The Effect of Numerical Mapping Techniques on Performance in Genomic Research

Seda Nur Gülocak; Bihter Daş

doi:10.35377/saucis...1191850

EN

The Effect of Numerical Mapping Techniques on Performance in Genomic Research

Abstract

In genomic signal processing applications, digitization of these signals is needed to process and analyze DNA signals. In the digitization process, the mapping technique to be chosen greatly affects the performance of the system for the genomic domain to be studied. The purpose of this review is to analyze how numerical mapping techniques used in digitizing DNA sequences affect performance in genomic studies. For this purpose, all digital coding techniques presented in the literature in the studies conducted in the last 10 years have been examined, and the numerical representations of these techniques are given in a sample DNA sequence. In addition, the frequency of use of these coding techniques in four popular genomic areas such as exon region identification, exon-intron classification, phylogenetic analysis, gene detection, and the min-max range of the performances obtained by using these techniques in that area are also given. This study is thought to be a guide for researchers who want to work in the field of bioinformatics.

Keywords

References

P. of P. G. B. of the C. for D. and P. G. M. Nei, M. Nei, S. Kumar, ve E. P. P. of B. M. Nei, Molecular Evolution and Phylogenetics. Oxford University Press, 2000.
M. Akhtar, J. Epps, ve E. Ambikairajah, “Signal Processing in Sequence Analysis: Advances in Eukaryotic Gene Prediction”, IEEE J. Sel. Top. Signal Process., vol. 2, no 3, pp 310-321, Jun. 2008, doi: 10.1109/JSTSP.2008.923854.
L. Das, J. K. Das, S. Mohapatra, ve S. Nanda, “DNA numerical encoding schemes for exon prediction: a recent history”, Nucleosides, Nucleotides & Nucleic Acids, c. 40, no 10, pp. 985-1017, Oct. 2021, doi: 10.1080/15257770.2021.1966797.
U. N. Wisesty, T. R. Mengko, ve A. Purwarianti, “Gene mutation detection for breast cancer disease: A review”, IOP Conf. Ser.: Mater. Sci. Eng., vol. 830, no 3, pp. 032051, Apr. 2020, doi: 10.1088/1757-899X/830/3/032051.
M. Raman Kumar ve N. K. Vaegae, “A new numerical approach for DNA representation using modified Gabor wavelet transform for the identification of protein coding regions”, Biocybernetics and Biomedical Engineering, vol. 40, no 2, pp. 836-848, Apr. 2020, doi: 10.1016/j.bbe.2020.03.007.
N. Yu, Z. Li, ve Z. Yu, “Survey on encoding schemes for genomic data representation and feature learning—from signal processing to machine learning”, Big Data Mining and Analytics, vol. 1, no 3, pp. 191-210, Sep. 2018, doi: 10.26599/BDMA.2018.9020018.
P. K. Kumari, “A Survey on Numerical Representation Of DNA Sequences”, Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, Apr. 2018, Erişim: 05 November 2021. [Online]. Access Adress: https://asianssr.org/index.php/ajct/article/view/417
L. Das, J. K. Das, S. Nanda, ve S. Mohapatra, “DNA Coding Sequence Prediction: A Review”, içinde 2018 International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC), Oct. 2018, vol. 1, pp. 1-6. doi: 10.1109/AESPC44649.2018.9033278.

