Related Experiment Video
Updated: Mar 9, 2026
Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
Published on: September 28, 2017
A comprehensive benchmark of RNA-RNA interaction prediction tools for all domains of life
Sinan Ugur Umu1,2, Paul P Gardner1,2,3
1School of Biological Sciences.
Motivation:
The aim of this study is to assess the performance of RNA-RNA interaction prediction tools for all domains of life.
Results:
Minimum free energy (MFE) and alignment methods constitute most of the current RNA interaction prediction algorithms. The MFE tools that include accessibility (i.e. RNAup, IntaRNA and RNAplex) to the final predicted binding energy have better true positive rates (TPRs) with a high positive predictive values (PPVs) in all datasets than other methods. They can also differentiate almost half of the native interactions from background. The algorithms that include effects of internal binding energies to their model and alignment methods seem to have high TPR but relatively low associated PPV compared to accessibility based methods.
Availability And Implementation:
We shared our wrapper scripts and datasets at Github (github.com/UCanCompBio/RNA_Interactions_Benchmark). All parameters are documented for personal use.
Contact:
sinan.umu@pg.canterbury.ac.nz.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Related Concept Videos
03:21RNA-seq
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
02:24Ribosome Profiling
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...
02:04Protein-protein Interfaces
01:23RNA Interference
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...