CallingCard-seq pipelineV2 (python3.6 + bash)

This resource provides the python code to reproduce key results described in

Brd4-bound enhancers drive cell intrinsic sex differences in glioblastoma. (2021) Proceedings of the National Academy of Sciences (PNAS) 118 (16). N Kfoury*, Z Qi* (* equal contribution) et al.

URL: https://pubmed.ncbi.nlm.nih.gov/33850013/

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Disclaimer

The pipeline has been tailored for the Washington University HTCF computing environment (https://htcf.wustl.edu/docs/) which uses the slurm queueing system (https://slurm.schedmd.com/tutorials.html). No guarantees are made about other systems, setups, configurations, etc.

Prerequisites

The scripts in the folder "alignment_and_quantification" require python3. If this is not your default python, change all instance of python in bulkRNACallingCards.sh to python3. In addition, you will need to install the following modules:

• pysam
• numpy
• pandas
• matplotlib
• twobitreader

The easiest way to install them is:

• If python3 is the default: pip install pysam numpy pandas matplotlib twobitreader
• Otherwise: pip3 install pysam numpy pandas matplotlib twobitreader

To figure out which version of python is installed by default:

• python -V

Instructions

Copy these files into your working directory.

Make a folder called logs in this directory.

Edit the following files:

• 5prime_barcodes_R1.txt One sample per line, each line lists the primer barcode used for that sample
• manifest_R1.txt One sample per line corresponding to 5prime_barcodes_R1.txt, each line listing the file containing the read1 sequences for that sample
• manifest_I7.txt One sample per line corresponding to 5prime_barcodes_R1.txt and manifest_R1.txt, each line listing the i7 index (index 1) sequence used to demultiplex this sample
• manifest_I5.txt Same instruction as per manifest_I7.txt, if the library was dual-indexed. Thus, each line lists the i5 index (index 2) sequence used to demultiplex this sample
• bulkRNACallingCards.sh Edit the following lines:
• Line 6: set --array= to be the number of samples you are analyzing. Example: if you are analyzing 5 samples, you would write: --array=1-5
• Line 10: set --mail-user= to your e-mail address, for notifications about your job submission
• Line 41: set transposase= to the transposase used in the experiment. Example: if you used piggyBac, you would write: transpoase="PB"
• Line 42: set genome= to the one appropriate for your experiment. Recommend values are hg38 for human and mm10 for mouse
• Line 46: set the terminal repeat sequence used in this experiment. This line should, collectively, specify the barcode and terminal repeat sequences expected at the start of every read1. Note that the barcode will be automatically stored in the $barcode variable. Therefore, the only thing to specify is the terminal repeat sequence expected to be found. A list of commonly-used terminal repeats is found in lines 36-39. Example: if you are expecting to find the long PB LTR sequence, you would write: -g "^"$barcode$Long_PB_LTR

Running the Scripts

As a sanity check, ensure that 5prime_barcodes_R1.txt and manifest_R1.txt (and manifest_I7.txt and manifest_I5.txt, if used) have the same number of lines, and that this number is equal to the range specified in line 6 (--array=). To run the scripts, enter sbatch bulkRNACallingCards.sh

Output

The major output of the scripts are aligned SAM files of insertions as well as CCF files for statistical analysis and visualization. These will be written to ../output_and_analysis/. The logs folder contains stdout and stderr files from the scripts. The *.out files contain information from the adapter trimming step, including the number and percent of reads in the read1 file containing the correct barcode-terminal repeat combination. The *.err files contain mapping information, specifically what fraction of the trimmed read1's mapped to the genome.

The output_and_analysis output files are mainl two types:

1. The aligned SAM files (*.sam).
2. The calling card files (*.ccf) that have genomic positions for insertions in a BED format.

Visulization by WashU Epigenome Browser

• The genome browser website: https://epigenomegateway.wustl.edu/
• A JSON file is need to upload data and create tracks on the browser. Here is the structure of a simple, two-track JSON file:
• Note that the URL field must specify the bgzipped file (.gz), not the tabix index file (.gz.tbi).
• Also note that all strings must be in quotation marks, while numerical values need not. Here, the "colorpositive" and "height" specifications are optional. To add more tracks, enclose them in braces within the brackets and separate the braces with commas. More information about supported file types and how to format them can be found at the WashU EpiGenome Browser wiki.
• Save the JSON file, either on your local computer or on the HTCF cluster in the public folder.
• To upload data, open the appropriate reference genome in the browser, click on "Tracks", then "Custom Tracks", then "Add new tracks". If you've stored the JSON file on your computer, click on the "Datahub by upload" button, then select your JSON file. If the file is on the cluster, click "Datahub by URL link" and provide the URL to the JSON file.
• Below are the genome browser view of the insertions of transcription factor SNAI2 in three diffrent cell lines