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Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

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Related Experiment Video

Updated: May 29, 2025

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Published on: July 10, 2020

mRNA-LM: full-length integrated SLM for mRNA analysis.

Sizhen Li¹, Shahriar Noroozizadeh^1,2,3, Saeed Moayedpour¹

¹Digital R&D, Sanofi, Cambridge, MA 02141, United States.

Nucleic Acids Research

|February 3, 2025

Summary

This summary is machine-generated.

Developing an mRNA language model improves vaccine design. mRNA-LM accurately predicts messenger RNA properties, enhancing vaccine and therapeutic development by optimizing sequence selection for efficiency and stability.

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Last Updated: May 29, 2025

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Area of Science:

Bioinformatics
Computational Biology
Molecular Biology

Background:

The success of SARS-CoV-2 mRNA vaccines highlights the potential of messenger RNA (mRNA) technology.
Selecting optimal mRNA sequences is crucial for vaccine and therapeutic efficiency, but remains a significant challenge.
mRNA sequence directly impacts translation efficiency, stability, and degradation rates, influencing overall vaccine performance.

Purpose of the Study:

To develop an integrated small language model, mRNA-LM, for comprehensive mRNA sequence modeling.
To enable accurate prediction of mRNA properties for improved vaccine and therapeutic design.
To overcome the limitations of existing methods in selecting optimal mRNA sequences.

Main Methods:

Developed mRNA-LM, an integrated small language model using contrastive language-image pretraining technology.
Combined three separate language models to analyze different mRNA segments.
Trained mRNA-LM on millions of diverse mRNA sequences across multiple species using unsupervised learning.

Main Results:

The unsupervised mRNA-LM model learned biologically relevant information, including evolutionary patterns and host-pathogen interactions.
Fine-tuning enabled mRNA-LM for various mRNA property prediction tasks.
The full-length integrated model demonstrated accurate predictions, outperforming previous methods.

Conclusions:

mRNA-LM provides a powerful tool for modeling entire mRNA sequences and predicting their properties.
This approach significantly improves the selection of appropriate mRNA sequences for enhanced vaccine and therapeutic development.
The model's ability to learn biological insights from sequence data opens new avenues for mRNA-based innovations.