Antigen-Library Screen Analysis

⚠️ Docker required 🐳

Generate Ag lib fasta file and bowtie2 indices

Ag lib fasta

Usage: ./convert_lib_to_fasta.sh library.tsv library.fasta
where library.tsv contains two columns, without header, where column 1 is the Ag unique IDs and column 2 the Ag sequences.

Bowtie2 indices

Usage: ./bowtie2index.py library.fasta

Run pipeline

Usage for one sample: ./main.py read1.fastq read2.fastq library.fasta
Options:
-l --log name of log file (default: log.txt)
-o --out output folder (default: out)
-t --threads number of cpus to use, for cutdadapt and bowtie2 steps (default: 4)
-k --keepSam keep the SAM file after counts are generated (default: False)
-K --keepTmp keep the tmp subfolder after pipeline finishes (default: False)

Usage for multiple sample:

paste samples.read1.txt samples.read2.txt | xargs -n 2 -P 1 bash -c 'python3 main.py "$0" "$1" library.fasta -t 12'

Where samples.read1.txt and samples.read2.txt are lists of paired read1 and read2 fastq files, respectively.

$ cat samples.read1.txt
/path/to/sample1.R1.fastq.gz
/path/to/sample2.R1.fastq.gz
/path/to/sample3.R1.fastq.gz
/path/to/sample4.R1.fastq.gz
...

and

$ cat samples.read2.txt
/path/to/sample1.R2.fastq.gz
/path/to/sample2.R2.fastq.gz
/path/to/sample3.R2.fastq.gz
/path/to/sample4.R2.fastq.gz
...

Join counts

Make one single count table for all samples You can use either the R or Python version:

• R: ./join_counts.R out library.tsv
• Python: ./join_counts.py out library.tsv

Where out is the output directory containing the count files for each sample ending with .counts or .counts.txt, and library.tsv contains the Ag unique IDs in column 1.