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Positional motif analysis reveals the extent of specificity of protein-RNA interactions observed by CLIP

View ORCID ProfileKlara Kuret, View ORCID ProfileAram Gustav Amalietti, View ORCID ProfileJernej Ule
doi: https://doi.org/10.1101/2021.12.07.471544
Klara Kuret
1National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
4Jozef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia
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Aram Gustav Amalietti
1National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
2The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
3Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, Queen Square, London, WC1N 3BG, UK
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Jernej Ule
1National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
2The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
3Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, Queen Square, London, WC1N 3BG, UK
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  • For correspondence: jernej.ule@ki.si
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Abstract

Background Crosslinking and immunoprecipitation (CLIP) is a method used to identify in vivo RNA– protein binding sites on a transcriptome-wide scale. With the increasing amounts of available data for RNA-binding proteins (RBPs), it is important to understand to what degree the enriched motifs specify the RNA binding profiles of RBPs in cells.

Results We develop positionally-enriched k-mer analysis (PEKA), a computational tool for efficient analysis of enriched motifs from individual CLIP datasets, which minimises the impact of technical and regional genomic biases by internal data normalisation. We cross-validate PEKA with mCross, and show that background correction by size-matched input doesn’t generally improve the specificity of detected motifs. We identify motif classes with common enrichment patterns across eCLIP datasets and across RNA regions, while also observing variations in the specificity and the extent of motif enrichment across eCLIP datasets, between variant CLIP protocols, and between CLIP and in vitro binding data. Thereby we gain insights into the contributions of technical and regional genomic biases to the enriched motifs, and find how motif enrichment features relate to the domain composition and low-complexity regions (LCRs) of the studied proteins.

Conclusions Our study provides insights into the overall contributions of regional binding preferences, protein domains and LCRs to the specificity of protein-RNA interactions, and shows the value of cross-motif and cross-RBP comparison for data interpretation. Our results are presented for exploratory analysis via an online platform in an RBP-centric and motif-centric manner (https://imaps.goodwright.com/apps/peka/). PEKA is available from https://github.com/ulelab/peka.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • E-mail: klara.kuret{at}ki.si, aram.amalietti{at}gmail.com

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 07, 2021.
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Positional motif analysis reveals the extent of specificity of protein-RNA interactions observed by CLIP
Klara Kuret, Aram Gustav Amalietti, Jernej Ule
bioRxiv 2021.12.07.471544; doi: https://doi.org/10.1101/2021.12.07.471544
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Positional motif analysis reveals the extent of specificity of protein-RNA interactions observed by CLIP
Klara Kuret, Aram Gustav Amalietti, Jernej Ule
bioRxiv 2021.12.07.471544; doi: https://doi.org/10.1101/2021.12.07.471544

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