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Biomolecules
Special Issues
Data Analysis and Tools for Mass Spectrometry-Based Omics
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Data Analysis and Tools for Mass Spectrometry-Based Omics

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 3925

Special Issue Editors

Prof. Dr. Seongho Kim

E-Mail Website
Guest Editor
Karmanos Cancer Institute, School of Medicine, Wayne State University, Detroit, MI, USA
Interests: clinical trial design; survival analysis; PK/PD; metabolomics
Special Issues, Collections and Topics in MDPI journals
Dr. Jaesik Jeong

E-Mail Website
Guest Editor
Statistics Department, Chonnam National University, Gwangju, Republic of Korea
Interests: high-throughput data analysis; development of hierarchical statistical model; statistical modeling in bioinformatics

Special Issue Information

Dear Colleagues,

Mass spectrometry (MS) is an analytical technique used to measure the mass-to-charge ratio (m/z) of molecules present in a sample. MS is often coupled with several separation tools such as liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Many omics studies, including metabolomics, lipidomics, and proteomics, heavily rely on MS for the identification and quantification of biochemical metabolites, lipids, proteins, etc. This Special Issue is focused on the current use of MS in biological and clinical research. Researchers working with MS are invited to submit their original research or review articles for publication in this Special Issue. The topics of interest include but are not limited to, the application of MS, computational and statistical methods, and bioinformatics tools. Both application and methodological research studies are welcome.

Prof. Dr. Seongho Kim
Dr. Jaesik Jeong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mass spectrometry
  • liquid chromatography-mass spectrometry (LC-MS)
  • gas chromatography-mass spectrometry (GC-MS)
  • bioinformatics
  • systems biology

Published Papers (2 papers)

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Research

28 pages, 9351 KiB  
Article
In Search of Disentanglement in Tandem Mass Spectrometry Datasets
by Krzysztof Jan Abram and Douglas McCloskey
Biomolecules 2023, 13(9), 1343; https://doi.org/10.3390/biom13091343 - 04 Sep 2023
Viewed by 1013
Abstract
Generative modeling and representation learning of tandem mass spectrometry data aim to learn an interpretable and instrument-agnostic digital representation of metabolites directly from MS/MS spectra. Interpretable and instrument-agnostic digital representations would facilitate comparisons of MS/MS spectra between instrument vendors and enable better and [...] Read more.
Generative modeling and representation learning of tandem mass spectrometry data aim to learn an interpretable and instrument-agnostic digital representation of metabolites directly from MS/MS spectra. Interpretable and instrument-agnostic digital representations would facilitate comparisons of MS/MS spectra between instrument vendors and enable better and more accurate queries of large MS/MS spectra databases for metabolite identification. In this study, we apply generative modeling and representation learning using variational autoencoders to understand the extent to which tandem mass spectra can be disentangled into their factors of generation (e.g., collision energy, ionization mode, instrument type, etc.) with minimal prior knowledge of the factors. We find that variational autoencoders can disentangle tandem mass spectra data with the proper choice of hyperparameters into meaningful latent representations aligned with known factors of variation. We develop a two-step approach to facilitate the selection of models that are disentangled, which could be applied to other complex and high-dimensional data sets. Full article
(This article belongs to the Special Issue Data Analysis and Tools for Mass Spectrometry-Based Omics)
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20 pages, 3756 KiB  
Article
Quality Control—A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome
by Svitlana Rozanova, Julian Uszkoreit, Karin Schork, Bettina Serschnitzki, Martin Eisenacher, Lars Tönges, Katalin Barkovits-Boeddinghaus and Katrin Marcus
Biomolecules 2023, 13(3), 491; https://doi.org/10.3390/biom13030491 - 07 Mar 2023
Cited by 6 | Viewed by 2322
Abstract
Proteomic studies using mass spectrometry (MS)-based quantification are a main approach to the discovery of new biomarkers. However, a number of analytical conditions in front and during MS data acquisition can affect the accuracy of the obtained outcome. Therefore, comprehensive quality assessment of [...] Read more.
Proteomic studies using mass spectrometry (MS)-based quantification are a main approach to the discovery of new biomarkers. However, a number of analytical conditions in front and during MS data acquisition can affect the accuracy of the obtained outcome. Therefore, comprehensive quality assessment of the acquired data plays a central role in quantitative proteomics, though, due to the immense complexity of MS data, it is often neglected. Here, we address practically the quality assessment of quantitative MS data, describing key steps for the evaluation, including the levels of raw data, identification and quantification. With this, four independent datasets from cerebrospinal fluid, an important biofluid for neurodegenerative disease biomarker studies, were assessed, demonstrating that sample processing-based differences are already reflected at all three levels but with varying impacts on the quality of the quantitative data. Specifically, we provide guidance to critically interpret the quality of MS data for quantitative proteomics. Moreover, we provide the free and open source quality control tool MaCProQC, enabling systematic, rapid and uncomplicated data comparison of raw data, identification and feature detection levels through defined quality metrics and a step-by-step quality control workflow. Full article
(This article belongs to the Special Issue Data Analysis and Tools for Mass Spectrometry-Based Omics)
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