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- Title
Uncovering transcriptional dark matter via gene annotation independent single-cell RNA sequencing analysis.
- Authors
Wang, Michael F. Z.; Mantri, Madhav; Chou, Shao-Pei; Scuderi, Gaetano J.; McKellar, David W.; Butcher, Jonathan T.; Danko, Charles G.; De Vlaminck, Iwijn
- Abstract
Conventional scRNA-seq expression analyses rely on the availability of a high quality genome annotation. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, genome annotations are often incomplete, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant transcriptional activity beyond the scope of the best available genome annotation by performing scRNA-seq analysis on any region in the genome for which transcriptional products are detected. Our tool generates a single-cell expression matrix for all transcriptionally active regions (TARs), performs single-cell TAR expression analysis to identify biologically significant TARs, and then annotates TARs using gene homology analysis. This procedure uses single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts and thereby uncovers biology to which scRNA-seq would otherwise be in the dark. Conventional single-cell RNA sequencing analysis rely on genome annotations that may be incomplete or inaccurate especially for understudied organisms. Here the authors present a bioinformatic tool that leverages single-cell data to uncover biologically relevant transcripts beyond the best available genome annotation.
- Subjects
GENES; DARK matter; RNA sequencing; NAKED mole rat; ANNOTATIONS; SEA urchins
- Publication
Nature Communications, 2021, Vol 12, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-021-22496-3