SLICER: A Novel Computational Pipeline for Dynamic Analysis of Complex Engineered DNA from Long-Read Sequencing

Long-Read Sequencing (LRS) technologies offer capabilities for characterizing complex engineered DNA constructs from Golden Gate and barcoded DNA variant assemblies, CRISPR engineered libraries or Multiplexed Assays of Variant Effect (MAVE) experiments. However, the heterogeneity of such molecules, combined with potential structural and length variability, presents analytical challenges. We present SLICER (Sequencing Long-read Identifier of Complex Element Regions), a pipeline for analyzing LRS of such constructs. SLICER dynamically identifies and extracts user-defined barcode/core elements per-read using an anchor-based method, accommodating positional/length variations and aligns these back to reference sequences. If absent, SLICER is capable of de novo reference prediction, a feature that can be insightful to identify unpredicted/aberrant phasing/combinatorial events. When benchmarked, SLICER’s d e novo reference prediction was accurate to within 1% of reference data. SLICER’s dynamic extraction and robust de novo reference capabilities provide an invaluable tool for synthetic and engineered biology applications, enabling comprehensive interrogation of complex barcoded DNA constructs and libraries. SLICER is available at https://github.com/mbassalbioinformatics/SLICER.