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Analysis of ribosomal RNA sequences by combinatorial clustering
P Xing1, C Kulikowski, I Muchnik
1DIMACS and CS Department, Rutgers University, Piscataway, NJ 08855-1179, USA. xingpoe@cs.rutgers.edu
Proceedings. International Conference on Intelligent Systems for Molecular Biology
|April 29, 2000
Summary
This study introduces a novel segmentation and clustering method for analyzing small subunit ribosomal RNA (rRNA) sequences. The approach effectively organizes sequence data, yielding classifications consistent with established phylogenetic trees.
Area of Science:
- Bioinformatics
- Molecular Biology
- Computational Biology
Background:
- Analyzing large ribosomal RNA (rRNA) sequence datasets is computationally challenging.
- Existing methods may struggle with the complexity of multi-aligned eukaryotic and prokaryotic sequences.
Purpose of the Study:
- To develop a novel computational procedure for analyzing multi-aligned small subunit rRNA sequences.
- To extract sequence subsets with shared features and organize rRNA data efficiently.
Main Methods:
- A three-step procedure involving sequence segmentation, local cluster extraction, and cluster intersection.
- Dynamic programming for sequence segmentation and identification of conserved segments.
- A polynomial procedure for extracting homogeneous clusters within conserved segments.
Main Results:
- The method successfully segments and clusters rRNA sequences based on common features.
- The developed algorithms are useful for processing large, gap-filled multi-alignments.
- The sequence classification achieved demonstrates high consistency with the eukaryotic phylogenetic tree.
Conclusions:
- The novel segmentation and clustering procedure provides an efficient framework for rRNA data organization and analysis.
- This method supports various rRNA analysis functions, including subalignment and phylogenetic analysis.
- The approach offers a robust tool for understanding evolutionary relationships within rRNA sequence data.