Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different ‘omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps are added to an already complex workflow, overly complicating the process. In some cases, limitations to existing text manipulation are such that desired analyses can only be carried out using highly sophisticated processing steps beyond the reach of even advanced users and developers. For users with some SQL knowledge, these text operations could be combined into single, concise query on a relational database. As a solution, we have developed the Query Tabular Galaxy tool, which leverages a SQLite database generated from tabular input data. This database can be queried and manipulated to produce transformed and customized tabular outputs compatible with downstream processing steps. Regular expressions can also be utilized for even more sophisticated manipulations, such as find and replace and other filtering actions. Using several Galaxy-based multi-omic workflows as an example, we demonstrate how the Query Tabular tool dramatically streamlines and simplifies the creation of multi-step analyses, efficiently enabling complicated textual manipulations and processing. This tool should find broad utility for users of the Galaxy platform seeking to develop and use sophisticated workflows involving text manipulation on tabular outputs.
Bibliographical noteFunding Information:
Grant information: This work was supported in part by NSF award 1458524 and NIH award U24CA199347 to T.J. Griffin and the Galaxy for proteomics (Galaxy-P) research team.
This work was supported in part by NSF award 1458524 and NIH award U24CA199347 to T.J. Griffin and the Galaxy for proteomics (Galaxy-P) research team.
We thank the Supercomputing Institute at the University of Minnesota for support and maintenance of software and hardware infrastructure used in the development of this tool. We also thank the Jetstream team at the University of Indiana for support and maintenance of software and hardware infrastructure used for hosting publicly accessible Galaxy instances described in this manuscript. Additionally, we would like to thank Kevin Murray from the Department of Veterinary Population Medicine at the University of Minnesota for contributing the metabolomics data set.
© 2019 Johnson JE et al.