Characterization of sequence features in IGHV3-53/66 antibodies that bind to SARS-CoV-2 RBD

Dependencies

• Python version 2.7
• R version 3.6
• PEAR

Installation

Easiest way to install PEAR is through Anaconda

And the following to install the needed package:

conda install -c bioconda pear

INPUT FILES

• ./data/VH3-53_RBD_antibodies.tsv: List of IGHV3-53/3-66 RBD antibodies from literatures
• ./data/IGKV1-9_CDRH3.txt: List of CDR H3 from IGHV3-53/3-66 RBD antibodies that pair with IGKV1-9

Analyzing IGHV3-53/3-66 RBD antibodies from literature

1. categorize antibodies based on CDR H3 length and light chain germline

   • run python script script/summarize_LC_vs_CDRH3_length.py
   • run R script script/plot_LC_vs_CDRH3_length.R for plotting

2. analyzing SHM for antibodies with different CDR H3 length

   • run python script script/format_SHM_table.py
   • run R script script/plot_SHM_heatmap.R for plotting

Analyzing yeast display screening NGS data

1. merge overlapping paired-end reads

   • pear -f [READ1 FASTQ FILE] -r [READ2 FASTQ FILE] -o fastq/S10

2. run analysis for the assembled files

   • run python script script/fastq2count.py to count the frequence of each variant of CDRH3 loop
   • run python script script/merge&calculate.py to calculate the relative enrichment of each variant

3. plotting results

   • run R script script/plot_yeast_display.R