Literature DB >> 16870711

The end of the microarray Tower of Babel: will universal standards lead the way?

Ernest S Kawasaki1.   

Abstract

Microarrays are the most common method of studying global gene expression, and may soon enter the realm of FDA-approved clinical/diagnostic testing of cancer and other diseases. However, the acceptance of array data has been made difficult by the proliferation of widely different array platforms with gene probes ranging in size from 25 bases (oligonucleotides) to several kilobases (complementary DNAs or cDNAs). The algorithms applied for image and data analysis are also as varied as the microarray platforms, perhaps more so. In addition, there is a total lack of universally accepted standards for use among the different platforms and even within the same array types. Due to this lack of coherency in array technologies, confusion in interpretation of data within and across platforms has often been the norm, and studies of the same biological phenomena have, in many cases, led to contradictory results. In this commentary/review, some of the causes of this confusion will be summarized, and progress in overcoming these obstacles will be described, with the goal of providing an optimistic view of the future for the use of array technologies in global expression profiling and other applications.

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Year:  2006        PMID: 16870711      PMCID: PMC2291790     

Source DB:  PubMed          Journal:  J Biomol Tech        ISSN: 1524-0215


  40 in total

1.  Prediction of cancer outcome with microarrays: a multiple random validation strategy.

Authors:  Stefan Michiels; Serge Koscielny; Catherine Hill
Journal:  Lancet       Date:  2005 Feb 5-11       Impact factor: 79.321

2.  Microarrays and molecular research: noise discovery?

Authors:  John P A Ioannidis
Journal:  Lancet       Date:  2005 Feb 5-11       Impact factor: 79.321

3.  Multiple-laboratory comparison of microarray platforms.

Authors:  Rafael A Irizarry; Daniel Warren; Forrest Spencer; Irene F Kim; Shyam Biswal; Bryan C Frank; Edward Gabrielson; Joe G N Garcia; Joel Geoghegan; Gregory Germino; Constance Griffin; Sara C Hilmer; Eric Hoffman; Anne E Jedlicka; Ernest Kawasaki; Francisco Martínez-Murillo; Laura Morsberger; Hannah Lee; David Petersen; John Quackenbush; Alan Scott; Michael Wilson; Yanqin Yang; Shui Qing Ye; Wayne Yu
Journal:  Nat Methods       Date:  2005-04-21       Impact factor: 28.547

4.  Standardizing global gene expression analysis between laboratories and across platforms.

Authors:  Theodore Bammler; Richard P Beyer; Sanchita Bhattacharya; Gary A Boorman; Abee Boyles; Blair U Bradford; Roger E Bumgarner; Pierre R Bushel; Kabir Chaturvedi; Dongseok Choi; Michael L Cunningham; Shibing Deng; Holly K Dressman; Rickie D Fannin; Fredrico M Farin; Jonathan H Freedman; Rebecca C Fry; Angel Harper; Michael C Humble; Patrick Hurban; Terrance J Kavanagh; William K Kaufmann; Kathleen F Kerr; Li Jing; Jodi A Lapidus; Michael R Lasarev; Jianying Li; Yi-Ju Li; Edward K Lobenhofer; Xinfang Lu; Renae L Malek; Sean Milton; Srinivasa R Nagalla; Jean P O'malley; Valerie S Palmer; Patrick Pattee; Richard S Paules; Charles M Perou; Ken Phillips; Li-Xuan Qin; Yang Qiu; Sean D Quigley; Matthew Rodland; Ivan Rusyn; Leona D Samson; David A Schwartz; Yan Shi; Jung-Lim Shin; Stella O Sieber; Susan Slifer; Marcy C Speer; Peter S Spencer; Dean I Sproles; James A Swenberg; William A Suk; Robert C Sullivan; Ru Tian; Raymond W Tennant; Signe A Todd; Charles J Tucker; Bennett Van Houten; Brenda K Weis; Shirley Xuan; Helmut Zarbl
Journal:  Nat Methods       Date:  2005-04-21       Impact factor: 28.547

5.  Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

Authors:  M Schena; D Shalon; R W Davis; P O Brown
Journal:  Science       Date:  1995-10-20       Impact factor: 47.728

6.  Independence and reproducibility across microarray platforms.

Authors:  Jennie E Larkin; Bryan C Frank; Haralambos Gavras; Razvan Sultana; John Quackenbush
Journal:  Nat Methods       Date:  2005-04-21       Impact factor: 28.547

7.  Expression monitoring by hybridization to high-density oligonucleotide arrays.

Authors:  D J Lockhart; H Dong; M C Byrne; M T Follettie; M V Gallo; M S Chee; M Mittmann; C Wang; M Kobayashi; H Horton; E L Brown
Journal:  Nat Biotechnol       Date:  1996-12       Impact factor: 54.908

8.  Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data.

