Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins

Abstract

Background: A critical step in uncovering rules of RNA processing is to study the in vivo regulatory networks of RNA binding proteins (RBPs). Crosslinking and immunoprecipitation (CLIP) methods enable mapping RBP targets transcriptome-wide, but methodological differences present challenges to large-scale analysis across datasets. The development of enhanced CLIP (eCLIP) enabled the mapping of targets for 150 RBPs in K562 and HepG2, creating a unique resource of RBP interactomes profiled with a standardized methodology in the same cell types. Results: Our analysis of 223 eCLIP datasets reveals a range of binding modalities, including highly resolved positioning around splicing signals and mRNA untranslated regions that associate with distinct RBP functions. Quantification of enrichment for repetitive and abundant multicopy elements reveals 70% of RBPs have enrichment for non-mRNA element classes, enables identification of novel ribosomal RNA processing factors and sites, and suggests that association with retrotransposable elements reflects multiple RBP mechanisms of action. Analysis of spliceosomal RBPs indicates that eCLIP resolves AQR association after intronic lariat formation, enabling identification of branch points with single-nucleotide resolution, and provides genome-wide validation for a branch point-based scanning model for 3′ splice site recognition. Finally, we show that eCLIP peak co-occurrences across RBPs enable the discovery of novel co-interacting RBPs. Conclusions: This work reveals novel insights into RNA biology by integrated analysis of eCLIP profiling of 150 RBPs with distinct functions. Further, our quantification of both mRNA and other element association will enable further research to identify novel roles of RBPs in regulating RNA processing.

nearBynding: A flexible pipeline characterizing protein binding to local RNA structure

“…RBPs are common across both cell lines. Genome-wide RNA structure profiling showed that 3' untranslated regions (UTR), which are targets for many RBPs, are generally highly structured in cells [29,30]. Therefore, in order to test our pipeline on a robust dataset, we restricted our analysis of RBP binding to 3'UTR regions.…”

“…We selected 29 different RBPs in HepG2 and K562 cells that demonstrate strong, reproducible binding signals at 3'UTRs based on analysis from ref. [30] (Fig. S5A), which came to 40 unique cell type-RBP combinations.…”

“…We thank Leung lab members, Fertig lab members, and Tim Nieuwenhuis for crowd-sourced validation and ongoing suggestions for the project; Kurt Weir for discussion and reading the manuscript; Arjun Bhutkar for the suggestion to use Wasserstein distances; and Eric Van Nostrand for providing processed data from ref. [30].…”

Human cells contain myriad excised linear intron RNAs with links to gene regulation and potential utility as biomarkers

“…and clustering was performed based on the adjusted p-value for each enriched category using Seaborn ClusterMap. The function, cellular localization, and protein motif information for the RBPs are summarized below using information from (Van Nostrand et al 2020) supplemented by information from mammalian RNA granule and stress granule protein databases (Nunes et al 2019; Youn et al 2019), and AGO1-4 and DICER information from the UniProt database (The UniProt Consortium 2018). RBP are color coded by protein function: red, RNA splicing-related function; orange, miRNA-related functions blue, both an RNA splicing- and a miRNA-related function; black, RBPs whose primary function is not RNA splicing -or miRNA-related.…”

ChiRA: an integrated framework for chimeric read analysis from RNA-RNA interactome and RNA structurome data

“…Quantification helps to assess the true origin of a read segment in the case of multi-mapping. It has been shown that proper quantification of multi-mapped reads led to the discovery of novel protein-RNA interactions from CLIP-seq data [ 32 , 33 ]. A study on RNA-seq data revealed that the expression of genes with multi-mapped reads was underestimated by common quantification methods [ 31 ].…”