Evaluating data integrity in ribosome footprinting datasets through modelled polysome profiles.

Area of Science:

• Molecular Biology
• Genomics
• Biochemistry

Background:

• Studying protein synthesis regulation requires assessing transcriptome-wide ribosome binding to mRNAs.
• Polysome profiling and ribosome footprinting (Ribo-Seq) are key methods, offering different resolutions.
• Ribo-Seq provides ribosome densities on individual transcripts when combined with mRNA expression data.

Purpose of the Study:

• To develop methods for relating the information content of polysome profiling and Ribo-Seq.
• To reconstruct theoretical polysome profiles from ribosome footprinting data.
• To establish a tool for validating the quality of Ribo-Seq datasets.

Main Methods:

• Development of computational methods to reconstruct polysome profiles from Ribo-Seq data.
• Comparison of reconstructed polysome profiles with experimental polysome profiling data.
• Analysis of inconsistencies in RNA and Ribo-Seq data to identify non-physiological features.

Main Results:

• The developed method successfully reconstructs theoretical polysome profiles from Ribo-Seq data.
• Both polysome profiling and Ribo-Seq are validated as reliable experimental tools.
• Identification of non-physiological features in some published Ribo-Seq datasets, linked to data inconsistencies.

Conclusions:

• Reconstructed polysome profiles are valuable for assessing the global quality of Ribo-Seq datasets.
• The method provides a simple, visual approach for dataset quality assessment.
• This approach is useful for validating new Ribo-Seq datasets during early analysis stages.