Important areas in Data Management in Genome Database Systems
- Nonstandard and unstructured data.
Most of the genome data are
nonstandard and unstructured. It is not clear that every position in a DNA
sequence should be treated as a data object.
There are structural data like proteins, but they need a 3D
representation. Techniques from GIS (geographic information systems) and CAD
(computer aided design) as well as from geometric modeling need to be applied
for efficient indexing and querying. Scientists are currently using tools like
BLAST or PSBLAST to do pattern searches and this capability needs to be
integrated into the DBMS.
- Complex
query processing.
As discussed in the characteristics of genome databases,
since the similarity of sequences, graphs and 3-D shapes it has become really
hard to implement queries. Relational DBMS and Object DBMS are not capable of
processing these type of queries. Therefore DBMS developers have implemented
path oriented queries and specialized libraries to cater this requirement.
- Data
interpretation and Meta data management.
Decent mechanisms should be implemented in the database
system manage the meta data, because in
a database system like this is enough meta data should be provided to the
scientists for the interpretation purposes and they need to be maintained in a
virtuous manner. In order to do that several techniques have been implemented
such as use of Annotations and Ontology.
- Data
integration across related databases.
Various genome databases interrelated with each other and
there should be a proper management tool to handle these kinds of cross related
database links. Currently no uniform interfaces or consolidation of data has
been done so that information can be accessed in an integrated fashion in any
given context or by any particular classification.
- Need for
a set of uniform data management solutions.
There is a tremendous need to have this kind of uniform data
management solution because of “typical problems in databases of heterogeneous
data integration - multiple models, multiple formats, different underlying files
and database systems, and a large amount of context-sensitive semantic
content.”
This is a brief explanation on the key areas of data management in genome database systems. In the next post I hope to give your guys a mush more deeper analysis in to genome database systems. Till then Good bye folks. :D
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