Erhan Kenar

An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics

We present a computational pipeline for the quantification of peptides and proteins in label-free LC-MS/MS data sets. The pipeline is composed of tools from the OpenMS software framework and is applicable to the processing of large experiments (50+ samples). We describe several enhancements that we have introduced to OpenMS to realize the implementation of this pipeline. They include new algorithms for centroiding of raw data, for feature detection, for the alignment of multiple related measurements, and a new tool for the calculation of peptide and protein abundances. Where possible, we compare the performance of the new algorithms to that of their established counterparts in OpenMS. We validate the pipeline on the basis of two small data sets that provide ground truths for the quantification. There, we also compare our results to those of MaxQuant and Progenesis LC-MS, two popular alternatives for the analysis of label-free data. We then show how our software can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.

OpenMS: a flexible open-source software platform for mass spectrometry data analysis

High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.

Automated Label-free Quantification of Metabolites from Liquid Chromatography–Mass Spectrometry Data

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithms robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems.

Complexity and exact algorithms for vertex multicut in interval and bounded treewidth graphs

Multicut is a fundamental network communication and connectivity problem. It is defined as: given an undirected graph and a collection of pairs of terminal vertices, find a minimum set of edges or vertices whose removal disconnects each pair. We mainly focus on the case of removing vertices, where we distinguish between allowing or disallowing the removal of terminal vertices. Complementing and refining previous results from the literature, we provide several NP-completeness and (fixedparameter) tractability results for restricted classes of graphs such as trees, interval graphs, and graphs of bounded treewidth.

Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry.

MS‐based proteomics and metabolomics are rapidly evolving research fields driven by the development of novel instruments, experimental approaches, and analysis methods. Monolithic analysis tools perform well on single tasks but lack the flexibility to cope with the constantly changing requirements and experimental setups. Workflow systems, which combine small processing tools into complex analysis pipelines, allow custom‐tailored and flexible data‐processing workflows that can be published or shared with collaborators. In this article, we present the integration of established tools for computational MS from the open‐source software framework OpenMS into the workflow engine Konstanz Information Miner (KNIME) for the analysis of large datasets and production of high‐quality visualizations. We provide example workflows to demonstrate combined data processing and visualization for three diverse tasks in computational MS: isobaric mass tag based quantitation in complex experimental setups, label‐free quantitation and identification of metabolites, and quality control for proteomics experiments.

Clinical and non-targeted metabolomic profiling of homozygous carriers of Transcription Factor 7-like 2 variant rs7903146

An important role of the type 2 diabetes risk variant rs7903146 in TCF7L2 in metabolic actions of various tissues, in particular of the liver, has recently been demonstrated by functional animal studies. Accordingly, the TT diabetes risk allele may lead to currently unknown alterations in human. Our study revealed no differences in the kinetics of glucose, insulin, C-peptide and non-esterified fatty acids during an OGTT in homozygous participants from a German diabetes risk cohort (n = 1832) carrying either the rs7903146 CC (n = 15) or the TT (n = 15) genotype. However, beta-cell function was impaired for TT carriers. Covering more than 4000 metabolite ions the plasma metabolome did not reveal any differences between genotypes. Our study argues against a relevant impact of TCF7L2 rs7903146 on the systemic level in humans, but confirms the role in the pathogenesis of type 2 diabetes in humans as a mechanism impairing insulin secretion.

qPortal: A platform for data-driven biomedical research

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis. We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software’s strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy). qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on high-performance computing systems via coupling of workflow management systems. Integration of project and data management as well as workflow resources in one place present clear advantages over existing solutions.

qPortal - A science gateway for biomedical applications.

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. Nowadays, it is common practice to make extensive use of highthroughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for large amounts of storage, data management, and easily accessible means of analysis. We present qPortal, a web-based science gateway providing users with an intuitive way to manage and analyze quantitative biological data. Pre-programmed analysis pipelines, quality control workflows, and visualization tools are offered to the user. Through intensive user interactions, appropriate data models have been developed. These models build the biological data management system and provide possibilities to annotate data, query existing metadata for statistics and future re-analysis on a high-performance computing system via a coupling to gUSE, a workflow management system. ACKNOWLEDGMENT The authors thank Luis de la Garza and Jens Krüger for their generous help with setting up the gUSE framework. The authors thank the openBIS Helpdesk for their fast and insightful answers regarding our questions. M.C.C., S.C., D.W., O.K. and S.N. acknowledge funding from Deutsche Forschungsgemeinschaft (core facilities initiative, KO-2313/6-1 and KO-23132, Institutional Strategy of the University of Tübingen, ZUK 63). All authors acknowledge funding from Deutsche Forschungsgemeinschaft (Institutional Strategy of the University of Tübingen, ZUK 63). PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.2839v1 | CC BY 4.0 Open Access | rec: 1 Mar 2017, publ: 1 Mar 2017

Intuitive Web-Based Experimental Design for High-Throughput Biomedical Data

Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model.

„Metabolomics“ in der Diabetesforschung

ZusammenfassungZu den etablierten Ansätzen der Diabetesforschung kam unlängst eine neue vielversprechende Strategie hinzu, die Analyse des Metaboloms. Biomedizinische „Metabolomics“-Analysen beinhalten die Untersuchung von Metabolitmustern in Körperflüssigkeiten, Geweben oder Proben aus Zellkulturexperimenten mit dem Ziel, möglichst viele dieser Stoffwechselzwischenprodukte und -endprodukte gleichzeitig zu erfassen. In der Diabetesforschung können durch Metabolomics-Untersuchungen sowohl neue Erkenntnisse zum pathogenetischen Geschehen des Prädiabetes sowie des Diabetes und seiner Spätschäden als auch neue diagnostische Marker entdeckt werden. Der Übersichtsbeitrag gibt neben einem allgemeinen Teil über Metabolomics-Analysen einen Überblick über aktuelle Forschungsergebnisse von Metabolomics-Untersuchungen in der Diabetesforschung.AbstractRecently a new promising strategy has been introduced to the well-established approaches in diabetes research. Biomedical metabolomic analyses comprise the examination of metabolite patterns in different body fluids, tissues or samples from cell culture experiments with the objective to maximize the simultaneous detection of intermediate and end products of metabolism. Metabolomic analysis in diabetes research could provide new insights in the pathogenetic scenario of prediabetes, diabetes and its late complications as well as the discovery of novel diagnostic biomarkers. This review provides an overview of metabolomic analyses and a summary of current research results in metabolomics in diabetes research.