Towards Pattern Detection of Proprotein Convertase Subtilisin/kexin type 9(PCSK9) Gene in Bioinformatics Big Data

  • Umar Draz, Tariq Ali*, Sana Yasin

Abstract

Data is increasing rapidly not only in every passing day but also in every passing second. The management of this huge amount of data (big data) is very difficult without the data handling tools and techniques. These big data techniques are specially designed to handle large volume of data that cannot be easily managed by the traditional databases. In the field of bioinformatics data exists not only in large volume but also in different formats, for example in the field of bioinformatics. In this paper, the deep analysis of proprotein convertase Subtilisin/kexin type-9 (PCSK9) is done through the gapped and un-gapped Patterns detection that regulates the cell surfaces. This gene plays a significant role to control the cholesterol that is a waxy plump ingredient and yield from foods that originate from the animals. Recently formal methods and its formal specification are successfully implemented in different types of scenarios. As simulation work does not provide the correctness of the model and its experiments, so in this case, the formal methods not only provide the proof of correctness under the examination problem but also give its syntax and semantic verification. The verification and validation of the pattern detection of PCSK9 is done by the Vienna development Method Specification Language that is implemented through VDM-SL Tool box. To check the diversity between the genes, PCSK9 is compared with other PCSK types and this comparison is done by TOMTOM tool box..

Published
2018-10-22
How to Cite
SANA YASIN, Umar Draz, Tariq Ali*,. Towards Pattern Detection of Proprotein Convertase Subtilisin/kexin type 9(PCSK9) Gene in Bioinformatics Big Data. NFC IEFR Journal of Engineering and Scientific Research, [S.l.], v. 6, p. 160-165, oct. 2018. ISSN 2521-0114. Available at: <http://nijesr.com/ojs/index.php/archive/article/view/185>. Date accessed: 13 dec. 2018.
Section
Articles