Data integration in microbial genomics ... - Jacobs University

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92 7. Summary and discussion http://mixs.gensc.org/report/1 is a good entry point to suggest and discuss improvements ’bottom-up’. 7.3 Megx.net: A unified view on the data The megx.net platform with its on-line Genes Mapserver interface to access georeferenced sequence data, provides users with a unified view on the contextual and sequence data. The platform exemplifies the immediate benefits of data integration. Usage statistics show that especially the Geographic-Basic Local Alignment and Search Tool (BLAST) service, was on average accessed 750 times per month from March 2010 until March 2011. When users get hits for their search sequence, through linking to SILVA and ultimately the INSDC entries, they can access a lot of additional information. Also, a download of the complete list of hits is possible. This shows, that the data integration efforts in this direction are useful to the community. Furthermore, the integration of the environmental parameters such as temperature, nitrate, phosphate, salinity, silicate, dissolved oxygen, oxygen saturation, oxygen utilization, chlorophyll and environmental stability in the Genes Mapservers facilitates sequence data analysis in an environmental context. The integration of sequence data and environmental parameters by using the (x, y, z, t)-key-data tuple has proven to be very useful. This key-data-tuple truly is a minimal contextual data set that helps to link sequence data to many other data sources. The megx.net platform successfully demonstrates how the interpretability of sequence data is increased through data integration. The data sources that are currently integrated in megx.net are however only ”the tip of the iceberg”. The Global Ocean Survey (GOS) data set [Rusch et al., 2007] was the first big marine metagenome. It is reasonable to expect many more. It will require a lot of effort to integrate all these upcoming data sets. Also, the World Ocean Atlas (WOA) 2009 has been released. Megx.net is still using the previous WOA 2005. Further scientific environmental data resources like PANGAEA http://www.pangaea.de/ could be not only linked, but in-
7.4. In silico Hypothesis Generation 93 tegrated. If the MIxS standards are adopted widely, the integration efforts can be significantly reduced. However, if the effort necessary to integrate the data in the megx.net project, can not be done by a handful of people, it will be impossible to keep up with the pace of data accumulation. From this point of view, megx.net represents a proof of principle of a future-oriented approach that should be pursued. The megx.net platform has been classified as being at the interface between information and knowledge. This has been done, because the knowledge extraction itself still requires human input, even though the platform offers the users a lot of useful information 4 . That knowledge can be gained with this approach, has been demonstrated in chapter 6 and is discussed in the following. 7.4 Towards knowledge generation in silico To exemplify the power of in silico knowledge generation, a case study has been conducted (chapter 6). The study presents a computational approach to analyze large-scale metagenomic data sets that are enriched with contextual data in order to generate functional hypotheses. This was done by looking at the co-occurrence of domains of unknown function (DUF) across different habitats and by correlating their occurence with environmental parameters offered by the megx.net platform (chapter 5). If the ”x,y,z,t-key-data-tuple” had not been available, this analysis would not have been possible. The in silico approach demonstrates a way to quickly process and interpret large metagenomic data sets. The GOS data set was analyzed, which at that point of time included more than 10 million sequence reads from 79 sampling sites, all of which were georeferenced. The Hidden Markov Model (HMM) search returned a number of 473,251 hits. The graphical representation of co-occurring DUF hits in networks provided a quick and intuitive overview about this aspect of the data. Patterns were revealed and allowed interpretation already at this stage. Hypotheses about the function of protein families with previously no known function could be derived through the association to protein families with a puta- 4 There are no green buttons, that can be pressed to auto-generate scientific papers that contain explicit knowledge.
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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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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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40 3. CDinFusion Genomic Standards
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