Literature DB >> 17708771

A detailed transcript-level probe annotation reveals alternative splicing based microarray platform differences.

Joseph C Lee1, David Stiles, Jun Lu, Margaret C Cam.   

Abstract

BACKGROUND: Microarrays are a popular tool used in experiments to measure gene expression levels. Improving the reproducibility of microarray results produced by different chips from various manufacturers is important to create comparable and combinable experimental results. Alternative splicing has been cited as a possible cause of differences in expression measurements across platforms, though no study to this point has been conducted to show its influence in cross-platform differences.
RESULTS: Using probe sequence data, a new microarray probe/transcript annotation was created based on the AceView Aug05 release that allowed for the categorization of genes based on their expression measurements' susceptibility to alternative splicing differences across microarray platforms. Examining gene expression data from multiple platforms in light of the new categorization, genes unsusceptible to alternative splicing differences showed higher signal agreement than those genes most susceptible to alternative splicing differences. The analysis gave rise to a different probe-level visualization method that can highlight probe differences according to transcript specificity.
CONCLUSION: The results highlight the need for detailed probe annotation at the transcriptome level. The presence of alternative splicing within a given sample can affect gene expression measurements and is a contributing factor to overall technical differences across platforms.

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Year:  2007        PMID: 17708771      PMCID: PMC2000902          DOI: 10.1186/1471-2164-8-284

Source DB:  PubMed          Journal:  BMC Genomics        ISSN: 1471-2164            Impact factor:   3.969


  24 in total

1.  Genome-wide detection of alternative splicing in expressed sequences of human genes.

Authors:  B Modrek; A Resch; C Grasso; C Lee
Journal:  Nucleic Acids Res       Date:  2001-07-01       Impact factor: 16.971

2.  Large-scale transcriptional activity in chromosomes 21 and 22.

Authors:  Philipp Kapranov; Simon E Cawley; Jorg Drenkow; Stefan Bekiranov; Robert L Strausberg; Stephen P A Fodor; Thomas R Gingeras
Journal:  Science       Date:  2002-05-03       Impact factor: 47.728

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Authors:  Leming Shi; Laura H Reid; Wendell D Jones; Richard Shippy; Janet A Warrington; Shawn C Baker; Patrick J Collins; Francoise de Longueville; Ernest S Kawasaki; Kathleen Y Lee; Yuling Luo; Yongming Andrew Sun; James C Willey; Robert A Setterquist; Gavin M Fischer; Weida Tong; Yvonne P Dragan; David J Dix; Felix W Frueh; Frederico M Goodsaid; Damir Herman; Roderick V Jensen; Charles D Johnson; Edward K Lobenhofer; Raj K Puri; Uwe Schrf; Jean Thierry-Mieg; Charles Wang; Mike Wilson; Paul K Wolber; Lu Zhang; Shashi Amur; Wenjun Bao; Catalin C Barbacioru; Anne Bergstrom Lucas; Vincent Bertholet; Cecilie Boysen; Bud Bromley; Donna Brown; Alan Brunner; Roger Canales; Xiaoxi Megan Cao; Thomas A Cebula; James J Chen; Jing Cheng; Tzu-Ming Chu; Eugene Chudin; John Corson; J Christopher Corton; Lisa J Croner; Christopher Davies; Timothy S Davison; Glenda Delenstarr; Xutao Deng; David Dorris; Aron C Eklund; Xiao-hui Fan; Hong Fang; Stephanie Fulmer-Smentek; James C Fuscoe; Kathryn Gallagher; Weigong Ge; Lei Guo; Xu Guo; Janet Hager; Paul K Haje; Jing Han; Tao Han; Heather C Harbottle; Stephen C Harris; Eli Hatchwell; Craig A Hauser; Susan Hester; Huixiao Hong; Patrick Hurban; Scott A Jackson; Hanlee Ji; Charles R Knight; Winston P Kuo; J Eugene LeClerc; Shawn Levy; Quan-Zhen Li; Chunmei Liu; Ying Liu; Michael J Lombardi; Yunqing Ma; Scott R Magnuson; Botoul Maqsodi; Tim McDaniel; Nan Mei; Ola Myklebost; Baitang Ning; Natalia Novoradovskaya; Michael S Orr; Terry W Osborn; Adam Papallo; Tucker A Patterson; Roger G Perkins; Elizabeth H Peters; Ron Peterson; Kenneth L Philips; P Scott Pine; Lajos Pusztai; Feng Qian; Hongzu Ren; Mitch Rosen; Barry A Rosenzweig; Raymond R Samaha; Mark Schena; Gary P Schroth; Svetlana Shchegrova; Dave D Smith; Frank Staedtler; Zhenqiang Su; Hongmei Sun; Zoltan Szallasi; Zivana Tezak; Danielle Thierry-Mieg; Karol L Thompson; Irina Tikhonova; Yaron Turpaz; Beena Vallanat; Christophe Van; Stephen J Walker; Sue Jane Wang; Yonghong Wang; Russ Wolfinger; Alex Wong; Jie Wu; Chunlin Xiao; Qian Xie; Jun Xu; Wen Yang; Liang Zhang; Sheng Zhong; Yaping Zong; William Slikker
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5.  Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements.

