Library-on-library screen of betacoronavirus stem helix peptides vs S2P6 mutants

Obejctive

Studying the evolutionary trajectories of S2P6 for breadth expansion using a library-on-library screen that involves 27 unique betacoronavirus stem helix peptides and 1,024 S2P6 variants.

Dependencies

• Python (version 3.9)
• Biopython
• R (version 4.1)
• PEAR

Input files

• ./Fasta/SH_pep_ref.fa: A list of stem helix peptides in this experiment
• ./data/file_names.tsv: Filenames for the merged read files
• Raw PacBio seqeucing data in fastq format from NIH SRA database BioProject PRJNA1113356
• Raw Illumina seqeucing data in fastq format from NIH SRA database BioProject PRJNA1064076

Linking barcodes to variants based on PacBio sequencing data

1. Identify the sequences of stem helix peptide, S2P6 variant, and barcode in each read
   python3 script/PacBio_fastq2seq.py
   

   • Input file:
     
     • Fastq file from the PacBio sequencing
   • Output file:
     
     • data/barcode_SHpep_mutID.tsv

2. Filter barcodes with low read counts and perform error correction

   • Input file:
     • data/barcode_SHpep_mutID.tsv
     • ./Fasta/SH_pep_ref.fa
   • Ouput file:
     • ./data/barcode_count_info.tsv
       

Analyze the bacode sequencing data for the library-on-library screen

1. Merging Illumina seqeuncing reads
   python3 script/merge_reads.py
   

   • Input file:
     
     • All .fastq files in [fastq/]
       
   • Output files:
     
     • merged files in [fastq/merged]
       

2. Counting unique barcode sequences
   python3 script/fastq2count.py
   

   • Input file:
     
     • All merged fastq files in [fastq/merged]
       
     • ./data/file_names.tsv
       
   • Output files:
     
     • ./result/barcode_count.tsv
       

3. Splitting count file for faster processing
   python3 script/split_count_df.py
   

   • Input file:
     
     • ./result/barcode_count.tsv
       
   • Output files:
     
     • ./result/split_files/split_*.tsv
       

4. Indentifying pairs of stem helix peptide and S2P6 mutant
   python3 script/mut_ID.py
   

   • Input file:
     
     • ./result/split_files/split_*.tsv
       
     • ./data/barcode_count_info.tsv
       
   • Output files:
     
     • ./result/mut_count.tsv
       

5. Calculating the frequency of each variant
   python3 script/count2freq.py
   

   • Input file:
     
     • ./result/mut_count.tsv
       
   • Output files:
     
     • ./result/mut_freq.tsv
       

6. Calculate the expression scores and binding scores
   python3 script/freq2score.py
   

   • Input file:
     
     • ./result/mut_freq.tsv
       
   • Output files:
     
     • ./result/mut_scores.tsv
       

Plotting

1. Plot correlation between expression scores and binding scores
   python3 script/plot_replicate_qc.py
   

   • Input file:
     
     • ./result/mut_scores.tsv
       
   • Output files:
     
     • ./graph/QC/Binding_score_correlation.html
       
     • ./graph/QC/Binding_score_correlation_1*10-5.html
       
     • ./graph/QC/Binding_score_correlation_2*10-5.html
       
     • ./graph/QC/Expression_score_correlation.html
       
     • ./graph/QC/Expression_score_correlation_1*10-5.html
       
     • ./graph/QC/Expression_score_correlation_2*10-5.html
       

2. Plot correlation between binding scores and effect of different frequency cutoffs
   Rscript script/plot_QC.R
   

   • Input file:
     
     • ./result/mut_scores.tsv
       
   • Output files:
     
     • ./graph/QC_cutoff_freq_vs_cor.png
       
     • ./graph/QC_cutoff_freq_vs_variant_num.png
       
     • ./graph/QC_replicate_cor.png