RASTRO: Branching Point Decision-Making in Unitig Graphs

Authors

Behtari, S., Hooshmand, M.

Abstract

MotivationDe novo genome assembly relies on accurate path selection through complex graphs, yet unitig graph branching points remain a major ambiguity source. Multiple extensions may be equally plausible from topology alone, but sequence-level consistency of competing paths remains largely unexplored. ModelWe introduce RASTRO, a reference-free framework for scoring candidate paths at unitig branching points using nucleotide sequence information. RASTRO-S employs a sample-specific n-gram model to estimate extension plausibility via bits-per-base surprisal, while RASTRO-L uses the Evo2 pretrained DNA language model for autoregressive path scoring. Both operate directly on unitig graphs without reference genomes or assembled contigs. To evaluate branch decisions independently, we develop a read-based validation strategy using boundary k-mers spanning the junction between graph context and candidate extensions. ResultsExperiments on HIV-1 and SARS-CoV-2 unitig graphs from Logan show sequence-based models provide meaningful match with read-supported choices. Among 20,000 sampled branching points, unique read-supported choices were identified for 15,319 HIV-1 branches (76.6%) and 19,882 SARS-CoV-2 branches (99.4%). RASTRO-S improved match from 54.1% to 83.5% for HIV-1 and from 58.6% to 76.4% for SARS-CoV-2 as context length rose from n = 5 to n = 10. RASTRO-L also improved with context length, reaching 49.96% and 49.25% match. Both outperformed random selection and the longest-extension greedy baseline. ConclusionRASTRO provides an efficient, interpretable framework for evaluating ambiguous path decisions in unitig graphs using raw-read evidence rather than contig-level alignment. Results demonstrate the potential of lightweight, sample-adaptive sequence models for improving local decision-making in genome assembly workflows.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 08 Oct 2026.

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