Computer architecture: a quantitative approach
Computer architecture: a quantitative approach
PiQA: an algebra for querying protein data sets
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
MoBIoS: a metric-space DBMS to support biological discovery
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Declarative Querying for Biological Sequences
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
On Integrating Peptide Sequence Analysis and Relational Distance-Based Indexing
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
A distributed file system for a wide-area high performance computing infrastructure
WORLDS'06 Proceedings of the 3rd conference on USENIX Workshop on Real, Large Distributed Systems - Volume 3
Creating private network overlays for high performance scientific computing
Proceedings of the ACM/IFIP/USENIX 2007 International Conference on Middleware
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Performance analysis of a dual-tree algorithm for computing spatial distance histograms
The VLDB Journal — The International Journal on Very Large Data Bases
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With an increasingly large amount of sequences properly aligned, comparative sequence analysis can accurately identify not only common structures formed by standard base pairing but also new types of structural elements and constraints. However, traditional methods are too computationally expensive to perform well on large scale alignment and less effective with the sequences from diversified phylogenetic classifications. We propose a new approach that utilizes coevolutional rates among pairs of nucleotide positions using phylogenetic and evolutionary relationships of the organisms of aligned sequences. With a novel data schema to manage relevant information within a relational database, our method, implemented with a Microsoft SQL Server 2005, showed 90% sensitivity in identifying base pair interactions among 16S ribosomal RNA sequences from Bacteria, at a scale 40 times bigger and 50% better sensitivity than a previous study. The results also indicated covariation signals for a few sets of cross-strand base stacking pairs in secondary structure helices, and other subtle constraints in the RNA structure.