LORA: a polymorphic multi-sample LOng Read Assembly pipeline.

Abstract

Genome assembly from long-read sequencing data has become a standard approach for resolving complex genomic regions and producing high-contiguity assemblies. However, the diversity of available assemblers, their varying performance across species, and the need for reproducible workflows present ongoing challenges. We developed LORA, an easy-to-use and reproducible application for assembling genomes from long-read data. LORA integrates several well-established assemblers, including Canu, HiFiasm, Flye, and Unicycler, as well as more recent tools such as Necat and Pecat. It is implemented as a Snakemake pipeline to parallelize tasks and support seamless execution on both local machines and computing clusters. LORA includes multiple quality assessment steps, interactive HTML reports for interpretation, BLAST-based taxonomic identification, and completeness evaluation. Together, these features provide users with a comprehensive view of assembly quality and potential problematic. We illustrate the capabilities of LORA using datasets from bacterial genomes and unicellular eukaryotes, sequenced with both PacBio and Oxford Nanopore technologies, highlighting typical outcomes and common pitfalls encountered during long-read assemblies. LORA is distributed as part of the Sequana project, an open-source framework designed for reproducibility, maintainability, and straightforward deployment across computing environments.