4.8 Article

A high quality Arabidopsis transcriptome for accurate transcript-level analysis of alternative splicing

Journal

NUCLEIC ACIDS RESEARCH
Volume 45, Issue 9, Pages 5061-5073

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkx267

Keywords

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Funding

  1. Biotechnology and Biological Sciences Research Council (BBSRC) [BB/K013661/1, BB/K006568/1, BB/K006835/1]
  2. Scottish Government Rural and Environment Science and Analytical Services division (RESAS)
  3. Austrian Science Fund (FWF) [P26333, DK W1207]
  4. LABEX Saclay Plant Sciences
  5. BBSRC EASTBIO PhD studentships
  6. European Alternative Splicing Network of Excellence (EURASNET) [LSHG-CT-2005-518238]
  7. University of Dundee
  8. BBSRC [BB/K006568/1, BB/K013661/1, BB/K006835/1, 1785562] Funding Source: UKRI
  9. Biotechnology and Biological Sciences Research Council [1785562, BB/K013661/1, BB/K006835/1, BB/K006568/1] Funding Source: researchfish
  10. Austrian Science Fund (FWF) [P26333, W1207] Funding Source: Austrian Science Fund (FWF)

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Alternative splicing generates multiple transcript and protein isoforms from the same gene and thus is important in gene expression regulation. To date, RNA-sequencing (RNA-seq) is the standard method for quantifying changes in alternative splicing on a genome-wide scale. Understanding the current limitations of RNA-seq is crucial for reliable analysis and the lack of high quality, comprehensive transcriptomes for most species, including model organisms such as Arabidopsis, is a major constraint in accurate quantification of transcript isoforms. To address this, we designed a novel pipeline with stringent filters and assembled a comprehensive Reference Transcript Dataset for Arabidopsis (AtRTD2) containing 82,190 non-redundant transcripts from 34 212 genes. Extensive experimental validation showed that AtRTD2 and its modified version, AtRTD2-QUASI, for use in Quantification of Alternatively Spliced Isoforms, outperform other available transcriptomes in RNA-seq analysis. This strategy can be implemented in other species to build a pipeline for transcript-level expression and alternative splicing analyses.

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