scholarly journals Establishment and Maintenance of Alternative Chromatin States at a Multicopy Gene Locus

Cell ◽  
2011 ◽  
Vol 145 (4) ◽  
pp. 543-554 ◽  
Author(s):  
Manuel Wittner ◽  
Stephan Hamperl ◽  
Ulrike Stöckl ◽  
Wolfgang Seufert ◽  
Herbert Tschochner ◽  
...  
Keyword(s):  
Gene Locus ◽  
Chromatin States ◽  
Multicopy Gene

KERAGAMAN GENETIK INDUK UDANG WINDU DI PERAIRAN SELATAN PANGANDARAN DAN BINUANGEUN, JAWABARAT

2018 ◽  
Vol 5 (3) ◽  
Author(s):  
Ratu Siti Aliah
Keyword(s):  
Genetic Diversity ◽  
Population Genetic ◽  
Gene Locus ◽  
Penaeus Monodon ◽  
12S Rrna ◽  
Brood Stock ◽  
Population Genetic Diversity ◽  
Result Show ◽  
Mtdna Diversity

An evaluation of the Black Tiger Brood Stock (Penaeus monodon) genetic diversity of Pangandaran and Binuangeun was conducted by using the mtDNA diversity of two gene locus of CO I and 12S rRNA to understand their population genetic diversity. The result show that the brood stock of Pangandaran has 17 haplotipe, while from Binuangeun has 13 haplotipe. The result indicated that the genetic diversity of the Balck Tiger brood stock of Pangandaran was higher than thatBinuangeun.Key words : Genetic diversity, Black Tiger brood stock, Pangandaran, Binuangeun

Association of G72 gene locus with schizophrenia in Chinese females of Han ethnicity

2010 ◽  
Vol 30 (8) ◽  
pp. 826-829
Author(s):  
Yan HAN ◽  
Huan ZHANG ◽  
Qi-lan NING ◽  
Bo ZHONG ◽  
Qing-zhu SUN ◽  
...  
Keyword(s):  
Gene Locus

HCVS: Pinpointing Chromatin States Through Hierarchical Clustering and Visualization Scheme

2019 ◽  
Vol 14 (2) ◽  
pp. 148-156
Author(s):  
Nighat Noureen ◽  
Sahar Fazal ◽  
Muhammad Abdul Qadir ◽  
Muhammad Tanvir Afzal
Keyword(s):  
Hierarchical Clustering ◽  
Real Data ◽  
Cell Types ◽  
Computational Scheme ◽  
Data Set ◽  
Chromatin States ◽  
Functional Regions ◽  
Visualization Strategy ◽  
Hidden States ◽  
Next Generation Sequencing Ngs

Background: Specific combinations of Histone Modifications (HMs) contributing towards histone code hypothesis lead to various biological functions. HMs combinations have been utilized by various studies to divide the genome into different regions. These study regions have been classified as chromatin states. Mostly Hidden Markov Model (HMM) based techniques have been utilized for this purpose. In case of chromatin studies, data from Next Generation Sequencing (NGS) platforms is being used. Chromatin states based on histone modification combinatorics are annotated by mapping them to functional regions of the genome. The number of states being predicted so far by the HMM tools have been justified biologically till now. Objective: The present study aimed at providing a computational scheme to identify the underlying hidden states in the data under consideration. </P><P> Methods: We proposed a computational scheme HCVS based on hierarchical clustering and visualization strategy in order to achieve the objective of study. Results: We tested our proposed scheme on a real data set of nine cell types comprising of nine chromatin marks. The approach successfully identified the state numbers for various possibilities. The results have been compared with one of the existing models as well which showed quite good correlation. Conclusion: The HCVS model not only helps in deciding the optimal state numbers for a particular data but it also justifies the results biologically thereby correlating the computational and biological aspects.

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