Deep neural network for detecting arbitrary precision peptide features through attention based segmentation

Abstract A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in the LC-M...

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Autores principales:	Fatema Tuz Zohora, M. Ziaur Rahman, Ngoc Hieu Tran, Lei Xin, Baozhen Shan, Ming Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2e3d8f73b00a4ff9b1121f355defafae
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spelling	oai:doaj.org-article:2e3d8f73b00a4ff9b1121f355defafae2021-12-02T17:23:47ZDeep neural network for detecting arbitrary precision peptide features through attention based segmentation10.1038/s41598-021-97669-72045-2322https://doaj.org/article/2e3d8f73b00a4ff9b1121f355defafae2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97669-7https://doaj.org/toc/2045-2322Abstract A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in the LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters and human errors. As a solution, we propose PointIso, the first point cloud based arbitrary-precision deep learning network to address this problem. It consists of attention based scanning step for segmenting the multi-isotopic pattern of 3D peptide features along with the charge, and a sequence classification step for grouping those isotopes into potential peptide features. PointIso achieves 98% detection of high-quality MS/MS identified peptide features in a benchmark dataset. Next, the model is adapted for handling the additional ‘ion mobility’ dimension and achieves 4% higher detection than existing algorithms on the human proteome dataset. Besides contributing to the proteomics study, our novel segmentation technique should serve the general object detection domain as well.Fatema Tuz ZohoraM. Ziaur RahmanNgoc Hieu TranLei XinBaozhen ShanMing LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fatema Tuz Zohora M. Ziaur Rahman Ngoc Hieu Tran Lei Xin Baozhen Shan Ming Li Deep neural network for detecting arbitrary precision peptide features through attention based segmentation
description	Abstract A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in the LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters and human errors. As a solution, we propose PointIso, the first point cloud based arbitrary-precision deep learning network to address this problem. It consists of attention based scanning step for segmenting the multi-isotopic pattern of 3D peptide features along with the charge, and a sequence classification step for grouping those isotopes into potential peptide features. PointIso achieves 98% detection of high-quality MS/MS identified peptide features in a benchmark dataset. Next, the model is adapted for handling the additional ‘ion mobility’ dimension and achieves 4% higher detection than existing algorithms on the human proteome dataset. Besides contributing to the proteomics study, our novel segmentation technique should serve the general object detection domain as well.
format	article
author	Fatema Tuz Zohora M. Ziaur Rahman Ngoc Hieu Tran Lei Xin Baozhen Shan Ming Li
author_facet	Fatema Tuz Zohora M. Ziaur Rahman Ngoc Hieu Tran Lei Xin Baozhen Shan Ming Li
author_sort	Fatema Tuz Zohora
title	Deep neural network for detecting arbitrary precision peptide features through attention based segmentation
title_short	Deep neural network for detecting arbitrary precision peptide features through attention based segmentation
title_full	Deep neural network for detecting arbitrary precision peptide features through attention based segmentation
title_fullStr	Deep neural network for detecting arbitrary precision peptide features through attention based segmentation
title_full_unstemmed	Deep neural network for detecting arbitrary precision peptide features through attention based segmentation
title_sort	deep neural network for detecting arbitrary precision peptide features through attention based segmentation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2e3d8f73b00a4ff9b1121f355defafae
work_keys_str_mv	AT fatematuzzohora deepneuralnetworkfordetectingarbitraryprecisionpeptidefeaturesthroughattentionbasedsegmentation AT mziaurrahman deepneuralnetworkfordetectingarbitraryprecisionpeptidefeaturesthroughattentionbasedsegmentation AT ngochieutran deepneuralnetworkfordetectingarbitraryprecisionpeptidefeaturesthroughattentionbasedsegmentation AT leixin deepneuralnetworkfordetectingarbitraryprecisionpeptidefeaturesthroughattentionbasedsegmentation AT baozhenshan deepneuralnetworkfordetectingarbitraryprecisionpeptidefeaturesthroughattentionbasedsegmentation AT mingli deepneuralnetworkfordetectingarbitraryprecisionpeptidefeaturesthroughattentionbasedsegmentation
_version_	1718380960044548096

Deep neural network for detecting arbitrary precision peptide features through attention based segmentation

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