Artículo
ASpli: integrative analysis of splicing landscapes through RNA-Seq assays
Mancini, Estefania
; Rabinovich, Andrés
; Iserte, Javier Alonso
; Yanovsky, Marcelo Javier
; Chernomoretz, Ariel
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Fecha de publicación:
03/2021
Editorial:
Oxford University Press
Revista:
Bioinformatics (Oxford, England)
ISSN:
1367-4803
e-ISSN:
1367-4811
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Genome-wide analysis of alternative splicing has been a very active field of research since the early days of Next Generation Sequencing technologies. Since then, ever-growing data availability and the development of increasingly sophisticated analysis methods have uncovered the complexity of the general splicing repertoire. A large number of splicing analysis methodologies exist, each of them presenting its own strengths and weaknesses. For instance methods exclusively relying on junction information do not take advantage of the large majority of reads produced in an RNA-seq assay, isoform reconstruction methods might not detect novel intron retention events, some solutions can only handle canonical splicing events, and many existing methods can only perform pairwise comparisons. In this contribution, we present ASpli, a computational suite implemented in R statistical language, that allows the identification of changes in both, annotated and novel alternative splicing events and can deal with simple, multi-factor or paired experimental designs. Our integrative computational workflow considers the same GLM model, applied to different sets of reads and junctions, in order to compute complementary splicing signals.Analyzing simulated and real data we found that the consolidation of these signals resulted in a robust proxy of the occurrence of splicing alterations. While the analysis of junctions allowed us to uncover annotated as well as non-annotated events, read coverage signals notably increased recall capabilities at a very competitive performance when compared against other state-of-the-art splicing analysis algorithms. ASpli is freely available from the Bioconductor project site https://www.bioconductor.org/packages/ASpli.
Palabras clave:
RNASEQ
,
ALTERNATIVE SPLICING
Archivos asociados
Licencia
Identificadores
Colecciones
Articulos(IFIBA)
Articulos de INST.DE FISICA DE BUENOS AIRES
Articulos de INST.DE FISICA DE BUENOS AIRES
Articulos(IIBBA)
Articulos de INST.DE INVEST.BIOQUIMICAS DE BS.AS(I)
Articulos de INST.DE INVEST.BIOQUIMICAS DE BS.AS(I)
Citación
Mancini, Estefania; Rabinovich, Andrés; Iserte, Javier Alonso; Yanovsky, Marcelo Javier; Chernomoretz, Ariel; ASpli: integrative analysis of splicing landscapes through RNA-Seq assays; Oxford University Press; Bioinformatics (Oxford, England); 3-2021; 1-9
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