Data integration in microbial genomics ... - Jacobs University

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34 2. MetaBar ples to contextual and, if available, sequence and species data derived from a labeled sample, thus, no hierarchy or processing method is encoded in the identifiers. Also, sample hierarchies and complex identifier schemes are avoided. This concept does not interfere with native laboratory sample tracking methods, yet ensures consistency in environmental contextual data capture. It is important that users have the flexibility to cover different sample types. MetaBar offers a single template in which a restricted part is parsed to the database and an unrestricted part of the spreadsheet can be changed to contain sample specific additional data. HandleBar offers a set of non-constrained sample templates depending on the sample type and also individual templates can be created. In MetaBar each sample can be extended with further parameters organized into types of report and environmental packages suggested by the GSC. In contrast to HandleBar, data entered into MetaBar’s acquisition spreadsheet is validated on input, ensuring correct format before upload to the MetaBar server. This avoids frequent rejection of the acquisition sheet. In HandleBar the validation is done by the web server and erroneous sheets have to be corrected retrospectively by the uploading user. The variety of export features are currently unique to MetaBar. MetaBar is integrated into the megx.net tool set and connected to MegDB. This offers opportunity to work with the data and to analyze them alone, or in the context of other research project data stored in the megx.net database. This level of integration necessitated a user authentication and authorization management system and SSL encryption. Consequently, the local installation of MetaBar requires modification of the open source code base. The software and a detailed installation manual are available at www.megx.net/metabar. However, accounts on the MetaBar installation hosted at the MPI for Marine Microbiology in Bremen can easily be given to interested users and an “anonymous” project exists where data of external users can be stored anonymously. It is the intention of the Microbial Genomics and Bioinformatics Group at the MPI-Bremen to support this tool as open source in the future. Applicability MetaBar has been developed at the Max Planck Institute for Ma-
2.6. Conclusion 35 rine Microbiology; however, the tool may be readily applied to a wide range of research fields outside the marine sciences. Contextual data fields relevant to air, host associated, human associated, sediment, soil, wastewater sludge or water samples are available via the “add GSC fields” function. The parameters in each of these environmental packages have been selected based on community usage and consensus (http://gensc.org/gc_wiki/index.php/MIENS). For example, fields requesting data on barometric pressure, carbon dioxide, carbon monoxide, chemical administration, humidity, methane, organism count, oxygen, oxygenation status of sample, perturbation, pollutants, respirable particulate matter, sample salinity, sample storage duration, sample storage location, sample storage temperature, solar irradiance, temperature, ventilation rate, ventilation type, volatile organic compounds, wind direction, and wind speed would be presented to users using the air environmental package. Users may easily add new, custom fields as columns using standard Microsoft R○ Excel R○ operations. Combined, the GSC extensions and freedom for customization generalize MetaBar’s applicability to any scenario necessitating the capture of contextual data describing a sample’s environmental origin. 2.6 Conclusion MetaBar offers an integrated contextual data acquisition, storage, and submission solution to the INSDC system. The impact of better contextual data availability and correctness in the primary sequence databases will greatly improve the possibilities to reach a higher level of data integration and interpretation to address basic ecological questions. MetaBar’s integration into the megx.net tool set and its export mechanisms offer extended analysis possibilities via comparison to other scientific studies and with complementary interpolated environmental data. The visualization of the sampling sites on the Genes Mapserver and in Google Earth R○ offers the users a simple way to show sampling events on the globe and to relate them to other publicly available scientific studies.
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
Page 8 and 9: 4.2 Introduction . . . . . . . . .
Page 10 and 11: 2 1. Introduction syntax: data leve
Page 12 and 13: 4 1. Introduction used to derive ne
Page 14 and 15: 6 1. Introduction croorganisms thri
Page 16 and 17: 8 1. Introduction the middle at the
Page 18 and 19: 10 1. Introduction marker genes hel
Page 20 and 21: 12 1. Introduction what kind and wh
Page 22 and 23: 14 1. Introduction possibilities li
Page 24 and 25: 16 1. Introduction tual data acquis
Page 26 and 27: 18 1. Introduction Doug Wendel, Owe
Page 29 and 30: CHAPTER 2 METABAR A tool for consis
Page 31 and 32: 2.2. Background 23 molecular sequen
Page 33 and 34: 2.2. Background 25 printing of data
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: 2.5. Discussion 33 Handlebar MetaBa
Page 45: 2.6. Conclusion 37 RK advised progr
Page 48 and 49: 40 3. CDinFusion Genomic Standards
Page 50 and 51: 42 3. CDinFusion sortium (GSC), an
Page 52 and 53: 44 3. CDinFusion tiFASTA file with
Page 54 and 55: 46 3. CDinFusion offered the option
Page 56 and 57: 48 3. CDinFusion AMD Opteron TM pro
Page 58 and 59: 50 3. CDinFusion able separately an
Page 60 and 61: 52 3. CDinFusion and upload it with
Page 63 and 64: CHAPTER 4 MIMARKS The Minimum infor
Page 65 and 66: 4.2. Introduction 57 edge generatio
Page 67 and 68: 4.3. Development of MIMARKS 59 in t
Page 69 and 70: 4.4. Survey of published parameters
Page 71 and 72: 4.5. The MIMARKS checklist 63 Resul
Page 73 and 74: 4.6. Conclusions and call for actio
Page 75 and 76: CHAPTER 5 MEGX.NET Integrated datab
Page 77 and 78: 5.3. New database structure and con
Page 79 and 80: 5.3. New database structure and con
Page 81 and 82: 5.4. User Access 73 5.4 User Access
Page 83 and 84: 5.4. User Access 75 tal context, co
Page 85: 5.5. Summary 77 access charge: Max
Page 88 and 89: 80 6. Domains of unknown function 6
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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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