M. Ahmad, L. T. Jung, ve A.-A. Bhuiyan, “From DNA to protein: Why genetic code context of nucleotides for DNA signal processing? A review”, Biomedical Signal Processing and Control, vol. 34, pp. 44-63, Apr. 2017, doi: 10.1016/j.bspc.2017.01.004.
X. Jin vd., “Similarity/dissimilarity calculation methods of DNA sequences: A survey”, Journal of Molecular Graphics and Modelling, vol. 76, pp. 342-355, Sep. 2017, doi: 10.1016/j.jmgm.2017.07.019.
G. Mendizabal-Ruiz, I. Román-Godínez, S. Torres-Ramos, R. A. Salido-Ruiz, ve J. A. Morales, “On DNA numerical representations for genomic similarity computation”, PLOS ONE, vol. 12, no 3, p. e0173288, Mar. 2017, doi: 10.1371/journal.pone.0173288.
S. Saini ve L. Dewan, “Comparison of Numerical Representations of Genomic Sequences: Choosing the Best Mapping for Wavelet Analysis”, Int. J. Appl. Comput. Math, vol. 3, no 4, pp. 2943-2958, Dec. 2017, doi: 10.1007/s40819-016-0277-1.
Mabrouk, M.S. Advanced Genomic Signal Processing Methods in DNA Mapping Schemes for Gene Prediction Using Digital Filters --Gene prediction, Digital filters, 3- Base periodicity, Exon, Intron, Bioinformatics, Genomic signal processing”, American Journal of Signal Processing, p. 13, 2017.
L. Das, J. K. Das, ve S. Nanda, “Identification of exon location applying kaiser window and DFT techniques”, içinde 2017 2nd International Conference for Convergence in Technology (I2CT), Apr. 2017, pp. 211-216. doi: 10.1109/I2CT.2017.8226123.
B. Das ve I. Turkoglu, “Classification of DNA sequences using numerical mapping techniques and Fourier transformation, Journal of the Faculty of Engineering and Arcitecture of Gazi University, 2016, doi: 10.17341/gazimmfd.278447.
M. Abo-Zahhad, S. M. Ahmed, ve S. A. Abd-Elrahman, “Integrated Model of DNA Sequence Numerical Representation and Artificial Neural Network for Human Donor and Acceptor Sites Prediction”, IJITCS, vol. 6, no 8, pp. 51-57, July. 2014, doi: 10.5815/ijitcs.2014.08.07.
M. Abo-Zahhad, S. M. Ahmed, ve S. A. Abd-Elrahman, “Genomic Analysis and Classification of Exon and Intron Sequences Using DNA Numerical Mapping Techniques”, IJITCS, vol. 4, no 8, pp. 22-36, July. 2012, doi: 10.5815/ijitcs.2012.08.03.
H. K. Kwan, B. Y. M. Kwan, ve J. Y. Y. Kwan, “Novel methodologies for spectral classification of exon and intron sequences”, EURASIP Journal on Advances in Signal Processing, vol. 2012, no 1, p. 50, Feb 2012, doi: 10.1186/1687-6180-2012-50.
S. D. Sharma, K. Shakya, ve S. N. Sharma, “Evaluation of DNA mapping schemes for exon detection”, içinde 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET), Mar. 2011, pp. 71-74. doi: 10.1109/ICCCET.2011.5762441.
F. Akalin, N. Yumusak, Classification of exon and intron regions obtained using digital signal processing techniques on the DNA genome sequencing with EfficientNetB7 architecture, GUMMFD, 37:3 (2022) 1355-1371.
F. Akalin and N. Yumuşak, “Classification of ALL and CML malignancies being among the main types of leukaemia with graph neural networks and fuzzy logic algorithm,” GUMMFD, Mar. 2022, doi: 10.17341/gazimmfd.1022624.
L. Das, S. Nanda, ve J. K. Das, “An integrated approach for identification of exon locations using recursive Gauss Newton tuned adaptive Kaiser window”, Genomics, vol. 111, no 3, pp. 284-296, May. 2019, doi: 10.1016/j.ygeno.2018.10.008.
A. C. H. Choong ve N. K. Lee, “Evaluation of convolutionary neural networks modeling of DNA sequences using ordinal versus one-hot encoding method”, içinde 2017 International Conference on Computer and Drone Applications (IConDA), Nov. 2017, pp. 60-65. doi: 10.1109/ICONDA.2017.8270400.
N. Chakravarthy, A. Spanias, L. D. Iasemidis, ve K. Tsakalis, “Autoregressive Modeling and Feature Analysis of DNA Sequences”, EURASIP J. Adv. Signal Process., vol. 2004, no 1, pp. 952689, Jan. 2004, doi: 10.1155/S111086570430925X.
