OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets

The availability of user‐friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://www.ebi.ac.uk/ols ) is a popular centralized service to query, browse and navigate biomedical ontologies and controlled vocabularies. Recently, the OLS framework has been completely redeveloped (version 3.0), including enhancements in the data model, like the added support for Web Ontology Language based ontologies, among many other improvements. However, the new OLS is not backwards compatible and new software tools are needed to enable access to this widely used framework now that the previous version is no longer available. We here present the OLS Client as a free, open‐source Java library to retrieve information from the new version of the OLS. It enables rapid tool creation by providing a robust, pluggable programming interface and common data model to programmatically access the OLS. The library has already been integrated and is routinely used by several bioinformatics resources and related data annotation tools. Secondly, we also introduce an updated version of the OLS Dialog (version 2.0), a Java graphical user interface that can be easily plugged into Java desktop applications to access the OLS. The software and related documentation are freely available at https://github.com/PRIDE-Utilities/ols-client and https://github.com/PRIDE-Toolsuite/ols-dialog .     

The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience

The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R.We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found online.Mass spectrometry (MS)¹ has become a major analysis tool in the life sciences (1). It is currently used in different modes for several “omics” approaches, proteomics and metabolomics being the most prominent. In both disciplines, one major burden in the exchange, communication, and large-scale (re-) analysis of MS-based data is the significant number of software pipelines and, consequently, heterogeneous file formats used to process, analyze, and store these experimental results, including both identification and quantification data. Publication guidelines from scientific journals and funding agencies'' requirements for public data availability have led to an increasing amount of MS-based proteomics and metabolomics data being submitted to public repositories, such as those of the ProteomeXchange consortium (2) or, in the case of metabolomics, the resources from the nascent COSMOS (Coordination of Standards in Metabolomics) initiative (3).In the past few years, the Human Proteome Organization Proteomics Standards Initiative (PSI) has developed several vendor-neutral standard data formats to overcome the representation heterogeneity. The Human Proteome Organization PSI promotes the usage of three XML file formats to fully report the data coming from MS-based proteomics experiments (including related metadata): mzML (4) to store the “primary” MS data (the spectra and chromatograms), mzIdentML (5) to report peptide identifications and inferred protein identifications, and mzQuantML (6) to store quantitative information associated with these results.Even though the existence of the PSI standard data formats represents a huge step forward, these formats cannot address all use cases related to proteomics and metabolomics data exchange and sharing equally well. During the development of mzML, mzIdentML, and mzQuantML, the main focus lay on providing an exact and comprehensive representation of the gathered results. All three formats can be used within analysis pipelines and as interchange formats between independent analysis tools. It is thus vital that these formats be capable of storing the full data and analysis that led to the results. Therefore, all three formats result in relatively complex schemas, a clear necessity for adequate representation of the complexity found in MS-based data.An inevitable drawback of this approach is that data consumers can find it difficult to quickly retrieve the required information. Several application programming interfaces (APIs) have been developed to simplify software development based on these formats (7–9), but profound proteomics and bioinformatics knowledge still is required in order to use them efficiently and take full advantage of the comprehensive information contained.The new file format presented here, mzTab, aims to describe the qualitative and quantitative results for MS-based proteomics and metabolomics experiments in a consistent, simpler tabular format, abstracting from the mass spectrometry details. The format contains identifications, basic quantitative information, and related metadata. With mzTab''s flexible design, it is possible to report results at different levels ranging from a simple summary or subset of the complete information (e.g. the final results) to fairly comprehensive representation of the results including the experimental design. Many downstream analysis use cases are only concerned with the final results of an experiment in an easily accessible format that is compatible with tools such as Microsoft Excel® or R (10) and can easily be adapted by existing bioinformatics tools. Therefore, mzTab is ideally suited to make MS proteomics and metabolomics results available to the wider biological community, beyond the field of MS.mzTab follows a similar philosophy as the other tab-delimited format recently developed by the PSI to represent molecular interaction data, MITAB (11). MITAB is a simpler tab-delimited format, whereas PSI-MI XML (12), the more detailed XML-based format, holds the complete evidence. The microarray community makes wide use of the format MAGE-TAB (13), another example of such a solution that can cover the main use cases and, for the sake of simplicity, is often preferred to the XML standard format MAGE-ML (14). Additionally, in MS-based proteomics, several software packages, such as Mascot (15), OMSSA (16), MaxQuant (17), OpenMS/TOPP (18, 19), and SpectraST (20), also support the export of their results in a tab-delimited format next to a more complete and complex default format. These simple formats do not contain the complete information but are nevertheless sufficient for the most frequent use cases.mzTab has been designed with the same purpose in mind. It can be used alone or in conjunction with mzML (or other related MS data formats such as mzXML (21) or text-based peak list formats such as MGF), mzIdentML, and/or mzQuantML. Several highly successful concepts taken from the development process of mzIdentML and mzQuantML were adapted to the text-based nature of mzTab.In addition, there is a trend to perform more integrated experimental workflows involving both proteomics and metabolomics data. Thus, we developed a standard format that can represent both types of information in a single file.     

Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, bioinformatics analysis is becoming increasingly complex and convoluted, involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are designed as single‐tiered software application where the analytics tasks cannot be distributed, limiting the scalability and reproducibility of the data analysis. In this paper the key steps of metabolomics and proteomics data processing, including the main tools and software used to perform the data analysis, are summarized. The combination of software containers with workflows environments for large‐scale metabolomics and proteomics analysis is discussed. Finally, a new approach for reproducible and large‐scale data analysis based on BioContainers and two of the most popular workflow environments, Galaxy and Nextflow, is introduced to the proteomics and metabolomics communities.     

Charge state-selective separation of peptides by reversible modification of amino groups and strong cation-exchange chromatography: evaluation in proteomic studies using peptide-centric database searches

Here we describe an integrated approach for the selective separation of peptides from complex mixtures using strong cation-exchange chromatography. The procedure exploits the charge differences produced by reversible modification of primary amino groups in peptides, enabling their separation into three major fractions: 1) neutral peptides 2) peptides with one positive charge and 3) peptides with 2 or more positive charges. The procedure demonstrated an excellent selectivity which allowed restricted MS/MS ion searches with peptide-centric databases.     

Tissue-based absolute quantification using large-scale TMT and LFQ experiments

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.     

In silico analysis of accurate proteomics, complemented by selective isolation of peptides

Protein identification by mass spectrometry is mainly based on MS/MS spectra and the accuracy of molecular mass determination. However, the high complexity and dynamic ranges for any species of proteomic samples, surpass the separation capacity and detection power of the most advanced multidimensional liquid chromatographs and mass spectrometers. Only a tiny portion of signals is selected for MS/MS experiments and a still considerable number of them do not provide reliable peptide identification. In this article, an in silico analysis for a novel methodology of peptides and proteins identification is described. The approach is based on mass accuracy, isoelectric point (pI), retention time (t(R)) and N-terminal amino acid determination as protein identification criteria regardless of high quality MS/MS spectra. When the methodology was combined with the selective isolation methods, the number of unique peptides and identified proteins increases. Finally, to demonstrate the feasibility of the methodology, an OFFGEL-LC-MS/MS experiment was also implemented. We compared the more reliable peptide identified with MS/MS information, and peptide identified with three experimental features (pI, t(R), molecular mass). Also, two theoretical assumptions from MS/MS identification (selective isolation of peptides and N-terminal amino acid) were analyzed. Our results show that using the information provided by these features and selective isolation methods we could found the 93% of the high confidence protein identified by MS/MS with false-positive rate lower than 5%.