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Updated: Jun 22, 2025

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

Published on: April 30, 2011

Mudskipper detects combinatorial RNA binding protein interactions in multiplexed CLIP data.

Hsuanlin Her¹, Katherine L Rothamel², Grady G Nguyen²

¹Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Sanford Stem Cell Institute Innovation Center and Stem Cell Program, University of California, San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.

|July 2, 2024

Summary

This summary is machine-generated.

Mudskipper enhances RNA binding protein (RBP) site mapping by improving data analysis for multiplex crosslinking and immunoprecipitation (CLIP) experiments. This computational tool increases precision and recall for RBP binding site identification.

Keywords:

CLIP RNA RNA-binding proteins deep learning gene regulation splicing transcriptomics variant interpretation

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iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

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Published on: April 30, 2011

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Published on: May 24, 2017

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Understanding protein-RNA interactions is crucial for deciphering RNA processing and regulation.
Multiplexed crosslinking and immunoprecipitation (CLIP) technologies, like antibody-barcoded eCLIP (ABC), have advanced the high-throughput mapping of RNA binding protein (RBP) binding sites.
Existing multiplex CLIP datasets present challenges due to their multivariate nature and variable signal-to-noise ratios for different RBPs.

Purpose of the Study:

To develop a computational suite, Mudskipper, for improved analysis of multiplex CLIP datasets.
To address the challenges of multivariate data and non-uniform signal-to-noise ratios in mapping RBP binding sites.
To enhance the precision and recall of RBP binding site identification, particularly for low signal-to-noise ratio RBPs.

Main Methods:

Development of Mudskipper, a computational suite with two core components.
Implementation of a Dirichlet multinomial mixture model to handle the multivariate characteristics of ABC datasets.
Application of a soft-masking approach to identify and remove non-specific protein-RNA interactions.

Main Results:

Mudskipper demonstrates superior precision and recall compared to existing tools on multiplex CLIP datasets.
The computational suite effectively analyzes repetitive elements and small non-coding RNAs.
Mudskipper facilitates the unraveling of splicing outcomes and variant-associated disruptions.

Conclusions:

Mudskipper provides a versatile and effective computational solution for analyzing complex multiplex CLIP data.
The tool enhances the investigation of RNA binding protein-mediated regulation and disease mechanisms.
Mudskipper enables higher-throughput and more accurate studies of protein-RNA interactions.