MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data

Calling fusion genes from RNA-seq data is well established, but other transcriptional variants are difficult to detect using existing approaches. To identify all types of variants in transcriptomes we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference-free approach, combi...

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Autores principales: Cmero, Marek, Schmidt, Breon, Majewski, Ian J., Ekert, Paul G., Oshlack, Alicia, Davidson, Nadia M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8532352/
https://www.ncbi.nlm.nih.gov/pubmed/34686194
http://dx.doi.org/10.1186/s13059-021-02507-8
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author Cmero, Marek
Schmidt, Breon
Majewski, Ian J.
Ekert, Paul G.
Oshlack, Alicia
Davidson, Nadia M.
author_facet Cmero, Marek
Schmidt, Breon
Majewski, Ian J.
Ekert, Paul G.
Oshlack, Alicia
Davidson, Nadia M.
author_sort Cmero, Marek
collection PubMed
description Calling fusion genes from RNA-seq data is well established, but other transcriptional variants are difficult to detect using existing approaches. To identify all types of variants in transcriptomes we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference-free approach, combining de novo assembly of transcripts with differential expression analysis to identify up-regulated novel variants in a case sample. We compare MINTIE with eight other approaches, detecting > 85% of variants while no other method is able to achieve this. We posit that MINTIE will be able to identify new disease variants across a range of disease types. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02507-8.
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spelling pubmed-85323522021-10-25 MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data Cmero, Marek Schmidt, Breon Majewski, Ian J. Ekert, Paul G. Oshlack, Alicia Davidson, Nadia M. Genome Biol Method Calling fusion genes from RNA-seq data is well established, but other transcriptional variants are difficult to detect using existing approaches. To identify all types of variants in transcriptomes we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference-free approach, combining de novo assembly of transcripts with differential expression analysis to identify up-regulated novel variants in a case sample. We compare MINTIE with eight other approaches, detecting > 85% of variants while no other method is able to achieve this. We posit that MINTIE will be able to identify new disease variants across a range of disease types. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02507-8. BioMed Central 2021-10-22 /pmc/articles/PMC8532352/ /pubmed/34686194 http://dx.doi.org/10.1186/s13059-021-02507-8 Text en © The Author(s) 2021 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
Cmero, Marek
Schmidt, Breon
Majewski, Ian J.
Ekert, Paul G.
Oshlack, Alicia
Davidson, Nadia M.
MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data
title MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data
title_full MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data
title_fullStr MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data
title_full_unstemmed MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data
title_short MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data
title_sort mintie: identifying novel structural and splice variants in transcriptomes using rna-seq data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8532352/
https://www.ncbi.nlm.nih.gov/pubmed/34686194
http://dx.doi.org/10.1186/s13059-021-02507-8
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