Literature DB >> 15952881

Applications of DNA microarrays in biology.

Roland B Stoughton1.   

Abstract

DNA microarrays have enabled biology researchers to conduct large-scale quantitative experiments. This capacity has produced qualitative changes in the breadth of hypotheses that can be explored. In what has become the dominant mode of use, changes in the transcription rate of nearly all the genes in a genome, taking place in a particular tissue or cell type, can be measured in disease states, during development, and in response to intentional experimental perturbations, such as gene disruptions and drug treatments. The response patterns have helped illuminate mechanisms of disease and identify disease subphenotypes, predict disease progression, assign function to previously unannotated genes, group genes into functional pathways, and predict activities of new compounds. Directed at the genome sequence itself, microarrays have been used to identify novel genes, binding sites of transcription factors, changes in DNA copy number, and variations from a baseline sequence, such as in emerging strains of pathogens or complex mutations in disease-causing human genes. They also serve as a general demultiplexing tool to sort spatially the sequence-tagged products of highly parallel reactions performed in solution. A brief review of microarray platform technology options, and of the process steps involved in complete experiment workflows, is included.

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Mesh:

Year:  2005        PMID: 15952881     DOI: 10.1146/annurev.biochem.74.082803.133212

Source DB:  PubMed          Journal:  Annu Rev Biochem        ISSN: 0066-4154            Impact factor:   23.643


  73 in total

Review 1.  Dynamic changes in gene expression during human early embryo development: from fundamental aspects to clinical applications.

Authors:  Said Assou; Imène Boumela; Delphine Haouzi; Tal Anahory; Hervé Dechaud; John De Vos; Samir Hamamah
Journal:  Hum Reprod Update       Date:  2010-08-17       Impact factor: 15.610

2.  Fiber composite slices for multiplexed immunoassays.

Authors:  Jiyun Kim; Sangwook Bae; Seowoo Song; Keumsim Chung; Sunghoon Kwon
Journal:  Biomicrofluidics       Date:  2015-07-29       Impact factor: 2.800

3.  A bright but demanding future for core facilities.

Authors:  Clive Slaughter
Journal:  J Biomol Tech       Date:  2005-06

Review 4.  Novel susceptibility genes in inflammatory bowel disease.

Authors:  Colin Noble; Elaine Nimmo; Daniel Gaya; Richard K Russell; Jack Satsangi
Journal:  World J Gastroenterol       Date:  2006-04-07       Impact factor: 5.742

5.  The ABRF MARG microarray survey 2005: taking the pulse of the microarray field.

Authors:  Kevin L Knudtson; Herbert Auer; Andrew I Brooks; Chandi Griffin; George Grills; Susan Hester; Gregory Khitrov; Kathryn S Lilley; Aldo Massimi; Jay P Tiesman; Agnes Viale
Journal:  J Biomol Tech       Date:  2006-04

6.  Creating advanced multifunctional biosensors with surface enzymatic transformations.

Authors:  Hye Jin Lee; Alastair W Wark; Robert M Corn
Journal:  Langmuir       Date:  2006-06-06       Impact factor: 3.882

7.  Heterosis and polymorphisms of gene expression in an elite rice hybrid as revealed by a microarray analysis of 9198 unique ESTs.

Authors:  Yi Huang; Lida Zhang; Jianwei Zhang; Dejun Yuan; Caiguo Xu; Xianghua Li; Daoxiu Zhou; Shiping Wang; Qifa Zhang
Journal:  Plant Mol Biol       Date:  2006-08-29       Impact factor: 4.076

8.  Unraveling the molecular targets pertinent to junction restructuring events during spermatogenesis using the Adjudin-induced germ cell depletion model.

Authors:  Weiliang Xia; Dolores D Mruk; Will M Lee; C Yan Cheng
Journal:  J Endocrinol       Date:  2007-03       Impact factor: 4.286

9.  Transcriptome analysis of salinity stress responses in common wheat using a 22k oligo-DNA microarray.

Authors:  Kanako Kawaura; Keiichi Mochida; Yukiko Yamazaki; Yasunari Ogihara
Journal:  Funct Integr Genomics       Date:  2005-11-19       Impact factor: 3.410

10.  Identification of novel stem cell markers using gap analysis of gene expression data.

Authors:  Paul M Krzyzanowski; Miguel A Andrade-Navarro
Journal:  Genome Biol       Date:  2007       Impact factor: 13.583

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