Comparative Testing of DNA Segmentation Algorithms Using Benchmark Simulations
Author(s) -
Eran Elhaik,
Dan Graur,
Krešimir Josić́
Publication year - 2009
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/msp307
Subject(s) - segmentation , benchmark (surveying) , divergence (linguistics) , algorithm , biology , set (abstract data type) , homogeneous , domain (mathematical analysis) , pattern recognition (psychology) , computer science , artificial intelligence , mathematics , combinatorics , mathematical analysis , linguistics , philosophy , geodesy , programming language , geography
Numerous segmentation methods for the detection of compositionally homogeneous domains within genomic sequences have been proposed. Unfortunately, these methods yield inconsistent results. Here, we present a benchmark consisting of two sets of simulated genomic sequences for testing the performances of segmentation algorithms. Sequences in the first set are composed of fixed-sized homogeneous domains, distinct in their between-domain guanine and cytosine (GC) content variability. The sequences in the second set are composed of a mosaic of many short domains and a few long ones, distinguished by sharp GC content boundaries between neighboring domains. We use these sets to test the performance of seven segmentation algorithms in the literature. Our results show that recursive segmentation algorithms based on the Jensen-Shannon divergence outperform all other algorithms. However, even these algorithms perform poorly in certain instances because of the arbitrary choice of a segmentation-stopping criterion.
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