Supplementary information and data
Mining Frequent Stem Patterns from Unaligned RNA Sequences
Michiaki Hamada, Koji Tsuda, Taku Kudo, Taishin Kin and Kiyoshi Asai
Introduction
Our method RNAmine employs a graph theoretic representation of
RNA sequences, and detects all possible motifs
exhaustively using a graph mining algorithm.
The motif detection problem boils down to finding frequently appearing
patterns in a set of directed and labeled graphs.
See original paper and supplementary paper for detail algorithms and results.
Software availability
The binary of RNAmine is available on request. Please mail to "hamada-michiaki AT aist DOT go DOT jp".
Technical details for our graph mining algorithm
In our algorithm, we use extended graph mining techniques based on gSpan algorithm.
Technical details for graph mining method used in our paper are available here.
This also includes supplementary figures and tables.
Dataset and results in our experiments
All dataset are available here.
Related links
- gSpan: Graph-Based Substructure Pattern Mining
- comRNA: A common RNA secondary structure predictor
- Vienna RNA package: RNA Secondary Structure Prediction and Comparison
- CMfinder: A Covariance Model Based RNA Motif Finding Algorithm
- Scarna: Stem Candidate Aligner for RNA, which is developed by our group
Last Update: 2006/05/12