Data integration in microbial genomics ... - Jacobs University

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14 1. Introduction possibilities like temporal or spatial comparisons are hampered or simply not possible and comparability of the data cannot be assured. The fact that “latitude, longitude, and time, elements of the key contextual data tuple (x,y,z,t), are only reported in 7.3% and 7.2% of all submissions“ [Hankeln et al., 2010] shows that the majority of public sequence data is not sufficiently annotated. This fact has been recognized by the Genomic Standards Consortium (GSC), which ”is an open-membership working body which formed in September 2005. The goal of this international community is to promote mechanisms that standardize the description of genomes and the exchange and integration of genomic data“ (www.gensc.org). The GSC developed a series of checklists to specify which data should be captured and stored along with sequence data [Field et al., 2008]. The INSDC databases support the storage of these parameters. Recently, the life science community has begun to develop tools that implement these standards and to actively integrate these different data sources. “Data integration is the process of combining data residing at different sources and providing the user with a unified view of these data“ [Lenzerini, 2002]. Once contextualized, a far greater scope of analyses can be performed. Studies in various disciplines of life science have already shown the power of contextual data enriched sequence studies. In marine microbiology it could be shown that there are conserved diversity patterns along the depth continuum [DeLong et al., 2006]. Furthermore, annually recurring diversity patterns could be identified in certain regions of the ocean [Fuhrman et al., 2006]. In the medical field the global outbreaks of epidemics can be monitored globally [Janies et al., 2007, Salzberg et al., 2007, Schriml et al., 2010] 14 . All these studies exemplify the potential of globally integrated data. The tighter the integration of sequence data with contextual data will be, the easier it will become to carry out sequence data analysis studies in larger contexts. This offers an approach to answer the basic questions ”Who is out there?”, ”How many of which kind?” and ”What are they doing?”. Moreover, knowledge will become obtainable about the complex mechanisms of the Earth’s biosphere on the micro and macro scale. 14 There are many more examples that show the increased interpretability of contextualized sequence data: [Tyson et al., 2004, Sogin et al., 2006, Seshadri et al., 2007, Huber et al., 2007, Rusch et al., 2007].
1.6. Research aims and motivation 15 1.6 Research aims and motivation After the relevance of the field of microbial genomics and the need for contextual and sequence data integration has been identified, the motivation of this thesis was to integrate contextual and sequence data in aid of biological knowledge generation in marine microbial genomics 15 . This has been done by the development of the specialized data integration tools MetaBar and CDinFusion (chapter 2 and 3), by contributing to the development of contextual data standards within the realm of the GSC (chapter 4) and by participating in the development of the marine ecological genomics platform (chapter 5). Finally, a sequence analysis study has been carried out that exemplifies the power of in silico knowledge generation in an environmental context (chapter 6). 1.7 Overall thesis structure To provide the reader with an overview about the structure of this thesis, the topics of the chapters are sorted into the knowledge pyramid 1.8 introduced in 1.1. The thesis follows a bottom-up approach. It starts Figure 1.8: Overall thesis structure along the DIKW on the data level at the base of the pyramid with the consistent contex- 15 This thesis has to be seen as a contribution to this. The realization of the formulated aims will only be possible in a community effort.
Page 1: Max Planck Institute for Marine Mic
Page 4 and 5: List of abbreviations BLAST Basic L
Page 6 and 7: Thesis abstract Deoxyribonucleic ac
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Page 41 and 42: 2.5. Discussion 33 Handlebar MetaBa
Page 43 and 44: 2.6. Conclusion 35 rine Microbiolog
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4.6. Conclusions and call for actio
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CHAPTER 5 MEGX.NET Integrated datab
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5.3. New database structure and con
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5.3. New database structure and con
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5.4. User Access 73 5.4 User Access
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5.4. User Access 75 tal context, co
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5.5. Summary 77 access charge: Max
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80 6. Domains of unknown function 6
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82 6. Domains of unknown function F
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84 6. Domains of unknown function p
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CHAPTER 7 SUMMARY AND DISCUSSION Th
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7.2. GSC standards development 89 T
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7.2. GSC standards development 91 o
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7.4. In silico Hypothesis Generatio
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7.5. Getting the most out of the da
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98 8. Conclusion and outlook nities
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100 8. Conclusion and outlook plina
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102 BIBLIOGRAPHY [Beynon-Davies, 20
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104 BIBLIOGRAPHY oceanic regions by
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106 BIBLIOGRAPHY [Giovannoni et al.
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108 BIBLIOGRAPHY [Kitano, 2002] Kit
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110 BIBLIOGRAPHY pathways in marine
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112 BIBLIOGRAPHY [Ramette, 2007] Ra
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114 BIBLIOGRAPHY (2006). Microbial
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116 BIBLIOGRAPHY [Williamson et al.
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CHAPTER 9 ACKNOWLEDGEMENTS First, I
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