qcML: an exchange format for quality control metrics from mass spectrometry experiments

Observations from the Proteomics Bench

Many challenges in proteomics result from the high-throughput nature of the experiments. This paper first presents pre-analytical problems, which still occur, although the call for standardization in omics has been ongoing for many years. This article also discusses aspects that affect bioinformatic analysis based on three sets of reference data measured with different orbitrap instruments. Despite continuous advances in mass spectrometer technology as well as analysis software, data-set-wise quality control is still necessary, and decoy-based estimation, although challenged by modern instruments, should be utilized. We draw attention to the fact that numerous young researchers perceive proteomics as a mature, readily applicable technology. However, it is important to emphasize that the maximum potential of the technology can only be realized by an educated handling of its limitations.

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

[The promise of omics in the precision medicine era]

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS): an open community for bioinformatics training and research

The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS; eubic-ms.org) was founded in 2014 to unite European computational mass spectrometry researchers and proteomics bioinformaticians working in academia and industry. EuBIC-MS maintains educational resources (proteomics-academy.org) and organises workshops at national and international conferences on proteomics and mass spectrometry. Furthermore, EuBIC-MS is actively involved in several community initiatives such as the Human Proteome Organization's Proteomics Standards Initiative (HUPO-PSI). Apart from these collaborations, EuBIC-MS has organised two Winter Schools and two Developers' Meetings that have contributed to the strengthening of the European mass spectrometry network and fostered international collaboration in this field, even beyond Europe. Moreover, EuBIC-MS is currently actively developing a community-driven standard dedicated to mass spectrometry data annotation (SDRF-Proteomics) that will facilitate data reuse and collaboration. This manuscript highlights what EuBIC-MS is, what it does, and what it already has achieved. A warm invitation is extended to new researchers at all career stages to join the EuBIC-MS community on its Slack channel (eubic.slack.com).

Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics

In any analytical discipline, data analysis reproducibility is closely interlinked with data quality. In this book chapter focused on mass spectrometry-based proteomics approaches, we introduce how both data analysis reproducibility and data quality can influence each other and how data quality and data analysis designs can be used to increase robustness and improve reproducibility. We first introduce methods and concepts to design and maintain robust data analysis pipelines such that reproducibility can be increased in parallel. The technical aspects related to data analysis reproducibility are challenging, and current ways to increase the overall robustness are multifaceted. Software containerization and cloud infrastructures play an important part.We will also show how quality control (QC) and quality assessment (QA) approaches can be used to spot analytical issues, reduce the experimental variability, and increase confidence in the analytical results of (clinical) proteomics studies, since experimental variability plays a substantial role in analysis reproducibility. Therefore, we give an overview on existing solutions for QC/QA, including different quality metrics, and methods for longitudinal monitoring. The efficient use of both types of approaches undoubtedly provides a way to improve the experimental reliability, reproducibility, and level of consistency in proteomics analytical measurements.

QCMAP: An Interactive Web-Tool for Performance Diagnosis and Prediction of LC-MS Systems

The increasing role played by liquid chromatography-mass spectrometry (LC-MS)-based proteomics in biological discovery has led to a growing need for quality control (QC) on the LC-MS systems. While numerous quality control tools have been developed to track the performance of LC-MS systems based on a pre-defined set of performance factors (e.g., mass error, retention time), the precise influence and contribution of the performance factors and their generalization property to different biological samples are not as well characterized. Here, a web-based application (QCMAP) is developed for interactive diagnosis and prediction of the performance of LC-MS systems across different biological sample types. Leveraging on a standardized HeLa cell sample run as QC within a multi-user facility, predictive models are trained on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC-MS systems. It is demonstrated that the learned model can be applied to predict LC-MS system performance for brain samples generated from an independent study. By compiling these predictive models into our web-application, QCMAP allows users to benchmark the performance of their LC-MS systems using their own samples and identify key factors for instrument optimization. QCMAP is freely available from: http://shiny.maths.usyd.edu.au/QCMAP/.

