Linked Life Data

17602772

Source:http://linkedlifedata.com/resource/pubmed/id/17602772

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-2
pubmed:dateCreated
2007-11-12
pubmed:abstractText
Understanding dynamic processes and diversity in microbial communities is of key importance for combating pathogens and for stimulating beneficial bacteria. We have addressed these challenges utilising multivariate statistics for analyses of microbial community structures. We based our microbial community analyses on 16S rRNA gene data. This gene is by far the most widely applied genetic marker for phylogenetic and microbial community studies. Both probe and clone library data were analysed. We analysed the clone library data using a newly developed coordinate-based phylogenetic approach. By using coordinates, we avoid both DNA sequence alignments and the need for definition of operational taxonomic units (OTUs). The basic principle is to transform the sequence data to frequencies of multimers (short sequences of n=2 to 6), and then to use principal component analyses (PCA) for data compression into an orthogonal coordinate space. We used our coordinate method for global 16S rRNA gene analyses of prokaryotes. When comparing microbial communities, it is often important to determine the relationship between the microflora and knowledge about the samples analysed. We used partial least square regression (PLSR) to relate physical/chemical properties to microbial community composition. This was done by analysing both probe and clone library data using the effect of modified atmosphere packaging (MAP) on fish microflora as an example. We are currently investigating approaches to describe dynamic microbial community interactions. Our ultimate goal is to understand and model the main dynamic interactions in complete microbial communities.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0168-1605
pubmed:author
pubmed:issnType
Print
pubmed:day
30
pubmed:volume
120
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
95-9
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Use of multivariate statistics for 16S rRNA gene analysis of microbial communities.
pubmed:affiliation
MATFORSK Norwegian Food Research Institute, Osloveien 1, NO-1430 As, Norway. knut.rudi@matforsk.no
pubmed:publicationType
Journal Article, Review, Research Support, Non-U.S. Gov't