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BIOLOGICAL SCIENCES / APPLIED BIOLOGICAL SCIENCES
Accurately quantifying low-abundant targets amid similar sequences by revealing hidden correlations in oligonucleotide microarray data
*Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139; Biomedical Engineering Department, Northwestern University, Evanston, IL 60208; and Department of Biology, Temple University, Philadelphia, PA 19122
Edited by J. Craig Venter, The J. Craig Venter Institute, Rockville, MD, and approved June 13, 2006 (received for review February 22, 2006)
Microarrays have enabled the determination of how thousands of genes are expressed to coordinate function within single organisms. Yet applications to natural or engineered communities where different organisms interact to produce complex properties are hampered by theoretical and technological limitations. Here we describe a general method to accurately identify low-abundant targets in systems containing complex mixtures of homologous targets. We combined an analytical predictor of nonspecific probe–target interactions (cross-hybridization) with an optimization algorithm that iteratively deconvolutes true probe–target signal from raw signal affected by spurious contributions (cross-hybridization, noise, background, and unequal specific hybridization response). The method was capable of quantifying, with unprecedented specificity and accuracy, ribosomal RNA (rRNA) sequences in artificial and natural communities. Controlled experiments with spiked rRNA into artificial and natural communities demonstrated the accuracy of identification and quantitative behavior over different concentration ranges. Finally, we illustrated the power of this methodology for accurate detection of low-abundant targets in natural communities. We accurately identified Vibrio taxa in coastal marine samples at their natural concentrations (<0.05% of total bacteria), despite the high potential for cross-hybridization by hundreds of different coexisting rRNAs, suggesting this methodology should be expandable to any microarray platform and system requiring accurate identification of low-abundant targets amid pools of similar sequences.
microbial ecology | optimization algorithm | cross-hybridization | free energy | rRNA
Present address: Department of Microbiology and Molecular Genetics, Harvard Medical School, Boston, MA 02115.
Author contributions: L.A.M., V.B., and D.V. designed research; L.A.M., A.D., C.S., J.R.T., S.P.P., and C.L. performed research; E.L. contributed new reagents/analytic tools; L.A.M., V.B., D.V., and M.F.P. analyzed data; and L.A.M., V.B., and M.F.P. wrote the paper.
Conflict of interest statement: No conflicts declared.
This paper was submitted directly (Track II) to the PNAS office.
Data deposition: The probe sequences reported in this paper have been deposited in the National Center for Biotechnology Information (NCBI) Probe Database (PUIDs 6103259–6103399).
¶To whom correspondence should be addressed. E-mail: mpolz{at}mit.edu
© 2006 by The National Academy of Sciences of the USA
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