Publish Your Research Online
Get Recognition - International Audience
Request for an Author Account | Login | Submit Article
|HOME||FAQ||TOP AUTHORS||FORUMS||PUBLISH ARTICLE|
Expander: A Tool of BioinformaticsBY: Amna Adnan | Category: Bioinformatics | Submitted: 2010-07-05 09:13:45
Article Summary: "Bioinformatics has made tools for the storage of raw biological information. Expander is one of the tools which provides researchers the opportunity to analyze and observe the knowledge of gene expression present in this tool. It has the whole genome information of humans, fly, bacteria and some other organisms..."
The data of gene expression microarray consists of huge raw biological information. It is very difficult for the researchers to extract the information which they need. To sort out the huge biological data, they created a tool called Expander. Expander is the abbreviation of Expression Analyzer and Displayer. It is a java based tool and is concerned with the analysis of gene expression data.
How Expander works:-
The advantage of this tool is that it integrates all the analysis steps in one package. For example;
Preprocessing and Normalization:-
First the gene expression data is loaded in the Expander. The purpose of preprocessing is to remove the problems which arise in the analyzed chips. Expander makes it possible to filter the data of genes and separate the one which is needed for the analysis.
Identification of genes:-
In this step those genes are separated and identified from the raw biological data which are related with the gene expression.
Clustering and Biclustering analysis:-
The algorithms of clustering and biclustering make it possible to sort out the genes according to their expression. These algorithms put the genes into different clusters after analyzing their expressions. The genes with similar expression are placed in the same cluster. This method makes it easy to analyze the data properly and does not create any complexity.
Functional Enrichment Analysis:-
Once the gene sets which show co-expression, are identified then there comes the next step of assigning these sets or groups to some biological meaning. What researchers do, they use the Expander which analyzes these sets statistically. The statistical analysis helped researchers a lot because they were able to assign functions to those genes who had unknown functions. Expander provides opportunity to analyze the gene expression data of humans, rats, mice, yeast, chicken, fly, zebra fish, C. elegans, Arabidopsis, tomato and E.coli. It has made it easy for the user to avoid compilation of long data of gene expression. Gene ontology and some other databases have made it possible to compile the data sets into one place by taking information of all the data from their respective databases.
Algorithms of Expander:-
There are some algorithms in Expander which are developed to make it possible to analyze the gene expression data.
SAMBA is an algorithm of biclustering which is used to identify those genes which show similar behavior in the subsets. These genes are viewed under specific biological conditions. This algorithm provides ways to analyze the genes in biclusters statistically as the experimental data consists of hundreds and thousands of genes. It can also be stated that SAMBA works for the analysis of heterogeneous datasets.
It is a clustering algorithm which not only works for the gene expression data analysis but also is useful for other biological processes. The main purpose of this algorithm is to identify those genes in the cluster which are highly similar to each other in their expression. CLICK analyzes the data by three ways that is graphically, theoretically and statistically.
TANGO algorithm is concerned with the function of the genes. It checks for the genes in each cluster that have any particular function. GO annotation files for the gene expression data determine the functions of the genes because these files have the information about every organism's genes. Hyper geometric enrichment tests are preformed by the TANGO algorithm.
The algorithm of FAME shows whether the set of genes is rich with miRNA families or not by performing empirical tests through the technique of random permutation.
PRIMA stands for Promoter Integration in Microarray Analysis. The function of this algorithm is to check the groups of genes which have transcription factors. It finds those transcription factors that have enriched binding sites in the set of genes. When the genes are identified by clustering or biclustering algorithms then this algorithm works to analyze the genes in the given group.
About Author / Additional Info:
Comments on this article: (1 comments so far)
• Biofuels - A Solution for Non Renewable Sources
• Carotenoids- Introduction, Origin and Properties
• Biodegradation of Xenobiotic Compounds
• Targeted Disruption of Gene in Bacillus Anthracis
Latest Articles in "Bioinformatics" category:
• Career as Bioinformatician and Biostatistician
• Role of Bioinformatics in Drug Discovery
• Importance and Applications of Bioinformatics in Molecular Medicine
• Bioinformaticist vs. Bioinformatician - Definition, Differences and Career Outlook
• Bioinformatics Application in Nanotechnology
• How Bioinformatics Handles the Biological Data?
• Application of Bioinformatics in Medicine
• Prenatal Diagnosis via Bioinformatics Skills
• Applications of Bioinformatics in Agriculture
• Next Generation Sequencing Technologies: 454 Pyrosequencing
• GenScan: Bioinformatics Software For Structure Prediction and Analysis of Gene
• Pairwise Sequence Alignment For Sequence Similarity
• Applications of Bioinformatics in Biotechnology
• Introduction to Bioinformatics: Role of Mathematics and Technology
• Why and How of Normalization in Microarray Data Analysis
• Steps in Microarray Data Analysis - Part I
• Steps in Microarray Data Analysis - Part II
• Bilirubin Metabolism And its Role in Neonatal Jaundice
• Bioenergetics, Enzymes And the Energy of Activation
Important Disclaimer: All articles on this website are for general information only and is not a professional or experts advice. We do not own any responsibility for correctness or authenticity of the information presented in this article, or any loss or injury resulting from it. We do not endorse these articles, we are neither affiliated with the authors of these articles nor responsible for their content. Please see our disclaimer section for complete terms.
Copyright © 2010 biotecharticles.com - Do not copy articles from this website.
ARTICLE CATEGORIES :
| Disclaimer/Privacy/TOS | Submission Guidelines | Contact Us