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A Biologically-Inspired Computational Solution for Protein Coding Regions Identification in Noisy DNA Sequences

A Biologically-Inspired Computational Solution for Protein Coding Regions Identification in Noisy DNA Sequences

Muneer Ahmad
Copyright: © 2016 |Pages: 16
ISBN13: 9781466697928|ISBN10: 146669792X|EISBN13: 9781466697935
DOI: 10.4018/978-1-4666-9792-8.ch010
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MLA

Ahmad, Muneer. "A Biologically-Inspired Computational Solution for Protein Coding Regions Identification in Noisy DNA Sequences." Biologically-Inspired Energy Harvesting through Wireless Sensor Technologies, edited by Vasaki Ponnusamy, et al., IGI Global, 2016, pp. 201-216. https://doi.org/10.4018/978-1-4666-9792-8.ch010

APA

Ahmad, M. (2016). A Biologically-Inspired Computational Solution for Protein Coding Regions Identification in Noisy DNA Sequences. In V. Ponnusamy, N. Zaman, T. Low, & A. Amin (Eds.), Biologically-Inspired Energy Harvesting through Wireless Sensor Technologies (pp. 201-216). IGI Global. https://doi.org/10.4018/978-1-4666-9792-8.ch010

Chicago

Ahmad, Muneer. "A Biologically-Inspired Computational Solution for Protein Coding Regions Identification in Noisy DNA Sequences." In Biologically-Inspired Energy Harvesting through Wireless Sensor Technologies, edited by Vasaki Ponnusamy, et al., 201-216. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9792-8.ch010

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Abstract

Biologically inspired computational solutions for protein coding regions identification are termed as optimized solutions that could enhance regions of interest in noisy DNA signals contrary to contemporary identification. Exponentially growing genomic data needs better protein translation. The solutions proposed so far rely on statistical, digital signal processing and Fourier transforms approaches lacking the reflection for optimal biologically inspired identification of coding regions. This paper presents a peculiar biologically inspired solution for coding regions identification based on wavelet transforms with notion of a peculiar indicator sequence. DNA signal noise has been reduced considerably and exon peaks can be discriminated from introns significantly. A comparative analysis performed over datasets commonly used for protein coding identification revealed the outperformance of proposed solution in power spectral density estimation graphs and numerical discrimination measure's calculations. The significant results achieved depict 75% reduction in computational complexity than Binary indicator sequence method and 32% to 266% improvement than other methods in literature (as a comparison with standard NCBI range). The significance in results has been achieved by efficiently denosing the target DNA signal employing wavelets and peculiar indicator sequence.

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