Detection of Periodicities in Gene Sequences: A Maximum Likelihood Approach

Author

Arora, Raman ; Sethares, William A.

Author_Institution

Univ. of Wisconsin-Madison, Madison

fYear

2007

fDate

10-12 June 2007

Firstpage

1

Lastpage

4

Abstract

A novel approach is presented to the detection of homological, eroded and latent periodicities in DNA sequences. Each symbol in a DNA sequence is assumed to be generated from an information source with an underlying probability mass function (pmf) in a cyclic manner. The number of sources can be interpreted as the periodicity of the sequence. The maximum likelihood estimates are developed for the pmfs of the information sources as well as the period of the DNA sequence. The statistical model can also be utilized for building probabilistic representations of RNA families.

Keywords

DNA; genetic engineering; genetics; maximum likelihood estimation; DNA sequences; gene sequences; information source; latent periodicities; maximum likelihood approach; periodicities detection; probability mass function; statistical model; Biological system modeling; Buildings; DNA; Drives; Maximum likelihood detection; Maximum likelihood estimation; Probability; RNA; Random variables; Sequences;

fLanguage

English

Publisher

ieee

Conference_Titel

Genomic Signal Processing and Statistics, 2007. GENSIPS 2007. IEEE International Workshop on

Conference_Location

Tuusula

Print_ISBN

978-1-4244-0998-3

Electronic_ISBN

978-1-4244-0999-0

Type

conf

DOI

10.1109/GENSIPS.2007.4365836

Filename

4365836