Proceedings of the EuBIC developer's meeting 2018

The inaugural European Bioinformatics Community (EuBIC) developer's meeting was held from January 9th to January 12th 2018 in Ghent, Belgium. While the meeting kicked off with an interactive keynote session featuring four internationally renowned experts in the field of computational proteomics, its primary focus were the hands-on hackathon sessions which featured six community-proposed projects revolving around three major topics: Here, we present an overview of the scientific program of the EuBIC developer's meeting and provide a starting point for follow-up on the covered projects.

QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories

The increasing number of biomedical and translational applications in mass spectrometry-based proteomics poses new analytical challenges and raises the need for automated quality control systems. Despite previous efforts to set standard file formats, data processing workflows and key evaluation parameters for quality control, automated quality control systems are not yet widespread among proteomics laboratories, which limits the acquisition of high-quality results, inter-laboratory comparisons and the assessment of variability of instrumental platforms. Here we present QCloud, a cloud-based system to support proteomics laboratories in daily quality assessment using a user-friendly interface, easy setup, automated data processing and archiving, and unbiased instrument evaluation. QCloud supports the most common targeted and untargeted proteomics workflows, it accepts data formats from different vendors and it enables the annotation of acquired data and reporting incidences. A complete version of the QCloud system has successfully been developed and it is now open to the proteomics community (http://qcloud.crg.eu). QCloud system is an open source project, publicly available under a Creative Commons License Attribution-ShareAlike 4.0.

Proteomics Standards Initiative: Fifteen Years of Progress and Future Work

The Proteomics Standards Initiative (PSI) of the Human Proteome Organization (HUPO) has now been developing and promoting open community standards and software tools in the field of proteomics for 15 years. Under the guidance of the chair, cochairs, and other leadership positions, the PSI working groups are tasked with the development and maintenance of community standards via special workshops and ongoing work. Among the existing ratified standards, the PSI working groups continue to update PSI-MI XML, MITAB, mzML, mzIdentML, mzQuantML, mzTab, and the MIAPE (Minimum Information About a Proteomics Experiment) guidelines with the advance of new technologies and techniques. Furthermore, new standards are currently either in the final stages of completion (proBed and proBAM for proteogenomics results as well as PEFF) or in early stages of design (a spectral library standard format, a universal spectrum identifier, the qcML quality control format, and the Protein Expression Interface (PROXI) web services Application Programming Interface). In this work we review the current status of all of these aspects of the PSI, describe synergies with other efforts such as the ProteomeXchange Consortium, the Human Proteome Project, and the metabolomics community, and provide a look at future directions of the PSI.

Protein complex analysis: From raw protein lists to protein interaction networks

The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.

Proteogenomics: Key Driver for Clinical Discovery and Personalized Medicine

Proteogenomics is a multi-omics research field that has the aim to efficiently integrate genomics, transcriptomics and proteomics. With this approach it is possible to identify new patient-specific proteoforms that may have implications in disease development, specifically in cancer. Understanding the impact of a large number of mutations detected at the genomics level is needed to assess the effects at the proteome level. Proteogenomics data integration would help in identifying molecular changes that are persistent across multiple molecular layers and enable better interpretation of molecular mechanisms of disease, such as the causal relationship between single nucleotide polymorphisms (SNPs) and the expression of transcripts and translation of proteins compared to mainstream proteomics approaches. Identifying patient-specific protein forms and getting a better picture of molecular mechanisms of disease opens the avenue for precision and personalized medicine. Proteogenomics is, however, a challenging interdisciplinary science that requires the understanding of sample preparation, data acquisition and processing for genomics, transcriptomics and proteomics. This chapter aims to guide the reader through the technology and bioinformatics aspects of these multi-omics approaches, illustrated with proteogenomics applications having clinical or biological relevance.

