Abstract— The goal of workflow systems is to put away the disadvantages of the state-of-the-art methods of scientific data analysis, mostly in Perl or similar scripting languages. Scientific workflow systems enable the development of analysis pipelines, provenance management, process control, recovery, scheduling and parallelization of individual tasks, understandability and sharing of workflows among the scientific community. There are several workflow systems to design bioinformatics workflows. The objective of this work is to identify the frequent workflow patterns or substructures in a corpus of Galaxy bioinformatics workflows obtained from myExperiment. Frequent sub graph discovery (FSG) algorithm used in analyzing the workflows. Seventy-one reusable workflow patterns identified with a 5% minimum support threshold. As future work planning to annotate the identified frequent patterns and to encode the identified patterns in the workflow systems with the objective of improving the usability by providing a high-level abstract interface to the user.

Index Terms— Frequent subgraph mining, galaxy workflows, substructures in workflows, workflow abstraction.

C. R. Wijesinghe and A. R. Weerasinghe are with University of Colombo School of Computing, Colombo 7, Sri Lanka. C. R. Wijesinghe is also with Faculty of Graduate Studies, University of Colombo, Colombo 7, Sri Lanka.

Cite: C. R. Wijesinghe, A. R. Weerasinghe, " Mining Frequent Patterns in Bioinformatics Workflows," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 10, no. 4, pp. 161-169, 2020.

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