Mass spectrometry-based proteomics: advances in data processing

Edited by:
Andreas Hildebrandt: Johannes Gutenberg University Mainz, Germany

BMC Bioinformatics called for submissions to our Collection on mass spectrometry-based proteomics.

While the principles of mass spectrometry date back to the late 19^th-early 20^th century, the field experienced tremendous advances since then and has recently gained renewed importance with the advent of the omics and high-throughput approaches.

This Collection welcomed submissions on novel computational approaches, software and statistical methods for the analysis of mass spectrometry data for proteomics.

Meet the Guest Editor

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Andreas Hildebrandt: Johannes Gutenberg University Mainz, Germany

Andreas Hildebrandt is a Professor for Bioinformatics and Scientific Computing at the Institute for Computer Science of Johannes Gutenberg University Mainz. His research interests include modelling and simulation of biological systems, computational proteomics, structural bioinformatics, machine learning, scientific computing, and privacy preserving data mining. He as authored or co-authored more than 100 papers and book chapters on topics related to these research fields. From 2017-2023, he served as the director of the Gutenberg Teaching Council, and is a member of the editorial board of the Journal of Computational Science.

About the collection

BMC Bioinformatics is calling for submissions to our Collection on mass spectrometry-based proteomics.

Mass spectrometry, an analytical technique to measure the mass-to-charge ratio of ions in a biological sample, is an indispensable tool in large-scale studies of the proteome. It allows to identify and obtain structural information on proteins based on the unique fragmentation the sample is subjected to.

While the principles of mass spectrometry date back to the late 19^th-early 20^th century, the field experienced tremendous advances since then and has recently gained renewed importance with the advent of the omics and high-throughput approaches.

This Collection welcomes submissions on novel computational approaches, software and statistical methods for the analysis of mass spectrometry data for proteomics.

Image credit: [M] Sodel Vladyslav / stock.adobe.com

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles.

Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select ["Mass spectrometry-based proteomics: advances in data processing"] from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.