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Finding short DNA motifs using permuted markov models

Finding short DNA motifs using permuted markov models Finding Short DNA Motifs Using Permuted Markov Models Xiaoyue Zhao — Haiyan Huang , Terence P. Speed ¡ , {xiaoyue, hhuang, terry}@stat.berkeley.edu ABSTRACT Many short DNA motifs such as transcription factor binding sites (TFBS) and splice sites exhibit strong local as well as non-local dependence. We introduce permuted variable length Markov models (PVLMM) which could capture the potentially important dependencies among positions, and apply them to the problem of detecting splice and TFB sites. They have been satisfactory from the viewpoint of prediction performance, and also give ready biological interpretations of the sequence dependence observed. The issue of model selection is also studied. 1. INTRODUCTION It is an important and also challenging task to identify short biological motifs such as transcription factor binding sites (TFBS) and splice sites, where the gene expression machinery interacts with nucleic acids. The binding of transcription factors to speci c DNA sequences near the transcription start site, is one of the rst steps determining whether a gene is turned on or o €. Splice sites are the boundaries between exons and introns, consisting of 5 ™ donor sites at the exon/intron junctions, and 3 ™ acceptor sites at the intron/exon junctions. The variability http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Finding short DNA motifs using permuted markov models

Zhao, Xiaoyue; Huang, Haiyan; Speed, Terence P.
Association for Computing Machinery — Mar 27, 2004
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References (33)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2004 by ACM Inc.
ISBN
1-58113-755-9
doi
10.1145/974614.974624
Publisher site
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Abstract

Finding Short DNA Motifs Using Permuted Markov Models Xiaoyue Zhao — Haiyan Huang , Terence P. Speed ¡ , {xiaoyue, hhuang, terry}@stat.berkeley.edu ABSTRACT Many short DNA motifs such as transcription factor binding sites (TFBS) and splice sites exhibit strong local as well as non-local dependence. We introduce permuted variable length Markov models (PVLMM) which could capture the potentially important dependencies among positions, and apply them to the problem of detecting splice and TFB sites. They have been satisfactory from the viewpoint of prediction performance, and also give ready biological interpretations of the sequence dependence observed. The issue of model selection is also studied. 1. INTRODUCTION It is an important and also challenging task to identify short biological motifs such as transcription factor binding sites (TFBS) and splice sites, where the gene expression machinery interacts with nucleic acids. The binding of transcription factors to speci c DNA sequences near the transcription start site, is one of the rst steps determining whether a gene is turned on or o €. Splice sites are the boundaries between exons and introns, consisting of 5 ™ donor sites at the exon/intron junctions, and 3 ™ acceptor sites at the intron/exon junctions. The variability

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