ProteinCenter enables researchers to mine proteomics data quickly and easily, remove redundancy, compare data sets, and get instant biological overview of large data sets
ProteinCenter integrates experimental systems biology data with the contents of a large number of public protein databases.
Key ProteinCenter advantages include:.
Adding biological annotation to large sets of proteins in seconds - this provides an instant biological overview of what the data contain.
Truly comprehensive, biologically relevant annotations (such as GO and OMIM annotations) are automatically added to experimental data to ensure that researchers are not missing out on important details.
For each protein, all annotation data have been consolidated and categorised, and it is presented in a manner that optimises the analysis of the experimental data.
Removing redundancy in lists of accession numbers ProteinCenter integrates sequence information and annotation from all major publicly available protein databases including: GenBank, RefSeq, EMBL, UniProt, Swiss-Prot, Trembl, PIR, IPI, Ensembl, and many more.
It contains more than seven million different proteins derived from 30 million accession codes from past and present versions of the public databases.
ProteinCenter handles all the tedious accession number bookkeeping, such that sequences referred to by multiple accession codes are merged into non-redundant ones.
Comparing multiple lists of proteins, regardless of accession number system and age.
With ProteinCenter, multiple data sets of thousands of proteins can be compared to display the true overlaps or differences in a matter of minutes.
This works independently of the original database source.
Thus, for instance, one can compare lists of IPI to Swiss-Prot accession keys regardless of their age as well.
This feature also can be used to compare experimental data sets with results from published scientific studies.
Filtering based on biologically relevant parameters and experimental observations simultaneously.
With ProteinCenter it is straightforward to filter data sets quickly to find the proteins of interest.
Multiple filters may be combined on both biological information and experimental data to extract the really important findings in a study (for example, to find all kinases that are up-regulated among thousands of accession codes in seconds).
