Abstract
Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 236-249 |
| Number of pages | 14 |
| Journal | Journal of Computational Biology |
| Volume | 22 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jan 1 2015 |
| Externally published | Yes |
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Keywords
- algorithms
- gene expression
- genetic analysis
- genome analysis
- next-generation sequencing
ASJC Scopus subject areas
- Modeling and Simulation
- Molecular Biology
- Genetics
- Computational Mathematics
- Computational Theory and Mathematics
Cite this
A stationary wavelet entropy-based clustering approach accurately predicts gene expression. / Nguyen, Nha H.; Vo, An; Choi, Inchan; Won, Kyoung Jae.
In: Journal of Computational Biology, Vol. 22, No. 3, 01.01.2015, p. 236-249.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A stationary wavelet entropy-based clustering approach accurately predicts gene expression
AU - Nguyen, Nha H.
AU - Vo, An
AU - Choi, Inchan
AU - Won, Kyoung Jae
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation.
AB - Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation.
KW - algorithms
KW - gene expression
KW - genetic analysis
KW - genome analysis
KW - next-generation sequencing
UR - http://www.scopus.com/inward/record.url?scp=84924763000&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84924763000&partnerID=8YFLogxK
U2 - 10.1089/cmb.2014.0221
DO - 10.1089/cmb.2014.0221
M3 - Article
VL - 22
SP - 236
EP - 249
JO - Journal of Computational Biology
JF - Journal of Computational Biology
SN - 1066-5277
IS - 3
ER -