Comprehensive and scalable quantification of splicing differences with MntJULiP

Tools for differential splicing detection have failed to provide a comprehensive and consistent view of splicing variation. We present MntJULiP, a novel method for comprehensive and accurate quantification of splicing differences between two or more conditions. MntJULiP detects both changes in intro...

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Detalles Bibliográficos
Autores principales: Yang, Guangyu, Sabunciyan, Sarven, Florea, Liliana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9472403/
https://www.ncbi.nlm.nih.gov/pubmed/36104797
http://dx.doi.org/10.1186/s13059-022-02767-y
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author Yang, Guangyu
Sabunciyan, Sarven
Florea, Liliana
author_facet Yang, Guangyu
Sabunciyan, Sarven
Florea, Liliana
author_sort Yang, Guangyu
collection PubMed
description Tools for differential splicing detection have failed to provide a comprehensive and consistent view of splicing variation. We present MntJULiP, a novel method for comprehensive and accurate quantification of splicing differences between two or more conditions. MntJULiP detects both changes in intron splicing ratios and changes in absolute splicing levels with high accuracy, and can find classes of variation overlooked by other tools. MntJULiP identifies over 29,000 differentially spliced introns in 1398 GTEx brain samples, including 11,242 novel introns discovered in this dataset. Highly scalable, MntJULiP can process thousands of samples within hours to reveal splicing constituents of phenotypic differentiation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02767-y.
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spelling pubmed-94724032022-09-15 Comprehensive and scalable quantification of splicing differences with MntJULiP Yang, Guangyu Sabunciyan, Sarven Florea, Liliana Genome Biol Method Tools for differential splicing detection have failed to provide a comprehensive and consistent view of splicing variation. We present MntJULiP, a novel method for comprehensive and accurate quantification of splicing differences between two or more conditions. MntJULiP detects both changes in intron splicing ratios and changes in absolute splicing levels with high accuracy, and can find classes of variation overlooked by other tools. MntJULiP identifies over 29,000 differentially spliced introns in 1398 GTEx brain samples, including 11,242 novel introns discovered in this dataset. Highly scalable, MntJULiP can process thousands of samples within hours to reveal splicing constituents of phenotypic differentiation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02767-y. BioMed Central 2022-09-14 /pmc/articles/PMC9472403/ /pubmed/36104797 http://dx.doi.org/10.1186/s13059-022-02767-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Yang, Guangyu
Sabunciyan, Sarven
Florea, Liliana
Comprehensive and scalable quantification of splicing differences with MntJULiP
title Comprehensive and scalable quantification of splicing differences with MntJULiP
title_full Comprehensive and scalable quantification of splicing differences with MntJULiP
title_fullStr Comprehensive and scalable quantification of splicing differences with MntJULiP
title_full_unstemmed Comprehensive and scalable quantification of splicing differences with MntJULiP
title_short Comprehensive and scalable quantification of splicing differences with MntJULiP
title_sort comprehensive and scalable quantification of splicing differences with mntjulip
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9472403/
https://www.ncbi.nlm.nih.gov/pubmed/36104797
http://dx.doi.org/10.1186/s13059-022-02767-y
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