RNA Search with Decision Trees and Partial Covariance Models

The use of partial covariance models to search for RNA family members in genomic sequence databases is explored. The partial models are formed from contiguous subranges of the overall RNA family multiple alignment columns. A binary decision-tree framework is presented for choosing the order to apply the partial models and the score thresholds on which to make the decisions. The decision trees are chosen to minimize computation time subject to the constraint that all of the training sequences are passed to the full covariance model for final evaluation. Computational intelligence methods are suggested to select the decision tree since the tree can be quite complex and there is no obvious method to build the tree in these cases. Experimental results from seven RNA families shows execution times of 0.066-0.268 relative to using the full covariance model alone. Tests on the full sets of known sequences for each family show that at least 95 percent of these sequences are found for two families and 100 percent for five others. Since the full covariance model is run on all sequences accepted by the partial model decision tree, the false alarm rate is at least as low as that of the full model alone

Computation Intelligence Method to Find Generic Non-Coding RNA Search Models

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

Efficient known ncRNA search including pseudoknots

BACKGROUND: Searching for members of characterized ncRNA families containing pseudoknots is an important component of genome-scale ncRNA annotation. However, the state-of-the-art known ncRNA search is based on context-free grammar (CFG), which cannot effectively model pseudoknots. Thus, existing CFG-based ncRNA identification tools usually ignore pseudoknots during search. As a result, dozens of sequences that do not contain the native pseudoknots are reported by these tools. When pseudoknot structures are vital to the functions of the ncRNAs, these sequences may not be true members. RESULTS: In this work, we design a pseudoknot search tool using multiple simple sub-structures, which are derived from knot-free and bifurcation-free structural motifs in the underlying family. We test our tool on a contiguous 22-Mb region of the Maize Genome. The experimental results show that our work competes favorably with other pseudoknot search methods. CONCLUSIONS: Our sub-structure based tool can conduct genome-scale pseudoknot-containing ncRNA search effectively and efficiently. It provides a complementary pseudoknot search tool to Infernal. The source codes are available at http://www.cse.msu.edu/~chengy/knotsearch