Designing biomedical proteomics experiments: state-of-the-art and future perspectives.

With the current expanded technical capabilities to perform mass spectrometry-based biomedical proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that ignoring the importance of a good design leads to an unprecedented rate of false discoveries which would poison our results, more and more tools are developed to help researchers designing proteomic experiments. In this review, we apply statistical thinking to go through the entire proteomics workflow for biomarker discovery and validation and relate the considerations that should be made at the level of hypothesis building, technology selection, experimental design and the optimization of the experimental parameters.

Open-Source, Platform-Independent Library and Online Scripting Environment for Accessing Thermo Scientific RAW Files.

Mass spectrometers typically output data in proprietary binary formats. While converter suites and standardized XML formats have been developed in response, these conversion steps come with non-negligible computational time and storage space overhead. As a result, simple, everyday data inspection tasks are often beyond the skills of the mass spectrometrist, who is unable to freely access the acquired data. We therefore here describe the unthermo library for convenient, platform-independent access to Thermo Scientific RAW files and the associated online playground to transform small and easily understandable scriptlets into executable programs for end-users. By fostering the provision of code examples and snippet exchange, the interested mass spectrometrist or researcher can use this playground to quickly assemble custom scripts for their particular purpose. In this way, the data in these RAW files can be mined much more readily and directly by the user, and fast, automated raw data extraction or analysis can finally become part of the daily routine of the mass spectrometrist.

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.

jqcML: an open-source java API for mass spectrometry quality control data in the qcML format.

The awareness that systematic quality control is an essential factor to enable the growth of proteomics into a mature analytical discipline has increased over the past few years. To this aim, a controlled vocabulary and document structure have recently been proposed by Walzer et al. to store and disseminate quality-control metrics for mass-spectrometry-based proteomics experiments, called qcML. To facilitate the adoption of this standardized quality control routine, we introduce jqcML, a Java application programming interface (API) for the qcML data format. First, jqcML provides a complete object model to represent qcML data. Second, jqcML provides the ability to read, write, and work in a uniform manner with qcML data from different sources, including the XML-based qcML file format and the relational database qcDB. Interaction with the XML-based file format is obtained through the Java Architecture for XML Binding (JAXB), while generic database functionality is obtained by the Java Persistence API (JPA). jqcML is released as open-source software under the permissive Apache 2.0 license and can be downloaded from https://bitbucket.org/proteinspector/jqcml .

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

Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml.

Machine learning applications in proteomics research: how the past can boost the future.

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.

Asn3, a reliable, robust, and universal lock mass for improved accuracy in LC-MS and LC-MS/MS.

The use of internal calibrants (the so-called lock mass approach) provides much greater accuracy in mass spectrometry based proteomics. However, the polydimethylcyclosiloxane (PCM) peaks commonly used for this purpose are quite unreliable, leading to missing calibrant peaks in spectra and correspondingly lower mass measurement accuracy. Therefore, we here introduce a universally applicable and robust internal calibrant, the tripeptide Asn3. We show that Asn3 is a substantial improvement over PCM both in terms of consistent detection and resulting mass measurement accuracy. Asn3 is also very easy to adopt in the lab, as it requires only minor adjustments to the analytical setup.