Open-target sparse sensing of biological agents using DNA microarray
| Autor: | David K. Walburger, Haley B. Skaer, James C. Diggans, Mojdeh Mohtashemi, Felicia N Sutton, Matthew W. Peterson |
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| Přispěvatelé: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Mohtashemi, Mojdeh |
| Rok vydání: | 2011 |
| Předmět: |
Oligonucleotides
Computational biology Biology lcsh:Computer applications to medicine. Medical informatics Biochemistry Sensitivity and Specificity DNA sequencing Structural Biology Partial least squares regression Computer Simulation Least-Squares Analysis lcsh:QH301-705.5 Molecular Biology Oligonucleotide Array Sequence Analysis Bacteria Oligonucleotide Applied Mathematics Statistical model Molecular biology Computer Science Applications lcsh:Biology (General) Nucleic acid lcsh:R858-859.7 Regression Analysis False positive rate DNA microarray Biosensor Research Article |
| Zdroj: | BMC Bioinformatics BioMed Central BMC Bioinformatics, Vol 12, Iss 1, p 314 (2011) |
| ISSN: | 1471-2105 |
| Popis: | Background Current 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. Results A 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. Conclusions We 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. Mitre Corporation |
| Databáze: | OpenAIRE |
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