Invas: an inversion-aware method for transcriptome assembly.

Authors

Xuedong Wang, Feiyue Wang, Chenzhao Feng, Chaoyang Sun, Shuaicheng Li

Published in

Nature communications. Jul 11, 2026. Epub Jul 11, 2026.

Abstract

Intragenic inversions reverse sequence orientation within genes and create non-collinear splice junctions that standard transcript assemblers miss, resulting in incomplete reconstruction and biased quantification. Here we show Invas, an inversion-aware framework operating with bulk short-read sequencing data. Invas integrates whole-genome sequencing breakpoints with transcriptomic sequencing evidence, rescues unmapped reads, and assembles isoforms using conjugate flow optimization. In silico across 42,000 events, Invas demonstrates high precision and recall and improves quantification of unaffected transcripts compared with conventional tools. We apply Invas to seven disease cohorts, identifying recurrent germline susceptibility variants and somatic drivers. Furthermore, Invas facilitates the discovery of somatic inversion-derived tumor-specific antigens with strong predicted immunogenicity. We release Invas and InvasDB as community resources to enable accurate characterization of inversion-affected genes, filling a critical gap in structural variant-aware transcriptomics.

PMID:
42436115
Bibliographic data and abstract were imported from PubMed on 12 Jul 2026.

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