R. K. M ve N. K. Vaegae, “Walsh code based numerical mapping method for the identification of protein coding regions in eukaryotes”, Biomedical Signal Processing and Control, vol. 58, no. 101859, Ap. 2020, doi: 10.1016/j.bspc.2020.101859.
B. Das, S. Toraman, and İ. Türkoğlu, “A novel genome analysis method with the entropy-based numerical technique using pretrained convolutional neural networks,” Turk J Elec Eng & Comp Sci, vol. 28, no. 4, pp. 1932–1948, Jul. 2020, doi: 10.3906/elk-1909-119.
P. Bernaola-Galván, I. Grosse, P. Carpena, J. L. Oliver, R. Román-Roldán, ve H. E. Stanley, “Finding Borders between Coding and Noncoding DNA Regions by an Entropic Segmentation Method”, Phys. Rev. Lett., vol. 85, no 6, pp. 1342-1345, Aug. 2000, doi: 10.1103/PhysRevLett.85.1342.
D. Nicorici ve J. Astola, “Segmentation of DNA into Coding and Noncoding Regions Based on Recursive Entropic Segmentation and Stop-Codon Statistics”, EURASIP J. Adv. Signal Process., vol. 2004, no 1, pp. 832471, Dec. 2004, doi: 10.1155/S1110865704309212.
N. Y. Song ve H. Yan, “Autoregressive modeling of DNA features for short exon recognition”, içinde 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec. 2010, pp. 450-455. doi: 10.1109/BIBM.2010.5706608.
Q. Zheng, T. Chen, W. Zhou, L. Xie, ve H. Su, “Gene prediction by the noise-assisted MEMD and wavelet transform for identifying the protein coding regions”, Biocybernetics and Biomedical Engineering, Vol. 41, no 1, 2021, doi: 10.1016/j.bbe.2020.12.005.
R. Harrison, Y. Li, ve I. Măndoiu, Ed., Bioinformatics Research and Applications: 11th International Symposium, ISBRA 2015 Norfolk, USA, June 7-10, 2015 Proceedings, c. 9096. Cham: Springer International Publishing, 2015. doi: 10.1007/978-3-319-19048-8.
Z. Abbas, H. Tayara, ve K. T. Chong, “4mCPred-CNN—Prediction of DNA N4-Methylcytosine in the Mouse Genome Using a Convolutional Neural Network”, Genes, vol. 12, no 2, Feb. 2021, doi: 10.3390/genes12020296.
P. Liò ve M. Vannucci, “Finding pathogenicity islands and gene transfer events in genome data”, Bioinformatics, vol. 16, no 10, pp. 932-940, Oct. 2000, doi: 10.1093/bioinformatics/16.10.932.
L. Zhang, F. Tian, S. Wang, ve X. Liu, “A novel coding method for gene mutation correction during protein translation process”, Journal of Theoretical Biology, vol. 296, pp. 33-40, Mar. 2012, doi: 10.1016/j.jtbi.2011.11.031.
F. Castro-Chavez, “Defragged Binary I Ching Genetic Code Chromosomes Compared to Nirenberg’s and Transformed into Rotating 2D Circles and Squares and into a 3D 100% Symmetrical Tetrahedron Coupled to a Functional One to Discern Start From Non-Start Methionines through a Stella Octangula”, J Proteome Sci Comput Biol, vol. 2012, no 1, pp. 3, 2012, doi: 10.7243/2050-2273-1-3.
M. Raman Kumar ve V. Naveen Kumar, “A Numerical Representation Method for a DNA Sequence Using Gray Code Method”, içinde Soft Computing for Problem Solving, Singapore, 2020, pp. 645-654. doi: 10.1007/978-981-15-0184-5_55.
L. Deng, H. Wu, X. Liu, ve H. Liu, “DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence”, International Journal of Molecular Sciences, vol. 22, no 11, Jan. 2021, doi: 10.3390/ijms22115521.
Q. Zhang, Z. Shen, ve D.-S. Huang, “Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network”, Sci Rep, vol. 9, no 1, p. 8484, June. 2019, doi: 10.1038/s41598-019-44966-x.
M. Randić, D. Butina, and J. Zupan, “Novel 2-D graphical representation of proteins,” Chemical Physics Letters, vol. 419, no. 4, pp. 528–532, Feb. 2006, doi: 10.1016/j.cplett.2005.11.091.
Z. Liu, B. Liao, W. Zhu, ve G. Huang, “A 2D graphical representation of DNA sequence based on dual nucleotides and its application”, International Journal of Quantum Chemistry, vol. 109, no 5, pp. 948-958, 2009, doi: 10.1002/qua.21919.
A. T. M. Bari, M. Reaz, A. T. Islam, H.-J. Choi, ve B.-S. Jeong, “Effective Encoding for DNA Sequence Visualization Based on Nucleotide’s Ring Structure”, Evolutionary bioinformatics online, vol. 9, pp. 251-61, July. 2013, doi: 10.4137/EBO.S12160.
