Linked Life Data

12547420

Source:http://linkedlifedata.com/resource/pubmed/id/12547420

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2003-1-27
pubmed:abstractText
The rapid growth of bio-sequence information has resulted in an increasing demand for reliable methods that group proteins. A few databases with curated alignments of protein families have demonstrated that expert-driven repositories can keep up with the data deluge in the genome era. These original resources implicitly identify domain-like modules in proteins. An increasing number of automatic methods have sprouted over the past few years that cluster the protein universe. Many of these implicitly dissect proteins into structural domain-like fragments. In a very coarse-grained evaluation, some of the automatic methods appear to be on par with expert-driven approaches. However, neither automatic nor manual methods are currently entirely up to the challenges of tasks such as target selection in structural genomics. Thus, we urgently need refined and sustained automatic clustering tools.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1367-5931
pubmed:author
pubmed:issnType
Print
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
5-11
pubmed:dateRevised
2009-8-25
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Domains, motifs and clusters in the protein universe.
pubmed:affiliation
CUBIC and North East Structural Genomics Consortium, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Review