Fairly effective methods exist for finding new noncoding RNA genes using search models based on known families of ncRNA genes (for example covariance models). However, these models only find new members of the existing families and are not useful in finding potential members of novel ncRNA families. Other problems with family-specific search include large processing requirements, ambiguity in defining which sequences form a family and lack of sufficient numbers of known sequences to properly estimate model parameters. An ncRNA search model is proposed which includes a collection of non-overlapping RNA hairpin structure covariance models. The hairpin models are chosen from a hairpin-model list compiled from many families in the Rfam non-coding RNA families database. The specific hairpin models included and the overall score threshold for the search model is determined through the use of a genetic algorithm.
Smith, Jennifer A.. (2010). "Computation Intelligence Method to Find Generic Non-Coding RNA Search Models". 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 1-5. http://dx.doi.org/10.1109/CIBCB.2010.5510341