LIMS and Clinical Data Management

In order to achieve more accurate disease prevention, diagnosis, and treatment, clinical and genetic data need extensive and systematically associated study. As one way to achieve precision medicine, a laboratory information management system (LIMS) can effectively associate clinical data in a macrocosmic aspect and genomic data in a microcosmic aspect. This chapter summarizes the application of the LIMS in a clinical data management and implementation mode. It also discusses the principles of a LIMS in clinical data management, as well as the opportunities and challenges in the context of medical informatics.

The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry

To have confidence in results acquired during biological mass spectrometry experiments, a systematic approach to quality control is of vital importance. Nonetheless, until now, only scattered initiatives have been undertaken to this end, and these individual efforts have often not been complementary. To address this issue, the Human Proteome Organization-Proteomics Standards Initiative has established a new working group on quality control at its meeting in the spring of 2016. The goal of this working group is to provide a unifying framework for quality control data. The initial focus will be on providing a community-driven standardized file format for quality control. For this purpose, the previously proposed qcML format will be adapted to support a variety of use cases for both proteomics and metabolomics applications, and it will be established as an official PSI format. An important consideration is to avoid enforcing restrictive requirements on quality control but instead provide the basic technical necessities required to support extensive quality control for any type of mass spectrometry-based workflow. We want to emphasize that this is an open community effort, and we seek participation from all scientists with an interest in this field.

A Golden Age for Working with Public Proteomics Data

Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature ‘omics’ disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets.

The field of proteomics has matured and diversified substantially over the past 10 years.

Proteomics data are increasingly shared through centralized, public repositories.

Standardization efforts have ensured that a large proportion of these public data can be read and processed by any interested researcher.

Because any proteomics data set is only partially understood, there is great opportunity for (orthogonal) reuse of public data.

While public proteomics data has so far remained outside ethics and privacy discussions, recent work indicates that there is an inherent risk.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

BatMass: a Java Software Platform for LC-MS Data Visualization in Proteomics and Metabolomics

Mass spectrometry (MS) coupled to liquid chromatography (LC) is a commonly used technique in metabolomic and proteomic research. As the size and complexity of LC-MS-based experiments grow, it becomes increasingly more difficult to perform quality control of both raw data and processing results. In a practical setting, quality control steps for raw LC-MS data are often overlooked, and assessment of an experiment's success is based on some derived metrics such as "the number of identified compounds". The human brain interprets visual data much better than plain text, hence the saying "a picture is worth a thousand words". Here, we present the BatMass software package, which allows for performing quick quality control of raw LC-MS data through its fast visualization capabilities. It also serves as a testbed for developers of LC-MS data processing algorithms by providing a data access library for open mass spectrometry file formats and a means of visually mapping processing results back to the original data. We illustrate the utility of BatMass with several use cases of quality control and data exploration.

Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry

Untargeted metabolomics aims at obtaining quantitative information on the highest possible number of low-molecular biomolecules present in a biological sample. Rather small changes in mass spectrometric spectrum acquisition parameters may have a significant influence on the detectabilities of metabolites in untargeted global-scale studies by means of high-performance liquid chromatography-mass spectrometry (HPLC-MS). Employing whole cell lysates of human renal proximal tubule cells, we present a systematic global-scale study of the influence of mass spectrometric scan parameters and post-acquisition data treatment on the number and intensity of metabolites detectable in whole cell lysates. Ion transmission and ion collection efficiencies in an Orbitrap-based mass spectrometer basically depend on the m/z range scanned, which, ideally, requires different instrument settings for the respective mass ranges investigated. Therefore, we split a full scan range of m/z 50-1000 relevant for metabolites into two separate segments (m/z 50-200 and m/z 200-1,000), allowing an independent tuning of the ion transmission parameters for both mass ranges. Three different implementations, involving either scanning from m/z 50-1000 in a single scan, or scanning from m/z 50-200 and from m/z 200-1000 in two alternating scans, or performing two separate HPLC-MS runs with m/z 50-200 and m/z 200-1000 scan ranges were critically assessed. The detected features were subjected to rigorous background filtering and quality control in order to obtain reliable metabolite features for subsequent differential quantification. The most efficient approach in terms of feature number, which forms the basis for statistical analysis, identification, and for generating biological hypotheses, was the separate analysis of two different mass ranges. This lead to an increase in the number of detectable metabolite features, especially in the higher mass range (m/z greater than 400), by 2.5 (negative mode) to 6-fold (positive mode) as compared to analysis involving a single scan range. The total number of features confidently detectable was 560 in positive ion mode, and 436 in negative ion mode.

