Pacybara: Accurate long-read sequencing for barcoded mutagenized allelic libraries

Long read sequencing technologies, an attractive solution for many applications, usually suffer from higher error rates. Alignment of multiple reads can improve base-calling accuracy, but some applications, e.g. the sequencing of mutagenized libraries where multiple distinct clones differ by one or few variants, require the use of barcodes or unique molecular identifiers. Unfortunately, not only can sequencing errors interfere with correct barcode identification, but a given barcode sequence may be linked to multiple independent clones within a given library. Here we focus on the target application of sequencing mutagenized libraries in the context of multiplexed assays of variant effects (MAVEs). MAVEs are increasingly used to create comprehensive genotype-phenotype maps that can aid clinical variant interpretation. Many MAVE methods use barcoded mutant libraries and thus require the accurate association of barcode with genotype, e.g. using long-read sequencing. Existing pipelines do not account for inaccurate sequencing or non-unique barcodes. Here, we describe Pacybara, which handles these issues by clustering long reads based on the similarities of (error-prone) barcodes while detecting the association of a single barcode with multiple genotypes. Pacybara also detects recombinant (chimeric) clones and reduces false positive indel calls. In an example application, we show that Pacybara increases the sensitivity of a MAVE-derived missense variant effect map.