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RPKM analysis calculates gene expression differences by normalizing the read counts for the total length of the gene and the number of mapped sequencing reads [ 101].
The absolute read counts were transformed into transcript abundances by normalizing the read counts of each miRNA using the cloning frequency (CF) in each library [14].
The absolute copy number for each gene in each cell line was calculated as above by normalizing the read depth to the median.
Gene expression intensity was calculated by normalizing the read counts to RPKM according to the gene length and total mapped reads, and genes with RPKM < 0.01 were removed.
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Average RE accessibility was calculated by normalizing the reads in each window to total reads, dividing by the number of regions of interest, and presented in reads per million.
First, based on the gene body-specific distribution patterns of the H3K27me3 and H3K36me3 modifications (Additional file 4), we quantitated the H3K27me3 and H3K36me3 modification levels by normalizing the reads as the number of reads per kilobase per million reads (RPKM) within the gene bodies.
We first measured the expression of protein-coding mRNAs by counting and normalizing the reads overlapping each transcript, generating measurements, for each transcript in each tissue, of the number overlapped reads per 1000 nt of RNA transcript length per million alignable reads (RPKM [11]), along with a normalized uncertainty derived using Poisson statistics [12].
To account for variation in number of reads sequenced within the 4 samples, read counts were adjusted by normalizing the total read count of each sample.
TE was calculated by normalizing the RFP reads with the mRNA reads of the same genes.
Reads per kilobase per million per gene values were calculated by normalizing the RNA reads and mapping them onto the reference genome.
This final bias is corrected by normalizing the corrected read depths for each usable restriction fragment by the corrected read depths from a euploid reference dataset.
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