Public proteomics data: How the field has evolved from sceptical inquiry to the promise of in silico proteomics

•

Proteomics data sharing moved from validation to re-use.

•

New tools and services make data very easily accessible.

•

Metadata provision can still benefit from improvements.

•

Quality control metrics will soon be reported along with submitted data.

•

Data re-use will enable the advent of actual in silico proteomics.

Unsupervised Quality Assessment of Mass Spectrometry Proteomics Experiments by Multivariate Quality Control Metrics

Despite many technological and computational advances, the results of a mass spectrometry proteomics experiment are still subject to a large variability. For the understanding and evaluation of how technical variability affects the results of an experiment, several computationally derived quality control metrics have been introduced. However, despite the availability of these metrics, a systematic approach to quality control is often still lacking because the metrics are not fully understood and are hard to interpret. Here, we present a toolkit of powerful techniques to analyze and interpret multivariate quality control metrics to assess the quality of mass spectrometry proteomics experiments. We show how unsupervised techniques applied to these quality control metrics can provide an initial discrimination between low-quality experiments and high-quality experiments prior to manual investigation. Furthermore, we provide a technique to obtain detailed information on the quality control metrics that are related to the decreased performance, which can be used as actionable information to improve the experimental setup. Our toolkit is released as open-source and can be downloaded from https://bitbucket.org/proteinspector/qc_analysis/ .

2016 update of the PRIDE database and its related tools

The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data. Since the beginning of 2014, PRIDE Archive (http://www.ebi.ac.uk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database. Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013. PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components. PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium. The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month). We outline some statistics on the current PRIDE Archive data contents. We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool. Finally, we will give a brief update on the resources under development 'PRIDE Cluster' and 'PRIDE Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive.

Platforms and Pipelines for Proteomics Data Analysis and Management

Since mass spectrometry was introduced as the core technology for large-scale analysis of the proteome, the speed of data acquisition, dynamic ranges of measurements, and data quality are continuously improving. These improvements are triggered by regular launches of new methodologies and instruments.

Proteomics Quality Control: Quality Control Software for MaxQuant Results

Mass spectrometry-based proteomics coupled to liquid chromatography has matured into an automatized, high-throughput technology, producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality analysis (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream analysis. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC-MS data generated by the MaxQuant software pipeline. PTXQC creates a QC report containing a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of experimental designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuant's Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and m/z. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC .

Multicenter experiment for quality control of peptide-centric LC-MS/MS analysis - A longitudinal performance assessment with nLC coupled to orbitrap MS analyzers

Proteomic technologies based on mass spectrometry (MS) have greatly evolved in the past years, and nowadays it is possible to routinely identify thousands of peptides from complex biological samples in a single LC-MS/MS experiment. Despite the advancements in proteomic technologies, the scientific community still faces important challenges in terms of depth and reproducibility of proteomics analyses. Here, we present a multicenter study designed to evaluate long-term performance of LC-MS/MS platforms within the Spanish Proteomics Facilities Network (ProteoRed-ISCIII). The study was performed under well-established standard operating procedures, and demonstrated that it is possible to attain qualitative and quantitative reproducibility over time. Our study highlights the importance of deploying quality assessment metrics routinely in individual laboratories and in multi-laboratory studies. The mass spectrometry data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000205.This article is part of a Special Issue entitled: HUPO 2014.

