Data integration in microbial genomics ... - Jacobs University

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94 7. Summary and discussion tive function. The interpretation of the graphical results furthermore affirmed these hypotheses, through the application of multivariate statistical methods. It could for instance be shown that the DUFs in the photosynthesis cluster, observed in the network graph, are frequently found in habitats with high chlorophyll values. In a next step, the hypotheses should be tested in wet-lab experiments. For that the genomes of marine organisms should be screened for these DUFs. If cultivated marine organisms contain these DUFs, targeted knock-out experiments may reveal, if the deactivation of these domains of function confirms the hypothesis about their function. If for example a DUF, hypothetically involved in photosynthesis, is deactivated, and the deactivation of this function hampers the ability of the organism to perform this function, finally knowledge about the function has been derived. In a community effort, these hypotheses may be tested on a large-scale. Therefore, this approach demonstrates how to arrive at the knowledge level through the intepretation and application of repeatable methods to contextualized sequence data. Without contextual data this study would not have been possible. 7.5 Getting the most out of the data Further strategies that are similar to the approach presented in chapter 6 need to be developed and applied. The aim must be, to make use of the power of computers to process large quantities of data 5 and to make use of the strength of human minds to interpret results and to draw conclusions. This can be an iterative process, where processing strategies are refined after humans interpreted results and developed new ideas how to improve the methods applied. For this, data has to be brought into a computable form. Different data integration strategies as well as the application of different database technologies to store and process the data need to be explored. The enrichment of the primary sequence data with contextual data offers an efficient way and seems to be a good starting point. The formalization of the data through the application of controlled vocab- 5 Meant are quantities that exceed the ability of human minds to process data.
7.5. Getting the most out of the data 95 ularies is a logical next step. This could be integrated into the GSC standards. Terms for certain parameters could for example easily be offered to users in user interfaces. This will increase the computability of the data and will lead to results that can be interpreted by humans more quickly. Ontologies offer even more ways to automatically derive new knowledge out the data. However, formalization requires human input. The development of ontologies and the translation of data into ontologies is an elaborate process. It has to be considered, if the effort-to-benefitratio justifies the work, which has to be additionally invested. The sheer quantity of the data could make it inevitable, to intensify the use of means to automatically infer knowledge. The requirements for human interpretation could be reduced, by drawing conclusions in silico. Solutions to this may be achieved through the application of the rich toolbox of artificial intelligence.
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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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4 1. Introduction used to derive ne
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6 1. Introduction croorganisms thri
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10 1. Introduction marker genes hel
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14 1. Introduction possibilities li
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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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Page 110 and 111: 102 BIBLIOGRAPHY [Beynon-Davies, 20
Page 112 and 113: 104 BIBLIOGRAPHY oceanic regions by
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Page 118 and 119: 110 BIBLIOGRAPHY pathways in marine
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Page 122 and 123: 114 BIBLIOGRAPHY (2006). Microbial
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