MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep n
- PDF / 6,299,417 Bytes
- 15 Pages / 595.276 x 790.866 pts Page_size
- 79 Downloads / 183 Views
Open Access
SOFTWARE
MSpectraAI: a powerful platform for deciphering proteome profiling of multi‑tumor mass spectrometry data by using deep neural networks Shisheng Wang1†, Hongwen Zhu2†, Hu Zhou2, Jingqiu Cheng1* and Hao Yang1* *Correspondence: [email protected]; [email protected] † Shisheng Wang and Hongwen Zhu have contributed equally to this work. 1 West China‑Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immu‑Nology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, No. 88, Keyuan South Road, Hi‑tech Zone, Chengdu 610041, China Full list of author information is available at the end of the article
Abstract Background: Mass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological samples. Nevertheless, most studies focus on the final proteins identified through a suite of algorithms by using partial MS spectra to compare with the sequence database, while the pattern recognition and classification of raw mass-spectrometric data remain unresolved. Results: We developed an open-source and comprehensive platform, named MSpectraAI, for analyzing large-scale MS data through deep neural networks (DNNs); this system involves spectral-feature swath extraction, classification, and visualization. Moreover, this platform allows users to create their own DNN model by using Keras. To evaluate this tool, we collected the publicly available proteomics datasets of six tumor types (a total of 7,997,805 mass spectra) from the ProteomeXchange consortium and classified the samples based on the spectra profiling. The results suggest that MSpectraAI can distinguish different types of samples based on the fingerprint spectrum and achieve better prediction accuracy in MS1 level (average 0.967). Conclusion: This study deciphers proteome profiling of raw mass spectrometry data and broadens the promising application of the classification and prediction of proteomics data from multi-tumor samples using deep learning methods. MSpectraAI also shows a better performance compared to the other classical machine learning approaches. Keyword: Raw mass spectrometry data, Proteome profiling, Feature swath extraction, Deep neural networks, Multi-tumor types, Leave-one-out cross prediction strategy
Background The comparison of molecular features from diverse physiological or disease states is vital for determining different potential biomarkers closely associated with specific diseases [1, 2]. For example, identification of cancer subtype-specific biomarkers and candidate drivers can reveal useful insights into disease pathogenesis and facilitate personalized © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative
Data Loading...
