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TableButler – a Windows based tool for processing large data tables generated with high-throughput methods

Christian Schwager1*, Ute Wirkner1, Amir Abdollahi12 and Peter E Huber1

Author Affiliations

1 Department of Radiation Oncology, German Cancer Research Center (DKFZ) and University of Heidelberg Medical School, Heidelberg, Germany

2 Center of Cancer Systems Biology, Dept. of Medicine, Caritas St. Elizabeth's Medical Center, Tufts University School of Medicine, Boston, Massachusetts, USA

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BMC Bioinformatics 2009, 10:235  doi:10.1186/1471-2105-10-235

Published: 29 July 2009

Abstract

Background

High-throughput "omics" based data analysis play emerging roles in life sciences and molecular diagnostics. This emphasizes the urgent need for user-friendly windows-based software interfaces that could process the diversity of large tab-delimited raw data files generated by these methods. Depending on the study, dozens to hundreds of these data tables are generated. Before the actual statistical or cluster analysis, these data tables have to be combined and merged to expression matrices (e.g., in case of gene expression analysis). Gene annotations as well as information concerning the samples analyzed may be appended, renewed or extended. Often additional data values shall be computed or certain features must be filtered out.

Results

In order to perform these tasks, we have developed a Microsoft Windows based application, "TableButler", which allows biologists or clinicians without substantial bioinformatics background to perform a plethora of data processing tasks required to analyze the large-scale data.

Conclusion

TableButler is a monolithic Windows application. It is implemented to handle, join and preprocess large tab delimited ASCII data files. The intuitive user interface enables scientists (e.g. biologists, clinicians or others) to setup workflows for their specific problems by simple drag-and drop like operations.

For more details about TableButler, visit http://www.OncoExpress.org/software/tablebutler webcite.