MS2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0.

Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS2Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS2Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS2Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS2Rescore offers a powerful solution to boost peptide identifications. MS2Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS2Rescore to be a valuable tool for the wider proteomics community. MS2Rescore is available at https://github.com/compomics/ms2rescore.

MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search.

Spectral library search can enable more sensitive peptide identification in tandem mass spectrometry experiments. However, its drawbacks are the limited availability of high-quality libraries and the added difficulty of creating decoy spectra for result validation. We describe MS Ana, a new spectral library search engine that enables high sensitivity peptide identification using either curated or predicted spectral libraries as well as robust false discovery control through its own decoy library generation algorithm. MS Ana identifies on average 36% more spectrum matches and 4% more proteins than database search in a benchmark test on single-shot human cell-line data. Further, we demonstrate the quality of the result validation with tests on synthetic peptide pools and show the importance of library selection through a comparison of library search performance with different configurations of publicly available human spectral libraries.

MS Annika 2.0 Identifies Cross-Linked Peptides in MS2-MS3-Based Workflows at High Sensitivity and Specificity.

Cross-linking mass spectrometry has become a powerful tool for the identification of protein-protein interactions and for gaining insight into the structures of proteins. We previously published MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable cross-linkers. In this publication, we present MS Annika 2.0, an updated version implementing a new search algorithm that, in addition to MS2 level, only supports the processing of data from MS2-MS3-based approaches for the identification of peptides from MS3 spectra, and introduces a novel scoring function for peptides identified across multiple MS stages. Detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika 2.0 on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines. We show that MS Annika detects up to 4 times more true unique cross-links while simultaneously yielding less false positive hits and therefore a more accurate FDR estimation than the other two search engines. All mass spectrometry proteomics data along with result files have been deposited to the ProteomeXchange consortium via the PRIDE partner repository with the dataset identifier PXD041955.

Toward an Integrated Machine Learning Model of a Proteomics Experiment.

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

MS Annika: A New Cross-Linking Search Engine.

Cross-linking mass spectrometry (XL-MS) has become a powerful technique that enables insights into protein structures and protein interactions. The development of cleavable cross-linkers has further promoted XL-MS through search space reduction, thereby allowing for proteome-wide studies. These new analysis possibilities foster the development of new cross-linkers, which not every search engine can deal with out of the box. In addition, some search engines for XL-MS data also struggle with the validation of identified cross-linked peptides, that is, false discovery rate (FDR) estimation, as FDR calculation is hampered by the fact that not only one but two peptides in a single spectrum have to be correct. We here present our new search engine, MS Annika, which can identify cross-linked peptides in MS2 spectra from a wide variety of cleavable cross-linkers. We show that MS Annika provides realistic estimates of FDRs without the need of arbitrary score cutoffs, being able to provide on average 44% more identifications at a similar or better true FDR than comparable tools. In addition, MS Annika can be used on proteome-wide studies due to fast, parallelized processing and provides a way to visualize the identified cross-links in protein 3D structures.

Universal Spectrum Explorer: A Standalone (Web-)Application for Cross-Resource Spectrum Comparison.

Here, we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can be either provided manually by the user (table format) or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high-quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other web sites (https://github.com/kusterlab/universal_spectrum_explorer).

MS Amanda 2.0: Advancements in the standalone implementation.

RATIONALE: Database search engines are the preferred method to identify peptides in mass spectrometry data. However, valuable software is in this context not only defined by a powerful algorithm to separate correct from false identifications, but also by constant maintenance and continuous improvements. METHODS: In 2014, we presented our peptide identification algorithm MS Amanda, showing its suitability for identifying peptides in high-resolution tandem mass spectrometry data and its ability to outperform widely used tools to identify peptides. Since then, we have continuously worked on improvements to enhance its usability and to support new trends and developments in this fast-growing field, while keeping the original scoring algorithm to assess the quality of a peptide spectrum match unchanged. RESULTS: We present the outcome of these efforts, MS Amanda 2.0, a faster and more flexible standalone version with the original scoring algorithm. The new implementation has led to a 3-5× speedup, is able to handle new ion types and supports standard data formats. We also show that MS Amanda 2.0 works best when using only the most common ion types in a particular search instead of all possible ion types. CONCLUSIONS: MS Amanda is available free of charge from https://ms.imp.ac.at/index.php?action=msamanda.

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).

PhoStar: Identifying Tandem Mass Spectra of Phosphorylated Peptides before Database Search.

Standard proteomics workflows use tandem mass spectrometry followed by sequence database search to analyze complex biological samples. The identification of proteins carrying post-translational modifications, for example, phosphorylation, is typically addressed by allowing variable modifications in the searched sequences. Accounting for these variations exponentially increases the combinatorial space in the database, which leads to increased processing times and more false positive identifications. The here-presented tool PhoStar identifies spectra that originate from phosphorylated peptides before database search using a supervised machine learning approach. The model for the prediction of phosphorylation was trained and validated with an accuracy of 97.6% on a large set of high-confidence spectra collected from publicly available experimental data. Its power was further validated by predicting phosphorylation in the complete NIST human and mouse high collision-dissociation spectral libraries, achieving an accuracy of 98.2 and 97.9%, respectively. We demonstrate the application of PhoStar by using it for spectra filtering before database search. In database search of HeLa samples the peptide search space was reduced by 27-66% while finding at least 97% of total peptide identifications (at 1% FDR) compared with a standard workflow.

