Malaysian Journal of Computer Science (ISSN 0127-9084)
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Article Information
Title:Biolets: Statistical Approach to Biological Random Sequence Generation
Auhtor(s): Vinod Chandra S. S,Gopakumar G,Achuthsankar S. Nair,
Journal:Malaysian Journal of Computer Science (ISSN 0127-9084)
Volume:21, No 2
Keywords:Biological Sequence Generation, Random DNA Sequence, Statistical Distributions, Sequence Analysis, Tandem Repeats
Abstract:Simulations of originally existent biological sequences are helpful for computational analysis and manipulations, rather than wet lab experiments, considerably reducing the cost and time associated with traditional wet lab experiments. The basic idea is to compare the results of a run on real data to many runs on random data. This paper discusses the need for random biological sequence analysis and an alternative for generating biological random sequences. A novel random generation algorithm which uses the statistical distributions to generate numbers and to map these numbers into biological random sequences is conceived. Algorithm for generating random sequences with normal and binomial distributions is implemented; random sequences with tandem repeats, GC base control and nucleotide position control are also incorporated by modifying this algorithm. The newly proposed algorithm and all the mentioned features are also implemented as a platform independent tool named Biolets which can be freely downloaded from the web site

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