MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC-MS/MS Data Analysis (Record no. 54733)

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control field 20250625162452.0
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Transcribing agency CICY
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Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) B-20647
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Title MCnebula: Critical Chemical Classes for the Classification and Boost Identification by Visualization for Untargeted LC-MS/MS Data Analysis
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Volume/sequential designation Analytical Chemistry, 95, p.9940-9948, 2023
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Summary, etc. Untargeted mass spectrometry is a robust tool for biology, but it usually requires a large amount of time on data analysis, especially for system biology. A framework called Multiple-Chemical nebula (MCnebula)was developed herein to facilitate the LC-MS data analysis process by focusing on critical chemical classes and visualization in multiple dimensions. This framework consists of three vital steps as follows: (1)abundance-based classes (ABC)selection algorithm, (2)critical chemical classes to classify "features" (corresponding to compounds), and (3)visualization as multiple Child-Nebulae (network graph)with annotation, chemical classification, and structure. Notably, MCnebula can be used to explore the classification and structural characteristic of unknown compounds beyond the limit of the spectral library. Moreover, it is intuitive and convenient for pathway analysis and biomarker discovery because of its function of ABC selection and visualization. MCnebula was implemented in the R language. A series of tools in R packages were provided to facilitate downstream analysis in an MCnebula-featured way, including feature selection, homology tracing of top features, pathway enrichment analysis, heat map clustering analysis, spectral visualization analysis, chemical information query, and output analysis reports. The broad utility of MCnebula was illustrated by a human-derived serum data set for metabolomics analysis. The results indicated that "Acyl carnitines" were screened out by tracing structural classes of biomarkers, which was consistent with the reference. A plant-derived data set was investigated to achieve a rapid annotation and discovery of compounds in E. ulmoides.
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Personal name Huang, L.
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Personal name Shan, Q.
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Personal name Lyu, Q.
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Personal name Zhang, S.
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Personal name Wang, L.
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Personal name Cao, G.
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Uniform Resource Identifier <a href="https://drive.google.com/file/d/1s8zyJ4RJxbYAXsiKLN3qu_-8k2O-kecJ/view?usp=drivesdk">https://drive.google.com/file/d/1s8zyJ4RJxbYAXsiKLN3qu_-8k2O-kecJ/view?usp=drivesdk</a>
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Source of classification or shelving scheme Clasificación local
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  Clasificación local     Ref1 CICY CICY Documento préstamo interbibliotecario 25.06.2025   B-20647 25.06.2025 25.06.2025 Documentos solicitados