Pacybara

Pacybara clusters long reads based on similarities of error-prone barcodes to enable accurate long-read sequencing of barcoded mutagenized allelic libraries for multiplexed assays of variant effects (MAVEs).

Key Features:

• Barcode Clustering: Clusters long reads based on similarities of error-prone barcodes to correct sequencing errors in barcode identification.
• Non-unique Barcode Detection: Detects barcodes that have been associated with multiple independent genotypes within a library.
• Recombinant Clone Detection: Identifies recombinant (chimeric) clones in barcoded mutant libraries.
• Indel Error Reduction: Reduces false positive indel calls in long-read sequencing data.
• HPC Compatibility: Runs on GNU/Linux HPC clusters via Slurm, PBS, or GridEngine schedulers, with a single-machine simplex version also available.

Scientific Applications:

• Multiplexed Assays of Variant Effects (MAVEs): Supports creation of comprehensive genotype-phenotype maps to aid clinical variant interpretation by accurately associating barcodes with genotypes in mutagenized libraries.
• Barcoded Mutant Library Sequencing: Enables accurate long-read sequencing of libraries where multiple distinct clones differ by one or few variants and require barcode-based identification.

Methodology:

Pacybara clusters long reads based on similarities of error-prone barcodes while detecting barcodes associated with multiple genotypes. It is implemented in R, Python, and bash.