The algorithm assigns a 1 to 10 RIN score, where level
10 RNA is completely intact. Because interpretation of
the electropherogram is automatic and not subject to individual
interpretation, universal and unbiased comparison of samples
is enabled and repeatability of experiments is improved.
The RIN algorithm was developed using neural networks
and adaptive learning in conjunction with a large database
of eukaryote total RNA samples, which were obtained
mainly from human, rat, and mouse tissues. The RIN score
is largely independent of the amount of RNA used and
the origin of the sample.