Input depth associated with human p53 ChIP-seq datasets

Description

This composite track contains the 40 ChIP-seq input tracks of the human p53 transcription factor binding sites based on ChIP-seq experiments generated by various labs. The tracks represent signals that were generated based on a uniform processing pipeline of all published raw data.

Display Conventions and Configuration

The default displays the input tracks from the p53 activated ChIP-seq data. Inputs from control, non-activated p53 ChIP-seq data can be visualized as additional subtracks. The individual ChIP-seq input tracks are organized by cell and condition by which p53 is induced or activated, where samples can be selected from a cell vs. condition matrix. Each track can be turned on/off individually.

Methods

Please note: For a full description of the methods used, refer to Nguyen et al. (2018) in the References section below.

ChIP-seq analysis workflow

Relevant ChIP-seq and associated input data sets were downloaded from publicly-available resources as listed in Supplemental Table ST1. All reads were clipped to a maximum length of 36 nucleotides (nt), then filtered to retain only sequences with a mean base quality score of at least 20. Filtered reads were aligned against the hg19 reference genome (excluding haplotype chromosomes) via Bowtie v0.12.8 with parameters “-m1 -v2” to accept only uniquely-mapped hits with a maximum of 2 mismatched bases. Multiple replicates from the same sample were merged, then duplicate reads were removed with MergeSamFiles.jar and MarkDuplicates.jar from the Picard tool suite v1.86 (http://broadinstitute.github.io/picard). For ChIP-seq data sets without an associated input sample, surrogate inputs were generated by randomly selecting 20 million uniquely-mapped, non-duplicate reads from other input data sets of the same cell type. Specifically, a surrogate U2OS input was made by downsampling the combined input data sets from DMSO, DXR, and Nutlin treatment conditions from Menendez et al., and a surrogate HCT116 input was made by downsampling the combined 5FU-treated input data sets from Botcheva and McCorkle and Wang et al. Depth tracks were generated with BEDTools genomeCoverageBed v2.17.0 and UCSC utility bedGraphToBigWig, after extending each uniquely-mapped, non-duplicate read to a length of 200 nt.

Credit

These data were analyzed by Nguyen et al., at the National Institutes of Health/National Institute of Environmental Health Sciences (NIH/NIEHS) in Research Triangle Park, North Carolina, USA. Please direct all questions to menendez@niehs.nih.gov.

https://www.niehs.nih.gov/research/resources/databases/p53/index.cfm

References

Nguyen TT, Grimm SA, Bushel PR, Li J, Li Y, Bennett BD, Lavender CA, Ward JM, Fargo DC, Anderson CW, Li L, Resnick MA, Menendez D. Revealing a human p53 universe. Nucleic Acids Res. 2018;46:8153-8167

Menendez, D., Nguyen, T.A., Freudenberg, J.M., Mathew, V.J., Anderson, C.W., Jothi, R. and Resnick, M.A. (2013) Diverse stresses dramatically alter genome-wide p53 binding and transactivation landscape in human cancer cells. Nucleic Acids Res., 41, 7286-7301.

Botcheva, K. and McCorkle, S.R. (2014) Cell context dependent p53 genome-wide binding patterns and enrichment at repeats. PLoS One, 9, e113492.

Wang, B., Niu, D., Lam, T.H., Xiao, Z. and Ren, E.C. (2013) Mapping the p53 transcriptome universe using p53 natural polymorphs. Cell Death Differ., 21, 521-532.