Normalized sample-level ATAC-seq (ATAC) data used for visualization and differential analysis
Details
Unfiltered ATAC sample-level data are only available via download from Google Cloud Storage.
For example, https://storage.googleapis.com/motrpac-rat-training-6mo-extdata/epigen-rda/ATAC_BAT_NORM_DATA.rda
is the file for brown adipose tissue (BAT) data. You can change the name of the file to specify other tissues including:
HEART, HIPPOC, KIDNEY, LIVER, LUNG, SKMGN (gastrocnemius skeletal muscle), and WATSC (subcutaneous white adipose tissue).
You can also use MotrpacRatTraining6mo::load_sample_data() or MotrpacRatTraining6mo::get_rdata_from_url()
to download raw and normalized sample-level data for ATAC and METHYL.
For more details about these files see the readme of this repository at
https://github.com/MoTrPAC/MotrpacRatTraining6mo/blob/main/README.md.
Data was processed with the ENCODE ATAC-seq pipeline (v1.7.0). Samples from a single sex and training time point, e.g., males trained for 2 weeks, were analyzed together as biological replicates in a single workflow. Briefly, adapters were trimmed with cutadapt v2.5 (Martin, 2011) and aligned to release 96 of the Ensembl Rattus norvegicus (rn6) genome (Dobin et al., 2013) with Bowtie 2 v2.3.4.3 (Langmead and Salzberg, 2012). Duplicate reads and reads mapping to the mitochondrial chromosome were removed. Signal files and peak calls were generated using MACS2 v2.2.4 (Gaspar, 2018), both from reads from each sample and pooled reads from all biological replicates. Pooled peaks were compared with the peaks called for each replicate individually using Irreproducibility Discovery Rate (Li et al., 2011) and thresholded to generate an optimal set of peaks.
The cloud implementation of the ENCODE ATAC-seq pipeline and source code for the post-processing steps are available at
https://github.com/MoTrPAC/motrpac-atac-seq-pipeline.
Optimal peaks (overlap.optimal_peak.narrowPeak.bed.gz) from all workflows were concatenated, trimmed to 200 base pairs around the summit,
and sorted and merged with bedtools v2.29.0 (Quinlan and Hall, 2010) to generate a master peak list. This peak list was intersected with
the filtered alignments from each sample using bedtools coverage with options -nonamecheck and -counts to generate a peak by
sample matrix of raw counts.
The remaining steps were applied separately on raw counts from each tissue. Peaks from non-autosomal chromosomes were removed, as well as peaks that did not have at least 10 read counts in four samples. Filtered raw counts were then quantile-normalized with limma-voom (Law et al., 2014).
For the subset of normalized data corresponding to training-regulated features at 5% IHW FDR, see ATAC_NORM_DATA_05FDR.