scholarly journals An evaluation of new criteria for CpG islands in the human genome as gene markers

2004 ◽  
Vol 20 (7) ◽  
pp. 1170-1177 ◽  
Author(s):  
Y. Wang ◽  
F. C.C. Leung
Keyword(s):  
Human Genome ◽  
Cpg Islands ◽  
Gene Markers ◽  
New Criteria

CpG islands as gene markers in the human genome

Genomics ◽  
1992 ◽  
Vol 13 (4) ◽  
pp. 1095-1107 ◽  
Author(s):  
Frank Larsen ◽  
Glenn Gundersen ◽  
Rodrigo Lopez ◽  
Hans Prydz
Keyword(s):  
Human Genome ◽  
Cpg Islands ◽  
Gene Markers

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Features of Methylation and Gene Expression in the Promoter-Associated CpG Islands Using Human Methylome Data

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Du ◽  
Leng Han ◽  
An-Yuan Guo ◽  
Zhongming Zhao
Keyword(s):  
Gene Expression ◽  
Methylation Level ◽  
Cpg Island ◽  
Expression Patterns ◽  
Cpg Islands ◽  
Gc Content ◽  
Gene Markers ◽  
A Genome ◽  
Genome Level ◽  
Lower Ratio

CpG islands are typically located in the 5′end of genes and considered as gene markers because they play important roles in gene regulation via epigenetic change. In this study, we compared the features of CpG islands identified by several major algorithms by setting the parameter cutoff values in order to obtain a similar number of CpG islands in a genome. This approach allows us to systematically compare the methylation and gene expression patterns in the identified CpG islands. We found that Takai and Jones’ algorithm tends to identify longer CpG islands but with weaker CpG island features (e.g., lower GC content and lower ratio of the observed over expected CpGs) and higher methylation level. Conversely, the CpG clusters identified by Hackenberg et al.’s algorithm using stringent criteria are shorter and have stronger features and lower methylation level. In addition, we used the genome-wide base-resolution methylation profile in two cell lines to show that genes with a lower methylation level at the promoter-associated CpG islands tend to express in more tissues and have stronger expression. Our results validated that the DNA methylation of promoter-associated CpG islands suppresses gene expression at the genome level.

scholarly journals Particle Swarm Optimization with Reinforcement Learning for the Prediction of CpG Islands in the Human Genome

PLoS ONE ◽  
2011 ◽  
Vol 6 (6) ◽  
pp. e21036 ◽  
Author(s):  
Li-Yeh Chuang ◽  
Hsiu-Chen Huang ◽  
Ming-Cheng Lin ◽  
Cheng-Hong Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Reinforcement Learning ◽  
Human Genome ◽  
Cpg Islands ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Intergenic, gene terminal, and intragenic CpG islands in the human genome

BMC Genomics ◽  
2010 ◽  
Vol 11 (1) ◽  
pp. 48 ◽  
Author(s):  
Yulia A Medvedeva ◽  
Marina V Fridman ◽  
Nina J Oparina ◽  
Dmitry B Malko ◽  
Ekaterina O Ermakova ◽  
...  
Keyword(s):  
Human Genome ◽  
Cpg Islands

scholarly journals CpGIMethPred: computational model for predicting methylation status of CpG islands in human genome

2013 ◽  
Vol 6 (Suppl 1) ◽  
pp. S13 ◽  
Author(s):  
Hao Zheng ◽  
Hongwei Wu ◽  
Jinping Li ◽  
Shi-Wen Jiang
Keyword(s):  
Computational Model ◽  
Human Genome ◽  
Methylation Status ◽  
Cpg Islands

A CpGCluster-Teaching–Learning-Based Optimization for Prediction of CpG Islands in the Human Genome

2018 ◽  
Vol 25 (2) ◽  
pp. 158-169 ◽  
Author(s):  
Cheng-Hong Yang ◽  
Yi-Cheng Chiang ◽  
Li-Yeh Chuang ◽  
Yu-Da Lin
Keyword(s):  
Human Genome ◽  
Cpg Islands ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Compositional searching of CpG islands in the human genome

2005 ◽  
Vol 71 (6) ◽  
Author(s):  
Pedro Luis Luque-Escamilla ◽  
José Martínez-Aroza ◽  
José L. Oliver ◽  
Juan Francisco Gómez-Lopera ◽  
Ramón Román-Roldán
Keyword(s):  
Human Genome ◽  
Cpg Islands
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