A bottom-up clustering algorithm to detect ncRNA molecules with a common secondary structure

Yair Horesh, Ron Unger

Research output: Contribution to journalArticlepeer-review


Recently, there has been much interest in exploring the universe of non-protein coding RNA molecules that operate in the cell. We suggested an approach, using a simple two-dimensional representation of RNA molecules that can identify common structural features of RNA molecules. Here, we address a common situation in which there is a large and diverse population of candidate molecules, and the task is to identify a small subset (or subsets) of RNA molecules that share a common structure. With certain constraints, our algorithm enumerates all possible sets of RNA molecules that have a common structure by first grouping together all molecules that have a single common structural feature and, using an iterative approach, search for subsets that share additional structural motifs. In a computational experiment, we were able to detect members of three small classes of RNA molecules, each containing several dozen members that were mixed in a population of 2778 non-coding sequences common to two trypanosome species.

Original languageEnglish
Pages (from-to)292-304
Number of pages13
JournalInternational Journal of Bioinformatics Research and Applications
Issue number3
StatePublished - 2005


  • RNA secondary structure
  • clustering
  • dot-matrix
  • novel ncRNA families


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