ReviewTrends in genetics : TIG2024
Computational methods for allele-specific expression in single cells.
Review in Trends in genetics : TIG, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed.
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
- LongAllele: a joint inference framework for allele-specific analysis on long-read bulk and single-cell RNA sequencing.bioRxiv : the preprint server for biology · 2026Article
- Experimental and computational methods for allelic imbalance analysis from single-nucleus RNA-seq data.Genome biology · 2026Article
- DAESC + : high-performance, integrated software for single-cell allele-specific expression data.BMC bioinformatics · 2026Article
- Review
- A mouse organoid platform for modeling cerebral cortex development and cis-regulatory evolution in vitro.Developmental cell · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Allele-specific expression (ASE) is a powerful signal that can be used to investigate multiple molecular mechanisms, such as cis-regulatory effects and imprinting. Single-cell RNA-sequencing (scRNA-seq) enables ASE characterization at the resolution of individual cells. In this review, we highlight the computational methods for processing and analyzing single-cell ASE data. We first describe a bioinformatics pipeline to obtain ASE counts from raw reads synthesized from previous literature. We then discuss statistical methods for detecting allelic imbalance and its variability across conditions using scRNA-seq data. In addition, we describe other methods that use single-cell ASE to address specific biological questions. Finally, we discuss future directions and emphasize the need for an integrated, optimized bioinformatics pipeline, and further development of statistical methods for different technologies.
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Registered trials
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.