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  RNA transcription and degradation of Alu retrotransposons depends on sequence features and evolutionary history

Baar, T., Dümcke, S., Gressel, S., Schwalb, B., Dilthey, A., Cramer, P., et al. (2022). RNA transcription and degradation of Alu retrotransposons depends on sequence features and evolutionary history. G3: Genes, Genomes, Genetics, 12(5): jkac054. doi:10.1093/g3journal/jkac054.

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 Creators:
Baar, T., Author
Dümcke, S., Author
Gressel, Saskia1, Author                 
Schwalb, Björn1, Author           
Dilthey, A., Author
Cramer, Patrick1, Author                 
Tresch, A., Author
Steinmetz, L. M., Editor
Affiliations:
1Department of Molecular Biology, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society, ou_3350224              

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Free keywords: Alu elements; Alu transcription; RNA labeling; retrotransposons
 Abstract: Alu elements are one of the most successful groups of RNA retrotransposons and make up 11% of the human genome with over 1 million individual loci. They are linked to genetic defects, increases in sequence diversity, and influence transcriptional activity. Still, their RNA me- tabolism is poorly understood yet. It is even unclear whether Alu elements are mostly transcribed by RNA Polymerase II or III. We have con- ducted a transcription shutoff experiment by a-amanitin and metabolic RNA labeling by 4-thiouridine combined with RNA fragmentation (TT-seq) and RNA-seq to shed further light on the origin and life cycle of Alu transcripts. We find that Alu RNAs are more stable than previ- ously thought and seem to originate in part from RNA Polymerase II activity, as previous reports suggest. Their expression however seems to be independent of the transcriptional activity of adjacent genes. Furthermore, we have developed a novel statistical test for detecting the expression of quantitative trait loci in Alu elements that relies on the de Bruijn graph representation of all Alu sequences. It controls for both statistical significance and biological relevance using a tuned k-mer representation, discovering influential sequence features missed by regular motif search. In addition, we discover several point mutations using a generalized linear model, and motifs of interest, which also match transcription factor-binding motifs.

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Language(s): eng - English
 Dates: 2022-01-252022-02-252022-03-072022
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1093/g3journal/jkac054
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Project name : We thank the Regional Computing Center of the University of Cologne (RRZK) for providing computing time on the DFG-funded (funding number: INST 216/512/1FUGG) High Performance Computing (HPC) system CHEOPS as well as support. TB is partly funded by the Sonderforschungsbereiche (SFB) 680 grant by the Deutsche Forschungsgemeinschaft (DFG).
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Title: G3: Genes, Genomes, Genetics
Source Genre: Journal
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Publ. Info: Bethesda : Genetics Society of America
Pages: 14 Volume / Issue: 12 (5) Sequence Number: jkac054 Start / End Page: - Identifier: ISSN: 2160-1836
CoNE: https://pure.mpg.de/cone/journals/resource/2160-1836