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98 8. Conclusion and outlook nities to automatically process the data. • Standards: The development, implementation of standards for contextual data have the potential to create a wide impact and to facilitate broad use of the data. This has to be done in a community effort and requires the adoption of the standards. • Knowledge: Finally, efficient strategies to gain knowledge which make use of the available contextual data, need to be developed. An example for that has been given in chapter 6. 8.2 Projects on the horizon The GOS project started the age of large-scale environmental metagenomic data sets. There are many follow-up projects with specific focuses on the horizon. The TARA ocean cruise (http://oceans.taraexpeditions.org/) and the Malaspina project (http://www.expedicionmalaspina.es/Malaspina/Main. do) are further exploring the oceans’ ecosystems. The human microbiome project (http://commonfund.nih.gov/hmp/) is investigating the microbial diversity in humans. The Earth microbiome project (http: //www.earthmicrobiome.org/) aims to comprehensively characterize the global microbial taxonomic and functional diversity. There is no doubt that these projects will create vast amounts of sequence data. With the MIxS standards in place, it can be expected that contextual data are recorded and publicly deposited along with these sequence data. Once contextualized, a dense network of data points will be created (schematically depicted as overlapping data clouds about “Organisms“, “Genes“ and the “Environment“ in figure 8.1). The denser this network becomes and the better these data are integrated through the usage of contextual data, the greater will be the scope of analysis possibilities. In data analyses it will become easier to distinguish signal from noise. More and more statistically meaningful signals will be detected in the ever growing integrated data set. In the years to come, the spotlight needs to be on the development of methods and strategies to detect these signals. To draw a hypothetical picture about where the contextual and sequence data integration
8.3. A contextual data utopia 99 might lead us, in the last section of this thesis a ’contextual data utopia’ is described. 8.3 A contextual data utopia Ideally, there will be a multitude of tools and mechanisms that help to capture, process and exchange contextual data in the future. Figure 8.1: A multidisciplinary integrative approach to ecosystems biology. From: [Glöckner and Joint, 2010] The tools and mechanisms are used by a large community. The quantity and quality of the sequence and contextual data increases dramatically. Knowledge generation is accelerated through data integration in various ways. Standards are so far developed and widely adopted that the data can be processed automatically and knowledge can be derived mainly in silico. The methods to process the data are so advanced, that it is easy for humans to interpret the results and to make wise decisions based on them. An ever growing integrated and contextualized sequence data set (compare figure 8.1) combined with a multidisci-
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Max Planck Institute for Marine Mic
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List of abbreviations BLAST Basic L
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Thesis abstract Deoxyribonucleic ac
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4.2 Introduction . . . . . . . . .
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2 1. Introduction syntax: data leve
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6 1. Introduction croorganisms thri
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16 1. Introduction tual data acquis
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18 1. Introduction Doug Wendel, Owe
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CHAPTER 2 METABAR A tool for consis
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2.2. Background 23 molecular sequen
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2.2. Background 25 printing of data
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2.4. Results 27 KML export function
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2.4. Results 29 Figure 2.3: Screens
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2.5. Discussion 31 taking samples i
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2.5. Discussion 33 Handlebar MetaBa
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2.6. Conclusion 35 rine Microbiolog
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2.6. Conclusion 37 RK advised progr
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40 3. CDinFusion Genomic Standards
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42 3. CDinFusion sortium (GSC), an
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44 3. CDinFusion tiFASTA file with
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