A Quantitative Understanding of Human Sex Chromosomal Genes release_cm5dg7qt2bcizk46ve6bo4easq

by Sk. Sarif Hassan, Pabitra Pal Choudhury, Antara Sengupta, Binayak Sahu, Rojalin Mishra, Devendra Kumar Yadav, Saswatee Panda, Dharamveer Pradhan, Shrusti Dash, Gourav Pradhan

Released as a article .

2013  

Abstract

In the last few decades, the human allosomes are engrossed in an intensive attention among researchers. The allosomes are now already been sequenced and found there are about 2000 and 78 genes in human X and Y chromosomes respectively. The hemizygosity of the human X chromosome in males exposes recessive disease alleles, and this phenomenon has prompted decades of intensive study of X-linked disorders. By contrast, the small size of the human Y chromosome, and its prominent long-arm heterochromatic region suggested absence of function beyond sex determination. But the present problem is to accomplish whether a given sequence of nucleotides i.e. a DNA is a Human X or Y chromosomal genes or not, without any biological experimental support. In our perspective, a proper quantitative understanding of these genes is required to justify or nullify whether a given sequence is a Human X or Y chromosomal gene. In this paper, some of the X and Y chromosomal genes have been quantified in genomic and proteomic level through Fractal Geometric and Mathematical Morphometric analysis. Using the proposed quantitative model, one can easily make probable justification or deterministic nullification whether a given sequence of nucleotides is a probable Human X or Y chromosomal gene or not, without seeking any biological experiment. Of course, a further biological experiment is essential to validate it as the probable Human X or Y chromosomal gene homologue. This study would enable Biologists to understand these genes in more quantitative manner instead of their qualitative features.
In text/plain format

Archived Files and Locations

application/pdf  1.6 MB
file_bzdtrkixhzar7f7eowy75x6jlq
web.archive.org (webarchive)
arxiv.org (repository)
Read Archived PDF
Archived
Type  article
Stage   submitted
Date   2013-12-02
Version   v2
Language   en ?
arXiv  1207.5289v2
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 94b2af07-4561-49d0-8964-7f4ba4fa9ccf
API URL: JSON