  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.