Development of data representation standards by the human proteome organization proteomics standards initiative

OBJECTIVE

To describe the goals of the Proteomics Standards Initiative (PSI) of the Human Proteome Organization, the methods that the PSI has employed to create data standards, the resulting output of the PSI, lessons learned from the PSI's evolution, and future directions and synergies for the group.

MATERIALS AND METHODS

The PSI has 5 categories of deliverables that have guided the group. These are minimum information guidelines, data formats, controlled vocabularies, resources and software tools, and dissemination activities. These deliverables are produced via the leadership and working group organization of the initiative, driven by frequent workshops and ongoing communication within the working groups. Official standards are subjected to a rigorous document process that includes several levels of peer review prior to release.

RESULTS

We have produced and published minimum information guidelines describing what information should be provided when making data public, either via public repositories or other means. The PSI has produced a series of standard formats covering mass spectrometer input, mass spectrometer output, results of informatics analysis (both qualitative and quantitative analyses), reports of molecular interaction data, and gel electrophoresis analyses. We have produced controlled vocabularies that ensure that concepts are uniformly annotated in the formats and engaged in extensive software development and dissemination efforts so that the standards can efficiently be used by the community.Conclusion In its first dozen years of operation, the PSI has produced many standards that have accelerated the field of proteomics by facilitating data exchange and deposition to data repositories. We look to the future to continue developing standards for new proteomics technologies and workflows and mechanisms for integration with other omics data types. Our products facilitate the translation of genomics and proteomics findings to clinical and biological phenotypes. The PSI website can be accessed at http://www.psidev.info.

iMonDB: Mass Spectrometry Quality Control through Instrument Monitoring

Over the past few years, awareness has risen that for mass-spectrometry-based proteomics methods to mature into everyday analytical and clinical practices, extensive quality assessment is mandatory. A currently overlooked source of qualitative information originates from the mass spectrometer itself. Apart from the actual mass spectral data, raw-data objects also contain parameter settings and sensory information about the mass instrument. This information gives a detailed account of the operation of the instrument, which eventually can be related to observations in mass spectral data. The advantage of instrument information at the lowest level is the high sensitivity to detect emerging defects in a timely fashion. To this end, we introduce the Instrument MONitoring DataBase (iMonDB), which allows us to automatically extract, store, and manage the instrument parameters from raw-data objects into a highly efficient database structure. This enables us to monitor the instrument parameters over a considerable time period. Time course information about the instrument performance is necessary to define the normal range of operation and to detect anomalies that may correlate with instrument failure. The proposed tools foster an additional handle on quality control and are released as open source under the permissive Apache 2.0 license. The tools can be downloaded from https://bitbucket.org/proteinspector/imondb.

Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry

MS-based proteomics and metabolomics are rapidly evolving research fields driven by the development of novel instruments, experimental approaches, and analysis methods. Monolithic analysis tools perform well on single tasks but lack the flexibility to cope with the constantly changing requirements and experimental setups. Workflow systems, which combine small processing tools into complex analysis pipelines, allow custom-tailored and flexible data-processing workflows that can be published or shared with collaborators. In this article, we present the integration of established tools for computational MS from the open-source software framework OpenMS into the workflow engine Konstanz Information Miner (KNIME) for the analysis of large datasets and production of high-quality visualizations. We provide example workflows to demonstrate combined data processing and visualization for three diverse tasks in computational MS: isobaric mass tag based quantitation in complex experimental setups, label-free quantitation and identification of metabolites, and quality control for proteomics experiments.

Viewing the proteome: how to visualize proteomics data?

Proteomics has become one of the main approaches for analyzing and understanding biological systems. Yet similar to other high-throughput analysis methods, the presentation of the large amounts of obtained data in easily interpretable ways remains challenging. In this review, we present an overview of the different ways in which proteomics software supports the visualization and interpretation of proteomics data. The unique challenges and current solutions for visualizing the different aspects of proteomics data, from acquired spectra via protein identification and quantification to pathway analysis, are discussed, and examples of the most useful visualization approaches are highlighted. Finally, we offer our ideas about future directions for proteomics data visualization.