Literature DB >> 19336255

RNA-Seq-quantitative measurement of expression through massively parallel RNA-sequencing.

Brian T Wilhelm1, Josette-Renée Landry.   

Abstract

The ability to quantitatively survey the global behavior of transcriptomes has been a key milestone in the field of systems biology, enabled by the advent of DNA microarrays. While this approach has literally transformed our vision and approach to cellular physiology, microarray technology has always been limited by the requirement to decide, a priori, what regions of the genome to examine. While very high density tiling arrays have reduced this limitation for simpler organisms, it remains an obstacle for larger, more complex, eukaryotic genomes. The recent development of "next-generation" massively parallel sequencing (MPS) technologies by companies such as Roche (454 GS FLX), Illumina (Genome Analyzer II), and ABI (AB SOLiD) has completely transformed the way in which quantitative transcriptomics can be done. These new technologies have reduced both the cost-per-reaction and time required by orders of magnitude, making the use of sequencing a cost-effective option for many experimental approaches. One such method that has recently been developed uses MPS technology to directly survey the RNA content of cells, without requiring any of the traditional cloning associated with EST sequencing. This approach, called "RNA-seq", can generate quantitative expression scores that are comparable to microarrays, with the added benefit that the entire transcriptome is surveyed without the requirement of a priori knowledge of transcribed regions. The important advantage of this technique is that not only can quantitative expression measures be made, but transcript structures including alternatively spliced transcript isoforms, can also be identified. This article discusses the experimental approach for both sample preparation and data analysis for the technique of RNA-seq.

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Year:  2009        PMID: 19336255     DOI: 10.1016/j.ymeth.2009.03.016

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  197 in total

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2.  Normalization, testing, and false discovery rate estimation for RNA-sequencing data.

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Review 9.  Mechanisms and evolution of control logic in prokaryotic transcriptional regulation.

Authors:  Sacha A F T van Hijum; Marnix H Medema; Oscar P Kuipers
Journal:  Microbiol Mol Biol Rev       Date:  2009-09       Impact factor: 11.056

10.  An Enumerative Combinatorics Model for Fragmentation Patterns in RNA Sequencing Provides Insights into Nonuniformity of the Expected Fragment Starting-Point and Coverage Profile.

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