A String-Matching Operation using Finite Automata and Online Interface for Bioinformatics Algorithms



Bioinformatics, A-BOM, Interface, Approximate Pattern Matching


In this study, I present a new web interface for major bioinformatics algorithms and introduce a novel approximate string-matching algorithm. My web interface executes major algorithms on the field for the use of computational biologists, students or any other interested researchers. In the web interface, algorithms come under three sections: Sequence alignment, pattern matching and motif finding. In each section, I introduce algorithms in order to find best fitting one for specific dataset and problem. The interface introduces execution time, memory usage and context specific results of algorithms such as alignment score. The interface utilizes emerging open source languages and tools. In order to develop light and user-friendly interface, all parts of the interface coded with Python language. On the other hand, Django is used for web interface. Second contribution of the study is novel A-BOM algorithm, which is designed for approximate pattern matching problem. The algorithm is approximate matching variation of Backward Oracle Matching. I compare my algorithm with popular approximate string-matching algorithms. Results denote that A-BOM introduces 30% to 80% short runtime improvement when compared to current approximate pattern matching algorithms on long patterns.

How to cite this article:
Pattnaik S. A String-Matching Operation using Finite Automata and Online Interface for Bioinformatics Algorithms. J Engr Desg Anal 2020; 3(2): 1-7.


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