For more information see the paper Fennell and Vassiliadis et al., 2021

• Python 3.7+
  • Biopython
• FLASh
• Starcode
• R 4.0+
  • Tidyverse

This process assumes familiarity with and access to a HPC cluster running the SLURM scheduler.

1. Amplify SPLINTR barcodes from each library plasmid pool by PCR (see attached PCR protocol).

• Perform PCRs in technical duplicate
• Use a different index combination in PCR2 for each replicate

2. Sequence library pool to a depth of at least 100-200M reads (~100X coverage for a 1 million barcode library) per PCR reaction.

• Lower diversity libraries will require less sequencing.
• Ideally perform single end 150bp or paired end 75bp sequencing, DO NOT perform single end 75bp as reads will not cover the entire barcode.

3. Generate a text file containing full path of each library fastq file. See example fastqs.txt

4. Confirm sequencing quality by running FastQC

• [OPTIONAL] if paired end 75bp sequencing was performed. Run flash.sh to combine paried-end reads into a single overlapped read that covers the entire barcode region.

5. Extract barcode reads from fastq files using filter_barcodes.sbatch which submits extractBarcodeReads.py scripts to a SLURM HPC

6. Run Starcode on the extracted barcode reads to cluster barcodes according to Edit distance. See starcode.sbatch

7. Perform QC and analysis of Starcode output in R using library-analysis-template.Rmd

Output will include a fasta file of barcode sequences within each library which can be used to generate a reference library for subsequent barcode alignment. See example output in library-analysis-template.html and test