Processing Metabolomics and Proteomics Data with Open Software by Winkler Robert;

Author:Winkler, Robert;
Language: eng
Format: epub
ISBN: 9781788019903
Publisher: Book Network Int'l Limited trading as NBN International (NBNi)
Published: 2020-02-15T00:00:00+00:00

6.1 Introduction

Computational mass spectrometry has seen exponential growth in recent years in data size and complexity, straining the existing infrastructure of many labs as they moved towards high-performance computing (HPC) and embraced big data paradigms. Transparent and reproducible data analysis has traditionally been challenging in the field due to a highly heterogeneous software environment, while algorithms and analysis workflows have grown increasingly complex. A multitude of competing and often incompatible file formats prevented objective algorithmic comparisons and, in some cases, access to specific software or file formats relied on a vendor license. Due to the fast technology progress in the field, many novel algorithms are proposed in the literature every year, but few are implemented with reusability, robustness, cross-platform compatibility and user-friendliness in mind, creating a highly challenging software and data storage environment that in some aspects is even opaque to experts.

The OpenMS software framework addresses these issues through a set of around 175 highly robust and transparent cross-platform tools with a focus on maximal flexibility. 1,3 Modern software engineering techniques ensure reproducibility between versions and minimize code duplication and putative errors in software. OpenMS is completely open-source, uses standardized data formats extensively and is available on all three major computing platforms (macOS, Windows, Linux). Multiple layers of access to the OpenMS algorithms exist for specialist, intermediate and novice users, providing low entrance barriers through sophisticated data visualization and graphical workflow managers.

The flexibility of OpenMS allows it to support a multitude of customizable and easily transmissible workflows in multi-omics data analysis, including metabolomics, lipidomics, and proteomics setups, supporting different quantitative approaches spanning label-free, isotopic, isobaric labeling techniques, as well as targeted proteomics. Its highly flexible structure and layered design allow different scientific groups to take full advantage of the software. Developers can fully exploit the sophisticated C++ library for tool and data structure development, while advanced Python bindings (pyOpenMS, see Chapter 16) wrap most of the classes, 4 providing an excellent solution for fast scripting and prototyping. Users, can either work on command line tool level or take advantage of industry-grade workflows systems, such as the KoNstanz Information MinEr (KNIME), 5,6 Galaxy, 7 Nextflow, 8 or Snakemake. 9 The framework is highly adaptable, allowing even novice users to generate complex workflows using the easy-to-learn graphical user interfaces of KNIME. Built-in support for most common workflow steps (such as popular proteomics search engines) ensures low entrance barriers while advanced users have high flexibility within the same framework. A modular and comprehensive codebase allows rapid development of novel methods as exemplified by the recent additions for metabolomics, SWATH-MS and cross-linking workflows.

In addition, a versatile visualization software (TOPPView) allows exploration of raw data as well as identification and quantification results. 10 The permissive BSD license encourages usage in commercial and academic projects, making the project especially suited for reference implementations of file formats and algorithms.

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