Linked Life Data

17193296

Source:http://linkedlifedata.com/resource/pubmed/id/17193296

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2006-12-28
pubmed:abstractText
Cluster analysis of gas-chromatographic (GC) data of ca. 500 bacterial isolates was used as an aid in detection and identification of new natural compounds. This approach reduces the number of GC/MS analysis (dereplication) and concomitantly improves the selection of samples with high probability to contain unknown natural products. Lipophilic bacterial extracts were derivatized and analyzed by GC under standardized conditions. A program was developed to convert chromatographic data into a two-dimensional matrix. Based on the results of hierarchical cluster analysis samples were selected for further investigation by GC/MS and NMR. This approach avoided unnecessary analysis of similar samples. By this method, the unusual oligoprenylsesquiterpenes 1 and 2 as well as new aromatic amides 7 and 8 were identified.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1612-1880
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
622-34
pubmed:dateRevised
2011-11-17
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Cluster analysis as selection and dereplication tool for the identification of new natural compounds from large sample sets.
pubmed:affiliation
Institute of Organic Chemistry, Technical University of Braunschweig, Hagenring 30, D-38106 Braunschweig.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't