TY - JOUR
T1 - Next-generation sequencing data interpretation
T2 - Enhancing reproducibility and accessibility
AU - Nekrutenko, Anton
AU - Taylor, James
N1 - Funding Information:
The authors are grateful for the support of the Galaxy Team (E. Afgan, G. Ananda, D. Baker, D. Blankenberg, D. Bouvier, D. Clements, N. Coraor, C. Eberhard, J. Goecks, J. Jackson, G. Von Kuster, R. Lazarus, R. Marenco and S. McManus). The visual analytics framework shown in Figure 1 has been built by J. Goecks. The authors’ laboratories are supported by US National Institutes of Health grants HG005133, HG004909 and HG006620 and US National Science Foundation grant DBI 0850103. Additional funding is provided, in part, by the Huck Institutes for the Life Sciences at Penn State, the Institute for Cyberscience at Penn State and a grant with the Pennsylvania Department of Health using Tobacco Settlement Funds. The Department specifically disclaims responsibility for any analyses, interpretations or conclusions.
PY - 2012/9
Y1 - 2012/9
N2 - 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.
AB - 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.
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U2 - 10.1038/nrg3305
DO - 10.1038/nrg3305
M3 - Review article
C2 - 22898652
AN - SCOPUS:84865226981
SN - 1471-0056
VL - 13
SP - 667
EP - 672
JO - Nature Reviews Genetics
JF - Nature Reviews Genetics
IS - 9
ER -