Open-target sparse sensing of biological agents using DNA microarray

BMC Bioinformatics

Research Article

David K Walburger¹  Matthew W Peterson¹  Haley B Skaer¹  Felicia N Sutton¹  James C Diggans¹  Mojdeh Mohtashemi² 

[1] Emerging & Disruptive Technologies, The MITRE Corporation, McLean, Virginia, USA;Emerging & Disruptive Technologies, The MITRE Corporation, McLean, Virginia, USA;MIT CSAIL, Cambridge, MA, USA;

关键词: Compressive Sense;    Partial Little Square Regression;    Mixed Sample;    Whole Genome Amplification;    Microarray Probe;   

DOI  :  10.1186/1471-2105-12-314

 received in 2011-03-28, accepted in 2011-07-29,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundCurrent biosensors are designed to target and react to specific nucleic acid sequences or structural epitopes. These 'target-specific' platforms require creation of new physical capture reagents when new organisms are targeted. An 'open-target' approach to DNA microarray biosensing is proposed and substantiated using laboratory generated data. The microarray consisted of 12,900 25 bp oligonucleotide capture probes derived from a statistical model trained on randomly selected genomic segments of pathogenic prokaryotic organisms. Open-target detection of organisms was accomplished using a reference library of hybridization patterns for three test organisms whose DNA sequences were not included in the design of the microarray probes.ResultsA multivariate mathematical model based on the partial least squares regression (PLSR) was developed to detect the presence of three test organisms in mixed samples. When all 12,900 probes were used, the model correctly detected the signature of three test organisms in all mixed samples (mean(R2 )) = 0.76, CI = 0.95), with a 6% false positive rate. A sampling algorithm was then developed to sparsely sample the probe space for a minimal number of probes required to capture the hybridization imprints of the test organisms. The PLSR detection model was capable of correctly identifying the presence of the three test organisms in all mixed samples using only 47 probes (mean(R2 )) = 0.77, CI = 0.95) with nearly 100% specificity.ConclusionsWe conceived an 'open-target' approach to biosensing, and hypothesized that a relatively small, non-specifically designed, DNA microarray is capable of identifying the presence of multiple organisms in mixed samples. Coupled with a mathematical model applied to laboratory generated data, and sparse sampling of capture probes, the prototype microarray platform was able to capture the signature of each organism in all mixed samples with high sensitivity and specificity. It was demonstrated that this new approach to biosensing closely follows the principles of sparse sensing.

【 授权许可】

CC BY   
© Mohtashemi et al; licensee BioMed Central Ltd. 2011