input sequence. Hence, this study aims at developing a new method that can classify promoters without assuming a fixed location of TSS. We draw an analogy between TSS identification and object detection, which is a well-known task in image analysis. Thus, TSSNet, a deep neural network model, is developed in this study and it can scan for the TSSs by taking windowed regions in a prokaryotic genome. The benchmark reveals that TSSNet has a recall rate higher than 80% in TSS identification when analyzing the genomic sequences. Our results suggest that an object detection model can be applied to the analysis of genomic sequences for finding the regulatory elements and key points that are important in functional genomics.

AB - Several computational methods have been developed to perform promoter classification in prokaryotic genomes. However, those methods were mostly designed to classify promoters only under the condition that the TSS must be located at a predefined anchor position in each input sequence. Hence, this study aims at developing a new method that can classify promoters without assuming a fixed location of TSS. We draw an analogy between TSS identification and object detection, which is a well-known task in image analysis. Thus, TSSNet, a deep neural network model, is developed in this study and it can scan for the TSSs by taking windowed regions in a prokaryotic genome. The benchmark reveals that TSSNet has a recall rate higher than 80% in TSS identification when analyzing the genomic sequences. Our results suggest that an object detection model can be applied to the analysis of genomic sequences for finding the regulatory elements and key points that are important in functional genomics.

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KW - object detection

KW - promoter classification

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ER -

Ni CE, Doan DP, Chiu YJ, Huang YH. TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites. In Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 221-224. (Proceedings - IEEE 22nd International Conference on Bioinformatics and Bioengineering, BIBE 2022). doi: 10.1109/BIBE55377.2022.00054

TSSNet – A Deep Neural Network Model for Predicting Prokaryotic Transcription Start Sites

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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