SUMMARY: In mass spectrometry-based proteomics, accurate peptide masses improve identifications, alignment and quantitation. Getting the most out of any instrument therefore requires proper calibration. Here, we present a new stand-alone software, mzRecal, for universal automatic recalibration of data from all common mass analyzers using standard open formats and based on physical principles. AVAILABILITY AND IMPLEMENTATION: mzRecal is implemented in Go and freely available on https://github.com/524D/mzRecal. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The corpus of bioinformatics resources is huge and expanding rapidly, presenting life scientists with a growing challenge in selecting tools that fit the desired purpose. To address this, the European Infrastructure for Biological Information is supporting a systematic approach towards a comprehensive registry of tools and databases for all domains of bioinformatics, provided under a single portal (https://bio.tools). We describe here the practical means by which scientific communities, including individual developers and projects, through major service providers and research infrastructures, can describe their own bioinformatics resources and share these via bio.tools.

MOTIVATION: Numerous software utilities operating on mass spectrometry (MS) data are described in the literature and provide specific operations as building blocks for the assembly of on-purpose workflows. Working out which tools and combinations are applicable or optimal in practice is often hard. Thus researchers face difficulties in selecting practical and effective data analysis pipelines for a specific experimental design. RESULTS: We provide a toolkit to support researchers in identifying, comparing and benchmarking multiple workflows from individual bioinformatics tools. Automated workflow composition is enabled by the tools' semantic annotation in terms of the EDAM ontology. To demonstrate the practical use of our framework, we created and evaluated a number of logically and semantically equivalent workflows for four use cases representing frequent tasks in MS-based proteomics. Indeed we found that the results computed by the workflows could vary considerably, emphasizing the benefits of a framework that facilitates their systematic exploration. AVAILABILITY AND IMPLEMENTATION: The project files and workflows are available from https://github.com/bio-tools/biotoolsCompose/tree/master/Automatic-Workflow-Composition. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Numerous software utilities operating on mass spectrometry (MS) data are described in the literature that provide specific operations as building blocks for the assembly of purpose-specific workflows. Working out which tools and combinations are applicable or optimal is often hard: insufficient annotation of tool functions and interfaces impedes finding viable tool combinations, and potentially compatible tools may not, in practice, operate together. Thus researchers face difficulties in selecting practical and effective data analysis pipelines for a specific experimental design.

This presentation describes a novel combination of text mining and quantitative structure-property relationships (QSPR) of small molecules, where text-mined ChEBI entries were retrieved by combining Europe PMC RESTful APIs in scientific workflows and matched with QSPR predictions from machine learning. Described applications include metabolomics data integration, experimental design, historical studies and business intelligence.

In mass spectrometry-based proteomics, peptides are typically identified from tandem mass spectra using spectrum comparison. A sequence search engine compares experimentally obtained spectra with those predicted from protein sequences, applying enzyme cleavage and fragmentation rules. To this, there are two main alternatives: spectral libraries and de novo sequencing. The former compares measured spectra with a collection of previously acquired and identified spectra in a library. De novo attempts to sequence peptides from the tandem mass spectra alone. We here present a theoretical framework and a data processing workflow for visualizing and comparing the results of these different types of algorithms. The method considers the three search strategies as different dimensions, identifies distinct agreement classes and visualizes the complementarity of the search strategies. We have included X! Tandem, SpectraST and PepNovo, as they are in common use and representative for algorithms of each type. Our method allows advanced investigation of how the three search methods perform relatively to each other and shows the impact of the currently used decoy sequences for evaluating the false discovery rates.

BACKGROUND: Peptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledge about the data as well as the algorithms themselves is needed. Most researchers therefore tend to use default parameters, which are not necessarily optimal. RESULTS: We have applied a new optimization framework for the Taverna scientific workflow management system (http://ms-utils.org/Taverna_Optimization.pdf) to find the best combination of parameters for a given scientific workflow to perform peptide-spectrum matching. The optimizations themselves are non-trivial, as demonstrated by several phenomena that can be observed when allowing for larger mass measurement errors in sequence database searches. On-the-fly parameter optimization embedded in scientific workflow management systems enables experts and non-experts alike to extract the maximum amount of information from the data. The same workflows could be used for exploring the parameter space and compare algorithms, not only for peptide-spectrum matching, but also for other tasks, such as retention time prediction. CONCLUSION: Using the optimization framework, we were able to learn about how the data was acquired as well as the explored algorithms. We observed a phenomenon identifying many ammonia-loss b-ion spectra as peptides with N-terminal pyroglutamate and a large precursor mass measurement error. These insights could only be gained with the extension of the common range for the mass measurement error tolerance parameters explored by the optimization framework.

Parameter sweeps are used by researchers with scientific domain-specific tools or workflows to submit a large collection of computational jobs whereby each single job of it only varies in certain parts. They require a more fine-grained distribution of jobs across resources, which also raise a significant challenge for efficient resource management in middleware environments that have been not specifically designed to perform parameter sweeps. This paper offers insights into parameter sweep solutions that support multi-disciplinary science environments via abstraction from resource management complexities using middleware. The solutions are based on use case requirements, enable efficient submission, enhanced usability, and standard compliance. We also apply a use case taken from the life science domain to demonstrate usefulness and efficiency of the solutions.