ArticleGenome biology2017
BRIE: transcriptome-wide splicing quantification in single cells.
Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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.
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
59 citing papers in PubMed.
- scASprofiler: profiling single-cell RNA splicing with a deep convolutional generative network.Briefings in bioinformatics · 2026Article
- ASPIRE: Accurate alternative splicing prediction from limited RNA sequencing data and a minimal gene set.PLoS computational biology · 2026Article
- Transcriptome Reprogramming in Heart Failure: The Hidden Splicing Code.Current cardiology reports · 2026Review
- Deep learning-driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives.Journal of translational medicine · 2026Review
- Single-cell mapping of alternative splicing linked to checkpoint immunotherapy response.Nucleic acids research · 2025Article
- Deciphering splicing heterogeneity at single-cell resolution by SCSES.Nature communications · 2025Article
- Computational methods for alternative polyadenylation and splicing in post-transcriptional gene regulation.Experimental & molecular medicine · 2025Review
- DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads.Nature communications · 2025Article
- Single-Cell Analysis of Alternative Splicing and Gene Regulatory Network Reveals Remarkable Expression and Regulation Dynamics During Human Early Embryonic Development.FASEB bioAdvances · 2025Article
- Shiba: a versatile computational method for systematic identification of differential RNA splicing across platforms.Nucleic acids research · 2025Article
- Shiba: A versatile computational method for systematic identification of differential RNA splicing across platforms.bioRxiv : the preprint server for biology · 2025Article
- AEnet: a practical tool to construct the splicing-associated phenotype atlas at a single cell level.GigaScience · 2025Article
- scTML: a pan-cancer single-cell landscape of multiple mutation types.Nucleic acids research · 2025Article
- Mining alternative splicing patterns in scRNA-seq data using scASfind.Genome biology · 2024Article
- Quantifying transcriptome diversity: a review.Briefings in functional genomics · 2024Review
- Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance.Nature communications · 2024Article
- Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.Briefings in bioinformatics · 2024Article
- LncSEA 2.0: an updated platform for long non-coding RNA related sets and enrichment analysis.Nucleic acids research · 2024Article
- CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5' scRNA-seq data.Nature communications · 2023Article
- The problem of selection bias in studies of pre-mRNA splicing.Nature communications · 2023Article
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
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stochasticity in transcription, but the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here, we present BRIE (Bayesian regression for isoform estimation), a Bayesian hierarchical model that resolves these problems by learning an informative prior distribution from sequence features. We show that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE, therefore, expands the scope of scRNA-seq experiments to probe the stochasticity of RNA processing.
Indexed as
Identifiers
What OpenQuestion holds
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.