Literature DB >> 18643308

Modeling background intensity in DNA microarrays.

K M Kroll1, G T Barkema, E Carlon.   

Abstract

DNA microarrays are devices that are able, in principle, to detect and quantify the presence of specific nucleic acid sequences in complex biological mixtures. The measurement consists in detecting fluorescence signals from several spots on the microarray surface onto which different probe sequences are grafted. One of the problems of the data analysis is that the signal contains a noisy background component due to nonspecific binding. We present a physical model for background estimation in Affymetrix Genechips. It combines two different approaches. The first is based on the sequence composition, specifically its sequence-dependent hybridization affinity. The second is based on the strong correlation of intensities from locations which are the physical neighbors of a specific spot on the chip. Both effects are incorporated in a background estimator which contains 24 free parameters, fixed by minimization on a training data set. In all data analyzed the sequence-specific parameters, obtained by minimization, are found to strongly correlate with empirically determined stacking free energies for RNA-DNA hybridization in solution. Moreover, there is an overall agreement with experimental background data and we show that the physics-based model that we propose performs on average better than purely statistical approaches for background calculations. The model thus provides an interesting alternative method for background subtraction schemes in Affymetrix Genechips.

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Year:  2008        PMID: 18643308     DOI: 10.1103/PhysRevE.77.061915

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  7 in total

1.  G-stack modulated probe intensities on expression arrays - sequence corrections and signal calibration.

Authors:  Mario Fasold; Peter F Stadler; Hans Binder
Journal:  BMC Bioinformatics       Date:  2010-04-27       Impact factor: 3.169

2.  Nonlinear transcriptomic response to dietary fat intake in the small intestine of C57BL/6J mice.

Authors:  Tenzin Nyima; Michael Müller; Guido J E J Hooiveld; Melissa J Morine; Marco Scotti
Journal:  BMC Genomics       Date:  2016-02-09       Impact factor: 3.969

3.  Thermodynamic scaling behavior in genechips.

Authors:  Alessandro Ferrantini; Joke Allemeersch; Paul Van Hummelen; Enrico Carlon
Journal:  BMC Bioinformatics       Date:  2009-01-06       Impact factor: 3.169

4.  Linear model for fast background subtraction in oligonucleotide microarrays.

Authors:  K Myriam Kroll; Gerard T Barkema; Enrico Carlon
Journal:  Algorithms Mol Biol       Date:  2009-11-16       Impact factor: 1.405

5.  Inverse Langmuir method for oligonucleotide microarray analysis.

Authors:  Geert C W M Mulders; Gerard T Barkema; Enrico Carlon
Journal:  BMC Bioinformatics       Date:  2009-02-20       Impact factor: 3.169

Review 6.  Microarray experiments and factors which affect their reliability.

Authors:  Roman Jaksik; Marta Iwanaszko; Joanna Rzeszowska-Wolny; Marek Kimmel
Journal:  Biol Direct       Date:  2015-09-03       Impact factor: 4.540

Review 7.  Transcriptomic Studies of Malaria: a Paradigm for Investigation of Systemic Host-Pathogen Interactions.

Authors:  Hyun Jae Lee; Athina Georgiadou; Thomas D Otto; Michael Levin; Lachlan J Coin; David J Conway; Aubrey J Cunnington
Journal:  Microbiol Mol Biol Rev       Date:  2018-04-25       Impact factor: 11.056

  7 in total

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