Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors

2021 ◽  
Author(s):  
Weigang Ge ◽  
Xiao Liang ◽  
Fangfei Zhang ◽  
Yifan Hu ◽  
Luang Xu ◽  
...  
Keyword(s):  
Spectral Library ◽  
Library Size ◽  
Proteome Coverage ◽  
Computational Optimization

scholarly journals Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

2020 ◽  
Author(s):  
Weigang Ge ◽  
Xiao Liang ◽  
Fangfei Zhang ◽  
Luang Xu ◽  
Nan Xiang ◽  
...  
Keyword(s):  
Protein Identification ◽  
A Priori ◽  
Peptide Identification ◽  
Colorectal Tumor ◽  
Spectral Library ◽  
Library Size ◽  
Test Dataset ◽  
Proteome Coverage ◽  
Data Independent Acquisition ◽  
Computational Strategy

AbstractEfficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

scholarly journals Sample Size-Comparable Spectral Library Enhances Data-Independent Acquisition-Based Proteome Coverage of Low-Input Cells

2021 ◽  
Author(s):  
Asad Ali Siyal ◽  
Eric Sheng-Wen Chen ◽  
Hsin-Ju Chan ◽  
Reta Birhanu Kitata ◽  
Jhih-Ci Yang ◽  
...  
Keyword(s):  
Sample Size ◽  
Spectral Library ◽  
Proteome Coverage ◽  
Low Input ◽  
Data Independent Acquisition

scholarly journals A hybrid spectral library combining DIA-MS data and a targeted virtual library substantially deepens the proteome coverage

2020 ◽  
Author(s):  
Ronghui Lou ◽  
Pan Tang ◽  
Kang Ding ◽  
Shanshan Li ◽  
Cuiping Tian ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Protein Identification ◽  
Transmembrane Protein ◽  
Peptide Identification ◽  
Protein Family ◽  
Virtual Library ◽  
Proteomic Profiling ◽  
Spectral Library ◽  
Proteome Coverage ◽  
Brain Tissues

AbstractData-independent acquisition mass spectrometry (DIA-MS) is a rapidly evolving technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein coupled receptors; ion channels; and transporters) in mouse brain tissues with increases in protein identification of 37-87%, and peptide identification of 58-161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement.

scholarly journals DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ronghui Lou ◽  
Weizhen Liu ◽  
Rongjie Li ◽  
Shanshan Li ◽  
Xuming He ◽  
...  
Keyword(s):  
Data Mining ◽  
In Silico ◽  
Deep Neural Network ◽  
Learning Models ◽  
Spectral Library ◽  
Proteome Coverage ◽  
Data Independent Acquisition ◽  
User Access ◽  
Model Training ◽  
Egf Signaling

AbstractPhosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.

scholarly journals Hybrid Spectral Library Combining DIA-MS Data and a Targeted Virtual Library Substantially Deepens the Proteome Coverage

iScience ◽  
2020 ◽  
Vol 23 (3) ◽  
pp. 100903 ◽  
Author(s):  
Ronghui Lou ◽  
Pan Tang ◽  
Kang Ding ◽  
Shanshan Li ◽  
Cuiping Tian ◽  
...  
Keyword(s):  
Virtual Library ◽  
Spectral Library ◽  
Proteome Coverage

scholarly journals DeepPhospho: Accelerate DIA phosphoproteome profiling by Deep Learning

2021 ◽  
Author(s):  
Wenqing Shui ◽  
Ronghui Lou ◽  
Weizhen Liu ◽  
Rongjie Li ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Models ◽  
Spectral Library ◽  
Proteome Coverage ◽  
Data Independent Acquisition ◽  
User Access ◽  
Egf Signaling

Abstract Phosphoproteomics integrating data-independent acquisition (DIA) has enabled deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a novel deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we established a new DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expanded the phosphoproteome coverage while maintaining high quantification performance, which led to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server to facilitate user access to predictions and library generation.

Creation of Soil Spectral Library for Marathwada Region

2016 ◽  
Vol 5 (1) ◽  
pp. 1787-1794
Author(s):  
Ramdas D. Gore ◽  
◽  
Reena H. Chaudhari ◽  
Bharti W. Gawali ◽  
Keyword(s):  
Spectral Library

USGS Digital Spectral Library splib06a

Data Series ◽  
10.3133/ds231 ◽  
2007 ◽  
Author(s):  
Roger N. Clark ◽  
Gregg A. Swayze ◽  
Richard A. Wise ◽  
K. Eric Livo ◽  
Todd M. Hoefen ◽  
...  
Keyword(s):  
Spectral Library

Separation and Detection of Caffeine, Theophylline and Theobromine from Coffee Varieties, Carbonated Soft Drinks and Alcoholic Beverages

2017 ◽  
Vol 68 (11) ◽  
pp. 2704-2707
Author(s):  
Delia Nica Badea ◽  
Codrina Levai
Keyword(s):  
Food Safety ◽  
Mobile Phase ◽  
Soft Drinks ◽  
Alcoholic Beverages ◽  
Instrumental Method ◽  
Spectral Library ◽  
Active Components ◽  
Carbonated Soft Drinks ◽  
Mass Detector ◽  
Methyl Xanthine

The paper evaluates the presence of methyl xanthine compounds: caffeine, theophylline, theobromine used as ingredients in carbonated soft drinks or as color and flavor ingredients in alcoholic beverages. The active components extracted from the selected products (coffee, tea, drinks) was separated and identified chromatographically using plates with silica nano -Sil NH2 / UV-254, mobile phase ethanol - water (50: 1, 50: 3, 50: 5; 50: 7; v / v) and 60 F254 plates, mobile phase acetone-toluene-chloroform (40:30:30 v / v). Separated caffeine and identified by TLC was analyzed using a HelWet Packard 5890 Gas Chromatograph equipped with MS 5972 mass detector and spectral library to confirm identification. This simple and rapid TLC, GC / MS instrumental method is useful in controlling traces of methyl xanthine compounds in food as a food safety measure.is useful in controlling traces compound of food products containing methylxanthines as a food safety measure.

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