S. Zou, L. Wang, ve J. Wang, “A 2D graphical representation of the sequences of DNA based on triplets and its application”, EURASIP Journal on Bioinformatics and Systems Biology, vol. 2014, no 1, pp. 1, Jan 2014, doi: 10.1186/1687-4153-2014-1.
B. Das ve I. Turkoglu, “A novel numerical mapping method based on entropy for digitizing DNA sequences”, Neural Comput & Applic, vol. 29, 8: 207-215, Apr. 2018, doi: 10.1007/s00521-017-2871-5.
A. Sankar, A. Nair, ve M. Thiru, “Visualization of genomic data using inter-nucleotide distance signals”, Jan. 2005.
Das, B. A deep learning model for identification of diabetes type 2 based on nucleotide signals. Neural Comput & Applic (2022). https://doi.org/10.1007/s00521-022-07121-8
Das, B. An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences. Chemometrics and Intelligent Laboratory Systems (2022),v. 230, 104680, ttps://doi.org/10.1016/j.chemolab.2022.104680
C.-T. Zhang ve J. Wang, “Recognition of protein coding genes in the yeast genome at better than 95% accuracy based on the Z curve”, Nucleic Acids Research, vol. 28, no 14, pp. 2804-2814, Tem. 2000, doi: 10.1093/nar/28.14.2804.
C. Yu, M. Deng, L. Zheng, R. L. He, J. Yang, ve S. S.-T. Yau, “DFA7, a New Method to Distinguish between Intron-Containing and Intronless Genes”, PLOS ONE, vol. 9, no 7, pp. e101363, Tem. 2014, doi: 10.1371/journal.pone.0101363.
R. R. Garafutdinov, A. R. Sakhabutdinova, P. A. Slominsky, F. G. Aminev, ve A. V. Chemeris, “A new digital approach to SNP encoding for DNA identification”, Forensic Science International, vol. 317, no. 110520, Dec. 2020, doi: 10.1016/j.forsciint.2020.110520.
T. Hoang, C. Yin, ve S. S.-T. Yau, “Numerical encoding of DNA sequences by chaos game representation with application in similarity comparison”, Genomics, vol. 108, no 3, pp. 134-142, Oct 2016, doi: 10.1016/j.ygeno.2016.08.002.
W. Deng ve Y. Luan, “Analysis of Similarity/Dissimilarity of DNA Sequences Based on Chaos Game Representation”, Abstract and Applied Analysis, vol. 2013, p. e926519, Mar. 2013, doi: 10.1155/2013/926519.
Z.-G. Yu ve V. Anh, “Time series model based on global structure of complete genome”, Chaos, Solitons & Fractals, vol. 12, no 10, pp. 1827-1834, Aug. 2001, doi: 10.1016/S0960-0779(00)00147-8.
C. Yin, “Encoding and Decoding DNA Sequences by Integer Chaos Game Representation”, Journal of Computational Biology, vol. 26, no 2, pp. 143-151, Feb. 2019, doi: 10.1089/cmb.2018.0173.
A. P. Boyle vd., “Comparative analysis of regulatory information and circuits across distant species”, Nature, vol. 512, no 7515, Aug. 2014, doi: 10.1038/nature13668.
Z. Mo vd., “One novel representation of DNA sequence based on the global and local position information”, Sci Rep, vol. 8, no 1, p. 7592, May. 2018, doi: 10.1038/s41598-018-26005-3.
D. Bielińska-Wąż ve P. Wąż, “Spectral-dynamic representation of DNA sequences”, Journal of Biomedical Informatics, vol. 72, pp. 1-7, Aug. 2017, doi: 10.1016/j.jbi.2017.06.001.
A. Czerniecka, D. Bielińska-Wąż, P. Wąż, ve T. Clark, “20D-dynamic representation of protein sequences”, Genomics, vol. 107, no 1, pp. 16-23, Jan. 2016, doi: 10.1016/j.ygeno.2015.12.003.
D. Zhang, “A New Numerical Method for DNA Sequence Analysis Based on 8-Dimensional Vector Representation”, Journal of Applied Mathematics and Physics, vol. 7, no 12, Dec. 2019, doi: 10.4236/jamp.2019.712204.
F. Ben Nasr ve A. E. Oueslati, “CNN for human exons and introns classification”, içinde 2021 18th International Multi-Conference on Systems, Signals Devices (SSD), Mar. 2021, pp. 249-254. doi: 10.1109/SSD52085.2021.9429303.
A. Rokas, “Phylogenetic Analysis of Protein Sequence Data Using the Randomized Axelerated Maximum Likelihood (RAXML) Program”, Current Protocols in Molecular Biology, vol. 96, no 1, pp. 19.11.1-19.11.14, 2011, doi: 10.1002/0471142727.mb1911s96.

Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Seda Nur Gülocak
0000-0003-1849-3555
Türkiye

Bihter Daş ^*
0000-0002-2498-3297
Türkiye

Publication Date

December 31, 2022

Submission Date

October 19, 2022

Acceptance Date

November 1, 2022

Published in Issue

Year 1970 Volume: 5 Number: 3

DOI

https://doi.org/10.35377/saucis...1191850

IZ

https://izlik.org/JA28GT57WW

Cite

RIS / Bibtex

APA

Gülocak, S. N., & Daş, B. (2022). The Effect of Numerical Mapping Techniques on Performance in Genomic Research. Sakarya University Journal of Computer and Information Sciences, 5(3), 315-340. https://doi.org/10.35377/saucis...1191850

AMA

1.Gülocak SN, Daş B. The Effect of Numerical Mapping Techniques on Performance in Genomic Research. SAUCIS. 2022;5(3):315-340. doi:10.35377/saucis.1191850

Chicago

Gülocak, Seda Nur, and Bihter Daş. 2022. “The Effect of Numerical Mapping Techniques on Performance in Genomic Research”. Sakarya University Journal of Computer and Information Sciences 5 (3): 315-40. https://doi.org/10.35377/saucis. 1191850.

EndNote

Gülocak SN, Daş B (December 1, 2022) The Effect of Numerical Mapping Techniques on Performance in Genomic Research. Sakarya University Journal of Computer and Information Sciences 5 3 315–340.

IEEE

[1]S. N. Gülocak and B. Daş, “The Effect of Numerical Mapping Techniques on Performance in Genomic Research”, SAUCIS, vol. 5, no. 3, pp. 315–340, Dec. 2022, doi: 10.35377/saucis...1191850.

ISNAD

Gülocak, Seda Nur - Daş, Bihter. “The Effect of Numerical Mapping Techniques on Performance in Genomic Research”. Sakarya University Journal of Computer and Information Sciences 5/3 (December 1, 2022): 315-340. https://doi.org/10.35377/saucis. 1191850.

JAMA

1.Gülocak SN, Daş B. The Effect of Numerical Mapping Techniques on Performance in Genomic Research. SAUCIS. 2022;5:315–340.

MLA

Gülocak, Seda Nur, and Bihter Daş. “The Effect of Numerical Mapping Techniques on Performance in Genomic Research”. Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 3, Dec. 2022, pp. 315-40, doi:10.35377/saucis. 1191850.

Vancouver

1.Seda Nur Gülocak, Bihter Daş. The Effect of Numerical Mapping Techniques on Performance in Genomic Research. SAUCIS. 2022 Dec. 1;5(3):315-40. doi:10.35377/saucis. 1191850