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We study global fluctuations of the guanine and cytosine base content (GC%) in mouse genomic DNA using spectral analyses. Power spectra S(f) of GC% fluctuations in all nineteen autosomal and two sex chromosomes are observed to have the universal functional form S(f)~1/f^α (α≈1) over several orders of magnitude in the frequency range 10^-7<f<10^-5 cycle/base, corresponding to long-ranging GC% correlations at distances between 100 kb and 10 Mb. S(f) for higher frequencies (f>10^-5 cycle/base) shows a flattened power-law function with α<1 across all twenty-one chromosomes. The substitution of about 38% interspersed repeats does not affect the functional form of S(f), indicating that these are not predominantly responsible for the long-ranged multi-scale GC% fluctuations in mammalian genomes. Several biological implications of the large-scale GC% fluctuation are discussed, including neutral evolutionary history by DNA duplication, chromosomal bands, spatial distribution of transcription units (genes), replication timing, and recombination hot spots.

This paper reviews applications of signal processing techniques to a number of areas in the field of genetics. We focus on techniques for analyzing DNA sequences, and briefly discuss applications of signal processing to DNA sequencing, and other related areas in genetics that can provide biologically significant information to assist with sequence analysis.

Pevzner and Sze¹⁹ have introduced the Planted (l,d)-Motif Problem to find similar patterns (motifs) in sequences which represent the promoter regions of co-regulated genes, where l is the length of the motif and d is the maximum Hamming distance around the similar patterns. Many algorithms have been developed to solve this motif problem. However, these algorithms either have long running times or do not guarantee the motif can be found. In this paper, we introduce new algorithms to solve this motif problem. Our algorithms can find motifs in reasonable time for not only the challenging (9, 2), (11, 3), (15, 5)-motif problems but for even longer motifs, say (20, 7), (30, 11) and (40, 15), which have never been seriously attempted by other researchers because of the large time and space required. Besides, our algorithms can be extended to find more complicated motifs structure called cis-regulatory modules (CRM).

In this study, a new genetic algorithm was developed to discover the best motifs in a set of DNA sequences. The main steps were: finding the potential positions in each sequence by using few voters (1–5 sequences), constructing the chromosomes from the potential positions, evaluating the fitness for each gene (position) and for each chromosome, calculating the new random distribution, and using the new distribution to generate the next generation. To verify the effectiveness of the proposed algorithm, several real and artificial datasets were used; the results are compared to the standard genetic algorithm, and Gibbs, MEME, and consensus algorithms. Although all the algorithms have low correlation with the correct motifs, the new algorithm exhibits higher accuracy, without sacrificing implementation time.

In this paper we present a novel 2-D graphical representation of DNA sequences in the first quadrant Cartesian coordinate system. This representation has been mathematically proven to be a zero length of circuit, i.e., without any degeneracy. A practicable formula based upon an iterative comparison method has been developed to calculate the frequencies of nucleic acid bases (A, T, G, and C) from the starting point to any considered point. Furthermore, this new graphical representation, in comparison with our previously published method, may have a unique application in representing the universal genetic code.

CpG Island (CGI) is considered to be one of the important segments of deoxyribonucleic acid (DNA) sequences. Out of the various epigenetic events which are associated with CGIs, some such events are like: CGIs are useful in the prediction of promoter region and subsequently for gene prediction, CGIs’ contribution in finding the epigenetic reasons of cancer is of great importance, CGIs can be used to identify chromosome inactivation. Therefore, the exact and maximum number of CGIs hidden in DNA sequences need to explored. A lot of computational, transform-based approaches have been developed and reported in literature for the identification of CGIs in DNA sequences since last many years. The problem associated with transform-based approaches is that the domain of functioning of algorithm requires to be changed which can probably lead to biasing and result in loss of important information in terms of CGIs. Hence, to provide a solution to this issue, a modified P-spectrum-based approach has been proposed here which does not suffer from domain transformation issue. Also, the performance of proposed algorithm has been tested on a large data set of 100 DNA sequences of human species and the performance has been compared with other recently reported methods of CGIs identification in DNA sequences. The results obtained prove that the proposed algorithm is better than the existing methods in terms of identification of more number of CGIs in DNA sequences. Therefore, the proposed algorithm has been considered as an efficient approach to enhance the sensitivity of CGIs identification.