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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/11645

id CONICETDig_febc7351c684df4720115d6e6d941e8e
oai_identifier_str oai:ri.conicet.gov.ar:11336/11645
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling 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
reponame_str CONICET Digital (CONICET)
collection 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
_version_ 1844613755547156480
score 13.070432