Assessing the Evolution of Gene Expression Using Microarray Data
Classical studies of the evolution of gene function have predominantly focused on mutations within protein coding regions. With the advent of microarrays, however, it has become possible to evaluate the transcriptional activity of a gene as an additional characteristic of function. Recent studies ha...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2008-01-01
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| Series: | Evolutionary Bioinformatics |
| Online Access: | https://doi.org/10.4137/EBO.S628 |
| _version_ | 1819037454256046080 |
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| author | Owen Z. Woody Andrew C. Doxey Brendan J. McConkey |
| author_facet | Owen Z. Woody Andrew C. Doxey Brendan J. McConkey |
| author_sort | Owen Z. Woody |
| collection | DOAJ |
| description | Classical studies of the evolution of gene function have predominantly focused on mutations within protein coding regions. With the advent of microarrays, however, it has become possible to evaluate the transcriptional activity of a gene as an additional characteristic of function. Recent studies have revealed an equally important role for gene regulation in the retention and evolution of duplicate genes. Here we review approaches to assessing the evolution of gene expression using microarray data, and discuss potential influences on expression divergence. Currently, there are no established standards on how best to identify and quantify instances of expression divergence. There have also been few efforts to date that incorporate suspected influences into mathematical models of expression divergence. Such developments will be crucial to a comprehensive understanding of the role gene duplications and expression evolution play in the emergence of complex traits and functional diversity. An integrative approach to gene family evolution, including both orthologous and paralogous genes, has the potential to bring strong predictive power both to the functional annotation of extant proteins and to the inference of functional characteristics of ancestral gene family members. |
| first_indexed | 2024-12-21T08:21:40Z |
| format | Article |
| id | doaj.art-60b5ce890585456e9d921b00410efb51 |
| institution | Directory Open Access Journal |
| issn | 1176-9343 |
| language | English |
| last_indexed | 2024-12-21T08:21:40Z |
| publishDate | 2008-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Evolutionary Bioinformatics |
| spelling | doaj.art-60b5ce890585456e9d921b00410efb512022-12-21T19:10:26ZengSAGE PublishingEvolutionary Bioinformatics1176-93432008-01-01410.4137/EBO.S628Assessing the Evolution of Gene Expression Using Microarray DataOwen Z. Woody0Andrew C. Doxey1Brendan J. McConkey2Department of Biology, University of Waterloo, Waterloo, Ontario CanadaDepartment of Biology, University of Waterloo, Waterloo, Ontario CanadaDepartment of Biology, University of Waterloo, Waterloo, Ontario CanadaClassical studies of the evolution of gene function have predominantly focused on mutations within protein coding regions. With the advent of microarrays, however, it has become possible to evaluate the transcriptional activity of a gene as an additional characteristic of function. Recent studies have revealed an equally important role for gene regulation in the retention and evolution of duplicate genes. Here we review approaches to assessing the evolution of gene expression using microarray data, and discuss potential influences on expression divergence. Currently, there are no established standards on how best to identify and quantify instances of expression divergence. There have also been few efforts to date that incorporate suspected influences into mathematical models of expression divergence. Such developments will be crucial to a comprehensive understanding of the role gene duplications and expression evolution play in the emergence of complex traits and functional diversity. An integrative approach to gene family evolution, including both orthologous and paralogous genes, has the potential to bring strong predictive power both to the functional annotation of extant proteins and to the inference of functional characteristics of ancestral gene family members.https://doi.org/10.4137/EBO.S628 |
| spellingShingle | Owen Z. Woody Andrew C. Doxey Brendan J. McConkey Assessing the Evolution of Gene Expression Using Microarray Data Evolutionary Bioinformatics |
| title | Assessing the Evolution of Gene Expression Using Microarray Data |
| title_full | Assessing the Evolution of Gene Expression Using Microarray Data |
| title_fullStr | Assessing the Evolution of Gene Expression Using Microarray Data |
| title_full_unstemmed | Assessing the Evolution of Gene Expression Using Microarray Data |
| title_short | Assessing the Evolution of Gene Expression Using Microarray Data |
| title_sort | assessing the evolution of gene expression using microarray data |
| url | https://doi.org/10.4137/EBO.S628 |
| work_keys_str_mv | AT owenzwoody assessingtheevolutionofgeneexpressionusingmicroarraydata AT andrewcdoxey assessingtheevolutionofgeneexpressionusingmicroarraydata AT brendanjmcconkey assessingtheevolutionofgeneexpressionusingmicroarraydata |