Database Commons
Database Commons

a catalog of worldwide biological databases

Database Profile

Sentra

General information

URL: http://wit.mcs.anl.gov/WIT2/Sentra
Full name:
Description: Sentra is a database of signal transduction proteins with the emphasis on microbial signal transduction. The database was updated to include classes of signal transduction systems modulated by either phosphorylation or methylation reactions such as PAS proteins and serine/threonine kinases, as well as the classical two-component histidine kinases and methyl-accepting chemotaxis proteins.
Year founded: 2000
Last update:
Version:
Accessibility:
Unaccessible
Country/Region: United Kingdom

Classification & Tag

Data type:
Data object:
NA
Database category:
Major species:
NA
Keywords:

Contact information

University/Institution: Argonne National Laboratory
Address: Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439, USA.
City:
Province/State:
Country/Region: United Kingdom
Contact name (PI/Team): Maltsev N
Contact email (PI/Helpdesk): maltsev@mcs.anl.gov

Publications

17135204
Sentra: a database of signal transduction proteins for comparative genome analysis. [PMID: 17135204]
D'Souza M, Glass EM, Syed MH, Zhang Y, Rodriguez A, Maltsev N, Galperin MY.

Sentra (http://compbio.mcs.anl.gov/sentra), a database of signal transduction proteins encoded in completely sequenced prokaryotic genomes, has been updated to reflect recent advances in understanding signal transduction events on a whole-genome scale. Sentra consists of two principal components, a manually curated list of signal transduction proteins in 202 completely sequenced prokaryotic genomes and an automatically generated listing of predicted signaling proteins in 235 sequenced genomes that are awaiting manual curation. In addition to two-component histidine kinases and response regulators, the database now lists manually curated Ser/Thr/Tyr protein kinases and protein phosphatases, as well as adenylate and diguanylate cyclases and c-di-GMP phosphodiesterases, as defined in several recent reviews. All entries in Sentra are extensively annotated with relevant information from public databases (e.g. UniProt, KEGG, PDB and NCBI). Sentra's infrastructure was redesigned to support interactive cross-genome comparisons of signal transduction capabilities of prokaryotic organisms from a taxonomic and phenotypic perspective and in the framework of signal transduction pathways from KEGG. Sentra leverages the PUMA2 system to support interactive analysis and annotation of signal transduction proteins by the users.

Nucleic Acids Res. 2007:35(Database issue) | 19 Citations (from Europe PMC, 2026-03-28)
10592266
SENTRA, a database of signal transduction proteins. [PMID: 10592266]
D'Souza M, Romine MF, Maltsev N.

SENTRA, available via URL http://wit.mcs.anl.gov/WIT2/Sentra/, is a database of proteins associated with microbial signal transduction. The database currently includes the classical two-component signal transduction pathway proteins and methyl-accepting chemotaxis proteins, but will be expanded to also include other classes of signal transduction systems that are modulated by phosphorylation or methylation reactions. Although the majority of database entries are from prokaryotic systems, eukaroytic proteins with bacterial-like signal transduction domains are also included. Currently SENTRA contains signal transduction proteins in 34 complete and almost completely sequenced prokaryotic genomes, as well as sequences from 243 organisms available in public databases (SWISS-PROT and EMBL). The analysis was carried out within the framework of the WIT2 system, which is designed and implemented to support genetic sequence analysis and comparative analysis of sequenced genomes.

Nucleic Acids Res. 2000:28(1) | 5 Citations (from Europe PMC, 2026-04-04)

Ranking

All databases:
5325/6932 (23.197%)
Gene genome and annotation:
1601/2039 (21.53%)
5325
Total Rank
24
Citations
0.923
z-index

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Record metadata

Created on: 2018-02-08
Curated by:
huma shireen [2018-09-04]
Zhaohua Li [2018-02-24]
Pei Wang [2018-02-08]