Comparing similar ordered trees in linear-time

AbstractWe describe a linear-time algorithm for comparing two similar ordered rooted trees with node labels. The method for comparing trees is the usual tree edit distance. We show that an optimal mapping that uses at most k insertions or deletions can then be constructed in O(nk3) where n is the size of the trees. The approach is inspired by the Zhang–Shasha algorithm for tree edit distance in combination with an adequate pruning of the search space based on the tree edit graph

PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions

Local gapped subforest alignment and its application in finding RNA structural motifs

10.1089/cmb.2006.13.702Journal of Computational Biology133702-718JCOB

Local Gapped Subforest Alignment and Its Application in Finding RNA Structural Motifs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)3341569-58

Efficient Algorithms for Local Forest Similarity

An ordered labelled tree is a tree where the left-to-right order among siblings is significant. Ordered labelled forests are sequences of ordered labelled trees. Given two ordered labelled forests F and G. the local forest similarity is to find two sub­ forests F\u27 and G\u27 of F and G respectively such that they are the most similar over all possible F\u27 and G\u27. In this thesis, we present efficient algorithms for the local forest similarity problem for two types of sub-forests: sibling subforests and closed subforests. Our algorithms can be used to locate the structural regions in RNA secondary structures since RNA molecules’ secondary structures could be represented as ordered labelled forests