Evidence map›Paper›PMID 41965748›Full record

ArticleGenome biology2026

Experimental and computational methods for allelic imbalance analysis from single-nucleus RNA-seq data.

Sean K Simmons, Xian Adiconis, Nathan Haywood, Jacob Parker, Zechuan Lin, Zhixiang Liao, Idil Tuncali, Asa Shin, Karthik Jagadeesh, Kirk Gosik and 14 more

Abstract read

PubMed Full text at PMC Publisher

In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it

0cells of the map it votes in

4citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

LongAllele: a joint inference framework for allele-specific analysis on long-read bulk and single-cell RNA sequencing.bioRxiv : the preprint server for biology · 2026
Article
Single-cell eQTL mapping reveals convergent glial-neuronal risk architecture in Parkinson's disease.bioRxiv : the preprint server for biology · 2026
Article
Experimental and computational methods for allelic imbalance analysis from single-nucleus RNA-seq data.Genome biology · 2026
Article
Integrating Long-Read Structural Variant Analysis with single-nucleus RNA-seq to Elucidate Gene Expression Effects in Disease.bioRxiv : the preprint server for biology · 2026
Article

4 · The record

Corrections and comments

Update of
Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data.2025

5 · Who and what money

Authors and funding

24 authors.

Sean K SimmonsAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA. seankenneths@gmail.com.ORCID http://orcid.org/0000-0002-1537-4000

Xian AdiconisAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0002-4052-2503

Nathan HaywoodAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0003-2213-5080

Jacob ParkerAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0001-9425-6418

Zechuan LinAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0003-2270-5194

Zhixiang LiaoAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0001-7014-5465

Idil TuncaliAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0001-7437-285X

Asa ShinBroad Clinical Labs, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.ORCID http://orcid.org/0009-0003-1362-6635

Karthik JagadeeshKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.ORCID http://orcid.org/0000-0002-0957-812X

Kirk GosikKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.ORCID http://orcid.org/0000-0001-7612-2176

Michael GatzenData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.ORCID http://orcid.org/0000-0001-5390-0705

Jonathan T SmithData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.ORCID http://orcid.org/0000-0002-4279-8220

Daniel N El KodsiAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0002-8896-0043

Yuliya KurasAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0001-5388-3669

Clare Baecher-AllanDept. of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.ORCID http://orcid.org/0000-0003-2405-8525

Geidy E SerranoAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0002-9527-2011

Thomas G BeachAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0003-3296-6128

Kiran GarimellaData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.ORCID http://orcid.org/0000-0002-6212-5736

Aziz M Al'KhafajiBroad Clinical Labs, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.ORCID http://orcid.org/0000-0002-4677-5667

Orit Rozenblatt-RosenKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.ORCID http://orcid.org/0000-0001-6313-3570

Aviv RegevKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.ORCID http://orcid.org/0000-0003-3293-3158

Xianjun DongAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0002-8052-9320

Clemens R ScherzerAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0002-0567-9193

Joshua Z LevinAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA.ORCID http://orcid.org/0000-0002-0170-3598

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Combining allele-specific expression (ASE) analysis with single-cell RNA-seq can elucidate how genomic variation affects RNA expression at the single-cell level. We explore how experimental and computational choices impact the power of ASE-based methods and develop a suite of single-cell ASE computational tools. With single-nucleus RNA-Seq, we extract more ASE information from reads in intronic than exonic regions. We show how read length can increase power and that hybrid selection improves power to detect ASE in targeted genes. We apply our methods to a Parkinson's disease cohort and show that ASE analysis has more power than eQTL analysis.

Indexed as

Allelic ImbalanceComputational BiologyRNA-SeqSequence Analysis, RNASingle-Cell AnalysisAllelesCell NucleusHumansParkinson DiseaseQuantitative Trait LociSingle-Cell Gene Expression AnalysisAllele-specific expressionParkinson’s diseaseRNA-seqSingle-cellVariant to function

Identifiers

PMID41965748

DOI10.1186/s13059-026-04062-6

PMCPMC13185290

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.