Journal article
PyBDA: a command line tool for automated analysis of big biological data sets.
- Dirmeier S Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
- Emmenlauer M Biozentrum, University of Basel, Basel, Switzerland.
- Dehio C Biozentrum, University of Basel, Basel, Switzerland.
- Beerenwinkel N Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. niko.beerenwinkel@bsse.ethz.ch.
- 2019-11-14
Published in:
- BMC bioinformatics. - 2019
Big data
Command line
Computing cluster
Data analysis
Grid engine
Machine learning
Pipeline
Algorithms
Automation
Computational Biology
Computing Methodologies
HeLa Cells
Humans
Image Processing, Computer-Assisted
Machine Learning
English
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.
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.
- Language
-
- English
- Open access status
- gold
- Identifiers
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- DOI 10.1186/s12859-019-3087-8
- PMID 31718539
- Persistent URL
- https://sonar.ch/global/documents/118245
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