Next-generation sequencing data interpretation: enhancing reproducibility and accessibility

被引：220

作者：

Nekrutenko, Anton ^{[1
,2
]}

Taylor, James ^{[3
,4
]}

机构：

[1] Penn State Univ, Huck Inst Life Sci, University Pk, PA 16802 USA

[2] Penn State Univ, Dept Biochem & Mol Biol, University Pk, PA 16802 USA

[3] Emory Univ, Dept Biol, Atlanta, GA 30322 USA

[4] Emory Univ, Dept Math & Comp Sci, Atlanta, GA 30322 USA

来源：

NATURE REVIEWS GENETICS | 2012年 / 13卷 / 09期

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

ENZYMATIC AMPLIFICATION; CLOUD; GALAXY; DNA; COMMUNITY; FRAMEWORK; HEAD; SEQ;

D O I：

10.1038/nrg3305

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Areas of life sciences research that were previously distant from each other in ideology, analysis practices and toolkits, such as microbial ecology and personalized medicine, have all embraced techniques that rely on next-generation sequencing instruments. Yet the capacity to generate the data greatly outpaces our ability to analyse it. Existing sequencing technologies are more mature and accessible than the methodologies that are available for individual researchers to move, store, analyse and present data in a fashion that is transparent and reproducible. Here we discuss currently pressing issues with analysis, interpretation, reproducibility and accessibility of these data, and we present promising solutions and venture into potential future developments.

引用

页码：667 / U93

页数：6

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