Evaluation of the performance of a tandem mass spectral library with mass spectral data extracted from literature

2011 ◽  
Vol 4 (3-4) ◽  
pp. 235-241 ◽  
Author(s):  
Philipp Würtinger ◽  
Herbert Oberacher
Keyword(s):  
Spectral Data ◽  
Tandem Mass ◽  
Mass Spectral Library ◽  
Spectral Library ◽  
Mass Spectral Data ◽  
Mass Spectral

scholarly journals Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4590
Author(s):  
Jiali Lv ◽  
Jian Wei ◽  
Zhenyu Wang ◽  
Jin Cao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spectral Data ◽  
Test Data ◽  
False Positive ◽  
Tandem Mass ◽  
True Positive ◽  
Mass Spectral Data ◽  
True Negative ◽  
Mass Spectral

Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purification and denoising will cost a lot of algorithm time. In this paper, we propose a model based on convolutional neural network (CNN) which can analyze the chemical peak information in the tandem mass spectrometry (MS/MS) data. Compared with traditional analyzing methods, CNN can reduce steps in data preprocessing. This model can extract features of different compounds and classify multi-label mass spectral data. When dealing with MS data of mixtures based on the Human Metabolome Database (HMDB), the accuracy can reach at 98%. In 600 MS test data, 451 MS data were fully detected (true positive), 142 MS data were partially found (false positive), and 7 MS data were falsely predicted (true negative). In comparison, the number of true positive test data for support vector machine (SVM) with principal component analysis (PCA), deep neural network (DNN), long short-term memory (LSTM), and XGBoost respectively are 282, 293, 270, and 402; the number of false positive test data for four models are 318, 284, 198, and 168; the number of true negative test data for four models are 0, 23, 7, 132, and 30. Compared with the model proposed in other literature, the accuracy and model performance of CNN improved considerably by separating the different compounds independent MS/MS data through three-channel architecture input. By inputting MS data from different instruments, adding more offset MS data will make CNN models have stronger universality in the future.

scholarly journals Creating a Reliable Mass Spectral–Retention Time Library for All Ion Fragmentation-Based Metabolomics

Metabolites ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 251 ◽  
Author(s):  
Ipputa Tada ◽  
Hiroshi Tsugawa ◽  
Isabel Meister ◽  
Pei Zhang ◽  
Rie Shu ◽  
...  
Keyword(s):  
Retention Time ◽  
Metabolite Identification ◽  
Mass Spectral Library ◽  
Spectral Library ◽  
Mass Spectral Data ◽  
Mass Spectral ◽  
Ion Fragmentation ◽  
Metabolomics Study ◽  
Construction Strategy

Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chemical spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liquid chromatography coupled to mass spectrometry (LC–MS) methods across laboratories. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across laboratories. This library construction strategy improves the confidence in annotation for AIF data in LC–MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.

Development of a dedicated peptide tandem mass spectral library for conservation science

2012 ◽  
Vol 728 ◽  
pp. 39-48 ◽  
Author(s):  
Wim Fremout ◽  
Maarten Dhaenens ◽  
Steven Saverwyns ◽  
Jana Sanyova ◽  
Peter Vandenabeele ◽  
...  
Keyword(s):  
Tandem Mass ◽  
Mass Spectral Library ◽  
Spectral Library ◽  
Conservation Science ◽  
Mass Spectral

scholarly journals Fast Parallel Tandem Mass Spectral Library Searching Using GPU Hardware Acceleration

2011 ◽  
Vol 10 (6) ◽  
pp. 2882-2888 ◽  
Author(s):  
Lydia Ashleigh Baumgardner ◽  
Avinash Kumar Shanmugam ◽  
Henry Lam ◽  
Jimmy K. Eng ◽  
Daniel B. Martin
Keyword(s):  
Hardware Acceleration ◽  
Tandem Mass ◽  
Mass Spectral Library ◽  
Spectral Library ◽  
Mass Spectral

scholarly journals A review of methods for interpretation of glycopeptide tandem mass spectral data

2015 ◽  
Vol 33 (3) ◽  
pp. 285-296 ◽  
Author(s):  
Han Hu ◽  
Kshitij Khatri ◽  
Joshua Klein ◽  
Nancy Leymarie ◽  
Joseph Zaia
Keyword(s):  
Spectral Data ◽  
Tandem Mass ◽  
Mass Spectral Data ◽  
Mass Spectral ◽  
Tandem Mass Spectral Data
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