Publications

Abstract : Phylogenetic tree reconstruction plays a key role in the field of bioinformatics for the understanding of evolutionary history of species and species traits. However, computation of such trees, especially for enormous data sets, is a challenging task. Maximum Parsimony method generates accurate results, although it is highly resource intensive. In this paper, we present a novel algorithm that improves over the original Maximum Parsimony method. Following a bottom-up approach of the divide-and-conquer methodology, the algorithm divides the input data set into sub-data sets. Sub-trees corresponding to the sub-data sets are generated and analysed in parallel via multiple threads of execution running over multiple cores of multiple processors. Finally, they are merged to generate the optimal evolution tree. Cluster cell formation is encouraged with a novel idea of utilizing bitcoins to reward participating users. We show that our method has reduces time complexity and analyse the impact of bitcoins on user participation willingness. The proposed algorithm is programming language independent. © Springer Nature Singapore Pte Ltd. 2018.