Leveraging transcript quantification for fast computation of alternative splicing profiles
- Autores
- Alamancos, Gael P.; Pages, Amadis; Trincado, Jose Luis; Bellora, Nicolás; Eyras, Eduardo
- Año de publicación
- 2015
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available data sets represents a major challenge in terms of computation time and storage requirements. We describe SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA-sequencing data compared with experimentally validated events. We assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, we show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large data sets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa.
Fil: Alamancos, Gael P.. Universitat Pompeu Fabra; España
Fil: Pages, Amadis. Universitat Pompeu Fabra; España. Centre for Genomic Regulation; España
Fil: Trincado, Jose Luis. Universitat Pompeu Fabra; España
Fil: Bellora, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación En Biodiversidad y Medioambiente; Argentina
Fil: Eyras, Eduardo. Institució Catalana de Recerca I Estudis Avancats; España. Universitat Pompeu Fabra; España - Materia
-
bioinformatics
RNA-seq
splicing
cancer - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/11645
Ver los metadatos del registro completo
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Leveraging transcript quantification for fast computation of alternative splicing profilesAlamancos, Gael P.Pages, AmadisTrincado, Jose LuisBellora, NicolásEyras, EduardobioinformaticsRNA-seqsplicingcancerhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available data sets represents a major challenge in terms of computation time and storage requirements. We describe SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA-sequencing data compared with experimentally validated events. We assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, we show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large data sets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa. Fil: Alamancos, Gael P.. Universitat Pompeu Fabra; EspañaFil: Pages, Amadis. Universitat Pompeu Fabra; España. Centre for Genomic Regulation; EspañaFil: Trincado, Jose Luis. Universitat Pompeu Fabra; EspañaFil: Bellora, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación En Biodiversidad y Medioambiente; ArgentinaFil: Eyras, Eduardo. Institució Catalana de Recerca I Estudis Avancats; España. Universitat Pompeu Fabra; EspañaCold Spring Harbor Lab Press2015-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/11645Alamancos, Gael P.; Pages, Amadis; Trincado, Jose Luis; Bellora, Nicolás; Eyras, Eduardo; Leveraging transcript quantification for fast computation of alternative splicing profiles; Cold Spring Harbor Lab Press; Rna (new York, N.y.); 21; 9; 7-2015; 1521-15311355-8382enginfo:eu-repo/semantics/altIdentifier/url/http://rnajournal.cshlp.org/content/21/9/1521.longinfo:eu-repo/semantics/altIdentifier/doi/10.1261/rna.051557.115info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4536314/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:59:04Zoai:ri.conicet.gov.ar:11336/11645instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:59:05.0CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Leveraging transcript quantification for fast computation of alternative splicing profiles |
title |
Leveraging transcript quantification for fast computation of alternative splicing profiles |
spellingShingle |
Leveraging transcript quantification for fast computation of alternative splicing profiles Alamancos, Gael P. bioinformatics RNA-seq splicing cancer |
title_short |
Leveraging transcript quantification for fast computation of alternative splicing profiles |
title_full |
Leveraging transcript quantification for fast computation of alternative splicing profiles |
title_fullStr |
Leveraging transcript quantification for fast computation of alternative splicing profiles |
title_full_unstemmed |
Leveraging transcript quantification for fast computation of alternative splicing profiles |
title_sort |
Leveraging transcript quantification for fast computation of alternative splicing profiles |
dc.creator.none.fl_str_mv |
Alamancos, Gael P. Pages, Amadis Trincado, Jose Luis Bellora, Nicolás Eyras, Eduardo |
author |
Alamancos, Gael P. |
author_facet |
Alamancos, Gael P. Pages, Amadis Trincado, Jose Luis Bellora, Nicolás Eyras, Eduardo |
author_role |
author |
author2 |
Pages, Amadis Trincado, Jose Luis Bellora, Nicolás Eyras, Eduardo |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
bioinformatics RNA-seq splicing cancer |
topic |
bioinformatics RNA-seq splicing cancer |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available data sets represents a major challenge in terms of computation time and storage requirements. We describe SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA-sequencing data compared with experimentally validated events. We assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, we show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large data sets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa. Fil: Alamancos, Gael P.. Universitat Pompeu Fabra; España Fil: Pages, Amadis. Universitat Pompeu Fabra; España. Centre for Genomic Regulation; España Fil: Trincado, Jose Luis. Universitat Pompeu Fabra; España Fil: Bellora, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación En Biodiversidad y Medioambiente; Argentina Fil: Eyras, Eduardo. Institució Catalana de Recerca I Estudis Avancats; España. Universitat Pompeu Fabra; España |
description |
Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available data sets represents a major challenge in terms of computation time and storage requirements. We describe SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA-sequencing data compared with experimentally validated events. We assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, we show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large data sets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-07 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/11645 Alamancos, Gael P.; Pages, Amadis; Trincado, Jose Luis; Bellora, Nicolás; Eyras, Eduardo; Leveraging transcript quantification for fast computation of alternative splicing profiles; Cold Spring Harbor Lab Press; Rna (new York, N.y.); 21; 9; 7-2015; 1521-1531 1355-8382 |
url |
http://hdl.handle.net/11336/11645 |
identifier_str_mv |
Alamancos, Gael P.; Pages, Amadis; Trincado, Jose Luis; Bellora, Nicolás; Eyras, Eduardo; Leveraging transcript quantification for fast computation of alternative splicing profiles; Cold Spring Harbor Lab Press; Rna (new York, N.y.); 21; 9; 7-2015; 1521-1531 1355-8382 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/http://rnajournal.cshlp.org/content/21/9/1521.long info:eu-repo/semantics/altIdentifier/doi/10.1261/rna.051557.115 info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4536314/ |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Cold Spring Harbor Lab Press |
publisher.none.fl_str_mv |
Cold Spring Harbor Lab Press |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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