Utilities for Mass Spectrometry Analysis of Proteins (UMSAP): Fast post-processing of mass spectrometry data

SummaryThe aim of this study was to characterize the protein profile of ovarian follicular fluid (FF) of brown brocket deer (Mazama gouazoubira). Five adult females received an ovarian stimulation treatment and the FF was collected by laparoscopy from small/medium (≤3.5 mm) and large (>3.5 mm) follicles. Concentrations of soluble proteins in FF samples were measured and proteins were analyzed by 1-D SDS-PAGE followed by tryptic digestion and tandem mass spectrometry. Data from protein list defined after a Mascot database search were analyzed using the STRAP software tool. For the protein concentration, no significant difference (P > 0.05) was observed between small/medium and large follicles: 49.2 ± 22.8 and 56.7 ± 27.4 μg/μl, respectively. Mass spectrometry analysis identified 13 major proteins, but with no significant difference (P > 0.05) between follicle size class. This study provides insight into elucidating folliculogenesis in brown brocket deer.

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Bio-Inspired Metaheuristic Optimization Algorithms for Biomarker Identification in Mass Spectrometry Analysis

International Journal of Natural Computing Research ◽

10.4018/jncr.2012040104 ◽

2012 ◽

Vol 3 (2) ◽

pp. 64-85 ◽

Cited By ~ 3

Author(s):

Syarifah Adilah Mohamed Yusoff ◽

Ibrahim Venkat ◽

Umi Kalsom Yusof ◽

Rosni Abdullah

Keyword(s):

Mass Spectrometry ◽

Chemical Properties ◽

Feature Subset Selection ◽

Mass Spectrometry Data ◽

Mass Spectrometry Analysis ◽

Computational Techniques ◽

Feature Subset ◽

Protein Patterns ◽

Spectrometry Analysis ◽

Biomarker Analysis

Mass spectrometry is an emerging technique that is continuously gaining momentum among bioinformatics researchers who intend to study biological or chemical properties of complex structures such as protein sequences. This advancement also embarks in the discovery of proteomic biomarkers through accessible body fluids such as serum, saliva, and urine. Recently, literature reveals that sophisticated computational techniques mimetic survival and natural processes adapted from biological life for reasoning voluminous mass spectrometry data yields promising results. Such advanced approaches can provide efficient ways to mine mass spectrometry data in order to extract parsimonious features that represent vital information, specifically in discovering disease-related protein patterns in complex proteins sequences. This article intends to provide a systematic survey on bio-inspired approaches for feature subset selection via mass spectrometry data for biomarker analysis.

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Mass Spectrometry Analysis of NMC622/Graphite Li-Ion Cells Electrolyte Degradation Products after Storage and Cycling

Journal of The Electrochemical Society ◽

10.1149/1945-7111/ac44bb ◽

2021 ◽

Author(s):

Rigaud Sébastien ◽

Ana Cristina Martinez Maciel ◽

Tristan Lombard ◽

Sylvie Grugeon ◽

Pierre Tran-Van ◽

...

Keyword(s):

Mass Spectrometry ◽

Degradation Products ◽

Principal Component ◽

Mass Spectrometry Data ◽

Mass Spectrometry Analysis ◽

Statistical Tool ◽

Decomposition Products ◽

Spectrometry Analysis ◽

Li Ion ◽

Triton X

Abstract With the aim of establishing a data simultaneous comparison, the Principal Component Analysis (PCA) statistical tool was applied to LiNi0.6Mn0.2Co0.2O2/graphite Li-ion cells electrolyte’s decomposition products detected by UHPLC-ESI-HRMS. Herein, we illustrate how the chemometric tool associated with mass spectrometry data can be relevant to provide information about the presence of unusual molecules. Indeed, pristine Triton X-100 surfactant molecules used in electrode elaboration process were detected after impregnation stage. However, as they chemically react and oxidize at a potential lower than 4.5V vs. Li/Li+, only surfactant derivatives and classical ageing molecules were observed, respectively, after storage and cycling stages at 55°C, leading to a triangle-type correlation circle. On the other hand, global schemes of LiPF6-based electrolyte degradation pathways were elaborated from a comparative study with literature to help interpret results in future electrolyte ageing studies.

