# BPPart and BPMax: RNA-RNA Interaction Partition Function and Structure Prediction for the Base Pair Counting Model

## TL;DR

This paper introduces BPPart and BPMax, fast algorithms for RNA-RNA interaction prediction based on a simple base pair counting model, offering significant speed improvements over previous thermodynamic models.

## Contribution

The study presents novel, simplified algorithms BPPart and BPMax for RNA-RNA interaction prediction that are much faster and require less memory than existing thermodynamic-based methods.

## Key findings

- BPPart and BPMax are 225 and 1350 times faster than piRNA.
- High correlation with piRNA predictions (over 0.85) at different temperatures.
- Discovery of potential RNA partners SNORD3D and TRAF3.

## Abstract

RNA-RNA interaction (RRI) is ubiquitous and has complex roles in the cellular functions. In human health studies, miRNA-target and lncRNAs are among an elite class of RRIs that have been extensively studied. Bacterial ncRNA-target and RNA interference are other classes of RRIs that have received significant attention. In recent studies, mRNA-mRNA interaction instances have been observed, where both partners appear in the same pathway without any direct link between them, or any prior knowledge about their relationship. Those recently discovered cases suggest that RRI scope is much wider than those aforementioned elite classes. We revisit our RNA-RNA interaction partition function algorithm, piRNA, which computes the partition function, base-pairing probabilities, and structure for the comprehensive Turner energy model using 96 different dynamic programming tables. In this study, we strategically retreat from sophisticated thermodynamic models to the much simpler base pair counting model. That might seem counter-intuitive at the first glance; our idea is to benefit from the advantages of such simple models in terms of running time and memory footprint and compensate for the associated information loss by adding machine learning components in the future. Here, simple weighted base pair counting is considered to obtain BPPart for Base-pair Partition function and BPMax for Base-pair Maximization, which use 9 and 2 tables respectively. They are empirically 225 and 1350 fold faster than piRNA. A correlation of 0.855 and 0.836 was achieved between piRNA and BPPart and between piRNA and BPMax, respectively, in 37 degrees, and 0.920 and 0.904 in -180 degrees. We also discover two partner RNAs, SNORD3D and TRAF3, and hypothesize their potential roles in genetic diseases. We envision fusion of machine learning methods with the proposed algorithms in the future.