Text mining of DNA sequence homology searches.
Primary tasks in analysis and annotation of expressed sequence tag (EST) datasets are to identify similarity among sequences by unsupervised clustering and assign putative function based on BLAST homology searches. We investigated the usefulness of text mining as a simple approach for further higher-level clustering of EST datasets using IBM Intelligent Miner for Text v2.3 tools. Agglomerative and k-means clustering tools were used to cluster BLASTx homology search documents from two onion EST datasets and optimised by pre-processing and pruning. Subjective evaluation confirmed that these tools provided biologically useful and complementary views of the two libraries, provided new insights into their composition and revealed clusters previously identified by human experts. We compared BLASTx textual clusters for two gene families with their DNA sequence-based clusters and confirmed that these shared similar morphology.
McCallum
John
J
Crop & Food Research, Christchurch, New Zealand. mccallumj@crop.cri.nz
Ganesh
Siva
S
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D016000:Cluster Analysis; D003628:Database Management Systems; D030561:Databases, Nucleic Acid; D020224:Expressed Sequence Tags; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012689:Sequence Homology, Nucleic Acid; D012984:Software