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Main Four Parts Services in Bioinformatics ServicesBY: Mandy Scott | Category: Bioinformatics | Submitted: 2016-03-15 04:54:18
Article Summary: "As the rapid development of life sciences and computer science, bioinformatics becomes a new subject in collection, process, storage, transmission, analysis and interpretation of biological information. .."
Main researching items are focusing on genomics and proteomics. This article is mainly introducing four parts---gene ontology(GO) analysis, KEGG, protein protein interaction networks and cluster analysis of bioinformatic services.
Gene ontology analysis
Why should gene ontology analysis be established?
The establishment of gene ontology analysis is aiming to solve the phenomenon of chaos definition in order to make the function description of gene products in various database consistent, and the inquiry in many database highly consistent. What's more, it also allows users to check features of gene products on various levels.
Classification of gene ontology analysis
1. Molecular function
2. Biological process
3. Cellular component
Applications of gene ontology analysis
Limitation: this analysis can only reflect the function of gene products in cells. However, it cannot reflect the expression of genes.
It can utilized in genome analysis, gene expression analysis and some other fields.
KEGG is also known as the kyoto encyclopedia of genes and genomes, which is used to help users to understand high-level functions and the biological system's utilities as a database resource. For example, it works well in the cell, the organism and the ecosystem. The PATHWAY database, as the major component of KEGG, is composed with graphical diagrams of biochemical pathways, such as, most metabolic pathways and some regulatory pathways. Playing an important role in genome, KEGG maintains the genes database for all organisms' gene catalogs with selected organisms and complete genomes. Other computational tools, such as the reconstruction of biochemical pathways, as well as the data collection contribution, can be offered by KEGG databases, which are now available and updated.
Protein protein interaction network
Protein is a very important substance for supporting our lives. Researching on the structure and function of protein can express the change mechanism of lives under physiological or pathological conditions directly. However, the protein protein interaction possesses a vital position in the achievement of protein functions. Its basis are cell structure, dynamic behavior and biochemical activities and cell life activities is the result of protein interactions. However, what is the proteins reacting with each others? They are involved in one metabolic pathway or biological process and belonging to the element of one structural complex or molecular machine. To research on protein interaction is aiming to establish the protein interaction network in mode cell systems, which are also known as interactome.
Cluster analysis refers to a process to classify physical or abstract objects groups into multiple classes consisted of similar objects. It is an important human behavior. The purpose of cluster analysis is to classify the collection data on the base of similarity. It is derived from in many areas, including mathematics, statistics and biology. There are two types of cluster analysis methods: hierachical clustering, including consolidation, decomposition, tree methods. Non-hierarchical clustering, including division clustering and spectral clustering. The main features of cluster method are simple and intuitive. And it is mainly utilized in the exploratory study and offering a plurality of possible solutions. However, the limitation is that it cannot tell users how many classes should be used.
These are main four parts involved in bioinformatics services. As the development of our technology, it is believed that bioinformatics will become more and more important.
About Author / Additional Info:
Mandy Scott, from Creative Proteomics , a biotechnology company, provides bioinformatics services, such as, gene ontology(GO) analysis, KEGG, protein protein interaction networks and cluster analysis.
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