PointIso: Point Cloud Based Deep Learning Model for Detecting Arbitrary-Precision Peptide Features in LC-MS Map through Attention Based Segmentation

• URL: http://arxiv.org/abs/2009.07250v1
• Date: Tue, 15 Sep 2020 17:34:14 GMT
• Title: PointIso: Point Cloud Based Deep Learning Model for Detecting Arbitrary-Precision Peptide Features in LC-MS Map through Attention Based Segmentation
• Authors: Fatema Tuz Zohora, M Ziaur Rahman, Ngoc Hieu Tran, Lei Xin, Baozhen Shan, Ming Li
• Abstract summary: PointIso is a point cloud based, arbitrary-precision deep learning network to address the problem of peptide feature detection. It achieves 98% detection of high quality MS/MS identifications in a benchmark dataset.
• Score: 5.495506445661776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters since different settings of the parameters result in significantly different outcomes. Therefore, we propose PointIso, to serve the necessity of an automated system for peptide feature detection that is able to find out the proper parameters itself, and is easily adaptable to different types of datasets. It consists of an attention based scanning step for segmenting the multi-isotopic pattern of peptide features along with charge and a sequence classification step for grouping those isotopes into potential peptide features. PointIso is the first point cloud based, arbitrary-precision deep learning network to address the problem and achieves 98% detection of high quality MS/MS identifications in a benchmark dataset, which is higher than several other widely used algorithms. Besides contributing to the proteomics study, we believe our novel segmentation technique should serve the general image processing domain as well.

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