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Open Access Highly Accessed Research article

Prediction of regulatory elements in mammalian genomes using chromatin signatures

Kyoung-Jae Won1, Iouri Chepelev1, Bing Ren2 and Wei Wang1*

Author Affiliations

1 Dept of Chemistry & Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0359, USA

2 Ludwig Institute for Cancer Research, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0653, USA

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BMC Bioinformatics 2008, 9:547  doi:10.1186/1471-2105-9-547

Published: 18 December 2008

Additional files

Additional file 1:

Figure S1. Probability density of Gaussian mixtures for the three HMM states trained on promoter and enhancer for each chromatin marker. The x-axis is the log ratio of ChIP-chip intensity. The black curve is the mixture of 3 Gaussian (red curves represent individual Gaussians. (A) H4ac. Analysis on the trained HMM for H4ac.

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Additional file 2:

Figure S1 (B). H3ac. Analysis on the trained HMM for H3ac.

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Additional file 3:

Figure S1 (C). H3K4me1. Analysis on the trained HMM for H3K4me1.

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Additional file 4:

Figure S1 (D). H3K4me2. Analysis on the trained HMM for H3K4me2.

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Additional file 5:

Figure S1 (E). H3K4me3. Analysis on the trained HMM for H3K4me3.

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Additional file 6:

Figure S1 (F). H3. Analysis on the trained HMM for H3.

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Additional file 7:

Figure S2 Active and inactive profile of the ChIP-Seq data. Tag counts at TSS are clustered considering the expression ratio. Histone profiles for active and inactive TSS.

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