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.
Usage:
./bowtie2index.py library.fasta
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)
Usage for multiple sample (requires GNU parallel installed):
parallel --link -a samples.read1.txt -a samples.read2.txt python3 main.py {1} {2} library.fasta -t 4
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
...
Make one single count table for all samples
Usage: ./join_counts.R out library.tsv
where out is the output directory containing the count files for each sample ending with .count and library.tsv contains the Ag unique IDs in column 1.