qPortal: A platform for data-driven biomedical research

PLoS One. 2018 Jan 19;13(1):e0191603. doi: 10.1371/journal.pone.0191603. eCollection 2018.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomedical Research* / statistics & numerical data
  • Computational Biology / methods
  • Computational Biology / statistics & numerical data
  • Computing Methodologies*
  • Database Management Systems / statistics & numerical data
  • Databases, Factual / statistics & numerical data
  • Databases, Genetic / statistics & numerical data
  • High-Throughput Nucleotide Sequencing / statistics & numerical data
  • Humans
  • Internet
  • Software*
  • User-Computer Interface
  • Workflow

Grants and funding

MCC, SC, DW, OK and SN acknowledge funding from Deutsche Forschungsgemeinschaft (core facilities initiative, KO-2313/6-1 and KO-2313-2, Institutional Strategy of the University of Tübingen, ZUK 63). SN acknowledges funding by the Sonderforschungsbereich SFB/TR 209 “Liver cancer” of the Deutsche Forschungsgemeinschaft (DFG). All authors acknowledge funding from Deutsche Forschungsgemeinschaft (Institutional Strategy of the University of Tübingen, ZUK 63). EK and OK acknowledge funding from the German Ministry of Research and Education (BMBF, grant no. 01ZX1301F). CM and OK acknowledge funding from the European Union (APERIM, contract no. 633592).