Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets

Autores
Gonzalez, Sergio Alberto; Rivarola, Maximo Lisandro; Ribone, Andrés Ignacio; Lew, Sergio Eduardo; Paniego, Norma Beatriz
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
De novo assembly of transcriptomes from species without reference genome remains a common problem in functional genomics. While methods and algorithms for transcriptome assembly are continually being developed and published, the quality of de novo assemblies using short reads depends on the complexity of the transcriptome and is limited by several types of errors. One problem to overcome is the research gap regarding the best method to use in each study to obtain high-quality de novo assembly. Currently, there are no established protocols for solving the assembly problem considering the transcriptome complexity. In addition, the accuracy of quality metrics used to evaluate assemblies remains unclear. In this study, we investigate and discuss how different variables accounting for the complexity of RNA-Seq data influence assembly results independently of the software used. For this purpose, we simulated transcriptomic short-read sequence datasets from high-quality full-length predicted transcript models with varying degrees of complexity. Subsequently, we conducted de novo assemblies using different assembly programs, and compared and classified the results using both reference-dependent and independent metrics. These metrics were assessed both individually and combined through multivariate analysis. The degree of alternative splicing and the fragment size of the paired-end reads were identified as the variables with the greatest influence on the assembly results. Moreover, read length and fragment size had different influences on the reconstruction of longer and shorter transcripts. These results underscore the importance of understanding the composition of the transcriptome under study, and making experimental design decisions related to the need to work with reads and fragments of different sizes. In addition, the choice of assembly software will positively impact the final assembly outcome. This selection will affect the completeness of represented genes and assembled isoforms, as well as contribute to error reduction.
Instituto de Biotecnología
Fil: Gonzalez, Sergio Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnologia y Biología Molecular; Argentina
Fil: Gonzalez, Sergio Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rivarola, Maximo Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ribone, Andrés Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lew, Sergio Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lew, Sergio Eduardo. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
Fil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Paniego, Norma Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fuente
Bioinformatics and Biology Insights 18 : 1-13 (2024)
Materia
ARN
Transcriptómica
Genética
Modelos de Simulación
RNA
Transcriptomics
Genetics
Simulation Models
De Novo Assembly
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/21747
Acceder
id INTADig_c3ce9e6789b6e47e92d696cc5184ae56
oai_identifier_str oai:localhost:20.500.12123/21747
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq DatasetsGonzalez, Sergio AlbertoRivarola, Maximo LisandroRibone, Andrés IgnacioLew, Sergio EduardoPaniego, Norma BeatrizARNTranscriptómicaGenéticaModelos de SimulaciónRNATranscriptomicsGeneticsSimulation ModelsDe Novo AssemblyDe novo assembly of transcriptomes from species without reference genome remains a common problem in functional genomics. While methods and algorithms for transcriptome assembly are continually being developed and published, the quality of de novo assemblies using short reads depends on the complexity of the transcriptome and is limited by several types of errors. One problem to overcome is the research gap regarding the best method to use in each study to obtain high-quality de novo assembly. Currently, there are no established protocols for solving the assembly problem considering the transcriptome complexity. In addition, the accuracy of quality metrics used to evaluate assemblies remains unclear. In this study, we investigate and discuss how different variables accounting for the complexity of RNA-Seq data influence assembly results independently of the software used. For this purpose, we simulated transcriptomic short-read sequence datasets from high-quality full-length predicted transcript models with varying degrees of complexity. Subsequently, we conducted de novo assemblies using different assembly programs, and compared and classified the results using both reference-dependent and independent metrics. These metrics were assessed both individually and combined through multivariate analysis. The degree of alternative splicing and the fragment size of the paired-end reads were identified as the variables with the greatest influence on the assembly results. Moreover, read length and fragment size had different influences on the reconstruction of longer and shorter transcripts. These results underscore the importance of understanding the composition of the transcriptome under study, and making experimental design decisions related to the need to work with reads and fragments of different sizes. In addition, the choice of assembly software will positively impact the final assembly outcome. This selection will affect the completeness of represented genes and assembled isoforms, as well as contribute to error reduction.Instituto de BiotecnologíaFil: Gonzalez, Sergio Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnologia y Biología Molecular; ArgentinaFil: Gonzalez, Sergio Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rivarola, Maximo Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ribone, Andrés Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lew, Sergio Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lew, Sergio Eduardo. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; ArgentinaFil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Paniego, Norma Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaSage Publications2025-03-20T12:21:22Z2025-03-20T12:21:22Z2024-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/21747https://journals.sagepub.com/doi/full/10.1177/117793222412749571177-9322https://doi.org/10.1177/11779322241274957Bioinformatics and Biology Insights 18 : 1-13 (2024)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-04T09:50:59Zoai:localhost:20.500.12123/21747instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-04 09:50:59.679INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets
title Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets
