New techniques for DNA sequence classification.
DNA sequence classification is the activity of determining whether or not an unlabeled sequence S belongs to an existing class C. This paper proposes two new techniques for DNA sequence classification. The first technique works by comparing the unlabeled sequence S with a group of active motifs discovered from the elements of C and by distinction with elements outside of C. The second technique generates and matches gapped fingerprints of S with elements of C. Experimental results obtained by running these algorithms on long and well conserved Alu sequences demonstrate the good performance of the presented methods compared with FASTA. When applied to less conserved and relatively short functional sites such as splice-junctions, a variation of the second technique combining fingerprinting with consensus sequence analysis gives better results than the current classifiers employing text compression and machine learning algorithms.
Department of Computer and Information Science, New Jersey Institute of Technology, University Heights, Newark 07102, USA. jason@cis.njit.edu
J T
JT
Wang
S
S
Rozen
B A
BA
Shapiro
D
D
Shasha
Z
Z
Wang
M
M
Yin
D000465:Algorithms; D020087:Alu Elements / Q000235:genetics; D001483:Base Sequence; D019295:Computational Biology; D016384:Consensus Sequence / Q000235:genetics*; D017124:Conserved Sequence / Q000235:genetics; D004247:DNA / Q000145:classification* / Q000235:genetics; D016172:DNA Fingerprinting; D005188:False Negative Reactions; D008970:Molecular Weight; D012326:RNA Splicing / Q000235:genetics; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics; D017422:Sequence Analysis, DNA; D012984:Software
D004247:DNA