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Mapping and differential expression analysis from short-read RNA-Seq data in model organisms |
Qiong-Yi Zhao1( ), Jacob Gratten1, Restuadi Restuadi1, Xuan Li2( ) |
1. The University of Queensland, Queensland Brain Institute, St Lucia, Qld 4072, Australia 2. Key Laboratory of Synthetic Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China |
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Abstract Recent advances in next-generation sequencing technology allow high-throughput RNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies. For model organisms with a reference genome, the first step in analysis of RNA-Seq data involves mapping of short-read sequences to the reference genome. Reference-guided transcriptome assembly is an optional step, which is recommended if the aim is to identify novel transcripts. Following read mapping, the primary interest of biologists in many RNA-Seq studies is the investigation of differential expression between experimental groups. In this review, we discuss recent developments in RNA-Seq data analysis applied to model organisms, including methods and algorithms for direct mapping, reference-guided transcriptome assembly and differential expression analysis, and provide insights for the future direction of RNA-Seq.
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Author Summary
RNA-Seq is a revolutionary methodology that employs high-throughput sequencing technologies to enable highly sensitive detection and quantification of RNA in biological samples. Mapping of RNA-Seq data to a reference is a fundamental step for all forms of RNA-Seq data analysis in model organisms, and differential expression analysis is the primary interest of biologists in many RNA-Seq studies. In this review we discuss recent developments in these two fields and provide insights for the future direction of RNA-Seq. We see our review as a resource for the community that will enable researchers to select the most appropriate tools for RNA-Seq data analysis. |
Keywords
RNA-Seq
mapping
reference-guided transcriptome assembly
differential expression analysis
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Corresponding Author(s):
Qiong-Yi Zhao,Xuan Li
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About author: Tongcan Cui and Yizhe Hou contributed equally to this work. |
Online First Date: 16 March 2016
Issue Date: 16 March 2016
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