LongAllele: a joint inference framework for allele-specific analysis on long-read bulk and single-cell RNA sequencing

Allele-specific analysis from RNA-seq is a powerful approach to characterize cis -regulatory effects. However, existing methods remain limited in both haplotype inference and allelic testing. Their haplotype-inference workflows separate variant calling, haplotype phasing, and read-haplotype assignment into sequential steps, failing to fully exploit within-read SNV linkage information and propagating errors into downstream allelic analysis. At the testing stage, they ignore non-phasable reads lacking heterozygous SNVs, biasing calls and inflating false positives, and remain incomplete across gene-, isoform-, and local-event-level variant effects. Here, we present LongAllele, a statistical framework that employs an expectation-maximization algorithm to jointly infer heterozygous variants, haplotype structure, and read-haplotype assignments from long-read bulk and single-cell RNA sequencing. LongAllele further introduces phasability-aware testing that explicitly accounts for non-phasable reads, avoiding inflated false-positive calls when haplotype information is incomplete. It also enables comprehensive allelic testing across gene-level ASE, isoform-level allele-specific transcript usage (ASTU), and local-event-level haplotype-associated exon and junction usage (HAEU and HAJU), providing a multi-scale view of cis -regulation. We applied LongAllele to long-read RNA-seq datasets spanning GTEx (multi-tissue bulk), peripheral blood mononuclear cells (single-cell), and human hippocampus (single-nucleus). LongAllele consistently revealed greater tissue and cell-type variability in expression-level than isoform-level allelic regulation, pinpointed high-impact regulatory variants including rare splice-site mutations missed by standalone variant callers, and showed that purifying selection constrains allelic imbalance at both gene and isoform levels. LongAllele offers a unified framework for haplotype-resolved cis -regulatory analysis across diverse cellular contexts.