SHIRLEY, Oct 30th, 2017 – Creative Proteomics, a world leading proteomics identification and analysis service provider. Except for the professional services, we also collect the newest biostatistics and bioinformatics tools that can be used to interpret proteomics data.
Proteomics experiments often produce large amounts of data. However, the simple identification and quantification of proteins from cell proteome or subtype proteins is not sufficient to adequately understand the complex mechanisms that occur in biological systems. Thus, the functional annotation analysis of the protein data set using the bioinformatics tool is critical to explaining the results of high-throughput proteomics. Although large-scale proteomics data are rapidly increasing, the biological interpretation of these results remains a challenging task. Biostatistics and bioinformatics tools have started to be applied in the interpretation of the proteomics data:
.GO functional annotation for proteomics data.
Functional annotations of proteomics data allow mining of bioinformatics databases to predict the function of proteins. According to its role in biological systems, the classification of genes and proteins is also the basis for analyzing the relationships and interactions between them.
GO annotation analysis service offered by Creative Proteomics involves 3 separate categories:
. Cellular component
. Molecular function
. Biological process
At present, there are two main methods for GO annotation – sequence similarity comparison (BLAST) and domain similarity comparison (InterProScan).
GO annotation on genes and proteins clarify the relationship between gene products and the terms being used to define. A gene product refers to a gene encoding RNA or protein product. Since a gene may encode a number of products with very different properties, the recommended GO annotation is for the gene product rather than the gene itself.
.GO Enrichment Analysis for Proteomics data
Enrichment analysis can be used to identify overexpression of biological information in long protein lists and allow visualization of biological processes. Enrichment analysis takes advantage of GO terminology to summarize the biological pathways which are most likely to be associated with proteomics data. The statistical method is used to compare the abundance of GO items in the data set and the natural abundance in the reference data set. Terms are extracted in the proteomics data set by calculating the p-value super-representation. More than 60 software tools have been developed to calculate concentration analysis by enrichment algorithms.
Different algorithms depends on whether a single item is tested at a time by single concentration analysis (SEA), or by the genome enrichment algorithm (GSEA).
.Network Analysis for Proteomics Data
Biological pathway analysis is a series of cytochemical reactions that contribute to biological effects. Since proteins are involved in chemical reactions, they can be combined in the pathway database so that we can explain the type of biogenic processes in proteomic data. The simplest method is to analyze a list of proteins that represent the abundance of a particular pathway.
There are several biological network models have been developed to predict the consequences of each biological pathway, and software which can be used to process large-scale proteome database with the results from enrichment analysis.
About Bioinformatics for Proteomics provide by Creative Proteomics:
Proteomics is an interdisciplinary domain which has benefited a lot from the genetic information of the Human Genome Project (HGP); it is also arising scientific research and exploration of proteomes from the complete level of intracellular protein composition, structure, and its own singular activity patterns. It is a valuable component of functional genomics. Proteomics research involves the separation, identification, qualitative, quantitative, and functional characterization of the entire protein profile of a given cell, tissue, and/or organism. Studying proteome also includes the profiling of isoforms, mutants, post-translational modifications, splice variants and protein-protein interactions. In this process, bioinformatics methods play a vital role for the analysis of proteomics.
Except for functional annotation, enrichment analysis and bioinformatics network analysis, Creative proteomics can also offer the following bioinformatics services in proteomics:
. Statistical analysis
. Proteomic analysis of post-translational modifications
Company Name: Creative Proteomics
Contact Person: Mandy Scott
Email: Send Email
Address:45-1 Ramsey Road
State: New York
Country: United States