Editorial pubs.acs.org/jpr
Cite This: J. Proteome Res. 2019, 18, 575−575
Special Issue on Software Tools and Resources: Acknowledging the Toolmakers of Science
J. Proteome Res. 2019.18:575-575. Downloaded from pubs.acs.org by 95.85.69.57 on 02/05/19. For personal use only.
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formats and have been added to bio.tools because these tools should have broad applicability in proteomics workflows and merit wide dissemination. The tools described in this Special Issue cover a range of applications and topics, useful in different stages in a data analysis workflow, from working with raw mass spectrometry data and evaluating data quality to visualizing protein interaction networks as an end point of an analysis. The diversity of the tools spans quantifying proteins to analyzing post-translational modifications and expanding analyses into metabolomics or transcriptomics. Some are web-based, whereas others have custom graphical user interfaces or are collections of R or Python functions for use in R or Python scripts, respectively. This diversity underscores the versatility of software tools available in proteomics and permits the selection of the best fit based on, for example, user expertise or analysis platform. As Editors of this Special Issue, we would like to express our sincere appreciation to the authors who submitted manuscripts and to the reviewers for their significant efforts in testing the software tools and for their constructive critiques of the manuscripts. We think this Special Issue provides an overview and acknowledgment of current efforts in the field, defines the state of the art, and brings to light a number of informatics challenges that still need to be addressed. We hope you will be as inspired by the diversity and excellence of the contributions in this Special Issue as we are.
n this Special Issue, we highlight novel and significantly updated software tools, web applications, and databases for data analysis and visualization in proteomics and related domains. Our goal is for this Special Issue to be an easily identifiable resource of informatics tools that have been specifically reviewed for their applicability and ease of adoption. As a benefit for their developers, the Special Issue enhances the visibility and dissemination of the tools in the proteomics community. With constantly evolving instrumentation and experimental methodology for data generation, the need for new and updated computational tools for proteomics never ceases. We therefore envision this Special Issue as the first in a biennial series. Successful production, documentation, sharing, and maintenance of purposeful, user-friendly tools deserve recognition and appreciation. Efficient, scalable software tools have become absolutely indispensable in big-data enterprises, including largescale proteomics efforts. But, at the same time, there continues to be a need for user-oriented tools that permit individual researchers to interrogate complex data sets and extract the information of relevance to them. Tools that work well and are fully fit-for-purpose are often taken for granted by researchers who are focused on the scientific findings of their investigation. However, when the necessary tool cannot be found, progress often hits an impasse. Because different researchers operate under different constraints, such as familiarity with scripting languages, hardware limitations, and data security concerns, no individual tool can work for everyone, even for similar data analysis tasks. In a taxonomy of tools, there are toolsets that are integrated into a monolithic graphical user interface constructed from libraries of tools, and there are functions that need to be assembled in executable scripts or workflows. Tools in the former class generally emphasize user-friendliness, visualization, and interaction with the data over scalability and automation, which are hallmarks of the latter. Both classes of tools address clear research needs in proteomics. In order to achieve the ultimate goal of efficient data analysis, tools must be combined into advanced pipelines and workflows. An essential requirement is the standardization of reporting results from proteomics analyses and the broad adoption of open formats such as those developed by the HUPO Proteomics Standards Initiative.1 The drive toward mass spectrometry data interoperability is still nascent because there are many tools that perform the same operation on the same types of data yet differ with respect to the formats of their input and/or output. Some of these differences are subtle variations of the interpretation of standard formats. Thus each tool has inherent limits to its compatibility with other tools, which must be resolved to successfully combine the tools in an executable pipeline. To help researchers find the tool they need to perform a specific function within the constraints of their workflow, there are registries that collate functional annotations of bioinformatics software tools. An example is bio.tools (https://bio.tools), with over 12 000 entries. The contributions in this Special Issue support open © 2019 American Chemical Society
Susan T. Weintraub* Department of Biochemistry and Structural Biology, The University of Texas Health Science Center at San Antonio
Michael R. Hoopmann Institute for Systems Biology
Magnus Palmblad
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Leiden University Medical Center
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected] ORCID
Susan T. Weintraub: 0000-0002-8328-7814 Michael R. Hoopmann: 0000-0001-7029-7792 Magnus Palmblad: 0000-0002-5865-8994 Notes
Views expressed in this editorial are those of the authors and not necessarily the views of the ACS.
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REFERENCES
(1) Orchard, S.; Hermjakob, H.; Apweiler, R. The Proteomics Standards Initiative. Proteomics 2003, 3, 1374−1376.
Special Issue: Software Tools and Resources 2019 Published: February 1, 2019 575
DOI: 10.1021/acs.jproteome.9b00022 J. Proteome Res. 2019, 18, 575−575
