RNA-seq data were aligned using bowtie2 (32) against the hg19 version of the human genome, and RSEM v1.2.12 software (33) was used to estimate raw read counts and Reads Per Kilobase of transcript, per Million (RPKM) using the Ensemble transcriptome. DESeq2 (34) was used to estimate the significance of differential expression between group pairs. Overall gene expression changes were considered significant if they passed FDR thresholds of <5%. Significance of overlap between genes was estimated using hypergeometric test.

ChIP-seq data were aligned using bowtie (35) against hg19 version of the human genome, and HOMER (36) was used to generate bigWig files and call significant peaks versus input and between pairs of samples using the –style histone option. Peaks that passed FDR threshold of <5% were considered. Normalized signals for significant peaks were derived from bigWig files using the bigWigAverageOverBed tool from the University of California, Santa Cruz toolbox (37) with mean0 option. Fold differences between samples were then calculated with the average input signal 0.4 used as a floor for the minimum allowed signal. Only peaks with a FDR of <5% that passed the additional cutoff of 4-fold versus input or 1.2-fold between wild-type and knockout conditions were considered significant. Genes that had the closest transcript’s transcription starting site (TSS) were assigned to each peak. ChIP-seq profile groups were generated using k-means clustering with Pearson correlation distance. All peaks with an FDR of <5% were used for global chromosome CAPH2 redistribution analysis. A number of CAPH2 peaks in ARID1A wild-type and knockout conditions per 1-MB bin were used to calculate log2 ratios (knockout/wild type) for bins with at least one peak. All ratios were then mean-centered, and the average for each chromosome was used as a measure of CAPH2 redistribution.

For in situ Hi-C data analysis, 76-bp paired-end reads were separately aligned to the human genome (hg19) using bowtie2 (32) with iterative alignment strategy. Redundant paired reads derived from a PCR bias, reads aligned to repetitive sequences, and reads with low mapping quality (MapQ < 30) were removed. Reads potentially derived from self-ligation and undigested products were also discarded. Hi-C biases in contact maps were corrected using the iterative correction and eigenvector (ICE) method (38). The ICE normalization was repeated 30 times. For compartment calculation, the ratio of observed and expected score of ICE-normalized contact matrices were calculated using a resolution of 200 kb. Converted matrices were subjected to the principal components analysis (PCA). The signs of PCA score were determined by gene density. A PCA score with positive values were defined as A-compartment, and negative values were defined as B-compartment, as previously described (10). PCA values were upper quartile–normalized and used to estimate the significance of compartment A-to-B or B-to-A switch with Bioconductor limma package (39). Enrichment of peaks within significantly switched compartments was estimated by Fisher’s exact test. TADs were identified according to Van et al. (40) on ICE-normalized contact matrices at a resolution of 40 kb, and the significance of change of boundary strength determined by insulation score was tested using limma package (39). Significance of difference of interchromosomal interactions was estimated using DESeq2 on raw counts, and P values were adjusted for multiple testing using Bonferroni correction. An FDR of <5% was used as a significance threshold. Average interchromosomal interaction values for each chromosome were used for analysis of association with chromosome sizes and NCAPH2 redistribution. Gene set enrichment analysis for enrichment of gene ontology biological processes, molecular functions, and pathways (KEGG and BIOCARTA) was done using DAVID software, and enrichments of at least twofold that passed the FDR cutoff of <5% were considered significant.

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