Targeted proteomics data interpretation with DeepMRM

Jungkap Park; Christopher Wilkins; Dmitry Avtonomov; Jiwon Hong; Seunghoon Back; Hokeun Kim; Nicholas Shulman; Brendan X. MacLean; Sang Won Lee; Sangtae Kim

doi:10.1016/j.crmeth.2023.100521

Targeted proteomics data interpretation with DeepMRM

Jungkap Park, Christopher Wilkins, Dmitry Avtonomov, Jiwon Hong, Seunghoon Back, Hokeun Kim, Nicholas Shulman, Brendan X. MacLean, Sang Won Lee, Sangtae Kim

Department of Chemistry

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.

Original language	English
Article number	100521
Journal	Cell Reports Methods
Volume	3
Issue number	7
DOIs	https://doi.org/10.1016/j.crmeth.2023.100521
Publication status	Published - 2023 Jul 24

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

CP: Systems biology
Skyline
machine learning
multiple reaction monitoring
object detection
peak detection
quality control
quantification
selected reaction monitoring
targeted proteomics

ASJC Scopus subject areas

Biotechnology
Biochemistry
Biochemistry, Genetics and Molecular Biology (miscellaneous)
Genetics
Radiology Nuclear Medicine and imaging
Computer Science Applications

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10.1016/j.crmeth.2023.100521

Cite this

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title = "Targeted proteomics data interpretation with DeepMRM",

abstract = "Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.",

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doi = "10.1016/j.crmeth.2023.100521",

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AU - Park, Jungkap

AU - Wilkins, Christopher

AU - Avtonomov, Dmitry

AU - Hong, Jiwon

AU - Back, Seunghoon

AU - Kim, Hokeun

AU - Shulman, Nicholas

AU - MacLean, Brendan X.

AU - Lee, Sang Won

AU - Kim, Sangtae

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AB - Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.

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KW - object detection

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KW - quality control

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Targeted proteomics data interpretation with DeepMRM

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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