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EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral
Abdul Rehman Basharat1, Xia Ning2, Xiaowen Liu1,3
1School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States.
Analytical Chemistry
|May 2, 2020
Summary
EnvCNN, a new convolutional neural network model, accurately evaluates isotopic envelopes in top-down mass spectrometry data. This method improves proteoform identification accuracy and statistical significance in spectral analysis.
Area of Science:
- Proteomics
- Analytical Chemistry
- Bioinformatics
Background:
- Top-down mass spectrometry is crucial for intact proteoform analysis.
- Spectral deconvolution is essential for processing complex mass spectrometry data.
- Scoring functions significantly impact the accuracy of spectral deconvolution.
Purpose of the Study:
- To introduce EnvCNN, a novel convolutional neural network model for evaluating isotopic envelopes.
- To enhance the accuracy and reliability of spectral deconvolution in top-down mass spectrometry.
- To improve the identification and statistical significance of proteoforms.
Main Methods:
- Development of EnvCNN, a convolutional neural network-based scoring function.
- Application of EnvCNN to spectral deconvolution in top-down mass spectrometry.
- Comparative analysis of EnvCNN against existing scoring functions.
Main Results:
- EnvCNN demonstrates superior performance in distinguishing correct isotopic envelopes.
- The model significantly increases the number of proteoform identifications.
- EnvCNN improves the statistical significance of identifications in spectral interpretation.
Conclusions:
- EnvCNN represents a significant advancement in spectral deconvolution for top-down mass spectrometry.
- The model enhances the accuracy and depth of proteoform analysis.
- EnvCNN offers a powerful tool for researchers in proteomics and related fields.
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