. ISSN 1684-1719. 2021. 1


English page


DOI https://doi.org/10.30898/1684-1719.2021.1.11



Digital signal processing-based approach to identify splicing mutations for detecting genetic diseases


P. Kumar Varadwaj 1, N. Purohit 1, T. Lahiri 1, V. Antisiperov 2

1 Indian Institute of Information Technology Allahabad, Devghat, Jhalwa, Prayagraj-211015, U. P. India

2 Kotelnikov Institute of Radioengineering and Electronics, Mokhovaya 11-7, Moscow, 125009, Russia


The paper was received on January 12, 2021


Abstract. More than ninety percent of genes in Homo Sapience are reported to exist as discontinuous segments of coding regions called as Exons and are separated by intervening non-coding regions, called Introns. During the splicing mechanism, the non-coding regions got removed and coding regions are joined together for producing the precursor messenger RNA. The site of these Exon-Intron splicing is called Splice Site. The anomalies caused due to genetic mutation in spice site during the processing of precursor m-RNA into mature m-RNA causes several genetic diseases like Cancer, Dementia, Epilepsy, Hematological Disorders, Parathyroid Deficiency etc. It is estimated that as many as 50% of disease-causing mutations affect splicing. The present invention describes the design of digital signal processing-based approach to detect these Splicing Site. A successful identification of the splice site will help in finding the mutations hence can be used as an inference tool for predicting genetic disease.

Keywords: biomedical signal processing, coding regions, splicing, statistic inference.


1. Akhtar, J. Epps, E. Ambikairajah. Signal processing in sequence analysis. IEEE Journal of Selected Topics in Signal Processing. 2008, Vol.2(3). P.310-321. https://doi.org/10.1109/JSTSP.2008.923854

2. Saxena A., Pitchaipillai G., Vardawaj P.K. Annotation of Human Genomic Sequence by Combining Existing Gene Prediction Tools Using Hybrid Approach. In:  Parashar M., Kaushik D., Rana O.F., Samtaney R., Yang Y., Zomaya A., editors.  Contemporary Computing. IC3 2012. Communications in Computer and Information Science, 2012. Vol 306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32129-0_48



Varadwaj P.K., Purohit N., Lahiri T., Antisiperov V. Digital signal processing-based approach to identify splicing mutations for detecting genetic diseases. Zhurnal Radioelektroniki [Journal of Radio Electronics]. 2021. No.1. https://doi.org/10.30898/1684-1719.2021.1.11