This is a quick tour of the major functions of Barleybase. Please click the
links, and corresponding pages will pop-up. Do not close this page during
your tour. Some of the popup pages may need further action at the page to
try out functions on that page. You may download our Power
Point tutorial on data
analysis and submission work flow.
A. Expression Profile-Based Filters
They are mainly used to identify differentially-expressed genes
(probe sets).
a. Single
Experiment Expression Profile Filter -- Work on one experiment a
time with a single filter or a composite
filter.
b.
Cross-Experiment Expression Profile Filter -- Cross-experiment
probe set querying.
B. Function2Expression
-- Gene Functional Annotation to Expression Profile
This is a collection of gene list creation methods, based on several types of
gene functional annotations.
a. GO2Expression -- Browse and search Gene Ontology
tree, and retrieve probe sets or
genes from selected GO classes.
b. Pathway2Expression
-- Find probe
sets corresponding to enzymes from your interested metabolic or regulatory
pathways. Available for the Arabidopsis ATH1 GeneChip only. Based on KEGG and
TAIR pathway data.
c. Domain2Expression -- Creates Gene lists
corresponding to predicted InterPro protein functional domains. Most of the functional domain information
comes from UniProt, TIGR. For other non-characterized exemplars, BarleyBase
conducts in-house annotation on with InterproScan.
d. GeneFamily2Expression -- Find probe
sets corresponding to a given gene family. Available for the Arabidopsis ATH1
GeneChip.
f.
Keyword2Expression -- Find probe sets matching single or several terms
in exemplar annotation.
g.
BLAST2Expression--
Finding
probe sets whose exemplars have similarity to your own sequences.
Cross-Platform is another form of
BLAST2Expression.
C.
Import Gene List
You may input a list of probe sets or exemplar names and retrieve
their expression from selected experiment. You can input any free text
containing the probe set names.
2. Manage Gene Lists
Manage
Gene List--
Management, analysis, visualization, export/import gene list.
Compare two gene lists and create new gene list.
3. Pattern Recognition on Gene Lists
Analyze Gene List by Hierarchical Clustering, K-Means
partition, Sammon's MDS, PCA, or SOM.
4. View Gene List Analysis
Result
View
Gene List
Analysis Result-- All pattern recognition results are saved
automatically for future access.
5. Functional
Interpretation of Gene Lists
FuncExpression-- Functional
interpretation of large scale gene expression data by gene
ontology, pathway.
1.
Experiment Level
Visualization helps in understanding data and in quality assessment:
2.
Hybridization Level Visualization by scatter plots and MVA plots to
show reproducibility/variability among hybridizations.
3. Gene List Visualization please follow links in II.2, shown as
Expression line graph or heatmap for probe sets.
4. Single Gene or Probe Set Visualization: For an example, please view the
Contig15950_at page
5. Probe Level Visualization: Bar-plots for raw PM/MM
intensities for probes of the probe sets, such as
Contig15950_at page.