Literature DB >> 15256402

Limited agreement among three global gene expression methods highlights the requirement for non-global validation.

Peter M Haverty1, Li-Li Hsiao, Steven R Gullans, Ulla Hansen, Zhiping Weng.   

Abstract

MOTIVATION: DNA microarrays have revolutionized biological research, but their reliability and accuracy have not been extensively evaluated. Thorough testing of microarrays through comparison to dissimilar gene expression methods is necessary in order to determine their accuracy.
RESULTS: We have systematically compared three global gene expression methods on all available histologically normal samples from five human organ types. The data included 25 Affymetrix high-density oligonucleotide array experiments, 23 expressed sequence tag based expression (EBE) experiments and 5 SAGE experiments. The reported gene-by-gene expression patterns showed a wide range of correlations between pairs of methods. This level of agreement was sufficient for accurate clustering of datasets from the same tissue and dissimilar methods, but highlights the need for thorough validation of individual gene expression measurements by alternate, non-global methods. Furthermore, analyses of mRNA abundance distributions indicate limitations in the EBE and SAGE methods at both high- and low-expression levels.

Entities:  

Mesh:

Year:  2004        PMID: 15256402     DOI: 10.1093/bioinformatics/bth421

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  10 in total

1.  A novel bioinformatics approach identifies candidate genes for the synthesis and feruloylation of arabinoxylan.

Authors:  Rowan A C Mitchell; Paul Dupree; Peter R Shewry
Journal:  Plant Physiol       Date:  2007-03-09       Impact factor: 8.340

2.  Identification of tissue-specific, abiotic stress-responsive gene expression patterns in wine grape (Vitis vinifera L.) based on curation and mining of large-scale EST data sets.

Authors:  Richard L Tillett; Ali Ergül; Rebecca L Albion; Karen A Schlauch; Grant R Cramer; John C Cushman
Journal:  BMC Plant Biol       Date:  2011-05-18       Impact factor: 4.215

3.  Comparison of hybridization-based and sequencing-based gene expression technologies on biological replicates.

Authors:  Fang Liu; Tor-Kristian Jenssen; Jeff Trimarchi; Claudio Punzo; Connie L Cepko; Lucila Ohno-Machado; Eivind Hovig; Winston Patrick Kuo
Journal:  BMC Genomics       Date:  2007-06-07       Impact factor: 3.969

4.  Evaluation of the similarity of gene expression data estimated with SAGE and Affymetrix GeneChips.

Authors:  Fred van Ruissen; Jan M Ruijter; Gerben J Schaaf; Lida Asgharnegad; Danny A Zwijnenburg; Marcel Kool; Frank Baas
Journal:  BMC Genomics       Date:  2005-06-14       Impact factor: 3.969

5.  Identification of regulatory targets of tissue-specific transcription factors: application to retina-specific gene regulation.

Authors:  Jiang Qian; Noriko Esumi; Yangjian Chen; Qingliang Wang; Itay Chowers; Donald J Zack
Journal:  Nucleic Acids Res       Date:  2005-06-20       Impact factor: 16.971

6.  Using a seed-network to query multiple large-scale gene expression datasets from the developing retina in order to identify and prioritize experimental targets.

Authors:  Laura A Hecker; Timothy C Alcon; Vasant G Honavar; M Heather West Greenlee
Journal:  Bioinform Biol Insights       Date:  2008-02-01

7.  Identification of novel reference genes using multiplatform expression data and their validation for quantitative gene expression analysis.

Authors:  Mi Jeong Kwon; Ensel Oh; Seungmook Lee; Mi Ra Roh; Si Eun Kim; Yangsoon Lee; Yoon-La Choi; Yong-Ho In; Taesung Park; Sang Seok Koh; Young Kee Shin
Journal:  PLoS One       Date:  2009-07-07       Impact factor: 3.240

8.  Transcriptomic and proteomic profiling of two porcine tissues using high-throughput technologies.

Authors:  Henrik Hornshøj; Emøke Bendixen; Lene N Conley; Pernille K Andersen; Jakob Hedegaard; Frank Panitz; Christian Bendixen
Journal:  BMC Genomics       Date:  2009-01-19       Impact factor: 3.969

9.  Evaluation of combining several statistical methods with a flexible cutoff for identifying differentially expressed genes in pairwise comparison of EST sets.

Authors:  Angelica Lindlöf; Marcus Bräutigam; Aakash Chawade; Olof Olsson; Björn Olsson
Journal:  Bioinform Biol Insights       Date:  2008-05-01

10.  Ovarian gene expression in the absence of FIGLA, an oocyte-specific transcription factor.

Authors:  Saurabh Joshi; Holly Davies; Lauren Porter Sims; Shawn E Levy; Jurrien Dean
Journal:  BMC Dev Biol       Date:  2007-06-13       Impact factor: 1.978

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.