Abstract
Identification of exons in eukaryotes using DSP techniques is a challenging task in genomic signal processing owing to the low density of coding regions. Although many DSP techniques have been proposed, still fast and accurate identification of exons is a great challenge. In this paper, an empirical mode decomposition (EMD) based adaptive noise canceller (ANC) along with a zero-phase anti-notch filter is proposed for improved identification of exons. An anti-notch filter extracts the period-3 property present in the exons and generates the feature, whereas the EMD-based ANC can remove the 1/f background noise present in the feature. The potential of the proposed technique is analyzed in comparison with other state-of-the-art methods at the nucleotide level using statistical features such as receiver operating characteristic curve, sensitivity, specificity, approximate correlation, and correlation coefficient. The proposed EMD-based ANC technique outperforms other discussed methods when applied to benchmark databases.
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Data availability statement
Dataset will be provided on request to malayakumar.h@vit.ac.in.
References
Abbasi O, Rostami A, Karimian G (2011) Identification of exonic regions in DNA sequences using cross-correlation and noise suppression by discrete wavelet transform. BMC Bioinform 12(430):1–10
Ahmad M, Jung LT, Bhuiyan A-A (2017) A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing. Comput Methods Prog Biomed 149:11–17
Akhtar M, Epps J, Ambikairajah E (2008) Signal processing in sequence analysis: advances in eukaryotic gene prediction. IEEE Sel Top Signal Process 2(3):310–321
Anastassiou D (2001) Genomic signal processing. IEEE Signal Process Mag 18(4):8–20
Awad MM et al (2016) MET exon 14 mutations in non-smell-cell lung cancer are associated with advanced age and stage-dependent MET genomic amplification and c-Met overexpression. J Clin Oncol 34(7):721–730
Burge C (1997) Identification of genes in human genomic DNA, Ph.D. dissertation, Stanford Univ., Stanford, CA
Burset M, Guigo R (1996) Evaluation of gene structure prediction programs. Genomics 34(3):353–367
Das L, Nanda S, Das JK (2019) An integrated approach for identification of exon locations using recursive Gauss Newton tuned adaptive Kaiser window. Genomics 111(3):284–296
George TP, Thomas TP (2010) Discrete wavelet transform de-noising in eukaryotic gene splicing. BMC Bioinform 11(S50):1–8
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristics (roc) curve. Radiology 143(1):29–36
Hayes MH (1996) Statistical digital signal processing and modelling. John Wiley & Sons, New York
Hota MK, Srivastava VK (2012a) Identification of protein coding regions using antinotch filters. Digit Signal Process 22(6):869–877
Hota MK, Srivastava VK (2012b) Multistage filters for identification of eukaryotic protein coding regions. Int J Biomath 5(2):1–18
Hota MK, Srivastava VK (2017) A multirate DSP structure for the identification of protein-coding regions. Int J Biomath 10(8):1–15
Huang NE, Shen Z, Long S, Wu MC, Shih HH, Zheng Q, Yen N-C (1998) The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc R Soc Lond A 454(1971):903–995
Marhon SA, Kremer SC (2016) Prediction of protein coding regions using a wide-range wavelet window method. IEEE/ACM Trans Comput Biol Bioinform 13(4):742–753
Mena-Chalco J, Carrer H, Zana Y, Cesar RM Jr (2008) Identification of protein coding regions using the modified Gabor-wavelet transform. IEEE/ACM Trans Comput Biol Bioinform 5(2):198–207
Rogic S, Mackworth AK, Ouellette FBF (2001) Evaluation of gene-finding programs on mammalian sequences. Genome Res 11(5):817–832
Sahu SS, Panda G (2011) Identification of protein-coding regions in DNA sequences using a time-frequency filtering approach. Genomics Proteomics Bioinform 9(1):45–55
Shakya DK, Saxana R, Sharma SN (2013) An adaptive window length strategy for eukaryotic CDS prediction. IEEE/ACM Trans Comput Biol Bioinform 10(5):1241–1252
Singh AK, Srivastava VK (2021) Improved filtering approach for identification of protein-coding regions by background noise reduction using S-G filter 10(1):1–16
Tiwari S, Ramachandran S, Bhattacharya A, Bhattacharya S, Ramaswamy R (1997) Prediction of probable genes by Fourier analysis of genomic sequences. CABIOS 13(3):263–270
Tuqan J, Rushdi A (2008) A DSP approach for finding the codon bias in DNA sequences. IEEE Sel Top Signal Process 2(3):343–356
Vaidyanathan PP, Yoon B-J (2004) The role of signal-processing concepts in genomics and proteomics. J Frankl Inst 341(1):111–135
Voss RF (1992) Evolution of long-range fractal correlations and 1/f noise in DNA base sequences. Phys Rev Lett 68(25):3805
Zhang W, Yan H (2011) Exon prediction using empirical mode decomposition and Fourier transform of structural profiles of DNA sequence. Pattern Recogn 45:947–955
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The author would like to thank the editor-in-chief and four anonymous reviewers for their constructive comments that improved the manuscript greatly.
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Hota, M.K. Empirical mode decomposition based adaptive noise canceller for improved identification of exons in eukaryotes. Netw Model Anal Health Inform Bioinforma 10, 60 (2021). https://doi.org/10.1007/s13721-021-00346-y
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DOI: https://doi.org/10.1007/s13721-021-00346-y