Authors:  Kerby Shedden; Wei Chen; Rork Kuick; Debashis Ghosh; James Macdonald; Kathleen R Cho; Thomas J Giordano; Stephen B Gruber; Eric R Fearon; Jeremy M G Taylor; Samir Hanash
Journal:  BMC Bioinformatics       Date:  2005-02-10       Impact factor: 3.169

9.  Correlation test to assess low-level processing of high-density oligonucleotide microarray data.

Authors:  Alexander Ploner; Lance D Miller; Per Hall; Jonas Bergh; Yudi Pawitan
Journal:  BMC Bioinformatics       Date:  2005-03-31       Impact factor: 3.169

10.  Relative transcript quantification by quantitative PCR: roughly right or precisely wrong?

Authors:  Rasmus Skern; Petter Frost; Frank Nilsen
Journal:  BMC Mol Biol       Date:  2005-04-26       Impact factor: 2.946

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  17 in total

Review 1.  Using genomics to understand intestinal biology.

Authors:  J C Fleet
Journal:  J Physiol Biochem       Date:  2007-03       Impact factor: 4.158

2.  Time-resolved Förster-resonance-energy-transfer DNA assay on an active CMOS microarray.

Authors:  David Eric Schwartz; Ping Gong; Kenneth L Shepard
Journal:  Biosens Bioelectron       Date:  2008-04-26       Impact factor: 10.618

Review 3.  Gene expression in the human brain: the current state of the study of specificity and spatiotemporal dynamics.

Authors:  Oksana Yu Naumova; Maria Lee; Sergei Yu Rychkov; Natalia V Vlasova; Elena L Grigorenko
Journal:  Child Dev       Date:  2012-11-12

Review 4.  Expectations, validity, and reality in gene expression profiling.

Authors:  Kyoungmi Kim; Stanislav O Zakharkin; David B Allison
Journal:  J Clin Epidemiol       Date:  2010-06-25       Impact factor: 6.437

5.  Gene expression profiling of mouse embryos with microarrays.

Authors:  Alexei A Sharov; Yulan Piao; Minoru S H Ko
Journal:  Methods Enzymol       Date:  2010       Impact factor: 1.600

6.  Age-related changes of gene expression in the neocortex: preliminary data on RNA-Seq of the transcriptome in three functionally distinct cortical areas.

Authors:  Oksana Yu Naumova; Dean Palejev; Natalia V Vlasova; Maria Lee; Sergei Yu Rychkov; Olga N Babich; Flora M Vaccarino; Elena L Grigorenko
Journal:  Dev Psychopathol       Date:  2012-11

Review 7.  Review of the literature examining the correlation among DNA microarray technologies.

Authors:  Carole L Yauk; M Lynn Berndt
Journal:  Environ Mol Mutagen       Date:  2007-06       Impact factor: 3.216

8.  Transcriptome characterization by RNA-Seq reveals the involvement of the complement components in noise-traumatized rat cochleae.

Authors:  M Patel; Z Hu; J Bard; J Jamison; Q Cai; B H Hu
Journal:  Neuroscience       Date:  2013-05-30       Impact factor: 3.590

Review 9.  Current status of methods to assess cancer drug resistance.

Authors:  Theodor H Lippert; Hans-Jörg Ruoff; Manfred Volm
Journal:  Int J Med Sci       Date:  2011-03-23       Impact factor: 3.738

10.  Evaluation of external RNA controls for the standardisation of gene expression biomarker measurements.

Authors:  Alison S Devonshire; Ramnath Elaswarapu; Carole A Foy
Journal:  BMC Genomics       Date:  2010-11-24       Impact factor: 3.969

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