Data Software Designing, Development and Automation: Based on Metabolomics and Lipidomics Studies

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 5901

Special Issue Editors

Institute of Neuroscience, Italian National Research Council, Rome, Italy
Interests: metabolomics; lipidomics; bioinformatics; central nervous system; autophagy
School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milano, Italy
Interests: metabolomics; lipidomics; ion mobility; mass spectrometry; spatial lipidomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, due to technological and methodological innovations, metabolomics and lipidomics analysis have become increasingly popular in both pre-clinical and clinical studies, resulting in an increase in raw data production and availability. This has led to the development of robust analytical pipeline beyond canonical targeted analysis such as, but not limited to, NMR based methods, such as HRMAS, and MS methods based on iterative data-dependent or data-independent acquisition, and, more recently, with the increase in the popularity of ion mobility, untargeted mass spectrometry imaging.

As for older omics disciplines, the development of software and computational pipeline ranging from raw data annotation to AI-based modeling is rising in the field. Despite that, developed tools require intensive manual handling, which limits the robustness and specialized personnel, hampering the extensive application of more computationally demanding approaches.

Therefore, this Special Issue of Metabolites will be dedicated to publishing current advances on the software design and computational pipeline to address recent challenges in metabolomics and lipidomics.

Dr. Giuseppe Martano
Prof. Dr. Giuseppe Paglia
Guest Editors

Manuscript Submission Information

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Keywords

  • metabolomics
  • lipidomics
  • mass spectrometry
  • nuclear magnetic resonance
  • bioinformatics
  • data mining
  • data modeling

Published Papers (2 papers)

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Research

13 pages, 1566 KiB  
Article
EASY-FIA: A Readably Usable Standalone Tool for High-Resolution Mass Spectrometry Metabolomics Data Pre-Processing
by Aurelia Morabito, Giulia De Simone, Manuela Ferrario, Francesca Falcetta, Roberta Pastorelli and Laura Brunelli
Metabolites 2023, 13(1), 13; https://doi.org/10.3390/metabo13010013 - 21 Dec 2022
Cited by 3 | Viewed by 1800
Abstract
Flow injection analysis coupled with high-resolution mass spectrometry (FIA-HRMS) is a fair trade-off between resolution and speed. However, free software available for data pre-processing is few, web-based, and often requires advanced user specialization. These tools rarely embedded blank and noise evaluation strategies, and [...] Read more.
Flow injection analysis coupled with high-resolution mass spectrometry (FIA-HRMS) is a fair trade-off between resolution and speed. However, free software available for data pre-processing is few, web-based, and often requires advanced user specialization. These tools rarely embedded blank and noise evaluation strategies, and direct feature annotation. We developed EASY-FIA, a free standalone application that can be employed for FIA-HRMS metabolomic data pre-processing by users with no bioinformatics/programming skills. We validated the tool′s performance and applicability in two clinical metabolomics case studies. The main functions of our application are blank subtraction, alignment of the metabolites, and direct feature annotation by means of the Human Metabolome Database (HMDB) using a minimum number of mass spectrometry parameters. In a scenario where FIA-HRMS is increasingly recognized as a reliable strategy for fast metabolomics analysis, EASY-FIA could become a standardized and feasible tool easily usable by all scientists dealing with MS-based metabolomics. EASY-FIA was implemented in MATLAB with the App Designer tool and it is freely available for download. Full article
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13 pages, 1640 KiB  
Article
MAVEN2: An Updated Open-Source Mass Spectrometry Exploration Platform
by Phillip Seitzer, Bryson Bennett and Eugene Melamud
Metabolites 2022, 12(8), 684; https://doi.org/10.3390/metabo12080684 - 25 Jul 2022
Cited by 3 | Viewed by 3322
Abstract
MAVEN, an open-source software program for analysis of LC-MS metabolomics data, was originally released in 2010. As mass spectrometry has advanced in the intervening years, MAVEN has been periodically updated to reflect this advancement. This manuscript describes a major update to the program, [...] Read more.
MAVEN, an open-source software program for analysis of LC-MS metabolomics data, was originally released in 2010. As mass spectrometry has advanced in the intervening years, MAVEN has been periodically updated to reflect this advancement. This manuscript describes a major update to the program, MAVEN2, which supports LC-MS/MS analysis of metabolomics and lipidomics samples. We have developed algorithms to support MS/MS spectral matching and efficient search of large-scale fragmentation libraries. We explore the ability of our approach to separate authentic from spurious metabolite identifications using a set of standards spiked into water and yeast backgrounds. To support our improved lipid identification workflow, we introduce a novel in-silico lipidomics library covering major lipid classes and compare searches using our novel library to searches with existing in-silico lipidomics libraries. MAVEN2 source code and cross-platform application installers are freely available for download from GitHub under a GNU permissive license [ver 3], as are the in silico lipidomics libraries and corresponding code repository. Full article
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