An efficient algorithm to detect palindromes in DNA sequences using periodicity transform

Gupta, Ravi P.; Mittal, Ankush; Gupta, Sumit

doi:10.1016/j.sigpro.2005.10.008

Cited by 21 publications

(8 citation statements)

References 12 publications

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“…, N − 1, are given by (14). Comparing (15) and 5, for small periods (e.g., p is a factor of N ), ϑ s p = λ s p , where λ s p is obtained by the standard periodicity transform on sequence x. Thus, x c p = x p for small periods indicating that the CPT is consistent with the standard periodicity transform for extracting high-frequency components.…”

Section: B Circular Periodicity Transform (Cpt)mentioning

confidence: 64%

“…Recently, driven by the ambitious exploration in biometrics [1]− [4] and bioinformatics [5]− [7], there has been an upsurge in research seeking to understand the intrinsic repetitive regularities in science and engineering database. Interpretation of repetitive regularities plays an essential role in pattern mining for many emerging applications such as biometric-based identity authentication [8]− [11], gene analysis and assembly [12]− [15], healthcare and disease diagnosis [16]− [18], drug design [19]− [20], and forecasting natural disasters such as earthquake and tsunami [22]− [24].…”

Section: Introductionmentioning

confidence: 99%

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Low-Complexity Analysis of Repetitive Regularities for Biometric Applications

Wang

Patel²

2007

JCP

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Presented in this paper is a joint algorithm optimization and architecture design framework for analysis of repetitive regularities. Two closely coupled algorithm optimization techniques, referred to as the prime subspace periodicity transform (PSPT) and circular periodicity transform (CPT), are developed that significantly reduce computational complexity while enable the extraction of a wide spectrum of periodic features. The proposed PSPTCPT algorithms lead to a parallel and resource sharing VLSI architecture. While most of the current systems rely on software solutions to performance feature extraction, the performance benefits rendered by the proposed framework show advantages in dealing with data-intensive computation for emerging applications in biometrics and bioinformatics. The explosive growth in database complexity combined with demand for fast analysis make the proposed framework a promising solution. Experimental results on an iris identification system demonstrate up to 99.2% accuracy and 24.6% − 41.9% reduction in computational complexity.

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Section: B Circular Periodicity Transform (Cpt)mentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Low-Complexity Analysis of Repetitive Regularities for Biometric Applications

Wang

Patel²

2007

JCP

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“…A palindrome is a sequence of letters or words such as racecar and madam I madam which are read the same in forward as well as in reverse direction [5]. The RECFIN finds the presence of palindrome in the given sequence which will be helpful in identifying the formation of protein.…”

Section: Four Types Of Molecules Adenine (A) Cytosine (C) Guanine (mentioning

confidence: 99%

Mining Association Rules in Dengue Gene Sequence with Latent Periodicity

Thangam

Vanniappan

2015

Computational Biology Journal

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The mining of periodic patterns in dengue database is an interesting research problem that can be used for predicting the future evolution of dengue viruses. In this paper, we propose an algorithm called Recurrence Finder (RECFIN) that uses the suffix tree for detecting the periodic patterns of dengue gene sequence. Also, the RECFIN finds the presence of palindrome which indicates the possibilities of formation of proteins. Further, this paper computes the periodicity of nucleic acid and amino acid sequences of any length. The periodicity based association rules are used to diagnose the type of dengue. The time complexity of the proposed algorithm is O(n 2 ). We demonstrate the effectiveness of the proposed approach by comparing the experimental results performed on dengue virus serotypes dataset with NCBI-BLAST algorithm.

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“…Another DSP approach for the same purpose was introduced by Rushdi and Tuqan [70]. Gupta et al [71] devised an efficient algorithm to detect palindromes in DNA sequences using a signal processing operation called periodicity transform. Providence [72] applied time-varying cellular automata to the problem of finding signals in DNA sequences.…”

Section: Other Studies On Genomic Sequences Using Dsp Techniquesmentioning

confidence: 99%

Digital Signal Processing in the Analysis of Genomic Sequences

Lorenzo-Ginori

Rodriguez-Fuentes

Abalo

et al. 2009

CBIO

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Digital Signal Processing (DSP) applications in Bioinformatics have received great attention in recent years, where new effective methods for genomic sequence analysis, such as the detection of coding regions, have been developed. The use of DSP principles to analyze genomic sequences requires defining an adequate representation of the nucleotide bases by numerical values, converting the nucleotide sequences into time series. Once this has been done, all the mathematical tools usually employed in DSP are used in solving tasks such as identification of protein coding DNA regions, identification of reading frames, and others. In this article we present an overview of the most relevant applications of DSP algorithms in the analysis of genomic sequences, showing the main results obtained by using these techniques, analyzing their relative advantages and drawbacks, and providing relevant examples. We finally analyze some perspectives of DSP in Bioinformatics, considering recent research results on algebraic structures of the genetic code, which suggest other new DSP applications in this field, as well as the new field of Genomic Signal Processing.

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An efficient algorithm to detect palindromes in DNA sequences using periodicity transform

Cited by 21 publications

References 12 publications

Low-Complexity Analysis of Repetitive Regularities for Biometric Applications

Low-Complexity Analysis of Repetitive Regularities for Biometric Applications

Mining Association Rules in Dengue Gene Sequence with Latent Periodicity

Digital Signal Processing in the Analysis of Genomic Sequences

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