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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set.

Fabrizio Pucci¹, Mehari B Zerihun^1,2,3, Emanuel K Peter¹

¹John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, 52428 Jülich, Germany.

RNA (New York, N.Y.)

|April 12, 2020

Summary

Predicting RNA structure is crucial for understanding its cellular roles. This study compares methods for predicting nucleotide contacts, finding minor differences in performance and highlighting the impact of data curation on prediction accuracy.

Keywords:

RNA contact prediction RNA structure prediction direct coupling analysis multiple sequence alignment

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Published on: December 1, 2023

Area of Science:

Molecular Biology
Structural Biology
Bioinformatics

Background:

RNA molecules perform vital cellular functions, but their roles are not fully understood.
Determining RNA's three-dimensional structure is essential for understanding its function, yet experimental structure resolution is challenging.
Advances in sequencing generate vast amounts of data, enabling statistical analysis like direct coupling analysis (DCA) for RNA structure prediction.

Purpose of the Study:

To quantify the improvement in RNA structure prediction using statistical methods.
To compare the performance of different nucleotide-nucleotide contact prediction methods.
To assess the robustness of these predictions based on contact definitions and sequence data curation.

Main Methods:

Curated a dataset of approximately 70 high-resolution RNA structures.
Compared various nucleotide-nucleotide contact prediction methods from existing literature.
Analyzed the impact of different contact definitions and sequence alignment procedures on prediction accuracy.

Main Results:

Observed only minor performance differences among the compared nucleotide-nucleotide contact prediction methods.
Evaluated the robustness of predictions across different contact definitions.
Demonstrated the significant influence of sequence data curation and alignment on prediction outcomes.

Conclusions:

Current nucleotide-nucleotide contact prediction methods show similar performance.
The accuracy of RNA structure prediction is highly sensitive to the quality and curation of homologous sequence data.
Further research is needed to optimize sequence data handling for improved RNA structure prediction.