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

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Authors: Zhuoran Xu, Kai Wang
Cited by: 2
References: 82

Abstract

Abstract 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 single-nucleotide variant (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 allele-specific expression (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 across biological contexts. We applied LongAllele to long-read RNA-seq datasets spanning GTEx (multi-tissue bulk), peripheral blood mononuclear cells (single-cell), human hippocampus (single-nucleus), and human cortex from two Alzheimer’s disease (AD) case-control cohorts (bulk, Oxford Nanopore and PacBio). LongAllele consistently revealed greater context dependence in expression-level than isoform-level allelic regulation across tissues, cell types, and disease states, and pinpointed high-impact regulatory variants including rare splice-site mutations missed by standalone variant callers. It further showed that purifying selection constrains allelic imbalance at both gene and isoform levels and resolved AD-associated variant effects in individual transcriptomes across long-read platforms.

Fields of Study & Subjects

Single-cell and spatial transcriptomics Cancer Genomics and Diagnostics Neuroinflammation and Neurodegeneration Mechanisms

Publication details

Journal / source
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Publication date
8 May 2026
Language
EN
Document type
Preprint