ChIP-seq in steatohepatitis and normal liver tissue identifies candidate disease mechanisms related to progression to cancer
1 Science for Life Laboratory, Department of Immunology, Genetics and Pathology, BMC, Uppsala University, PO BOX 815, Uppsala, SE 751 08, Sweden
2 Science for Life Laboratory, Department of Cell and Molecular Biology, BMC, Uppsala University, Uppsala, Sweden
3 Institute of Pathology, Medical University of Graz, Graz, Austria
4 Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, PL-02-106, Poland
5 Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, BMC, Uppsala, Sweden
BMC Medical Genomics 2013, 6:50 doi:10.1186/1755-8794-6-50Published: 8 November 2013
Additional file 1:
Shows the USF1 peaks and read counts for control and ASH.
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Additional file 2 Figure S1:
Comparison of ChIP-seq signals with ChIP-qPCR represents the good correlation between qPCR and ChIP-seq signal. Figure S2. Comparison of the ChIP-seq signal over the peak regions between disease and control for the histone modifications. Figure S3. The number of genes that contain histone modification peaks both in ASH and in control, only in ASH and only in control. Figure S4. Different biological processes identified using the genes associated with histone modifications in ASH. Figure S5. Different biological processes identified using the genes associated with histone modifications in control. Figure S6. Histone modification pattern for the genes associated with alcoholic liver disease and ASH. Figure S7. Fraction of peaks with USF1 motif ranked on peak height, with comparison to peaks called with MACS for the same dataset. Figure S8. Correlation between ASH and control signals for USF1 for peaks close to TSS. Table S1. Sanger sequencing results of SNPs identified at USF1 peaks and alleles identified for Genomic DNA and ChIP DNA of USF1. Allele frequencies obtained from dbSNP129 and AA, AB and BB indicate the frequencies calculated by using Hardy-Weinberg equation. Table S2. GWAS catalogue dbSNPs identified using ChIP-seq data of histone modifications in control. Table S3. GWAS catalogue dbSNPs identified using ChIP-seq data of histone modifications in ASH. Table S4. Novel SNPs identified using ChIP-seq data of histone modifications in control. Table S5. Novel SNPs identified using ChIP-seq data of histone modifications in ASH. Table S6. Primers used for USF1 qPCR validations, mRNA primers for USF1 and histone modifications.
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Additional file 3:
Peak regions with the highest differences in histone modification levels between ASH and control.
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Additional file 4:
List of genes within 2 kb from the histone modification peaks with highest difference between ASH and control. Peaks for all three histone modifications are combined. This is the list we have used for Gene Ontology.
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