Data integration in microbial genomics ... - Jacobs University

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Thesis abstract Deoxyribonucleic acid (DNA) is the primary structure that carries the genetic information of organisms in genomes. The introduction of the first DNA sequencing methods in 1977 marked a major breakthrough in life sciences. Today, these methods are widely applied and grant insight into the ’blueprints’ of organisms from all domains of life. The analysis of environmental microbial sequence data is becoming increasingly important in times of global climate change, because microbes are central catalysts in nutrient cycles such as the carbon cycle that profoundly affects Earth’s climate. Microbes perform almost all metabolic processes that are thermodynamically possible. DNA sequencing is carried out around the globe and the resulting data is submitted to the public repositories of the International Nucleotide Sequence Database Collaboration (INSDC). Data in the INSDC is accumulating exponentially. This trend shows the need for efficient data processing strategies in order to gain knowledge out of this ever increasing amount of sequence data. For this, it is important to annotate sequence data with as much contextual data as possible. Contextual data are data about the environmental context and the processing steps that were applied. These can range from data about the geographic location, sampling time, habitat, or about experimental procedures used to obtain the sequences up to video data recorded during sampling. Especially data about the geographic location (x, y, z) and the point in time (t), when samples are taken from the environment are essential. Comparability and interpretability are preserved. Ample analysis approaches become possible, when contextual and sequence data are integrated. In this doctoral thesis, data integration is promoted in three ways: Firstly, through the development of contextual data capture, submission and integration tools. Secondly, through the development of standards for contextual data and thirdly, through demonstration of in silico hypothesis generation for a large metagenomic data set.
Contents 1 Introduction 1 1.1 From data to knowledge . . . . . . . . . . . . . . . . . . . . 1 1.2 Relevance of microbiology . . . . . . . . . . . . . . . . . . 5 1.3 The primary data source . . . . . . . . . . . . . . . . . . . 7 1.4 Exponential sequence data growth . . . . . . . . . . . . . 10 1.5 Importance of contextual data . . . . . . . . . . . . . . . . 11 1.6 Research aims and motivation . . . . . . . . . . . . . . . . 15 1.7 Overall thesis structure . . . . . . . . . . . . . . . . . . . . 15 1.8 Publication overview . . . . . . . . . . . . . . . . . . . . . . 16 2 MetaBar 21 2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3 CDinFusion 39 3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4 Design and Implementation . . . . . . . . . . . . . . . . . 47 4 MIMARKS 55 4.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Page 1: Max Planck Institute for Marine Mic
Page 4 and 5: List of abbreviations BLAST Basic L
Page 8 and 9: 4.2 Introduction . . . . . . . . .
Page 10 and 11: 2 1. Introduction syntax: data leve
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Page 24 and 25: 16 1. Introduction tual data acquis
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Page 31 and 32: 2.2. Background 23 molecular sequen
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Page 35 and 36: 2.4. Results 27 KML export function
Page 37 and 38: 2.4. Results 29 Figure 2.3: Screens
Page 39 and 40: 2.5. Discussion 31 taking samples i
Page 41 and 42: 2.5. Discussion 33 Handlebar MetaBa
Page 43 and 44: 2.6. Conclusion 35 rine Microbiolog
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48 3. CDinFusion AMD Opteron TM pro
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50 3. CDinFusion able separately an
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52 3. CDinFusion and upload it with
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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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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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Data integration in microbial genomics ... - Jacobs University

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