Data integration in microbial genomics ... - Jacobs University

More documents

Recommendations

Info

30 2. MetaBar Figure 2.4: Screenshot of the graphical user interface II. Contextual data entries can be extended with GSC parameters. All contextual data can be exported and submitted to the INSDC databases. that is assigned to a certain user account. The web GUI possesses a cascading menu on the left which contains the “MetaBar admin features”, the “project admin features”, and the “MetaBar user features”, respectively. Furthermore, a sophisticated ownership and permission concept offers the users to share their data with other users in the same project giving them read or write permissions, or to prevent access for others. Project admins have the possibility to transfer the ownership of a set of samples to another user, create projects, assign users to a project and to remove users from a project. For a given MetaBar installation there is only one MetaBar admin who can create users, assign or dismiss project admins and delete samples or whole projects. A quick reference guide describing the general workflow of MetaBar from the acquisition to the submission of data is available on the website. Examples for metadata enriched INSDC database entries, created with MetaBar, are available through the accession numbers: [Gen- Bank:GU949561 and GenBank:GU949562]. 2.5 Discussion MetaBar can be used whenever it is necessary to capture contextual data that describe the environmental origin of a sample. The system has been tested in several studies in close collaboration with biologists
2.5. Discussion 31 taking samples in the field. By integrating their feedback MetaBar should qualify as user-friendly, scientist-centric software tool. Case study The above mentioned studies allow the typical MetaBar workflow to be generalized as follows. A scientist acting as the project administrator (PA) plans a sampling campaign together with two members in his research team (PM1, PM2). The project EXAMPLE is created in MetaBar and the users PM1 and PM2 are added to EXAMPLE. It is anticipated that PM1 will collect five push core samples of sediment while PM2 will collect five water samples. Thus, PM1 and PM2 create five barcodes each and download their acquisition spreadsheets. These barcodes are printed in multiple copies to label sample containers, appear in field notebooks and for contingency. During sample collection, PM1 and PM2 log contextual data such as latitude, longitude, depth and sampling time next to the barcode labels pasted in their field notebooks. The sample containers are labeled with the corresponding barcodes and transported back to the laboratory. The link between sample and environment is thus established. At the end of the sampling campaign the contextual data gathered in the field are transferred to PM1 and PM2’s acquisition spreadsheets. Barcodes pasted on the sample, the field records and present in the spreadsheet ensure fidelity and the data are then uploaded to the MetaBar server over the internet. PM1 and PM2 may enter further contextual data specific to their sampling environments by selecting the relevant GSC-compliant metadata packages (e.g. “sediment” and “water”, respectively) through the web GUI. The PA and both members of the project can now review the consolidated contextual data for errors or missing values. Corrective action at this stage improves the quality of the data prior to submission. During laboratory processing, every new subsample is labeled with a copy of the original sample’s barcode, preserving the link to the in situ sampling event. Native laboratory protocols and practices are otherwise unaffected and are documented in laboratory books. PM1 sequences the genomes of several sediment sample isolates and PM2 sequences microbial metagenomes from the community in the water sample. Congruent to the environmental extensions, GSC packages corresponding to various study types are available. PM1 and PM2 may use
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: 2.4. Results 29 Figure 2.3: Screens
Page 41 and 42: 2.5. Discussion 33 Handlebar MetaBa
Page 43 and 44: 2.6. Conclusion 35 rine Microbiolog
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
Page 90 and 91:
82 6. Domains of unknown function F
Page 92 and 93:
84 6. Domains of unknown function p
Page 95 and 96:
CHAPTER 7 SUMMARY AND DISCUSSION Th
Page 97 and 98:
7.2. GSC standards development 89 T
Page 99 and 100:
7.2. GSC standards development 91 o
Page 101 and 102:
7.4. In silico Hypothesis Generatio
Page 103:
7.5. Getting the most out of the da
Page 106 and 107:
98 8. Conclusion and outlook nities
Page 108 and 109:
100 8. Conclusion and outlook plina
Page 110 and 111:
102 BIBLIOGRAPHY [Beynon-Davies, 20
Page 112 and 113:
104 BIBLIOGRAPHY oceanic regions by
Page 114 and 115:
106 BIBLIOGRAPHY [Giovannoni et al.
Page 116 and 117:
108 BIBLIOGRAPHY [Kitano, 2002] Kit
Page 118 and 119:
110 BIBLIOGRAPHY pathways in marine
Page 120 and 121:
112 BIBLIOGRAPHY [Ramette, 2007] Ra
Page 122 and 123:
114 BIBLIOGRAPHY (2006). Microbial
Page 124 and 125:
116 BIBLIOGRAPHY [Williamson et al.
Page 127:
CHAPTER 9 ACKNOWLEDGEMENTS First, I
show all

Data integration in microbial genomics ... - Jacobs University

Create successful ePaper yourself

Delete template?

Save as template?