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Tools
BLAST Tools
A.
Use microarray element sequences to search other databases:
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NCBI
BLAST - Search the NCBI databases using the Barley1 exemplar sequences
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PlantGDB BLAST - Search the PlantGDB databases using the Barley1 exemplar sequences
B. Use your own sequence to search Barley1 and other Microarray Elements:
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BarleyBase
BLAST - Search the BarleyBase databases for matching
exemplars from Barley1 or ATH1.
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Dedicated BLAST - Search the Barley1 exemplars stored in PlantGDB
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Dedicated VMATCH - Search for Barley1 probes that match the sequence you provide.
C.
Microarray platform translator
(MPT): Mapping microarray elements between platforms. Or
mapping from GenBank AC/GI numbers to microarray platforms.
Microarray Data Analysis Tools
Microarray raw data acquisition are conducted with
Affymetrix Microarray Suite 5.0 (MAS5.0). Data normalization was
carried out with affy package of Bioconductor and MAS5.0 Suite. R and Bioconductor are used for
expression data analysis and some
visualizations:
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Bioconductor/R :
Bioconductor is an open source project aimed at
providing comprehensive capability for microarray data
analysis, visualization and annotation. It started in
fall of 2001, and quickly evolved into a high-profile
projects. It provides packages for analyzing both
Affymetrix oligo GeneChip data (Affy, affydata and
affycomp packages) and cDNA spotted microarray.
BarleyBase used its affy package for estimation of
expression values by Robust Multi-chip Average (RMA)
and MAS5.0 methods. A
note from
Dr. Dan Nettleton demonstrates how RMA estimation is carried out.
Bioconductor is developed with R, R is a GNU project, an
open language and environment for statistical computing.
It is a functional programming language and has powerful
graphical and visualization capabilities.
- MAS5.0: Affymetrix provides with
its Microarray Analysis Suite 5.0 the function to do absolute and
comparative expression estimation. MAS5.0 uses statistical algorithm
for normalization and calculate the expression values, he
presence/absence call and the p-values of calls.
- dChip:
This is one of the several mostly used normalization
method for estimating expression signals. It is free
for academic use, though it is not open source. dChip
used model-based estimate for gene expression indeces
(MBEI), which was proposed by Li and Wong (2001).
Bioconductor has a very rough approximation of
dChip's method. dChip is not used in BarleyBase.
Microarray Data Interpretation Tools
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Functionalize Expression (FuncExpression)
supports two way-integration of plant gene functional annotation and the
gene expression data. Multiple gene lists can be classified, compared
and visualized. The gene
function information includes the gene ontology classification,
InterPro protein functional domain prediction, metabolic pathway and
gene family information. It also support functional classification of
Plant GenBank sequences and TIGR Gene Indices.
Genomics Data Exploration Tools
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MapViewer
and SeqViewer
are the two complementary visualization tools developed
by TAIR for
integrative exploration of the genetic and physical
maps, markers, sequences and other annotation
information concerning Arabidopsis.
SeqViewer let user to search for names or
short sequences and view search hits against the whole
genome. MapViewer started from the other way, letting
user to select chromosome or segments, locating relevant
makers, clones and gene sequence information.
General Purpose Data Visualization Tools
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GGobi:The GGobi software is
a system for visualization of high-dimensional data in
interactive and dynamic way. It can be directly
access from R, Perl or Python. It
supports database like Postgres and MySQL and can
support XML formatted data.
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MATLAB:
MATLAB is a commercial high-level language that
integrates mathematical computing and visualization.
Similar to R, it is easily extensible by user.
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S-PLUS: This is the
commercial counterpart of R, which are both
implementation of S programming language.
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Manet :
MANET is a Macintosh-based software for interactive
visualization for studying multivariate features of
data sets with missing values.
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