Canada Spends Logo

Explore our Budget 2026 analysis

Grant: Discovery Grants Program - Individual

University of British Columbia

$35,700.00

Computational analysis of regulatory DNA sequences controlling gene transcription

FY 2013-14

Summary
All cells and organisms possess a DNA-based set of information that describes the proteins that can be constructed, as well as information for making RNA molecules that perform important roles in the biochemical processes of life. The information present in the DNA sequences is static -cells have the same DNA present without regard to the place, time or conditions in which the cell exists. To respond to the environment, cells selectively turn on or off (or up or down) genes, allowing production of RNA and proteins suitable for the conditions encountered. For instance, yeast producing alcohol from sugar in wine fermentation turn on genes that facilitate this process. The research proposal addresses the interdisciplinary application of computers to the analysis of the regulation of genes, with a specific emphasis on the transition of information from DNA into RNA (the process called transcription). The research program includes three majn areas of activity. First, software is developed to allow for the identification of specific DNA sequences that appear near genes that are turned on in a specific condition. Finding these ""on/off switches"" is a first step in deciphering how cells respond to changing conditions. This approach builds on artificial intelligence methods for pattern recognition. In the second step, we address non-random properties of DNA that complicate the analysis. In many organisms, genes that are ""always on"" have different mixtures of A,C,G and T, compared to genes that only turn on when needed. Searching for patterns is complicated by this mixture difference. Solving this seemingly minor problem could greatly improve the interpretation of expensive laboratory-generated data from across the life sciences. Linking of the on/off switch DNA sequences to the proteins that ""flip the switch"" is the focus of the third aim. We will computationally analyze text from published papers to predict which proteins control the process. The research program impacts research across all of life sciences, from agriculture to bioengineering, and from microbiology to biochemistry.
Program
Discovery Grants Program - Individual
Location
British Columbia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
355532-2010
Name nom
Wasserman, Wyeth
Department département
Medical Genetics
Organization id
2
Institution établissement
University of British Columbia
Province en
British Columbia
Province fr
Colombie-Britannique
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2013
Competition year année de concours
2010
Award amount
35700
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
1201
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Life sciences (including biotechnology)
Area of application fr
Sciences de la vie (y compris biotechnologie)
Research subject code
5303
Research subject group en
Molecular biology
Research subject group fr
Biologie moléculaire
Research subject en
Gene regulation
Research subject fr
Régulation des gènes
Partie
4
Nb partie
5
Application title
Computational analysis of regulatory DNA sequences controlling gene transcription
Keyword
NSERC
Application summary
All cells and organisms possess a DNA-based set of information that describes the proteins that can be constructed, as well as information for making RNA molecules that perform important roles in the biochemical processes of life. The information present in the DNA sequences is static -cells have the same DNA present without regard to the place, time or conditions in which the cell exists. To respond to the environment, cells selectively turn on or off (or up or down) genes, allowing production of RNA and proteins suitable for the conditions encountered. For instance, yeast producing alcohol from sugar in wine fermentation turn on genes that facilitate this process. The research proposal addresses the interdisciplinary application of computers to the analysis of the regulation of genes, with a specific emphasis on the transition of information from DNA into RNA (the process called transcription). The research program includes three majn areas of activity. First, software is developed to allow for the identification of specific DNA sequences that appear near genes that are turned on in a specific condition. Finding these ""on/off switches"" is a first step in deciphering how cells respond to changing conditions. This approach builds on artificial intelligence methods for pattern recognition. In the second step, we address non-random properties of DNA that complicate the analysis. In many organisms, genes that are ""always on"" have different mixtures of A,C,G and T, compared to genes that only turn on when needed. Searching for patterns is complicated by this mixture difference. Solving this seemingly minor problem could greatly improve the interpretation of expensive laboratory-generated data from across the life sciences. Linking of the on/off switch DNA sequences to the proteins that ""flip the switch"" is the focus of the third aim. We will computationally analyze text from published papers to predict which proteins control the process. The research program impacts research across all of life sciences, from agriculture to bioengineering, and from microbiology to biochemistry.