Computational protocol: ACF chromatin remodeling complex mediates stress–induced depressive–like behavior

Similar protocols

Protocol publication

[…] ChIP–seq data were aligned to the mouse genome (mm9) by CASAVA 1.8 (http://www.illumina.com/software/genome_analyzer_software.ilmn), and only unique reads were retained for analysis. FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was applied for quality control, and then SAMTools (http://samtools.sourceforge.net) was used to remove potential PCR duplicates. PhantomPeak (https://code.google.com/p/phantompeakqualtools/) was applied to estimate the quality and enrichment of the ChIP–seq dataset. Additional ENCODE quality metrics, such as the normalized strand coefficient (NSC) and the relative strand correlation (RSC), were calculated. For all samples in our research, NSC≥1.05, RSC≥0.8. Basic filtering and quality control confirmed that these samples were of strong quality and exceeded ENCODE standards; see and (raw and genome–browser compatible files can be accessed online at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=anenaoiqxzypjwz&acc=GSE54263). For the H3 MNase ChIP–seq libraries, 150+ million raw reads were obtained for each replicate. With ~70% reads uniquely mapped and less than 20% duplicative reads, there were ~100 million uniquely mapped, non–redundant reads per replicate. This is about 4–5x coverage of the mouse genome, and allows for sufficient analysis of genome–wide nucleosome mapping.For visualization of the ChIP–seq data genome–wide, ngs.plot (https://code.google.com/p/ngsplot/) was applied to visualize the dataset. All 3 replicates of the conditions were pooled, and normalized to 1 million reads. The density of BAZ1A binding 1 kb up– and downstream of TSSs of coding genes in Ensembl annotations were plotted. The Corrgram package in the R software (http://www.r–project.org/) was used to calculate and visualize the correlation between basal BAZ1A and SMARCA5. TDF files (all duplicative/redundant reads >2 removed) were applied in IGV for genome browser views of ChIP–seq tracks.Coincident binding sites between BAZ1A and SMARCA5 were identified using ChromHMM (http://compbio.mit.edu/ChromHMM/). First, all BAZ1A and SMARCA5 ChIP–seq data were binarized at 200 bp intervals. The intervals were designated as enriched or “1” if the fold enrichment threshold over input was 2.0 or greater, and the Poisson tail p–value was less than or equal to 1×10−6. Otherwise, the intervals were designated as not enriched or “0”. Then the binarized marks were fed into ChromHMM to estimate the states of the 200 bp intervals, iterating the training 200 times. The initiating number of states was 4, and based on state pruning strategy, we defined two states: one state represented low to no binding of the two factors and the other state represented high binding of both factors. To analyze the effect of CSDS, sites of high binding of both factors were extracted for control, susceptible, and resilient conditions. A site is considered enriched/specific in one condition only if no other condition contained that enrichment site within 2 kb.For analysis of nucleosome position and occupancy, DANPOS (https://code.google.com/p/danpos/) was applied in the dynamic analysis of nucleosomes. Analysis focused on the position shift and occupancy change events. The FDR cutoff was 0.01 for both events. For shift events, the cutoff of shifting distance was between 50 and 90 bp. For occupancy events, the FDR of difference between treatment and control was >0.01. The overlap of condition–enrichment of the ACF complex and nucleosome events was calculated using Fisher’s tests, with multiple tests corrected by Benjamini–Hochberg analysis. […]

Pipeline specifications

Software tools BaseSpace, FastQC, SAMtools, phantompeakqualtools, ngs.plot, ChromHMM, DANPOS
Organisms Mus musculus, Homo sapiens
Chemicals Adenosine Triphosphate