MrsRF: an efficient MapReduce algorithm for analyzing large collections of evolutionary trees. MapReduce is a parallel framework that has been used effectively to design large-scale parallel applications for large computing clusters. In this paper, we evaluate the viability of the MapReduce framework for designing phylogenetic applications. The problem of interest is generating the all-to-all Robinson-Foulds distance matrix, which has many applications for visualizing and clustering large collections of evolutionary trees. We introduce MrsRF (MapReduce Speeds up RF), a multi-core algorithm to generate a t × t Robinson-Foulds distance matrix between t trees using the MapReduce paradigm.

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References in zbMATH (referenced in 4 articles )

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  1. Radenski, Atanas; Ehwerhemuepha, Louis: Speeding-up codon analysis on the cloud with local MapReduce aggregation (2014) ioport
  2. Daugelaite, Jurate; O’Driscoll, Aisling; Sleator, Roy D.: An overview of multiple sequence alignments and cloud computing in bioinformatics (2013)
  3. Hong, Chun-Tao; Chen, De-Hao; Chen, Yu-Bei; Chen, Wen-Guang; Zheng, Wei-Min; Lin, Hai-Bo: Providing source code level portability between CPU and GPU with MapCG (2012) ioport
  4. Matthews, Suzanne; Williams, Tiffani L.: Mrsrf: an efficient mapreduce algorithm for analyzing large collections of evolutionary trees (2010) ioport