Data integration in microbial genomics ... - Jacobs University

14 1. Introduction
possibilities like temporal or spatial comparisons are hampered or simply
not possible and comparability of the data cannot be assured. The
fact that “latitude, longitude, and time, elements of the key contextual
data tuple (x,y,z,t), are only reported in 7.3% and 7.2% of all
submissions“ [Hankeln et al., 2010] shows that the majority of public
sequence data is not sufficiently annotated.
This fact has been
recognized by the Genomic Standards Consortium (GSC), which ”is
an open-membership working body which formed in September 2005.
The goal of this international community is to promote mechanisms
that standardize the description of genomes and the exchange and
integration of genomic data“ (www.gensc.org).
The GSC developed a series of checklists to specify which data should
be captured and stored along with sequence data [Field et al., 2008].
The INSDC databases support the storage of these parameters. Recently,
the life science community has begun to develop tools that
implement these standards and to actively integrate these different
data sources. “Data integration is the process of combining data residing
at different sources and providing the user with a unified view
of these data“ [Lenzerini, 2002].
Once contextualized, a far greater scope of analyses can be performed.
Studies in various disciplines of life science have already shown the
power of contextual data enriched sequence studies. In marine microbiology
it could be shown that there are conserved diversity patterns
along the depth continuum [DeLong et al., 2006].
Furthermore, annually
recurring diversity patterns could be identified in certain regions
of the ocean [Fuhrman et al., 2006].
In the medical field the
global outbreaks of epidemics can be monitored globally [Janies et al.,
2007, Salzberg et al., 2007, Schriml et al., 2010] 14 . All these studies
exemplify the potential of globally integrated data.
The tighter the
integration of sequence data with contextual data will be, the easier
it will become to carry out sequence data analysis studies in larger
contexts. This offers an approach to answer the basic questions ”Who
is out there?”, ”How many of which kind?” and ”What are they doing?”.
Moreover, knowledge will become obtainable about the complex
mechanisms of the Earth’s biosphere on the micro and macro scale.
14 There are many more examples that show the increased interpretability of contextualized
sequence data: [Tyson et al., 2004, Sogin et al., 2006, Seshadri et al., 2007, Huber et al., 2007,
Rusch et al., 2007].