TF ChIP-seq Track Settings

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TF ChIP-seq on NS5 cells

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Peaks

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	view^↓1	Antibody^↓2	Track Name^↓3
dense	Peaks	Ascl1	Ascl1 Peaks	Data format
full Configure	Signal	Ascl1	Ascl1 Signal	Data format
dense	Peaks	CTCF	CTCF Peaks	Data format
full Configure	Signal	CTCF	CTCF Signal	Data format
dense	Peaks	FoxO3	FoxO3 Peaks	Data format
full Configure	Signal	FoxO3	FoxO3 Signal	Data format
dense	Peaks	Max	Max Peaks	Data format
full Configure	Signal	Max	Max Signal	Data format
dense	Peaks	NFI	NFI Peaks	Data format
full Configure	Signal	NFI	NFI Signal	Data format
dense	Peaks	Olig2	Olig2 Peaks	Data format
full Configure	Signal	Olig2	Olig2 Signal	Data format
dense	Peaks	Smc1	Smc1 Peaks	Data format
full Configure	Signal	Smc1	Smc1 Signal	Data format
dense	Peaks	Sox2	Sox2 Peaks	Data format
full Configure	Signal	Sox2	Sox2 Signal	Data format
dense	Peaks	Sox21	Sox21 Peaks	Data format
full Configure	Signal	Sox21	Sox21 Signal	Data format
dense	Peaks	Sox9	Sox9 Peaks	Data format
full Configure	Signal	Sox9	Sox9 Signal	Data format
dense	Peaks	Tcf3	Tcf3 Peaks	Data format
full Configure	Signal	Tcf3	Tcf3 Signal	Data format

Assembly: Mouse July 2007 (NCBI37/mm9)

Data

The raw data for the transcription factors Ascl1, Max, NFI, Olig2, Sox2, Sox9, Sox21 and Tcf3 is available under the ArrayExpress accession number E-MTAB-2270.
The data for CTCF and Smca1 was retrieved from the Gene Expression Omnibus (GEO) with accession number GSE36203. The data for FoxO3 is in the same repository with accession number GSE48336.
As the input chromatin sample for the analysis off all factors we used the sample from ArrayExpress accession number E-MTAB-1423, except for FoxO3 in which case we used the input sample provided in the corresponding accession (GSE48336).

Methods

The raw reads were mapped to the mouse genome (mm9 including random chromosomes) with Bowtie version 0.12.5.
We used MACS version 2.0.9 to define bound regions for factors ChIP-seq data. As this tool is very sensitive to the unbalanced number of reads in the real and the input set, we decided to reduce the larger dataset to match the number of mapped reads in the smaller dataset by randomly sampling reads. Instead of using the tool included in the MACS software for this task, we designed a custom python script (balanceBAMFiles.py) that perform the sampling for pairs of treatment and input samples and determines the appropriate number of reads automatically.
For this process we only considered a maximum of two fully overlapping reads, discarding the rest. To correct for sampling bias we generated 10 different random samples on which we ran MACS specifying the shift size to 90, q value to 1e-2 and leaving the rest of parameters as default.
We subsequently collapsed the 10 different peak calling results for each set using another custom script (aggregatePeaksFromSubsampling.py) which reports only overlapping peaks in at least 9 of the 10 lists.
The resulting peak bed files were first filtered to discard peaks with q-value lower than 1e-5 and then converted into bigBed files using the tool bedToBigBed and the q-values bedGraph files were converted into bigWig file with the bedGraphToBigWig tool from the UCSC Genome Browser.

Credits

Data were generated and processed for the CISSTEM project. For inquiries, please contact Juan L. Mateo at the following address: mateojuan (at) uniovi.es

References

Mateo, J. L., van den Berg, D. L. C., Haeussler, M., Drechsel, D., Gaber, Z. B., Castro, D. S., ... Martynoga, B. (2015). Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal. Genome Research, 25(1), 41-56. DOI: 10.1101/gr.173435.114