CharmeRT: Boosting Peptide Identifications by Chimeric Spectra Identification and Retention Time Prediction.

Coeluting peptides are still a major challenge for the identification and validation of MS/MS spectra, but carry great potential. To tackle these problems, we have developed the here presented CharmeRT workflow, combining a chimeric spectra identification strategy implemented as part of the MS Amanda algorithm with the validation system Elutator, which incorporates a highly accurate retention time prediction algorithm. For high-resolution data sets this workflow identifies 38-64% chimeric spectra, which results in up to 63% more unique peptides compared to a conventional single search strategy.

Expanding the Use of Spectral Libraries in Proteomics.

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra.

Today's highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide identification algorithm. While most widely used search engines were developed when high-resolution mass spectrometry data were not readily available for fragment ion masses, we have designed a scoring algorithm particularly suitable for high mass accuracy. Our algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examined high mass accuracy data sets, with excellent overlap and identical peptide sequence identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available at http://ms.imp.ac.at/?goto=msamanda , is provided free of charge both as standalone version for integration into custom workflows and as a plugin for the Proteome Discoverer platform.

Improved homology-driven computational validation of protein-protein interactions motivated by the evolutionary gene duplication and divergence hypothesis.

BACKGROUND: Protein-protein interaction (PPI) data sets generated by high-throughput experiments are contaminated by large numbers of erroneous PPIs. Therefore, computational methods for PPI validation are necessary to improve the quality of such data sets. Against the background of the theory that most extant PPIs arose as a consequence of gene duplication, the sensitive search for homologous PPIs, i.e. for PPIs descending from a common ancestral PPI, should be a successful strategy for PPI validation. RESULTS: To validate an experimentally observed PPI, we combine FASTA and PSI-BLAST to perform a sensitive sequence-based search for pairs of interacting homologous proteins within a large, integrated PPI database. A novel scoring scheme that incorporates both quality and quantity of all observed matches allows us (1) to consider also tentative paralogs and orthologs in this analysis and (2) to combine search results from more than one homology detection method. ROC curves illustrate the high efficacy of this approach and its improvement over other homology-based validation methods. CONCLUSION: New PPIs are primarily derived from preexisting PPIs and not invented de novo. Thus, the hallmark of true PPIs is the existence of homologous PPIs. The sensitive search for homologous PPIs within a large body of known PPIs is an efficient strategy to separate biologically relevant PPIs from the many spurious PPIs reported by high-throughput experiments.

Proceedings of the EuBIC Winter School 2019.

The 2019 European Bioinformatics Community (EuBIC) Winter School was held from January 15th to January 18th 2019 in Zakopane, Poland. This year's meeting was the third of its kind and gathered international researchers in the field of (computational) proteomics to discuss (mainly) challenges in proteomics quantification and data independent acquisition (DIA). Here, we present an overview of the scientific program of the 2019 EuBIC Winter School. Furthermore, we can already give a small outlook to the upcoming EuBIC 2020 Developer's Meeting.

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.

Proceedings of the EuBIC Winter School 2017.

The 2017 EuBIC Winter School was held from January 10th to January 13th 2017 in Semmering, Austria. This meeting gathered international researchers in the fields of bioinformatics and proteomics to discuss current challenges in data analysis and biological interpretation. This article outlines the scientific program and exchanges that took place on this occasion and presents the current challenges of this ever-growing field. BIOLOGICAL SIGNIFICANCE: The EUPA bioinformatics community (EuBIC) organized its first winter school in January 2017. This successful event illustrates the growing need of the bioinformatics community in proteomics to gather and discuss current and future challenges in the field. In addition to the organization of yearly meetings, the young and active EuBIC community aims to develop new collaborative open source projects, spread bioinformatics knowledge in Europe, and actively promote data sharing through public repositories.

Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification.

In transfusion medicine, the identification of the Rhesus D type is important to prevent anti-D immunisation in Rhesus D negative recipients. In particular, the detection of the very low expressed DEL phenotype is crucial and hence constitutes the bottleneck of standard immunohaematology. The current method of choice, adsorption-elution, does not provide unambiguous results. We have developed a complementary method of high sensitivity that allows reliable identification of D antigen expression. Here, we present a workflow composed of high-resolution fluorescence microscopy, image processing, and machine learning that - for the first time - enables the identification of even small amounts of D antigen on the cellular level. The high sensitivity of our technique captures the full range of D antigen expression (including D+, weak D, DEL, D-), allows automated population analyses, and results in classification test accuracies of up to 96%, even for very low expressed phenotypes.