机构：[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.四川大学华西医院[2]Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
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.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).
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.
基金：
HY was supported by National Natural Science Foundation of China (Grant No. 81871475) and JC was supported by the 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University, Sichuan, China (ZYGD18014).
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2020]版：
大类|4 区计算机科学
小类|3 区生化研究方法3 区生物工程与应用微生物3 区数学与计算生物学
最新[2023]版：
大类|3 区生物学
小类|3 区生化研究方法3 区数学与计算生物学4 区生物工程与应用微生物
第一作者：
第一作者机构：[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.
共同第一作者：
通讯作者：
推荐引用方式(GB/T 7714)：
Shisheng Wang,Hongwen Zhu,Hu Zhou,et al.MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks.[J].BMC bioinformatics.2020,21(1):439.doi:10.1186/s12859-020-03783-0.
APA：
Shisheng Wang,Hongwen Zhu,Hu Zhou,Jingqiu Cheng&Hao Yang.(2020).MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks..BMC bioinformatics,21,(1)
MLA：
Shisheng Wang,et al."MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks.".BMC bioinformatics 21..1(2020):439
