Gene Expression Analysis
In a cDNA or gene expression profiling experiment the expression
levels of thousands of genes in a sample are measured and compared to the
expression of the same genes in different samples. This approach can be used to study the effects
of certain treatments, diseases or other conditions on gene expression. For
example, microarray-based gene expression profiling can be used to identify
genes whose expression changes in response to treatment by comparing the gene
expression levels of the control and treated tissues.
Find out more about Simbiot’s gene expression analysis
tools, including clustering and principal component analysis (PCA).
Time Course Analysis
Time course-based experiments are a special case of gene
expression analysis where time is treated as a continuous variable. These experiments allow scientists to analyze
the change in expression of genes over some time period. For example, embryonic development or the
effect of stress over time could be analyzed as time course experiments. This type of analysis requires specialized
algorithms and analysis tools.
Find out more about Simbiot’s time course analysis
tools.
SNP Genotyping
In a SNP genotyping experiment, microarrays can be used to identify alleles
(genotype) for known single nucleotide polymorphisms (SNPs) in the provided
genomic material. The resulting allele information can then be
used in genome wide association studies and linkage disequilibrium analysis.
Find out more about Simbiot’s genome wide association study
(GWAS) and linkage disequilibrium (LD) analysis tools.
Copy Number Analysis
SNP microarrays also could be used to measure quantities of DNA
that match a particular sequence, which allows these devices to measure changes
in copy numbers of chromosomal segments.
These copy number differences between samples are known as Copy Number
Variations (CNV).
Find out more about Simbiot’s copy number variation (CNV)
analysis tools.
Bioinformatics
Experimental design
The primary requirement for microarray experiments is the
availability of replicates. In general,
two types of replicates are possible:
- Biological
replicates: multiple tissue (or cell line) samples taken from different
individuals under the same biological conditions and
- Technical
replicates: multiple aliquots of RNA (or DNA)
obtained from the same biological samples
In practice, due to high cost, most experiments do not
utilize technical replicates. However,
no statistical analysis will be possible without sufficient number of
biological replicates. More biological
(or technical) replicates will lead to better results so careful cost-benefit
analysis should be performed to chose the appropriate number of replicates
within the cost constraints.
Please contact us with any question about experimental
design.
Standardization
Most journals require all submitted publications to be
reproducible. For microarray experiments
this means the information provided has to comply with Minimum Information
About a Microarray Experiment (MIAME) standard (Brazma, Hingamp et al.
2001).
The Simbiot’s data management system can store all information necessary for
MIAME compliant experiments.
Microarray and descriptive data generated by external
contract research organizations (CROs) or by an integrated Core Facility will
some times be delivered via a Laboratory Information Management System
(LIMS). Simbiot can effectively
integrate with these systems to automatically import data and descriptive
information.
Statistical analysis
Microarray data sets are commonly very large, and analytical
precision is influenced by a number of variables. Statistical challenges
include taking into account effects of background noise, data distribution and
appropriate normalization of the data.
Simbiot provides a wide choice of normalization, variance reduction and
transformation algorithms, including platform-specific functions.
Relation between probes and genes
It is important to remember that microarrays do not detect
genes. Microarrays detect fractionated cDNA
or DNA that hybridized to probes fixed on a
slide (or chip). Vendor provided probe
annotations are automatically imported into Simbiot and validated. Keep in mind that inaccurate annotations
could have significant impact on the validity of microarray analysis results.
Full annotation information, including the results of JBI’s
validations is easily accessible within Simbiot. Simbiot also provides functionality to
integrate annotations from custom or non-commercial microarrays.
Data warehousing
Microarray data are expensive to generate and contain
valuable information. Therefore it is
extremely important to properly maintain and manage these data as well as to provide
effective access to the previously generated data sets (historical data) allowing
researchers to easily construct novel in-silico experiments that combine newly
generated data, historical data as well as, possibly, data retrieved from
public repositories.
Simbiot’s private server implementations provide an
excellent solution for establishing cost effective and efficient repositories
for microarray data and experiments.
Collaboration
Microarray experiments are technically complicated and
frequently require active collaboration between researchers. Exchanging data between collaborators, even those
situated within the same Lab or Institute is not a trivial task. This process becomes even more complex when
the collaborators are based in different institutions, different cities or even
different countries.
Simbiot integrates powerful multi-lingual social networking
and sharing features that fully support scientific collaboration, including
exchange of data, analysis results and ideas.
Security
A related issue to collaboration and data exchange is
security of those data. Simbiot’s
effective security system allows researchers to share only the data they would
like and under the restrictions they impose.
All information deposited into Simbiot is encrypted, user access could
be restricted using biometric information and full non-volatile transaction log
is maintained.
References
Brazma, A., P. Hingamp, et al. (2001).
"Minimum information about a microarray experiment (MIAME)-toward standards
for microarray data." Nat Genet 29(4): 365-71.
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