Discover regulatory DNA elements using chromatin signatures and artificial neural network.
BACKGROUND
Recent large-scale chromatin states mapping efforts have revealed characteristic chromatin modification signatures for various types of functional DNA elements. Given the important influence of chromatin states on gene regulation and the rapid accumulation of genome-wide chromatin modification data, there is a pressing need for computational methods to analyze these data in order to identify functional DNA elements. However, existing computational tools do not exploit data transformation and feature extraction as a means to achieve a more accurate prediction.


RESULTS
We introduce a new computational framework for identifying functional DNA elements using chromatin signatures. The framework consists of a data transformation and a feature extraction step followed by a classification step using time-delay neural network. We implemented our framework in a software tool CSI-ANN (chromatin signature identification by artificial neural network). When applied to predict transcriptional enhancers in the ENCODE region, CSI-ANN achieved a 65.5% sensitivity and 66.3% positive predictive value, a 5.9% and 11.6% improvement, respectively, over the previously best approach.


METHODS
CSI-ANN is implemented in Matlab. The source code is freely available at http://www.medicine.uiowa.edu/Labs/tan/CSIANNsoft.zip


BACKGROUND
kai-tan@uiowa.edu


BACKGROUND
Supplementary Materials are available at Bioinformatics online.
Firpi
Hiram A
HA
Department of Internal Medicine, University of Iowa, 2294 CBRB, 285 Newton Road, Iowa City, IA 52242, USA.
Ucar
Duygu
D
Tan
Kai
K
D000465:Algorithms; D015496:CD4-Positive T-Lymphocytes / Q000378:metabolism; D002843:Chromatin / Q000737:chemistry*; D005786:Gene Expression Regulation; D006367:HeLa Cells; D042421:Histone Code; D006657:Histones / Q000378:metabolism; D006801:Humans; D016571:Neural Networks, Computer; D012045:Regulatory Sequences, Nucleic Acid
D002843:Chromatin; D006657:Histones
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