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Cell type-specific binding patterns reveal that TCF7L2 can be tethered to the genome by association with GATA3

Seth Frietze1, Rui Wang23, Lijing Yao1, Yu Gyoung Tak1, Zhenqing Ye3, Malaina Gaddis1, Heather Witt1, Peggy J Farnham1* and Victor X Jin3*

Author Affiliations

1 Department of Biochemistry and Molecular Biology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90089, USA

2 Department of Chemistry, Lanzhou University, Lanzhou 730000, China

3 Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA

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Genome Biology 2012, 13:R52  doi:10.1186/gb-2012-13-9-r52

Published: 5 September 2012

Additional files

Additional file 1:

Figure S1 - antibody validation.

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Additional file 2:

Table S1 - summary of ChIP-seq and RNA-seq experiments. All ChIP-seq experiments performed by the Farnham laboratory are listed; GEO numbers are provided and the data are availableat the UCSC genome browser

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Additional file 3:

Supplementary Methods.

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Additional file 4:

Table S2 - ChIP-seq reproducibility. To determine the reproducibility of the ChIP-seq data, we used the method of evaluating replicates as described in the ENCODE Standards document [53]. Briefly, the ENCODE consortium rules are as follows: '80% of the top 40% of the targets identified in one replicate should be contained within the list of targets from the other replicate'. This metric was chosen based on experiences of all the ENCODE production groups to allow an achievable threshold of reproducibility while producing high quality target lists. All ChIP-seq data for site-specific factors submitted to the UCSC browser as part of ENCODE have to pass this quality metric and, as can be seen in Table S2, all of the TCF7L2 data in our manuscript have passed. We note that the metric for reproducibility of 'broad peak' histone marks (such as H3K4me1) has not yet been established by ENCODE. Due to difficulties in calling peaks for such histone marks, the overlap is sometimes lower than 80%.

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Additional file 5:

Table S3 - all TCF7L2 peaks in six cell types. TCF7L2 peaks were called using BELT [54] and the merged datasets for each cell type (see Additional file 4 for the peak calling parameters for each dataset).

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Additional file 6:

Table S7 - summary of TCF7L2 peak characteristics. TCF7L2 peaks were called using BELT [54] and the merged datasets for each cell type (see Additional file 4 for the peak calling parameters for each dataset).

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Additional file 7:

Figure S2 - saturation analysis.

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Additional file 8:

Table S9 - amplified regions in the six cancer cell lines. Sole-search was used to call peaks for TCF7L2 in each of the six cancer cell lines, using input from each line as the specific control. One novel feature of Sole-search is that it provides a list of all amplified regions found in the input control. For each cancer cell line, a worksheet of all amplified regions is provided; column F indicates the fold amplification and column I indicates the chromosomal coordinates of the amplified region

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Additional file 9:

Table S10 - TCF7L2 peaks in amplified regions. The TCF7L2 peaks from each cell line were overlapped with the amplified regions from that same cell line. Presented in this table is a summary of all the overlaps and a worksheet for each cell line that lists each peak that is found in the amplified regions (column A indicates the chromosome and columns D and E indicate the chromosomal coordinates of the amplified region that contains the peak; column F indicates the fold amplification of the amplified region; and column I indicates the chromosomal coordinate of the TCF7L2 peak and the height of the peak).

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Additional file 10:

Table S4 - TCF7L2 peaks unique to each of the six cell types. To identify cell type-specific TCF7L2 peaks for a particular cell, we first combined the five sets of peaks from the other cell types, and then identified the unique set of peaks for the given cell type by removing sites in common with the combined set. For these analyses, the merged replicate TCF7L2 datasets were used.

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Additional file 11:

Figure S3 - ChIP-qPCR validation of TCF7L2 sites.

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Additional file 12:

Figure S4 - TCF7L2 binds to cell type-specific enhancer regions.

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Additional file 13:

Table S6 - TCF7L2 binding motifs in six cell types. We used our ChIPMotifs program to identify two canonical TCF7L2 motifs, W1 of 6 bp and W2 of 8 bp, for each cell type. We then used each of two motifs' position weight matrices to scan the sequences of the peaks to determine how many peaks contained the motifs; we examined the set of all peaks and the set of cell type-specific peaks for all six cell types.

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Additional file 14:

Figure S5 - motif recovery percentage plots.

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Additional file 15:

Figure S6 - Re-ChIP analysis of GATA3 and TCF7L2 sites.

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Additional file 16:

Table S8 - RNA-seq analysis of TCF7L2 and GATA3 knockdowns. The RNAseq data were processed by TopHat and Cufflinks programs essentially as described [55].

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Additional file 17:

Table S5 - primers used in ChIP-qPCR analyses. The sequences of positive and negative control primers used for ChIP-qPCR.

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