spellingShingle Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets
Gonzalez, Sergio Alberto
ARN
Transcriptómica
Genética
Modelos de Simulación
RNA
Transcriptomics
Genetics
Simulation Models
De Novo Assembly
title_short Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets
title_full Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets
title_fullStr Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets
title_full_unstemmed Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets
title_sort Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets
dc.creator.none.fl_str_mv Gonzalez, Sergio Alberto
Rivarola, Maximo Lisandro
Ribone, Andrés Ignacio
Lew, Sergio Eduardo
Paniego, Norma Beatriz
author Gonzalez, Sergio Alberto
author_facet Gonzalez, Sergio Alberto
Rivarola, Maximo Lisandro
Ribone, Andrés Ignacio
Lew, Sergio Eduardo
Paniego, Norma Beatriz
author_role author
author2 Rivarola, Maximo Lisandro
Ribone, Andrés Ignacio
Lew, Sergio Eduardo
Paniego, Norma Beatriz
author2_role author
author
author
author
dc.subject.none.fl_str_mv ARN
Transcriptómica
Genética
Modelos de Simulación
RNA
Transcriptomics
Genetics
Simulation Models
De Novo Assembly
topic ARN
Transcriptómica
Genética
Modelos de Simulación
RNA
Transcriptomics
Genetics
Simulation Models
De Novo Assembly
dc.description.none.fl_txt_mv De novo assembly of transcriptomes from species without reference genome remains a common problem in functional genomics. While methods and algorithms for transcriptome assembly are continually being developed and published, the quality of de novo assemblies using short reads depends on the complexity of the transcriptome and is limited by several types of errors. One problem to overcome is the research gap regarding the best method to use in each study to obtain high-quality de novo assembly. Currently, there are no established protocols for solving the assembly problem considering the transcriptome complexity. In addition, the accuracy of quality metrics used to evaluate assemblies remains unclear. In this study, we investigate and discuss how different variables accounting for the complexity of RNA-Seq data influence assembly results independently of the software used. For this purpose, we simulated transcriptomic short-read sequence datasets from high-quality full-length predicted transcript models with varying degrees of complexity. Subsequently, we conducted de novo assemblies using different assembly programs, and compared and classified the results using both reference-dependent and independent metrics. These metrics were assessed both individually and combined through multivariate analysis. The degree of alternative splicing and the fragment size of the paired-end reads were identified as the variables with the greatest influence on the assembly results. Moreover, read length and fragment size had different influences on the reconstruction of longer and shorter transcripts. These results underscore the importance of understanding the composition of the transcriptome under study, and making experimental design decisions related to the need to work with reads and fragments of different sizes. In addition, the choice of assembly software will positively impact the final assembly outcome. This selection will affect the completeness of represented genes and assembled isoforms, as well as contribute to error reduction.
Instituto de Biotecnología
Fil: Gonzalez, Sergio Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnologia y Biología Molecular; Argentina
Fil: Gonzalez, Sergio Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Rivarola, Maximo Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ribone, Andrés Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lew, Sergio Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lew, Sergio Eduardo. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
Fil: Paniego, Norma Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Paniego, Norma Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description De novo assembly of transcriptomes from species without reference genome remains a common problem in functional genomics. While methods and algorithms for transcriptome assembly are continually being developed and published, the quality of de novo assemblies using short reads depends on the complexity of the transcriptome and is limited by several types of errors. One problem to overcome is the research gap regarding the best method to use in each study to obtain high-quality de novo assembly. Currently, there are no established protocols for solving the assembly problem considering the transcriptome complexity. In addition, the accuracy of quality metrics used to evaluate assemblies remains unclear. In this study, we investigate and discuss how different variables accounting for the complexity of RNA-Seq data influence assembly results independently of the software used. For this purpose, we simulated transcriptomic short-read sequence datasets from high-quality full-length predicted transcript models with varying degrees of complexity. Subsequently, we conducted de novo assemblies using different assembly programs, and compared and classified the results using both reference-dependent and independent metrics. These metrics were assessed both individually and combined through multivariate analysis. The degree of alternative splicing and the fragment size of the paired-end reads were identified as the variables with the greatest influence on the assembly results. Moreover, read length and fragment size had different influences on the reconstruction of longer and shorter transcripts. These results underscore the importance of understanding the composition of the transcriptome under study, and making experimental design decisions related to the need to work with reads and fragments of different sizes. In addition, the choice of assembly software will positively impact the final assembly outcome. This selection will affect the completeness of represented genes and assembled isoforms, as well as contribute to error reduction.
publishDate 2024
dc.date.none.fl_str_mv 2024-12
2025-03-20T12:21:22Z
2025-03-20T12:21:22Z
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/20.500.12123/21747
https://journals.sagepub.com/doi/full/10.1177/11779322241274957
1177-9322
https://doi.org/10.1177/11779322241274957
url http://hdl.handle.net/20.500.12123/21747
https://journals.sagepub.com/doi/full/10.1177/11779322241274957
https://doi.org/10.1177/11779322241274957
identifier_str_mv 1177-9322
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Sage Publications
publisher.none.fl_str_mv Sage Publications
dc.source.none.fl_str_mv Bioinformatics and Biology Insights 18 : 1-13 (2024)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
_version_ 1842341438311890944
score 12.623145

Publicaciones similares