Frequent co-regulation of splicing and polyadenylation by RNA-binding proteins inferred with MAPP
Bak, M.; van Nimwegen, E.; Schmidt, R.; Zavolan, M.; Gruber, A. J.
Show abstract
Maturation of eukaryotic pre-mRNAs via splicing, 3 end cleavage and polyadenylation is modulated across cell types and conditions by a variety of RNA-binding proteins (RBPs). Although over 1500 proteins are associated with RNAs in human cells, their binding motifs, targets and functions still remain to be elucidated, especially in the complex environment of human tissues and in the context of diseases. To overcome the lack of methods for systematic and automated detection of sequence motif-guided changes in pre-mRNA processing based on RNA sequencing (RNA-seq) data we have developed MAPP (Motif Activity on Pre-mRNA Processing). We demonstrate MAPPs functionality by applying it to RNA-seq data from 284 RBP knock-down experiments in the ENCODE project, from which MAPP not only infers position-dependent impact profiles of known regulators, but also reveals RBPs that modulate both the inclusion of cassette exons and the poly(A) site choice. Among these, the Polypyrimidine Tract Binding Protein 1 (PTBP1) has a similar activity in glioblastoma samples. This highlights the ability of MAPP to unveil global regulators of mRNA processing under physiological and pathological conditions.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Differential Analysis of RNA Structure Probing Experiments at Nucleotide Resolution: Uncovering Regulatory Functions of RNA Structure 97%
- Massively parallel reporter perturbation assay uncovers temporal regulatory architecture during neural differentiation 96%
- RNA modifications detection by comparative Nanopore direct RNA sequencing 96%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Assessing Conservation of Alternative Splicing with Evolutionary Splicing Graphs 96%
- Contrasting and Combining Transcriptome Complexity Captured by Short and Long RNA Sequencing Reads 95%
- Quantitative occupancy of myriad transcription factors from one DNase experiment enables efficient comparisons across conditions 95%
Similar papers in this journal
- DeepLocRNA: An Interpretable Deep Learning Model for Predicting RNA Subcellular Localization with domain-specific transfer-learning 96%
- Estimating the power of sequence covariation for detecting conserved RNA structure 95%
- Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.