Data Storage

beefzoologistΒιοτεχνολογία

21 Φεβ 2013 (πριν από 4 χρόνια και 8 μήνες)

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Data Storage


Microarrays produce a vast amount of
data.


“National Center for Biotechnology
Information (NCBI) Gene Expression
Omnibus” (GEO) (Edgar et al. 2002)


“KEGG EXPRESSION” database (Kanehisa
et al. 2002)


“ArrayExpress Database” (Brazma et al. 2003)


“Stanford Microarray Database” (Ball et al.
2005)


Data Storage


The minimum information about a microarray
experiment (MIAME) specification


It comprise


data on the experimental design


information on the array design


the samples used


the RNA extraction and labeling


hybridization procedures and parameters


experimental data


image data,


raw data,


data after normalization, and after averaging of replicates.


a detailed description of strategy and controls used
for normalization


For describing all these data in a
structured way, a special XML format
called microarray gene expression markup
language (MAGE
-
ML) has been proposed


Clustering to expression data


assumption that similar expression levels
might indicate related biological function



Before any mathematical clusters analysis
can be done


First, a distance measure between data points
has to be selected.


Second, a function that defines the quality of
clustering results must be chosen.


Finally, the algorithm for clustering needs to
be selected.


Hierarchical data clustering
produces a tree structure

In K
-
means clustering, centroids,
drawn as stars, are dispersed by the
user, and data points are assigned
to the clusters in an iterative
algorithm.

Self
-
organizing maps start with a
regular grid of centroids, represented
as stars. Pulling the centroids to the

centers of the clusters they represent
in an iterative algorithm identifies the
clusters

Conclusion


The system biology approach requires a fundamental
framework involving several distinct steps


(1) definition of all components of a system


(2) systematic perturbation and monitoring of the
components either genetically or by modification of the
environment


(3) reconcile the experimentally observed responses with
those predicted by a quantitative model


(4) design and perform new perturbations to distinguish
between multiple or competing model hypothesis

(Ideker et al. 2001).


Conclusion


Microarrays can be used in microbiology
for a multitude of differing applications,
from the study of


gene regulation


bacterial response to environmental
changes


genome organization


evolutionary questions up to taxonomic
and environmental studies.