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Article: BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

TitleBRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments
Authors
KeywordsSingle-cell RNA-seq
Differential alternative splicing
Differential momentum genes
Variational Bayes
Issue Date2021
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.genomebiology.com
Citation
Genome Biology, 2021, v. 22, article no. 251 How to Cite?
AbstractRNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.
Persistent Identifierhttp://hdl.handle.net/10722/304898
ISSN
2012 Impact Factor: 10.288
2020 SCImago Journal Rankings: 9.027
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHuang, Y-
dc.contributor.authorSanguinetti, G-
dc.date.accessioned2021-10-05T02:36:47Z-
dc.date.available2021-10-05T02:36:47Z-
dc.date.issued2021-
dc.identifier.citationGenome Biology, 2021, v. 22, article no. 251-
dc.identifier.issn1474-7596-
dc.identifier.urihttp://hdl.handle.net/10722/304898-
dc.description.abstractRNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.-
dc.languageeng-
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.genomebiology.com-
dc.relation.ispartofGenome Biology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectSingle-cell RNA-seq-
dc.subjectDifferential alternative splicing-
dc.subjectDifferential momentum genes-
dc.subjectVariational Bayes-
dc.titleBRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments-
dc.typeArticle-
dc.identifier.emailHuang, Y: yuanhua@hku.hk-
dc.identifier.authorityHuang, Y=rp02649-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s13059-021-02461-5-
dc.identifier.pmid34452629-
dc.identifier.pmcidPMC8393734-
dc.identifier.scopuseid_2-s2.0-85113687831-
dc.identifier.hkuros325780-
dc.identifier.volume22-
dc.identifier.spagearticle no. 251-
dc.identifier.epagearticle no. 251-
dc.identifier.isiWOS:000690963600001-
dc.publisher.placeUnited Kingdom-

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