Linked Life Data

17385748

Source:http://linkedlifedata.com/resource/pubmed/id/17385748

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2007-9-3
pubmed:abstractText
Time-series transcriptional profiling experiments are becoming increasingly popular, in light of the abundance of information regarding a biological system's regulation that they are expected to reveal. However, identification of differentially expressed genes as a function of time and comparison between physiological states based on the genes' variability in significance level over time remain intriguing tasks, due to certain limitations in the currently available algorithms. Based on the principles of significance analysis of microarrays (SAM) method, we developed an algorithm that allows for the identification of the differentially expressed genes at each time-point of a time sequence, using a common reference distribution and significance threshold for all time-points. These results are further explored in a systematic way to extract information about (a) individual gene and gene class variability in significance level with time, (b) gene and time-point correlation based on (a), and (c) gene class comparison based on (a). All algorithms have been programmed in C language in the form of four executable files for both Windows and Macintosh platforms under the overall name MiTimeS. MiTimeS was validated in the context of real transcriptomic data. It enables the extraction of biologically relevant information from the dynamic transcriptomic profiles currently unnoticed from the available algorithms. The applicability of MiTimeS is not limited to transcriptomic data, but it could be accordingly used for the analysis of dynamic data from other cellular fingerprints.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0006-3592
pubmed:author
pubmed:copyrightInfo
Copyright 2007 Wiley Periodicals, Inc.
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
98
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
668-78
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Significance analysis of time-series transcriptomic data: a methodology that enables the identification and further exploration of the differentially expressed genes at each time-point.
pubmed:affiliation
Metabolic Engineering and Systems Biology Laboratory, Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, Maryland, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't