DNA Microarrays: Experimental Issues, Data Analysis, and Application to Bacterial Systems

Dharmadi, Yandi; González, Ramón

doi:10.1021/bp0400240

Cited by 104 publications

(39 citation statements)

References 129 publications

(164 reference statements)

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“…tent by applying transcriptomics or proteomics to study the differential expression of genes or proteins, respectively, on a genome-wide scale. Especially DNA microarrays have found widespread use to study the transcriptional response of numerous bacterial species to a variety of different growth conditions and stimuli (13,16,49,60). While a thorough analysis of the (putative) function of differentially expressed genes gives some insight into the biology of an adaptational response, it rarely gives hints regarding the regulatory processes underlying such differential gene expression.…”

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confidence: 99%

Cell Envelope Stress Response inBacillus licheniformis: Integrating Comparative Genomics, Transcriptional Profiling, and Regulon Mining To Decipher a Complex Regulatory Network

et al. 2006

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The envelope is an essential structure of the bacterial cell, and maintaining its integrity is a prerequisite for survival. To ensure proper function, transmembrane signal-transducing systems, such as two-component systems (TCS) and extracytoplasmic function (ECF) factors, closely monitor its condition and respond to harmful perturbations. Both systems consist of a transmembrane sensor protein (histidine kinase or antifactor, respectively) and a corresponding cytoplasmic transcriptional regulator (response regulator or factor, respectively) that mediates the cellular response through differential gene expression. The regulatory network of the cell envelope stress response is well studied in the gram-positive model organism Bacillus subtilis. It consists of at least two ECF factors and four two-component systems. In this study, we describe the corresponding network in a close relative, Bacillus licheniformis. Based on sequence homology, domain architecture, and genomic context, we identified five TCS and eight ECF factors as potential candidate regulatory systems mediating cell envelope stress response in this organism. We characterized the corresponding regulatory network by comparative transcriptomics and regulon mining as an initial screening tool. Subsequent in-depth transcriptional profiling was applied to define the inducer specificity of each identified cell envelope stress sensor. A total of three TCS and seven ECF factors were shown to be induced by cell envelope stress in B. licheniformis. We noted a number of significant differences, indicative of a regulatory divergence between the two Bacillus species, in addition to the expected overlap in the respective responses.

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Cell Envelope Stress Response inBacillus licheniformis: Integrating Comparative Genomics, Transcriptional Profiling, and Regulon Mining To Decipher a Complex Regulatory Network

et al. 2006

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“…Among the solutions to solve the problem, self-assembly is the most promising alternative. A variety of techniques have been developed for DNA hybridization detection, including fluorescence imaging [47], electrochemical [48], micro-gravimetrical [49], bioluminescence [50], chemiluminescence [51] and electrogenerated chemiluminescence (ECL) techniques [52]. ECL technique has many distinct advantages over fluorescence technique because it does not involve a light source and avoids the attendant problems of scattered light and impurities luminescent.…”

Section: Cnts/dna Nanoconjugatesmentioning

confidence: 99%

Dispersions Based on Carbon Nanotubes – Biomolecules Conjugates

Capek¹

2011

Carbon Nanotubes - Growth and Applications

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“…The development of techniques for spot-by-spot analysis of microarray data has been the subject of much research effort; and many of the key results and the still unresolved issues have been discussed in several reviews (Allison et al, 2006;Dharmadi and Gonzalez, 2004;Nadon and Shoemaker, 2002;Quackenbush, 2001;Sebastiani et al, 2003).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Ogunnaike

Gelmi

Edwards

2010

Journal of Theoretical Biology

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A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference, Journal of Theoretical Biology, doi:10.1016Biology, doi:10. /j.jtbi.2010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Fundamental probability models and statistical inference 302-831-1048)A c c e p t e d m a n u s c r i p t 2 AbstractGene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays.

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DNA Microarrays: Experimental Issues, Data Analysis, and Application to Bacterial Systems

Cited by 104 publications

References 129 publications

Cell Envelope Stress Response inBacillus licheniformis: Integrating Comparative Genomics, Transcriptional Profiling, and Regulon Mining To Decipher a Complex Regulatory Network

Cell Envelope Stress Response inBacillus licheniformis: Integrating Comparative Genomics, Transcriptional Profiling, and Regulon Mining To Decipher a Complex Regulatory Network

Dispersions Based on Carbon Nanotubes – Biomolecules Conjugates

A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

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