Recent advances in computational analysis of mass spectrometry for proteomic profiling

Sun, Clement S.; Markey, Mia K.

doi:10.1002/jms.1909

Cited by 20 publications

(13 citation statements)

References 46 publications

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“…selection of m/z -values which correspond to high and relevant peaks. The aim of the peak picking is to reduce the number of m/z -values by neglecting those values corresponding to noise signals or to non-specific baseline signals; for more on noise and baseline see [26], for more on the physical TOF model influencing the peak shape see [27], for more on statistical modelling of noise and baseline see [28]. Various peak picking methods for MALDI mass spectra are available and are implemented in mass spectrometry software packages.…”

Section: Methodsmentioning

confidence: 99%

MALDI imaging mass spectrometry: statistical data analysis and current computational challenges

2012

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Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging mass spectrometry, also called MALDI-imaging, is a label-free bioanalytical technique used for spatially-resolved chemical analysis of a sample. Usually, MALDI-imaging is exploited for analysis of a specially prepared tissue section thaw mounted onto glass slide. A tremendous development of the MALDI-imaging technique has been observed during the last decade. Currently, it is one of the most promising innovative measurement techniques in biochemistry and a powerful and versatile tool for spatially-resolved chemical analysis of diverse sample types ranging from biological and plant tissues to bio and polymer thin films. In this paper, we outline computational methods for analyzing MALDI-imaging data with the emphasis on multivariate statistical methods, discuss their pros and cons, and give recommendations on their application. The methods of unsupervised data mining as well as supervised classification methods for biomarker discovery are elucidated. We also present a high-throughput computational pipeline for interpretation of MALDI-imaging data using spatial segmentation. Finally, we discuss current challenges associated with the statistical analysis of MALDI-imaging data.

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Section: Methodsmentioning

confidence: 99%

MALDI imaging mass spectrometry: statistical data analysis and current computational challenges

2012

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“…In shotgun proteomics, a protein mixture is digested into peptides that, in turn, are loaded onto one-or (at least) two-dimensional chromatography-based separation system. Peptides are then eluted into a tandem mass spectrometer, resorting to an automated system, and the resulting tandem mass spectrometry data is analyzed by powerful computational software packages (Sun and Markey 2011). Because peptides are more easily separated by liquid chromatography than proteins, a peptide-based proteomic analysis can be carried out considerably faster and cheaper than complete gel-based studies.…”

Section: De Vs Modern Proteomicsmentioning

confidence: 99%

Proteome signatures—how are they obtained and what do they teach us?

Costa

Ferreira

Amado

et al. 2015

Appl Microbiol Biotechnol

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The dawn of a new Proteomics era, just over a decade ago, allowed for large-scale protein profiling studies that have been applied in the identification of distinctive molecular cell signatures. Proteomics provides a powerful approach for identifying and studying these multiple molecular markers in a vast array of biological systems, whether focusing on basic biological research, diagnosis, therapeutics, or systems biology. This is a continuously expanding field that relies on the combination of different methodologies and current advances, both technological and analytical, which have led to an explosion of protein signatures and biomarker candidates. But how are these biological markers obtained? And, most importantly, what can we learn from them? Herein, we briefly overview the currently available approaches for obtaining relevant information at the proteome level, while noting the current and future roles of both traditional and modern proteomics. Moreover, we provide some considerations on how the development of powerful and robust bioinformatics tools will greatly benefit high-throughput proteomics. Such strategies are of the utmost importance in the rapidly emerging field of immunoproteomics, which may play a key role in the identification of antigens with diagnostic and/or therapeutic potential and in the development of new vaccines. Finally, we consider the present limitations in the discovery of new signatures and biomarkers and speculate on how such hurdles may be overcome, while also offering a prospect for the next few years in what could be one of the most significant strategies in translational medicine research.

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“…Data mining techniques have been widely used to analyze data from many areas of biology; in particular, various machine learning methods have been applied to data generated by analytical techniques of genomics, transcriptomics and metabolomics to classify unknown samples and identify genes relevant to the state of the disease. Currently, similar methods are applied in the field of proteomics, and more specifically, in the analysis of data generated by MS 8 . In many studies, MS generates large data files containing lists of many peaks.…”

Section: Ms-based Proteomics and Data Miningmentioning

confidence: 99%

Data mining in mass spectrometry-based proteomics studies

Phan

Nguyen

2020

Sci. Tech. Dev. J.- Eng. Tech.

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The post-genomic era consists of experimental and computational efforts to meet the challenge of clarifying and understanding the function of genes and their products. Proteomic studies play a key role in this endeavour by complementing other functional genomics approaches, encompasses the large-scale analysis of complex mixtures, including the identification and quantification of proteins expressed under different conditions, the determination of their properties, modifications and functions. Understanding how biological processes are regulated at the protein level is crucial to understanding the molecular basis of diseases and often highlights the prevention, diagnosis and treatment of diseases. High-throughput technologies are widely used in proteomics to perform the analysis of thousands of proteins. Specifically, mass spectrometry (MS) is an analytical technique for characterizing biological samples and is increasingly used in protein studies because of its targeted, nontargeted, and high performance abilities. However, as large data sets are created, computational methods such as data mining techniques are required to analyze and interpret the relevant data. More specifically, the application of data mining techniques in large proteomic data sets can assist in many interpretations of data; it can reveal protein-protein interactions, improve protein identification, evaluate the experimental methods used and facilitate the diagnosis and biomarker discovery. With the rapid advances in mass spectrometry devices and experimental methodologies, MS-based proteomics has become a reliable and necessary tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed a great expansion of our knowledge of human diseases with the adoption of proteomic technologies based on MS, which leads to many interesting discoveries. Here, we review recent advances of data mining in MS-based proteomics in biomedical research. Recent research in many fields shows that proteomics goes beyond the simple classification of proteins in biological systems and finally reaches its initial potential – as an essential tool to aid related disciplines, notably biomedical research. From here, there is great potential for data mining in MS-based proteomics to move beyond basic research, into clinical research and diagnostics.

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Recent advances in computational analysis of mass spectrometry for proteomic profiling

Cited by 20 publications

References 46 publications

MALDI imaging mass spectrometry: statistical data analysis and current computational challenges

MALDI imaging mass spectrometry: statistical data analysis and current computational challenges

Proteome signatures—how are they obtained and what do they teach us?

Data mining in mass spectrometry-based proteomics studies

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