Detection and prediction of alternative splicing in Arabidopsis thaliana.
Alternative splicing is an important process for increasing the diversity arising from a single gene. Presently, most studies aimed at detecting alternatively spliced genes use Expressed Sequence Tags (ESTs). However, the EST studies based on spliced transcripts analyse sequences by alignment rather than sequence patterns. Second, EST libraries can be of uncertain quality. To address these issues and to improve the quality of detection and prediction for alternative splicing, we propose a method that primarily uses pre-mRNAs. It is achieved by a decision tree algorithm using triplet nucleotides as attributes for each chromosome in Arabidopsis thaliana. In addition, we propose a novel algorithm for accurate prediction.
Park
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M
Department of Computer Science, University of Massachusetts, Lowell, MA, 01854, USA. mpark@cs.uml.edu
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Daniels
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D000465:Algorithms; D017398:Alternative Splicing; D017360:Arabidopsis / Q000235:genetics* / Q000378:metabolism; D001483:Base Sequence; D019295:Computational Biology; D003198:Computer Simulation; D016384:Consensus Sequence; D017124:Conserved Sequence; D030561:Databases, Nucleic Acid; D003663:Decision Trees; D020224:Expressed Sequence Tags; D018745:Genome, Plant; D008957:Models, Genetic; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D012322:RNA Precursors / Q000235:genetics / Q000378:metabolism; D022821:RNA Splice Sites; D018749:RNA, Plant / Q000235:genetics / Q000378:metabolism
D012322:RNA Precursors; D022821:RNA Splice Sites; D018749:RNA, Plant