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• W3105992022 abstract "RNA-seq datasets can contain millions of intron reads per sequenced library that are typically removed from downstream analysis. Only reads overlapping annotated exons are considered to be informative since mature mRNA is assumed to be the major component sequenced, especially when examining poly(A) RNA samples. In this paper, we demonstrate that intron reads are informative and that pre-mRNA is the major source of intron signal. Making use of pre-mRNA signal, our index method combines differential expression analyses from intron and exon counts to categorise changes observed in each count set, giving additional genes with evidence of transcriptional changes when compared to a classic approach. Considering the importance of intron retention in some biological systems, another novel method, superintronic , looks for evidence of intron retention after accounting for the presence of pre-mRNA signal. The results presented here overcomes deficiencies and biases in previous works related to intron reads by exploring multiple sources for intron reads simultaneously using a data-driven approach, and provides a broad overview into how intron reads can be utilised in relation to multiple aspects of transcriptional biology." @default.
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• W3105992022 date "2018-06-21" @default.
• W3105992022 modified "2023-09-27" @default.
• W3105992022 title "Covering all your bases: incorporating intron signal from RNA-seq data" @default.
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• W3105992022 doi "https://doi.org/10.1101/352823" @default.
• W3105992022 hasPublicationYear "2018" @default.
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