# Download https://github.com/PacificBiosciences/pb-CpG-tools/raw/main/conda_env_cpg.yaml
# vim conda_env_cpg.yaml
#name: pbcpg
#channels:
# - bioconda
# - conda-forge
# - defaults
#dependencies:
# - python=3.9
# - tensorflow=2.7
# - numpy=1.20.0
# - biopython
# - pandas
# - pysam
# - tqdm
# - pybigwig
conda env create -f conda_env_cpg.yaml
source activate pbcpg
conda install -c bioconda samtools
conda install -c bioconda pbmm2
# Download "hg38.analysisSet.fa.gz" from http://hgdownload.cse.ucsc.edu/goldenpath/hg38/bigZips/analysisSet/
The model for calculating the modification probabilties across CpG context is available in https://github.com/PacificBiosciences/pb-CpG-tools/tree/v1.2.0/pileup_calling_model
conda create -y -n masisoseq -c bioconda pbskera
source activate masisoseq
conda install -y -c bioconda lima
conda install -y -c bioconda samtools
conda install -c bioconda isoseq3
conda install -c bioconda pbccs
conda install -c bioconda minimap2
# For _02.remove_5mc_from_bam.sh
conda create -y -n masisoseq2 -c bioconda argparse pysam tqdm matplotlib numpy
source activate masisoseq2
10X Cell Barcode downloaded from https://github.com/10XGenomics/cellranger/blob/master/lib/python/cellranger/barcodes/737K-august-2016.txt
The sequence data used in this study (e.g. PacBio HiFi Data) were submitted to the Sequence Read Archive with BioProject Accession Number PRJNA1071193*. (*Note: SRA records will be accessible with the link above after 2024-12-31 or upon pulication of our findings)