MOTIVATION: Advances in mass spectrometry have led to the development of mass spectrometers with ion mobility spectrometry capabilities and dual-source instrumentation; however, the current software ecosystem lacks interoperability with downstream data analysis using open-source software and pipelines. RESULTS: Here, we present TIMSCONVERT, a data conversion high-throughput workflow from timsTOF Pro/fleX mass spectrometer raw data files to mzML and imzML formats that incorporates ion mobility data while maintaining compatibility with data analysis tools. We showcase several examples using data acquired across different experiments and acquisition modalities on the timsTOF fleX MS. AVAILABILITY AND IMPLEMENTATION: TIMSCONVERT and its documentation can be found at https://github.com/gtluu/timsconvert and is available as a standalone command-line interface tool for Windows and Linux, NextFlow workflow and online in the Global Natural Products Social (GNPS) platform. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Project-oriented environments are key to supporting the co-operative work essential to collaborative research activities. However, personnel and resources in project-oriented environments are typically diverse and heterogeneous as they come from both internal as well as external domains. Providing a robust data security system in such an environment becomes critical. The ideal access control architecture should manage access to resources not only based on roles but also based on the specific nature of each resource and its involvement within the project. Traditional role-based access control (RBAC) does not consider the context which often modifies the responsibility given to resources. We propose using an enhanced role-based access control (RBAC) mechanism to address this problem. Specifically, we discuss the implementation of RBAC using ontological methods borrowed from semantic web technology. We used an ontology-based approach for specification and implementation of the RBAC in a collaborative system used within a research group to manage proteomics data, where the access control policy depends on how the project team hierarchy is structured. We describe the design and implementation of this system in this paper. We also provide a preliminary evaluation of the implementation. We find there are several advantages to using ontological methods to implement RBAC. The most significant of these is standardization, which is essential for portability. Also key is modifiability as the actual roles are defined by the ontology itself. Since data access is provided through URI handling moving to a federated system is made easier. This becomes very important in collaborative environments as the data in question is invariably distributed.

There is a growing interest in the use of cloud computing for scientific applications, including scientific workflows. Key attractions of the cloud include the pay-as-you-go model and elasticity. While the elasticity offered by clouds can be beneficial for many applications and use-scenarios, it also imposes significant challenges in the development of applications or services. For example, no general framework exists that can enable a scientific workflow to execute in a dynamic fashion, i.e. exploiting elasticity of clouds and automatically allocating and deal locating resources to meet time and/or cost constraints. This paper presents a case-study in creating a dynamic cloud workflow implementation of a scientific application. We work with Mass Matrix, an application which searches proteins and peptides from tandem mass spectrometry data. In order to use cloud resources, we first parallelize the search method used in this algorithm. Next, we create a flexible workflow using the Pegasus Workflow Management System. Finally, we add a new dynamic resource allocation module, which can use fewer or a larger number of resources based on a time constraint specified by the user. We evaluate our implementation using several different datasets, and show that the application scales quite well, and that our dynamic framework is effective in meeting time constraints.

BACKGROUND: High-throughput shotgun proteomics data contain a significant number of spectra from non-peptide ions or spectra of too poor quality to obtain highly confident peptide identifications. These spectra cannot be identified with any positive peptide matches in some database search programs or are identified with false positives in others. Removing these spectra can improve the database search results and lower computational expense. RESULTS: A new algorithm has been developed to filter tandem mass spectra of poor quality from shotgun proteomic experiments. The algorithm determines the noise level dynamically and independently for each spectrum in a tandem mass spectrometric data set. Spectra are filtered based on a minimum number of required signal peaks with a signal-to-noise ratio of 2. The algorithm was tested with 23 sample data sets containing 62,117 total spectra. CONCLUSIONS: The spectral screening removed 89.0% of the tandem mass spectra that did not yield a peptide match when searched with the MassMatrix database search software. Only 6.0% of tandem mass spectra that yielded peptide matches considered to be true positive matches were lost after spectral screening. The algorithm was found to be very effective at removal of unidentified spectra in other database search programs including Mascot, OMSSA, and X!Tandem (75.93%-91.00%) with a small loss (3.59%-9.40%) of true positive matches.

SUMMARY: We report a software scheme for automated diagnosis of liquid chromatography tandem mass spectrometry (LC-MS/MS) system performance. The proposed software scheme provides a robust framework for establishing automated diagnosis of LC-MS/MS system performance for a variety of instruments and experiments. This schematic consists of four main software components: (i) data conversion, (ii) peptide identification, (iii) LC retention time analysis and (iv) system performance evaluation. The implementation of a standard approach for assessing LC-MS/MS system performance enables researchers to apply reliable metrics to assess their workflows performance over different batch experiments. Furthermore, the results from system diagnosis can provide feedback to the workflow to stop batch analysis if system performance falls below prescribed thresholds. A basic implementation of the approach based on the MassMatrix database search and LC retention time analysis programs is presented. AVAILABILITY: An open source implementation of the LC-MS/MS system diagnosis software based on the MassMatrix database search program is freely available to non-commercial users and can be downloaded at www.massmatrix.net.

BACKGROUND: Rejection of false positive peptide matches in database searches of shotgun proteomic experimental data is highly desirable. Several methods have been developed to use the peptide retention time as to refine and improve peptide identifications from database search algorithms. This report describes the implementation of an automated approach to reduce false positives and validate peptide matches. RESULTS: A robust linear regression based algorithm was developed to automate the evaluation of peptide identifications obtained from shotgun proteomic experiments. The algorithm scores peptides based on their predicted and observed reversed-phase liquid chromatography retention times. The robust algorithm does not require internal or external peptide standards to train or calibrate the linear regression model used for peptide retention time prediction. The algorithm is generic and can be incorporated into any database search program to perform automated evaluation of the candidate peptide matches based on their retention times. It provides a statistical score for each peptide match based on its retention time. CONCLUSION: Analysis of peptide matches where the retention time score was included resulted in a significant reduction of false positive matches with little effect on the number of true positives. Overall higher sensitivities and specificities were achieved for database searches carried out with MassMatrix, Mascot and X!Tandem after implementation of the retention time based score algorithm.

BACKGROUND: Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become one of the most used tools in mass spectrometry based proteomics. Various algorithms have since been developed to automate the process for modern high-throughput LC-MS/MS experiments. RESULTS: A probability based statistical scoring model for assessing peptide and protein matches in tandem MS database search was derived. The statistical scores in the model represent the probability that a peptide match is a random occurrence based on the number or the total abundance of matched product ions in the experimental spectrum. The model also calculates probability based scores to assess protein matches. Thus the protein scores in the model reflect the significance of protein matches and can be used to differentiate true from random protein matches. CONCLUSION: The model is sensitive to high mass accuracy and implicitly takes mass accuracy into account during scoring. High mass accuracy will not only reduce false positives, but also improves the scores of true positive matches. The algorithm is incorporated in an automated database search program MassMatrix.