Authors:  Scott L Carter; Aron C Eklund; Brigham H Mecham; Isaac S Kohane; Zoltan Szallasi
Journal:  BMC Bioinformatics       Date:  2005-04-25       Impact factor: 3.169

6.  Three microarray platforms: an analysis of their concordance in profiling gene expression.

Authors:  David Petersen; G V R Chandramouli; Joel Geoghegan; Joanne Hilburn; Jonathon Paarlberg; Chang Hee Kim; David Munroe; Lisa Gangi; Jing Han; Raj Puri; Lou Staudt; John Weinstein; J Carl Barrett; Jeffrey Green; Ernest S Kawasaki
Journal:  BMC Genomics       Date:  2005-05-05       Impact factor: 3.969

7.  AceView: a comprehensive cDNA-supported gene and transcripts annotation.

Authors:  Danielle Thierry-Mieg; Jean Thierry-Mieg
Journal:  Genome Biol       Date:  2006-08-07       Impact factor: 13.583

8.  Identical probes on different high-density oligonucleotide microarrays can produce different measurements of gene expression.

Authors:  LanMin Zhang; Sean J Yoder; Steven A Enkemann
Journal:  BMC Genomics       Date:  2006-06-15       Impact factor: 3.969

9.  Weighing our measures of gene expression.

Authors:  John Quackenbush
Journal:  Mol Syst Biol       Date:  2006-11-14       Impact factor: 11.429

10.  Transcript-based redefinition of grouped oligonucleotide probe sets using AceView: high-resolution annotation for microarrays.

Authors:  Jun Lu; Joseph C Lee; Marc L Salit; Margaret C Cam
Journal:  BMC Bioinformatics       Date:  2007-03-29       Impact factor: 3.169

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  8 in total

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2.  Integrating multiple genome annotation databases improves the interpretation of microarray gene expression data.

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4.  Combining transcriptional datasets using the generalized singular value decomposition.

Authors:  Andreas W Schreiber; Neil J Shirley; Rachel A Burton; Geoffrey B Fincher
Journal:  BMC Bioinformatics       Date:  2008-08-08       Impact factor: 3.169

5.  Development and evaluation of new mask protocols for gene expression profiling in humans and chimpanzees.

Authors:  Donna M Toleno; Gabriel Renaud; Tyra G Wolfsberg; Munirul Islam; Derek E Wildman; Kimberly D Siegmund; Joseph G Hacia
Journal:  BMC Bioinformatics       Date:  2009-03-05       Impact factor: 3.169

6.  SpliceCenter: a suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies.

Authors:  Michael C Ryan; Barry R Zeeberg; Natasha J Caplen; James A Cleland; Ari B Kahn; Hongfang Liu; John N Weinstein
Journal:  BMC Bioinformatics       Date:  2008-07-18       Impact factor: 3.169

7.  TIPMaP: a web server to establish transcript isoform profiles from reliable microarray probes.

Authors:  Neelima Chitturi; Govindkumar Balagannavar; Darshan S Chandrashekar; Sadashivam Abinaya; Vasan S Srini; Kshitish K Acharya
Journal:  BMC Genomics       Date:  2013-12-27       Impact factor: 3.969

8.  Narrowing down the targets for yield improvement in rice under normal and abiotic stress conditions via expression profiling of yield-related genes.

Authors:  Amit K Tripathi; Ashwani Pareek; Sudhir K Sopory; Sneh L Singla-Pareek
Journal:  Rice (N Y)       Date:  2012-12-22       Impact factor: 4.783

  8 in total

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