EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral Deconvolution.
Abdul Rehman BasharatXia NingXiaowen LiuPublished in: Analytical chemistry (2020)
Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic envelopes and extracts monoisotopic masses of precursor or fragment ions. The performance of spectral deconvolution methods relies heavily on their scoring functions, which distinguish correct envelopes from incorrect ones. A good scoring function increases the accuracy of deconvoluted masses reported from mass spectra. In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating isotopic envelopes. We show that the model outperforms other scoring functions in distinguishing correct envelopes from incorrect ones and that it increases the number of identifications and improves the statistical significance of identifications in top-down spectral interpretation.
Keyphrases
- convolutional neural network
- optical coherence tomography
- mass spectrometry
- data analysis
- dual energy
- liquid chromatography
- deep learning
- computed tomography
- machine learning
- big data
- magnetic resonance
- magnetic resonance imaging
- ultrasound guided
- liquid chromatography tandem mass spectrometry
- capillary electrophoresis
- quantum dots
- contrast enhanced ultrasound