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Updated: Feb 21, 2026

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data.

David Heller^1,2, Ralf Krestel², Uwe Ohler³

¹Max Planck Institute for Molecular Genetics, Ihnestr. 63-73 14195 Berlin, Germany.

Nucleic Acids Research

|October 5, 2017

Summary

This summary is machine-generated.

We developed ssHMM, a novel RNA motif finder that integrates sequence and structure to better understand RNA-binding protein interactions. This tool accurately identifies sequence-structure motifs, aiding in the discovery of new RNA-binding protein targets.

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Last Updated: Feb 21, 2026

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins

Published on: July 2, 2010

Area of Science:

Computational biology
Molecular biology
Bioinformatics

Background:

RNA-binding proteins (RBPs) are crucial for post-transcriptional RNA regulation, recognizing target RNAs through sequence-structure motifs.
The precise role of RNA structure in RBP binding, especially alongside sequence motifs, remains incompletely understood.
Current RNA motif finders often inadequately incorporate RNA structure or use non-interpretable models.

Purpose of the Study:

To develop a novel computational method, ssHMM, for identifying sequence-structure motifs of RNA-binding proteins.
To fully capture the interplay between RNA sequence and secondary structure preferences for RBPs.
To provide a tool for discovering novel motifs for uncharacterized RBPs.

Main Methods:

Developed ssHMM, a hidden Markov model (HMM)-based RNA motif finder utilizing Gibbs sampling.
Implemented a model that fully integrates RNA sequence and secondary structure information.
Designed for intuitive visualization as a graph to aid biological interpretation.

Main Results:

ssHMM directly generates combined sequence-structure motifs from large sequence datasets.
Achieved high motif recovery rates on synthetic data and successfully identified known RBP motifs from CLIP-Seq data.
Demonstrated linear scalability and superior speed compared to MEMERIS and RNAcontext on large datasets, performing comparably to GraphProt.

Conclusions:

ssHMM offers a powerful and interpretable approach for identifying RNA sequence-structure motifs.
The tool facilitates the discovery of novel motifs for uncharacterized RBPs, exemplified by its application to the YY1 protein.
ssHMM provides a computationally efficient and accurate method for RBP motif analysis.