Data integration in microbial genomics ... - Jacobs University

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4 1. Introduction used to derive new expressions from one or more expressions called premises. An ontology is a “formal, explicit specification of a shared conceptualisation“ [Gruber, 1993]. Ontologies provide a shared vocabulary, which can be used to model a domain. A domain is comprised of objects and/or concepts that exist and their properties and relations. Knowledge encoded in FOL or ontologies can be stored in so-called knowledge bases. ”In artificial intelligence, FOL is used ... to encode knowledge that then can be stored and used by a computer” [Etchemendy, 1999]. Computers can then infer new knowledge from previously encoded knowledge. Inference is defined as “a way to add new sentences to the knowledge base by deriving new sentences from old“ [Norvig, 2009]. An example in natural language is to infer from the premises “All men are mortal” and ”Socrates is a man” that “Socrates is mortal” 3 . The larger a knowledge base, the more applicable are computers to infer new knowledge. Computers are able to rapidly compute vast amounts of data and knowledge, while human minds become overstrained by sheer quantity. Whereas knowledge can be computed, wisdom is a domain that is still reserved to humans exclusively, perhaps because it ”has more to do with human intuition, understanding, interpretation and actions, than with systems” [Rowley, 2007]. Figure 1.3: Knowledge pyramid, modified from: [Rowley, 2007] The ”data information knowledge wisdom hierarchy” (DIKW) or 3 Whereas natural language is often ambiguous, FOL and ontologies are not. The logic in these sentences is extracted and formalized to unambiguous expressions that can be computed [Etchemendy, 1999].
1.2. Relevance of microbiology 5 ”Knowledge Pyramid” shown in figure 1.3 is a model that is often used in the information and knowledge literature to provide a combined overview about the key terms and how they are related. The shape of the pyramid implies that there are a lot of data at the bottom of the pyramid. These data can be used to obtain information. On the next level there is fewer information that can be used to obtain knowledge. Finally, knowledge can be used to obtain wisdom.“Each of the higher types in the hierarchy includes the categories that fall below it” [Ackoff, 1989]. Shown on the left and right of the pyramid are variables that change on each level of the hierarchy from “high“ to “low“. Data at the lowest level can be computed easily. They have a low meaning and require few human input. It is a tedious task that requires a lot of human input, to model a knowledge domain as an ontology that then can be computed. The higher the level in the pyramid, the lower is the computer input programmability and the more human input is required. At the top of the pyramid the meaning, applicability and value is the highest but the programmability is the lowest. After the key terms and the knowledge pyramid have been explained, the field of microbiology is briefly introduced. Subsequently, the relevant bioinformatic approaches are explained, and how they can be applied to come from data to knowledge in marine microbial genomics. 1.2 Relevance of microbiology Our planet is inhabited by an unseen majority of microorganisms 4 [Whitman et al., 1998] that are central catalysts in the global cycling of nutrients. Microorganisms form two separate and extremely diverse domains 5 of life that are collectively termed the prokaryotes. It is estimated that microbes contribute between 50% and 90% to global primary production [Falkowski et al., 1998]. Almost all life on earth directly or indirectly relies on this production of organic compounds from atmospheric or aquatic carbon dioxide [Field et al., 1998]. Mi- 4 The total number of prokaryotes is estimated to be 4 − 6 ∗ 10 30 on Earth [Whitman et al., 1998]. 5 The domains are Archaea and Bacteria.
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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CHAPTER 4 MIMARKS The Minimum infor
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4.2. Introduction 57 edge generatio
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4.3. Development of MIMARKS 59 in t
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4.4. Survey of published parameters
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4.5. The MIMARKS checklist 63 Resul
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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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CHAPTER 7 SUMMARY AND DISCUSSION Th
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7.2. GSC standards development 89 T
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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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Data integration in microbial genomics ... - Jacobs University

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