Literature DB >> 19715439

Applications of new sequencing technologies for transcriptome analysis.

Olena Morozova1, Martin Hirst, Marco A Marra.   

Abstract

Transcriptome analysis has been a key area of biological inquiry for decades. Over the years, research in the field has progressed from candidate gene-based detection of RNAs using Northern blotting to high-throughput expression profiling driven by the advent of microarrays. Next-generation sequencing technologies have revolutionized transcriptomics by providing opportunities for multidimensional examinations of cellular transcriptomes in which high-throughput expression data are obtained at a single-base resolution.

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Year:  2009        PMID: 19715439     DOI: 10.1146/annurev-genom-082908-145957

Source DB:  PubMed          Journal:  Annu Rev Genomics Hum Genet        ISSN: 1527-8204            Impact factor:   8.929


  178 in total

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2.  Statistical design and analysis of RNA sequencing data.

Authors:  Paul L Auer; R W Doerge
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3.  Cancer: Genomics of metastasis.

Authors:  Joe Gray
Journal:  Nature       Date:  2010-04-15       Impact factor: 49.962

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Review 5.  Single-cell and regional gene expression analysis in Alzheimer's disease.

Authors:  Ruby Kwong; Michelle K Lupton; Michal Janitz
Journal:  Cell Mol Neurobiol       Date:  2012-01-22       Impact factor: 5.046

Review 6.  RNA-Seq technology and its application in fish transcriptomics.

Authors:  Xi Qian; Yi Ba; Qianfeng Zhuang; Guofang Zhong
Journal:  OMICS       Date:  2013-12-31

7.  Meet me halfway: when genomics meets structural bioinformatics.

Authors:  Sungsam Gong; Catherine L Worth; Tammy M K Cheng; Tom L Blundell
Journal:  J Cardiovasc Transl Res       Date:  2011-02-25       Impact factor: 4.132

8.  Synthetic spike-in standards for RNA-seq experiments.

Authors:  Lichun Jiang; Felix Schlesinger; Carrie A Davis; Yu Zhang; Renhua Li; Marc Salit; Thomas R Gingeras; Brian Oliver
Journal:  Genome Res       Date:  2011-08-04       Impact factor: 9.043

9.  A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data.

Authors:  Yan Zhou; Bin Yang; Junhui Wang; Jiadi Zhu; Guoliang Tian
Journal:  BMC Genomics       Date:  2021-06-26       Impact factor: 3.969

10.  Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples.

Authors:  John Vivian; Jordan M Eizenga; Holly C Beale; Olena M Vaske; Benedict Paten
Journal:  JCO Clin Cancer Inform       Date:  2020-02
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