Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets
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 complexi...
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SAGE Publishing
2024-12-01
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| Series: | Bioinformatics and Biology Insights |
| Online Access: | https://doi.org/10.1177/11779322241274957 |
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| author | Gonzalez Sergio Alberto Rivarola Maximo Ribone Andres Lew Sergio Paniego Norma |
| author_facet | Gonzalez Sergio Alberto Rivarola Maximo Ribone Andres Lew Sergio Paniego Norma |
| author_sort | Gonzalez Sergio Alberto |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-23dd421cfef6480fbace02d4a2ac6ff8 |
| institution | OA Journals |
| issn | 1177-9322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SAGE Publishing |
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| series | Bioinformatics and Biology Insights |
| spelling | doaj-art-23dd421cfef6480fbace02d4a2ac6ff82025-08-20T02:31:23ZengSAGE PublishingBioinformatics and Biology Insights1177-93222024-12-011810.1177/11779322241274957Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq DatasetsGonzalez Sergio Alberto0Rivarola Maximo1Ribone Andres2Lew Sergio3Paniego Norma4Instituto de Agrobiotecnología y Biología Molecular (IABIMO), CICVyA, Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires, ArgentinaConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, ArgentinaConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, ArgentinaInstituto de Ingeniería Biomédica, Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, ArgentinaConsejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, ArgentinaDe 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.https://doi.org/10.1177/11779322241274957 |
| spellingShingle | Gonzalez Sergio Alberto Rivarola Maximo Ribone Andres Lew Sergio Paniego Norma Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets Bioinformatics and Biology Insights |
| title | 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_short | 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 |
| url | https://doi.org/10.1177/11779322241274957 |
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