Size-dependence of three-periodicity and long-range correlations in DNA sequences

Chechetkin, V. R.; Turygin, A. Yu.

doi:10.1016/0375-9601(95)00047-7

Cited by 59 publications

(41 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The importance of measuring different periodicities for a given DNA in order to determine the locations of protein-coding regions has already been addressed by Fickett (1992), and these periodicities have been used as discriminant features in several studies of gene prediction (Silverman and Linsker 1986;Fickett and Tung 1992;Chechetkin and Turygin 1995;Tiwari et al 1997;Herzel et al 1999;Anastassiou 2000). The Discrete Fourier Transform (DFT) is a powerful tool for studying periodicities.…”

Section: Periodicities In Dna Sequences and Dft Analysismentioning

confidence: 99%

“…These methods are based on different measures for discriminating between protein-coding regions and noncoding regions. Some of the measures are based on statistical regularities in genes or exons, which are not present in introns and intergenic sections, such as, for example, differences in codon usage (Staden and McLachlan 1982), hexamer counts (Claverie and Bouguelerat 1986;Farber et al 1992;Fickett and Tung 1992), codon position asymmetry (Fickett 1982), different periodicities (Fickett 1982;Silverman and Linsker 1986;Chechetkin and Turygin 1995;Tiwari et al 1997;Herzel et al 1999;Trifonov 1998;Anastassiou 2000), autocorrelations, nucleotide frequencies (Shulman et al 1981;Fickett 1982;Borodovsky et al 1986Borodovsky et al , 1994, entropy measures (Almagor 1985), and many others. Other measures are based on signals of the gene expression machinery (reviewed in Mathé et al 2002).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Gene Prediction by Spectral Rotation Measure: A New Method for Identifying Protein-Coding Regions

Kotlar

Lavner²

2003

Genome Res.

138

114

View full text Add to dashboard Cite

A new measure for gene prediction in eukaryotes is presented. The measure is based on the Discrete Fourier Transform (DFT) phase at a frequency of 1/3, computed for the four binary sequences for A, T, C, and G. Analysis of all the experimental genes of S. cerevisiae revealed distribution of the phase in a bell-like curve around a central value, in all four nucleotides, whereas the distribution of the phase in the noncoding regions was found to be close to uniform. Similar findings were obtained for other organisms. Several measures based on the phase property are proposed. The measures are computed by clockwise rotation of the vectors, obtained by DFT for each analysis frame, by an angle equal to the corresponding central value. In protein coding regions, this rotation is assumed to closely align all vectors in the complex plane, thereby amplifying the magnitude of the vector sum. In noncoding regions, this operation does not significantly change this magnitude. Computing the measures with one chromosome and applying them on sequences of others reveals improved performance compared with other algorithms that use the 1/3 frequency feature, especially in short exons. The phase property is also used to find the reading frame of the sequence.Gene prediction analysis, and specifically, the computational methods for finding the location of protein-coding regions in uncharacterized genomic DNA sequences, is one of the central issues in bioinformatics (Fickett 1996;Salzberg et al. 1998). For a given DNA sequence of an organism, in which the genes and other functional structures are not already known, it is very important to have an accurate and reliable tool for automatic annotation of the sequence: the number and location of genes, the location of exons and introns (in eukaryotes), and their exact boundaries (Claverie 1997). Therefore, along with standard molecular methods, many new methods for finding distinctive features of protein-coding regions have been proposed in the past two decades (see reviews by Fickett 1996;Claverie 1997;Mathé et al. 2002). These methods are based on different measures for discriminating between protein-coding regions and noncoding regions. Some of the measures are based on statistical regularities in genes or exons, which are not present in introns and intergenic sections, such as, for example, differences in codon usage (Staden and McLachlan 1982), hexamer counts (Claverie and

show abstract

Section: Periodicities In Dna Sequences and Dft Analysismentioning

confidence: 99%

mentioning

confidence: 99%

Gene Prediction by Spectral Rotation Measure: A New Method for Identifying Protein-Coding Regions

Kotlar

Lavner²

2003

Genome Res.

138

114

View full text Add to dashboard Cite

show abstract

“…No such peak is observed in noncoding region. This characteristic, referred to as the 3-periodicity property [1,2], has been used in [1] and [3] to design gene prediction algorithms. In this paper, we provide theoretical justification for the 3-periodicity property by defining a new parameter referred to as the position count function (PCF), which measures the number of times different nucleotides appear in the three phases within a DNA codon.…”

Section: Introductionmentioning

confidence: 99%

A Fast DFT based Gene Prediction Algorithm for Identification of Protein Coding Regions

Datta

Asif

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

View full text Add to dashboard Cite

show abstract

“…En este contexto una solución viable es la implementación de herramientas matemáticas propias del procesamiento de señales genómicas [9] . El procesamiento de señales genómicas es un área multidisciplinaria que utiliza herramientas propias del procesamiento digital de señales para extraer información con un significado biológico específico [10,11] Dentro de este campo, uno de los hallazgos más estudiados e importantes en las regiones codificantes de proteínas es la existencia de un pico en la frecuencia f/3 y la ausencia de este pico en las no codificantes [12,18] . Sin embargo, las regiones codificantes solo representan el 1% del genoma humano y por varios años el resto del genoma fue considerado ADN "basura" [19,20] .…”

Section: Introductionunclassified

Statistical analysis of the Fourier Spectra of the ENCODE regulatory regions

Paredes

Romo-Vázquez

Vélez-Pérez

et al.

View full text Add to dashboard Cite

RESUMENEn la actualidad, nuevas bases de datos genómicos (secuencias de ADN) son puestas al alcance del dominio público para su análisis. La bioinformática ha desarrollado algoritmos para extraer información y características de dichas secuencias. Sin embargo, estos algoritmos bioinformáticos tienen limitaciones. Una alternativa es utilizar herramientas propias del procesamiento digital de señales (DSP) adaptadas a secuencias genómicas (procesamiento de señales genómicas -GSP). El presente trabajo versa sobre el análisis de los cuatro primeros momentos centrales (media, desviación estándar, asimetría y curtosis) y dos momentos estadísticos (mediana y varianza) de los espectros frecuenciales de las 15 Regiones Reguladoras (RRs) de la base de datos ENCODE con el objetivo de estudiar diferencias estadísticas y frecuencias características. La base de datos seleccionada es "mapeada". Luego, la FFT es calculada a estas señales genómicas y finalmente los momentos estadísticos son implementados. Los resultados muestran la existencia de 3 grupos de RRs utilizando la media, mediana y curtosis. La desviación estándar y la varianza, parecen no resaltar información importante. Finalmente, la asimetría revela un comportamiento homogéneo ante la presencia de valores atípicos en algunas RRs. Estas observaciones permiten inferir que la periodicidad dentro de la secuencia está relacionada o podría determinar la función biológica que desempeña la misma secuencia.

show abstract

Size-dependence of three-periodicity and long-range correlations in DNA sequences

Cited by 59 publications

References 24 publications

Gene Prediction by Spectral Rotation Measure: A New Method for Identifying Protein-Coding Regions

Gene Prediction by Spectral Rotation Measure: A New Method for Identifying Protein-Coding Regions

A Fast DFT based Gene Prediction Algorithm for Identification of Protein Coding Regions

Statistical analysis of the Fourier Spectra of the ENCODE regulatory regions

Contact Info

Product

Resources

About