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PyBDA: a command line tool for automated analysis of big biological data sets

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Dirmeier Simon, Emmenlauer Mario, Dehio Christoph, Beerenwinkel Niko,
Project Bacterial Type IV Secretion (T4S): Cellular, Molecular, and Evolutionary Basis of the Subversion of Host Cell Functions by Translocated Effector Proteins
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Original article (peer-reviewed)

Journal BMC Bioinformatics
Volume (Issue) 20(1)
Page(s) 564 - 564
Title of proceedings BMC Bioinformatics
DOI 10.1186/s12859-019-3087-8

Open Access

URL http://doi.org/10.1186/s12859-019-3087-8
Type of Open Access Publisher (Gold Open Access)

Abstract

Background: Analysing large and high-dimensional biological data sets poses significant computational difficulties for bioinformaticians due to lack of accessible tools that scale to hundreds of millions of data points. Results: We developed a novel machine learning command line tool called PyBDA for automated, distributed analysis of big biological data sets. By using Apache Spark in the backend, PyBDA scales to data sets beyond the size of current applications. It uses Snakemake in order to automatically schedule jobs to a high-performance computing cluster. We demonstrate the utility of the software by analyzing image-based RNA interference data of 150 million single cells. Conclusion: PyBDA allows automated, easy-to-use data analysis using common statistical methods and machine learning algorithms. It can be used with simple command line calls entirely making it accessible to a broad user base. PyBDA is available at https://pybda.rtfd.io .
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