Identifying Driver Potential in Passenger Genes Using Chemical Properties of Mutated and Surrounding Amino Acids View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Jayanta Kumar Das , Richa Singh , Pabitra Pal Choudhury , Bidyut Roy

ABSTRACT

One of the major challenging tasks today is to understand uncontrolled growth of tumor. Generally, two types of genes and mutations are observed in tumor cells. The driver mutations, within genes, confer a selective growth advantage and are responsible for causing the cancer. The passenger mutations are observed in those genes that, generally, do not provide growth advantage of cells in tumor. In tumor genome, more frequently mutated genes are considered as driver, whereas less frequent mutations are known as passenger genes. The prime aim of the present article is to identify the set of passenger genes that may have driver potential. The current analysis completely deals with the amino acid sequence and embedded chemical properties of amino acids present in both driver and passenger proteins. We picked up mutated and surrounding 21 amino acids, i.e. one mutated non-synonymous amino acid in the middle and 10 amino acids on the both sides of the mutated amino acid, in driver proteins and compared the presence of this length of amino acids having the same mutated amino acid, in passenger protein sequence. In this comparison of pairwise alignment, we generated similarity score between driver and passenger proteins. Based on the similarity index (i.e., alignment score) above the median value, we considered a set of passenger genes as having driver potential. Some of these passenger genes also possess reported biological functions and are found in the pathways of cancer development. So, these passenger genes or proteins, with diver potential, may play crucial role for cancer development. More... »

PAGES

107-118

References to SciGraph publications

Book

TITLE

Computational Intelligence and Big Data Analytics

ISBN

978-981-13-0543-6
978-981-13-0544-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-981-13-0544-3_10

DOI

http://dx.doi.org/10.1007/978-981-13-0544-3_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106894283


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