{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T01:24:07Z","timestamp":1787793847296,"version":"build-2784847793"},"posted":{"date-parts":[[2026,5,8]]},"group-title":"Bioinformatics","reference-count":82,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["HD105354"],"award-info":[{"award-number":["HD105354"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["HG013359"],"award-info":[{"award-number":["HG013359"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Children\u2013s Hospital of Philadelphia Research Institute"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,6,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Allele-specific analysis from RNA-seq is a powerful approach to characterize\n                  <jats:italic>cis<\/jats:italic>\n                  -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\n                  <jats:italic>cis<\/jats:italic>\n                  -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\u2019s 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.\n                <\/jats:p>","DOI":"10.64898\/2026.05.05.722992","type":"posted-content","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T23:00:20Z","timestamp":1778281220000},"source":"Crossref","is-referenced-by-count":2,"title":["LongAllele: a joint inference framework for allele-specific analysis on long-read bulk and single-cell RNA sequencing"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6733-541X","authenticated-orcid":false,"given":"Zhuoran","family":"Xu","sequence":"first","affiliation":[{"name":"Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA"},{"name":"Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children\u2019s Hospital of Philadelphia, Philadelphia, PA, 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5585-982X","authenticated-orcid":false,"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children\u2019s Hospital of Philadelphia, Philadelphia, PA, 19104, USA"},{"name":"Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026061220500643000_2026.05.05.722992v2.1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkae1070"},{"key":"2026061220500643000_2026.05.05.722992v2.2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaz1776"},{"key":"2026061220500643000_2026.05.05.722992v2.3","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1419161111"},{"key":"2026061220500643000_2026.05.05.722992v2.4","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.11752"},{"key":"2026061220500643000_2026.05.05.722992v2.5","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-017-0004-9"},{"key":"2026061220500643000_2026.05.05.722992v2.6","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-018-1417-1"},{"key":"2026061220500643000_2026.05.05.722992v2.7","doi-asserted-by":"publisher","DOI":"10.1126\/science.1222794"},{"key":"2026061220500643000_2026.05.05.722992v2.8","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1038\/s41576-023-00598-6","article-title":"Interpreting 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