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XFlow: An algorithm for extracting ion chromatograms

10.1101/2019.12.27.889253 ◽

2019 ◽

Author(s):

Mathew Gutierrez ◽

Rob Smith

Keyword(s):

Mass Spectrometry ◽

Open Source ◽

Analytical Methods ◽

Feature Detection ◽

Detection Algorithm ◽

Mass Spectrometry Data ◽

Mass Spectrometry Analysis ◽

Target Acquisition ◽

Spectrometry Analysis ◽

Broad Applicability

AbstractMass spectrometry is a fundamental tool for modern proteomics. The increasing availability of mass spectrometry data paired with the increasing sensitivity and fidelity of the instruments necessitates new and more potent analytical methods. To that end, we have created and present XFlow, a feature detection algorithm for extracting ion chromatograms from MS1 LC-MS data. XFlow is a parameter-free procedurally agnostic feature detection algorithm that utilizes the latent properties of ion chromatograms to resolve them from the surrounding noise present in MS1 data. XFlow is designed to function on either profile or centroided data across different resolutions and instruments. This broad applicability lends XFlow strong utility as a one-size-fits-all method for MS1 analysis or target acquisition for MS2. XFlow is written in Java and packaged with JS-MS, an open-source mass spectrometry analysis toolkit.

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The FAST-AIMS Clinical Mass Spectrometry Analysis System

Advances in Bioinformatics ◽

10.1155/2009/598241 ◽

2009 ◽

Vol 2009 ◽

pp. 1-4

Author(s):

Nafeh Fananapazir ◽

Alexander Statnikov ◽

Constantin F. Aliferis

Keyword(s):

Mass Spectrometry ◽

Performance Estimation ◽

Mass Spectrometry Data ◽

Clinical Proteomics ◽

Classification Model ◽

Mass Spectrometry Analysis ◽

Prognostic Models ◽

Spectrometry Analysis ◽

And Performance ◽

Analysis System

Within clinical proteomics, mass spectrometry analysis of biological samples is emerging as an important high-throughput technology, capable of producing powerful diagnostic and prognostic models and identifying important disease biomarkers. As interest in this area grows, and the number of such proteomics datasets continues to increase, the need has developed for efficient, comprehensive, reproducible methods of mass spectrometry data analysis by both experts and nonexperts. We have designed and implemented a stand-alone software system, FAST-AIMS, which seeks to meet this need through automation of data preprocessing, feature selection, classification model generation, and performance estimation. FAST-AIMS is an efficient and user-friendly stand-alone software for predictive analysis of mass spectrometry data. The present resource review paper will describe the features and use of the FAST-AIMS system. The system is freely available for download for noncommercial use.

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MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion

10.1101/2020.05.22.110726 ◽

2020 ◽

Author(s):

Alexander R. Pelletier ◽

Yun-En Chung ◽

Zhibin Ning ◽

Nora Wong ◽

Daniel Figeys ◽

...

Keyword(s):

Mass Spectrometry ◽

Real Time ◽

Mass Spectra ◽

Protein Identification ◽

Learning Algorithm ◽

Mass Spectrometry Data ◽

Mass Spectrometry Analysis ◽

Complex Samples ◽

Spectrometry Analysis ◽

Abundance Proteins

ABSTRACTMass spectrometry-based proteomics technologies are the prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS’ efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

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MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization

Metabolites ◽

10.3390/metabo11080492 ◽

2021 ◽

Vol 11 (8) ◽

pp. 492

Author(s):

Luca Nicolotti ◽

Jeremy Hack ◽

Markus Herderich ◽

Natoiya Lloyd

Keyword(s):

Mass Spectrometry ◽

Data Processing ◽

Programming Languages ◽

Detailed Comparison ◽

Mass Spectrometry Data ◽

Mass Spectrometry Analysis ◽

Molecular Feature ◽

Metabolomics Data ◽

Spectrometry Analysis ◽

Mass Spectrometry Technology

Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.

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