qPortal: A platform for data-driven biomedical research

<div><p>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.</p><p>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).</p><p>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.</p></div

Project view in qPortal.

<p>The main view of the Project Browser shows general information about the project such as the name, description, investigators, and a status. Users can navigate to other views like the project graph, available datasets, and the workflow submission view by clicking on the corresponding tabs.</p

qPortal software components.

<p>qPortal consists of several software components which have been implemented. The Java library Liferay Utils is connected to each portlet and ensures authentication of users and enables Liferay functionality for our portlets Project Wizard and Project Browser. These provide users with means of project creation and management via another Java library wrapping the openBIS API (openBIS Client). Import, configuration and submission of workflows via the communication with the gUSE API and openBIS is handled by our implemented Java library Workflow Handler. Java libraries are shown in dashed line boxes whereas the two presented main portlets of qPortal are shown in regular boxes.</p

Project view in qPortal.

<p>The main view of the Project Browser shows general information about the project such as the name, description, investigators, and a status. Users can navigate to other views like the project graph, available datasets, and the workflow submission view by clicking on the corresponding tabs.</p

qPortal setup.

<p>qPortal is connected to openBIS and the workflow engine. The workflow engine is connected to a high-performance computing cluster. Users may create projects, upload their data (Datamover), and analzye it through qPortal (Project Wizard and Project Browser). Results are automatically written back to the database and presented on the portal (Project Browser).</p

Workflow result presentation.

<p>Results of workflow runs will be directly visualized in qPortal. In this case results of one OptiType and one FastQC run are visualized.</p

Project registration.

<p>One part of the project registration through the Project Wizard as first step of the project management workflow realized by qPortal: values of the experimental design variables ethnicity and sex are input by the user. Resulting combinations are shown and can be adjusted by the user.</p

qPortal workflow admin panel.

<p>Configuration of workflows is done once through the workflow admin panel in qPortal. Besides the name, version, and a description of the worklfow, associated experiment and samples types in openBIS have to be selected. Workflow runs and their results will be stored in the database as data and meta information. Additionally, the parameters of the workflow which will be shown to the user can be selected (highlighted in blue in this example).</p

Workflow selection and submission.

<p>All available workflows for the corresponding project can be seen in the workflow tab. After selecting the workflow, users select input files and specify the parameter values. If the submission was successful, users will be notified.</p

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