Oligonucleotide microarray identification of Bacillus anthracis strains using support vector machines.
The capability of a custom microarray to discriminate between closely related DNA samples is demonstrated using a set of Bacillus anthracis strains. The microarray was developed as a universal fingerprint device consisting of 390 genome-independent 9mer probes. The genomes of B. anthracis strains are monomorphic and therefore, typically difficult to distinguish using conventional molecular biology tools or microarray data clustering techniques. Using support vector machines (SVMs) as a supervised learning technique, we show that a low-density fingerprint microarray contains enough information to discriminate between B. anthracis strains with 90% sensitivity using a reference library constructed from six replicate arrays and three replicates for new isolates.
Doran
M
M
Intelligent Multimedia Processing Laboratory, School of Computer Science, Telecommunications and Information Systems, DePaul University, Chicago, USA. doran_michael@msn.com
Raicu
D S
DS
Furst
J D
JD
Settimi
R
R
Schipma
M
M
Chandler
D P
DP
D000465:Algorithms; D001185:Artificial Intelligence; D001408:Bacillus anthracis / Q000145:classification / Q000235:genetics* / Q000302:isolation & purification*; D002874:Chromosome Mapping / Q000379:methods; D016172:DNA Fingerprinting / Q000379:methods*; D004269:DNA, Bacterial / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods
D004269:DNA, Bacterial