Ali Oghabian (@gacatag.bsky.social)

New paper out in BMC Genomics ! Poly(A)+ & rRNA-depleted #RNAseq capture long transcripts differently, introducing major biases in transcript & splice-junction detection with important implications for disease studies. doi.org/10.1186/s128... #Transcriptomics #Splicing #Genomics #Bioinformatics

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Poly(A)+ selection limits detection of long and alternatively spliced transcripts compared with rRNA depletion in RNA-Sequencing - BMC Genomics The eukaryotic transcriptome diversity arises largely from alternative splicing. One of the widely used high-throughput methods to study this diversity is RNA sequencing. RNA sequencing has become a cornerstone of both basic biology and precision medicine, facilitating the quantification of gene and transcript expression, as well as the characterization of alternative splicing events and regulatory biological pathways in these studies. As there is a wide interest in studying non-ribosomal RNAs, which constitute about 20% of cellular RNAs, it is common to either select for poly(A)+ RNAs or to deplete ribosomal RNAs during the library preparation stage of RNA sequencing. At the time of library preparation, poly(A)+ selected RNA-Seq captures the polyadenylated transcripts, whereas rRNA-depleted RNA-Seq pools a broader spectrum of RNA species, including non-polyadenylated and premature transcripts. Using blood and skeletal muscle transcriptomics datasets, we examined how these two library enrichment techniques influence transcript representation, transcript-body coverage, and splice junction detection. We observed that poly(A)+ selected libraries display length-dependent differences, reduced splice junction representation and pronounced 3’ end coverage bias for transcripts of total transcription length over 5 kb. In contrast, rRNA depletion provides a more uniform 5′-3′ coverage, an improved detection of splice junctions, and a robust detection of long disease-relevant transcripts. These differences are evident in the detection of extremely large transcripts, such as the sarcomeric genes OBSCN (~ 39 kb) and TTN (> 100 kb). This study discusses how RNA-Seq library preparation techniques capture different RNA types and emphasizes the importance of interpreting poly(A)+ selected and rRNA depleted data in the appropriate biological and clinical contexts. https://doi.org/10.1186/s12864-026-12944-z