Jie Ren1, Kai Song, Fengzhu Sun, Minghua Deng, Gesine Reinert. 1. School of Mathematics, Peking University, Beijing 100871, PR China, Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089-2910, USA, MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China and Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
Abstract
MOTIVATION: Recently, a range of new statistics have become available for the alignment-free comparison of two sequences based on k-tuple word content. Here, we extend these statistics to the simultaneous comparison of more than two sequences. Our suite of statistics contains, first, C(*)1 and C(S)1, extensions of statistics for pairwise comparison of the joint k-tuple content of all the sequences, and second, C(*)2, C(S)2 and C(geo)2, averages of sums of pairwise comparison statistics. The two tasks we consider are, first, to identify sequences that are similar to a set of target sequences, and, second, to measure the similarity within a set of sequences. RESULTS: Our investigation uses both simulated data as well as cis-regulatory module data where the task is to identify cis-regulatory modules with similar transcription factor binding sites. We find that although for real data, all of our statistics show a similar performance, on simulated data the Shepp-type statistics are in some instances outperformed by star-type statistics. The multiple alignment-free statistics are more sensitive to contamination in the data than the pairwise average statistics. AVAILABILITY: Our implementation of the five statistics is available as R package named 'multiAlignFree' at be http://www-rcf.usc.edu/∼fsun/Programs/multiAlignFree/multiAlignFreemain.html. CONTACT: reinert@stats.ox.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Recently, a range of new statistics have become available for the alignment-free comparison of two sequences based on k-tuple word content. Here, we extend these statistics to the simultaneous comparison of more than two sequences. Our suite of statistics contains, first, C(*)1 and C(S)1, extensions of statistics for pairwise comparison of the joint k-tuple content of all the sequences, and second, C(*)2, C(S)2 and C(geo)2, averages of sums of pairwise comparison statistics. The two tasks we consider are, first, to identify sequences that are similar to a set of target sequences, and, second, to measure the similarity within a set of sequences. RESULTS: Our investigation uses both simulated data as well as cis-regulatory module data where the task is to identify cis-regulatory modules with similar transcription factor binding sites. We find that although for real data, all of our statistics show a similar performance, on simulated data the Shepp-type statistics are in some instances outperformed by star-type statistics. The multiple alignment-free statistics are more sensitive to contamination in the data than the pairwise average statistics. AVAILABILITY: Our implementation of the five statistics is available as R package named 'multiAlignFree' at be http://www-rcf.usc.edu/∼fsun/Programs/multiAlignFree/multiAlignFreemain.html. CONTACT: reinert@stats.ox.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Matthew J Blow; David J McCulley; Zirong Li; Tao Zhang; Jennifer A Akiyama; Amy Holt; Ingrid Plajzer-Frick; Malak Shoukry; Crystal Wright; Feng Chen; Veena Afzal; James Bristow; Bing Ren; Brian L Black; Edward M Rubin; Axel Visel; Len A Pennacchio Journal: Nat Genet Date: 2010-08-22 Impact factor: 38.330