Dynamic covariation between gene expression and genome characteristics.
Kivioja, Teemu ; Tiirikka, Timo ; Siermala, Markku and Vihinen, Mauno LU
(2008)
In Gene
410(1).
p.53-66
- Abstract
- Gene and protein expression is controlled so that cells can react to changing intra- and extracellular signals by modulating biochemical networks and pathways. We have previously shown that gene expression and the properties of expressed proteins are dynamically correlated. Here we investigated correlations between gene related parameters and gene expression patterns, and found statistically significant correlations in microarray datasets for different cell types, organisms and processes, including human B and T cell stimulation, cell cycle in HeLa cells, infection in intestinal epithelial cells, Drosophila melanogaster life span, and Saccharomyces cerevisiae cell cycle. Our method was applied to time course datasets individually for each... (More)
- Gene and protein expression is controlled so that cells can react to changing intra- and extracellular signals by modulating biochemical networks and pathways. We have previously shown that gene expression and the properties of expressed proteins are dynamically correlated. Here we investigated correlations between gene related parameters and gene expression patterns, and found statistically significant correlations in microarray datasets for different cell types, organisms and processes, including human B and T cell stimulation, cell cycle in HeLa cells, infection in intestinal epithelial cells, Drosophila melanogaster life span, and Saccharomyces cerevisiae cell cycle. Our method was applied to time course datasets individually for each time point. We derived from sequence information numerous parameters for nucleotide composition, two-base composition, codon usage, skew parameters, and codon bias. In addition to coding regions, we also investigated correlations for complete genes and introns. Significant dynamic correlations were identified for each of the analyses. Our method also proved useful for detecting dynamic shifts in gene expression profiles, such as in the D. melanogaster dataset. Detection of changes in the properties of expressed genes and proteins might be useful for predicting or following biological processes, responses, growth, differentiation and possibly in related disorders. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3635195
- author
- Kivioja, Teemu
; Tiirikka, Timo
; Siermala, Markku
and Vihinen, Mauno
LU
- publishing date
- 2008
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Drosophila melanogaster: genetics, Saccharomyces cerevisiae: cytology, Saccharomyces cerevisiae: genetics
- in
- Gene
- volume
- 410
- issue
- 1
- pages
- 53 - 66
- publisher
- Elsevier
- external identifiers
-
- pmid:18191345
- scopus:40649092037
- pmid:18191345
- ISSN
- 1879-0038
- DOI
- 10.1016/j.gene.2007.11.018
- language
- English
- LU publication?
- no
- id
- a93226ac-669a-462d-a3f1-0eac7c8347d7 (old id 3635195)
- alternative location
- http://www.ncbi.nlm.nih.gov/pubmed/18191345?dopt=Abstract
- date added to LUP
- 2016-04-04 09:31:15
- date last changed
- 2022-01-29 18:16:21
@article{a93226ac-669a-462d-a3f1-0eac7c8347d7,
abstract = {{Gene and protein expression is controlled so that cells can react to changing intra- and extracellular signals by modulating biochemical networks and pathways. We have previously shown that gene expression and the properties of expressed proteins are dynamically correlated. Here we investigated correlations between gene related parameters and gene expression patterns, and found statistically significant correlations in microarray datasets for different cell types, organisms and processes, including human B and T cell stimulation, cell cycle in HeLa cells, infection in intestinal epithelial cells, Drosophila melanogaster life span, and Saccharomyces cerevisiae cell cycle. Our method was applied to time course datasets individually for each time point. We derived from sequence information numerous parameters for nucleotide composition, two-base composition, codon usage, skew parameters, and codon bias. In addition to coding regions, we also investigated correlations for complete genes and introns. Significant dynamic correlations were identified for each of the analyses. Our method also proved useful for detecting dynamic shifts in gene expression profiles, such as in the D. melanogaster dataset. Detection of changes in the properties of expressed genes and proteins might be useful for predicting or following biological processes, responses, growth, differentiation and possibly in related disorders.}},
author = {{Kivioja, Teemu and Tiirikka, Timo and Siermala, Markku and Vihinen, Mauno}},
issn = {{1879-0038}},
keywords = {{Drosophila melanogaster: genetics; Saccharomyces cerevisiae: cytology; Saccharomyces cerevisiae: genetics}},
language = {{eng}},
number = {{1}},
pages = {{53--66}},
publisher = {{Elsevier}},
series = {{Gene}},
title = {{Dynamic covariation between gene expression and genome characteristics.}},
url = {{http://dx.doi.org/10.1016/j.gene.2007.11.018}},
doi = {{10.1016/j.gene.2007.11.018}},
volume = {{410}},
year = {{2008}},
}