Literature DB >> 18855686

Serial Analysis of Gene Expression (SAGE): 13 years of application in research.

Sergey V Anisimov1.   

Abstract

A number of molecular methods of gene expression analysis can approach genomic level. Among those, Serial Analysis of Gene Expression (SAGE) stands out. Unlike many other techniques, SAGE allows both qualitative and quantitative analysis of previously unknown transcripts. Over the course of the last 13 years, SAGE has became a recognized tool of large-scale gene expression profiling, being used extensively in human, animal, yeast and plant studies of various nature. A number of important adaptations was introduced both to the protocol of SAGE library construction and to the analytical algorithm employed. Moreover, some variations of the original protocol (MAGE, SADE, microSAGE, miniSAGE, longSAGE, superSAGE, deepSAGE, etc.) were derived to improve the utility of SAGE in certain conditions. Current review aims comparing the benefits and drawbacks of the techniques for high-throughput gene expression analysis (including SAGE) in a realistic, balanced manner. Issues related to modifications to the original protocol and further development of the SAGE are discussed.

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Year:  2008        PMID: 18855686     DOI: 10.2174/138920108785915148

Source DB:  PubMed          Journal:  Curr Pharm Biotechnol        ISSN: 1389-2010            Impact factor:   2.837


  28 in total

Review 1.  Genomics and bioinformatics resources for crop improvement.

Authors:  Keiichi Mochida; Kazuo Shinozaki
Journal:  Plant Cell Physiol       Date:  2010-03-05       Impact factor: 4.927

2.  Mechanotransduction Mechanisms for Intraventricular Diastolic Vortex Forces and Myocardial Deformations: Part 2.

Authors:  Ares Pasipoularides
Journal:  J Cardiovasc Transl Res       Date:  2015-05-14       Impact factor: 4.132

3.  Comparing transcription rate and mRNA abundance as parameters for biochemical pathway and network analysis.

Authors:  Brewster Hayles; Sailu Yellaboina; Degeng Wang
Journal:  PLoS One       Date:  2010-03-26       Impact factor: 3.240

4.  Developmental staging of male murine embryonic gonad by SAGE analysis.

Authors:  Tin-Lap Lee; Yunmin Li; Diana Alba; Queenie P Vong; Shao-Ming Wu; Vanessa Baxendale; Owen M Rennert; Yun-Fai Chris Lau; Wai-Yee Chan
Journal:  J Genet Genomics       Date:  2009-04       Impact factor: 4.275

5.  A Prevalence of Imprinted Genes within the Total Transcriptomes of Human Tissues and Cells.

Authors:  Sergey V Anisimov
Journal:  Mol Biol Int       Date:  2012-09-11

6.  Digital gene expression analysis of two life cycle stages of the human-infective parasite, Trypanosoma brucei gambiense reveals differentially expressed clusters of co-regulated genes.

Authors:  Nicola J Veitch; Paul C D Johnson; Urmi Trivedi; Sandra Terry; David Wildridge; Annette MacLeod
Journal:  BMC Genomics       Date:  2010-02-22       Impact factor: 3.969

7.  Transcriptome analysis of Nicotiana tabacum infected by Cucumber mosaic virus during systemic symptom development.

Authors:  Jie Lu; Zhi-Xin Du; Jun Kong; Ling-Na Chen; Yan-Hong Qiu; Gui-Fen Li; Xiao-Hua Meng; Shui-Fang Zhu
Journal:  PLoS One       Date:  2012-08-28       Impact factor: 3.240

8.  Identification of differentially-expressed genes associated with pistil abortion in Japanese apricot by genome-wide transcriptional analysis.

Authors:  Ting Shi; Zhihong Gao; Liangju Wang; Zhen Zhang; Weibing Zhuang; Hailong Sun; Wenjun Zhong
Journal:  PLoS One       Date:  2012-10-16       Impact factor: 3.240

9.  Different transcriptional profiles of RAW264.7 infected with Mycobacterium tuberculosis H37Rv and BCG identified via deep sequencing.

Authors:  Fengguang Pan; Yaya Zhao; Seng Zhu; Changjiang Sun; Liancheng Lei; Xin Feng; Wen Yu Han
Journal:  PLoS One       Date:  2012-12-19       Impact factor: 3.240

10.  5'-Serial Analysis of Gene Expression studies reveal a transcriptomic switch during fruiting body development in Coprinopsis cinerea.

Authors:  Chi Keung Cheng; Chun Hang Au; Sarah K Wilke; Jason E Stajich; Miriam E Zolan; Patricia J Pukkila; Hoi Shan Kwan
Journal:  BMC Genomics       Date:  2013-03-20       Impact factor: 3.969

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