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DETECTION OF EXON AND INTRON REGIONS IN DNA SEQUENCES BY THE PROPOSED HASHING FUNCTION

Year 2023, , 26 - 41, 30.09.2023
https://doi.org/10.59313/jsr-a.1281084

Abstract

Chromosomes, which are formed by the combination of DNA and special proteins, are structures that can show some changes with the effect of genetic or environmental factors. The DNA molecule in these structures carries vital information in elucidating critical information about life. DNA, which is formed by the combination of sugar, phosphate and organic bases, has exon and intron regions separation. Information about the processes in the life cycle of cells, the changes experienced by stem cells, the regulations in the growth and development stage, the development status of cancer, mutation occurrences and protein synthesis are stored in exon regions. Distinguishing exon regions that form 3% of a cell's DNA is challenging. However, detecting diseases on genetically based facts offers more precise outputs. For this reason, analyses were made on the BCR-ABL gene and BRCA-1 mutation carrier genes to analyse leukemia and breast cancer, which are genetically based diseases. First, these genes obtained from the NCBI gene bank were digitized by integer mapping technique. The digitized sequences were given as input to the hash function. This proposed hash function consists of the steps of finding the logarithmic equivalent of the total number of digitized organic bases, summing all logarithmic equivalents, rounding to the nearest integer, expressing it in binary and placing it in the hash table. These outputs, which define the exon and intron regions, were shown as clusters to find the new input region easily. The collision cluster is the binary representation of key values representing both exon and intron regions for the same region. The main goal is to have a small number of elements in this cluster. With the proposed hierarchy in this study, only one collision occurred for BCR-ABL and BRCA-1 genes. Accuracy rates of the proposed approach based on a mathematical basis and independent of nucleotide length were obtained 93.33%, and 96%, respectively.

References

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  • [28] Akalın, F., and Yumuşak, N. (2023). Classification of ALL and CML malignancies being among the main types of leukaemia with graph neural networks and fuzzy logic algorithm. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(2), 707–719, 2023.
  • [29] Yetim, E. (2018). Meme manyetik rezonans görüntülemede BI-RADS kategori 3 lezyonlar; takip sonuçları. Akdeniz Üniversitesi Tıp Fakültesi Radyoloji Anabilim Dalı, Uzmanlık Tezi.
  • [30] Yumuşak, N., and Adak. M.F. (2016). C/C++ ile veri yapıları.
  • [31] Das, B., and Turkoglu, I. (2018). A novel numerical mapping method based on entropy for digitizing DNA sequences. Neural Computing and Applications, 29(8), 207–215.
  • [32] Marhon, S. A., and Kremer, S. C. (2011). Protein coding region prediction based on the adaptive representation method. Canadian Conference on Electrical and Computer Engineering, 000415–000418.
  • [33] Li, J., Zhang, L., Li, H., Ping, Y., Xu, Q., Wang, R., Tan, R., Zhen, W., Liu, B., and Wang, Y. (2019). Integrated entropy-based approach for analyzing exons and introns in DNA sequences. BMC Bioinformatics, 20.
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Year 2023, , 26 - 41, 30.09.2023
https://doi.org/10.59313/jsr-a.1281084

