RRBS read counts data after filtering for CpG sites with methylation coverage of >=10 in all samples; used as input for the site clustering pipeline.
Format
A data frame of sites as rows. Each sample has two columns: one for the methylated counts ("Me"), and nother for the unmethylated counts ("Un").
Details
Unfiltered METHYL 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/METHYL_BAT_RAW_COUNTS.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.
Reads were demultiplexed with bcl2fastq (version 2.20) using options
--use-bases-mask Y*,I8Y*,I*,Y* --mask-short-adapter-reads 0 --minimum-trimmed-read-length 0
(Illumina, San Diego, CA, USA), and UMIs in the index FASTQ files were attached to the read FASTQ files.
The regular 5' and 3' adapters were trimmed with TrimGalore (v1.18), and the diversity adapter
that is about 0 to 3 bases of RDD (R=A or G and D=A, G, or T) that is added before YGG
(Y=C or T depending on the methylation) from the YGG MspI cut signature was trimmed with
the NuGEN script "trimRRBSdiversityAdaptCustomers.py" (https://github.com/nugentechnologies/NuMetRRBS).
FastQC (v0.11.8) was used to generate pre-alignment QC metrics3. Bismark (v0.20.0) was used to index
and align reads to release 96 of the Ensembl Rattus norvegicus (rn6) genome and gene annotation.
As the lambda genome was spiked into each sample to determine the bisulfite conversion efficiency,
the lambda genome (GenBank: J02459.1) was also indexed. Default parameters were used for Bismark’s
bismark_genome_preparation in the alignment step. Bismark output BAM files were first formatted
using a custom script; Bismark’s deduplicate_bismark -p --barcode was used
to remove PCR duplicates from the bam files; and Bismark’s bismark_methylation_extractor
--comprehensive --bedgraph was used to quantify methylated and unmethylated coverages for all the
CpG sites. Bowtie 2 (v2.3.4.3) was used to index and align reads to globin, rRNA, and phix sequences
in order to quantify the percent of reads that mapped to these contaminants and spike-ins5.
SAMtools (v1.3.1) was used to compute mapping percentages to different chromosomes6.
UMIs were used to accurately quantify PCR duplicates with NuGEN’s "nodup.py" script
(https://github.com/tecangenomics/nudup). QC metrics from every stage of the quantification pipeline
were compiled, in part with multiQC (v1.6)7. The openWDL-based implementation of the RRBS pipeline on
Google Cloud Platform is available on GitHub (https://github.com/MoTrPAC/motrpac-rrbs-pipeline).