Abstract

References

  • [1] Kocabıyık, V.B. (2011). ALL ve KML’li hastalarda BCR ve ABL genlerindeki mutasyonların incelenmesi. Yüksek Lisans Tezi, Selçuk Üniversitesi Sağlık Bilimleri Enstitüsü, Konya.
  • [2] Khodaei, A., Feizi-Derakhshi, M.R., and Mozaffari-Tazehkand, B. (2020). A pattern recognition model to distinguish cancerous DNA sequences via signal processing methods. Soft Computing, 24(21), 16315–16334.
  • [3] Das B., and Türkoglu, I. (2016). Classification of DNA sequences using numerical mapping techniques and Fourier transformation. Journal of the Faculty of Engineering and Architecture of Gazi University, 31(4), 921–932, 2016.
  • [4] Barman, S., Saha, S., Mandal, A., and Roy M. (2012). Prediction of protein coding regions of a DNA sequence through spectral analysis. 2012 International Conference on Informatics, Electronics and Vision, ICIEV 2012.
  • [5] Hota, M. K., and Srivastava, V. K. (2010). Performance analysis of different DNA to numerical mapping techniques for identification of protein coding regions using tapered window based short-time discrete Fourier transform. ICPCES 2010 - International Conference on Power, Control and Embedded Systems 2010, 0–3.
  • [6] Daş, B. (2018). DNA dizilimlerinden hastalık tanılanması için işaret işleme temelli yeni yaklaşımların geliştirilmesi. Doktora Tezi, Fırat Üniversitesi Fen Bilimleri Enstitüsü, Elazığ, 83s.
  • [7] Al-jaboriy, S.S., Sjarif, N.N.A., Chuprat, S., and Abduallah, W.M. (2019). Acute lymphoblastic leukemia segmentation using local pixel information. Pattern Recognition Letters, 125, 85–90.
  • [8] Scotti F. (2005). Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. CIMSA 2005-IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 20–22.
  • [9] Kutlu, H., Avci, E., and Özyurt, F. (2020). White blood cells detection and classification based on regional convolutional neural networks. Medical Hypotheses, 135.
  • [10] Chakraborty, S., and Gupta, V. (2016). DWT based cancer identification using EIIP. Proceedings - 2016 2nd International Conference on Computational Intelligence and Communication Technology, CICT 2016, 718–723.
  • [11] Das, L., Das J.K., and Nanda, S. (2020). Detection of exon location in eukaryotic DNA using a fuzzy adaptive Gabor wavelet transform. Genomics, 112, 4406–4416.
  • [12] Das, L., Nanda, S., and Das, J.K. (2019). An integrated approach for identification of exon locations using recursive gauss newton tuned adaptive kaiser window. Genomics, 111, 284–296.
  • [13] Gupta, R., Mittal, A., Singh, K., Bajpai, P., and Prakash, S. (2007). A time series approach for identification of exons and introns. 10th International Conference on Information Technology (ICIT 2007), 91–93.
  • [14] Hsu, C.H., Chen, X., Lin, W., Jiang, C., Zhang, Y., Hao, Z., and Chung, Y.C. (2021). Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning. Measurement, 175.
  • [15] Aydın, G. (2017). Quercetinin KML kök hücreleri üzerine sitotoksik etkilerinin moleküler düzeyde incelenmesi. Erciyes Üniversitesi, Sağlık Bilimleri Enstitüsü, Yüksek Lisans Tezi, Kayseri.
  • [16] Arslan, S. (2014). KML ve ALL Tanılı Hastalarda BCR/ABL füzyon geni mutasyonlarının taranması. Eskişehir Osmangazi Üniversitesi Sağlık Bilimleri Enstitüsü, Yüksek Lisans Tezi, 76s.
  • [17] Audic S., and Claverie, J. M. (1998). Self-identification of protein-coding regions in microbial genomes. Proceedings of the National Academy of Sciences of the United States of America, 95(17), 10026–10031.
  • [18] Zhang, M.Q. (1998). Statistical features of human exons and their flanking regions. Human Molecular Genetics, 7(5), 919–932, 1998.
  • [19] Snyder, E.E., and Stormo, G.D. (1995). Identification of protein coding regions in genomic DNA. Journal of Molecular Biology, 248(1), 1–18.
  • [20] Mereuta, S., and Munteanu, V. (2007). A new information theoretic approach to exon - intron classification. ISSCS 2007 - International Symposium on Signals, Circuits and Systems, Proceedings 2007, 2, 497–500.
  • [21] Mena-Chalco, J., Carrer, H., Zana, Y., and Cesar, R. M. (2008). Identification of protein coding regions using the modified gabor-wavelet transform. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 5(2), 198–206.
  • [22] Kar, S., and Ganguly, M. (2022). Study of effectiveness of FIR and IIR filters in exon identification: a comparative approach. Materials Today: Proceedings, 58, 437–444.
  • [23] M, R. K., and Vaegae, N. K. (2020). Walsh code based numerical mapping method for the identification of protein coding regions in eukaryotes. Biomedical Signal Processing and Control, 58.
  • [24] Singh, N., Nath, R., and Singh, D.B. (2022). Splice-site identification for exon prediction using bidirectional LSTM-RNN approach. Biochemistry and Biophysics Reports, 30.
  • [25] Ben Nasr, F., and Oueslati, A.E. (2021). CNN for human exons and introns classification. 18th International Multi-Conference on Systems. Signals & Devices SSD'21 2021, 249–254.
  • [26] Ben Nasrand, F., Oueslati, A.E. (2022). A new automatic method for human coding and non-coding zones characterization and classification based on FCGR coding and CNN classifier. International Conference on Advanced Technologies for Signal and Image Processing, ATSIP, 8–9.
  • [27] Akalın, F., and Yumuşak, N. (2022). Classification of exon and intron regions obtained using digital signal processing techniques on the DNA genome sequencing with EfficientNetB7 architecture. Journal of the Faculty of Engineering and Architecture of Gazi University, 37(3), 1355–1371.
  • [28] Akalın, F., and Yumuşak, N. (2023). Classification of ALL and CML malignancies being among the main types of leukaemia with graph neural networks and fuzzy logic algorithm. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(2), 707–719, 2023.
  • [29] Yetim, E. (2018). Meme manyetik rezonans görüntülemede BI-RADS kategori 3 lezyonlar; takip sonuçları. Akdeniz Üniversitesi Tıp Fakültesi Radyoloji Anabilim Dalı, Uzmanlık Tezi.
  • [30] Yumuşak, N., and Adak. M.F. (2016). C/C++ ile veri yapıları.
  • [31] Das, B., and Turkoglu, I. (2018). A novel numerical mapping method based on entropy for digitizing DNA sequences. Neural Computing and Applications, 29(8), 207–215.
  • [32] Marhon, S. A., and Kremer, S. C. (2011). Protein coding region prediction based on the adaptive representation method. Canadian Conference on Electrical and Computer Engineering, 000415–000418.
  • [33] Li, J., Zhang, L., Li, H., Ping, Y., Xu, Q., Wang, R., Tan, R., Zhen, W., Liu, B., and Wang, Y. (2019). Integrated entropy-based approach for analyzing exons and introns in DNA sequences. BMC Bioinformatics, 20.
  • [34] Hota, M. K., and Srivastava, V. K. (2012). Identification of protein coding regions using antinotch filters. Digital Signal Processing: A Review Journal, 22(6), 869–877.
There are 34 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Fatma Akalın 0000-0001-6670-915X

Nejat Yumuşak 0000-0001-5005-8604

Publication Date September 30, 2023
Submission Date April 11, 2023
Published in Issue Year 2023

Cite

IEEE F. Akalın and N. Yumuşak, “DETECTION OF EXON AND INTRON REGIONS IN DNA SEQUENCES BY THE PROPOSED HASHING FUNCTION”, JSR-A, no. 054, pp. 26–41, September 2023, doi: 10.59313/jsr-a.1281084.