Reference number : GC-2017-Q1-03467, amendment number : , agreement type : G, recipient legal name : ZHANG, ZHAOLEI (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The human genome contains over 22,000 genes, which remarkably is not much more than the single cell budding yeast or a fruit fly. So what makes humans so much more complex? The answer lies in the immensely complex and delicate ways by which the human genes are regulated or turned on and off. Glitches in these regulations often result in protein products being made at a wrong amount or at wrong time or wrong place, which in turn can cause diseases such as cancer. In order to better understand how human cells work and function, it is crucial to understand how each of the 22,000 genes is regulated and how groups of genes are turned on and off to fulfill the required cellular task. 
 
Supported by a previous Discovery Grant, my students and I have made significant progress in developing computing methods in studying gene regulation and genetic variations. For example, we developed a suite of software tools that can accurately predict microRNA target genes and identify miRNA regulations that are important in human diseases. We also developed algorithms that can integrate transcription factors (TFs), microRNAs and other regulation elements into a comprehensive regulatory network, and computational tools for analyzing such network. 
 
In the next funded period, we will further improve and extend these algorithms so that they can run efficiently and quickly on a genomic scale. Our software tool will be able to quickly scan genome sequences and identify mutations that disrupt regulatory elements. These tools will be able to integrate different type of regulatory elements, such as microRNAs, transcription factors (TF), RNA binding proteins (RBP), RNA structure, and DNA or RNA methylation, into one comprehensive network model. Interestingly many of these regulatory mechanisms can cross-talk to each other. For example, microRNAs and RBPs can compete for the same target position in the 3&rsquo;UTR, and methylation in these target sites can mask the binding by microRNAs or RBPs. Our integrated regulatory network that will help researchers to quickly and accurately identify and prioritize mutations or mutated regulations. 
 
With the rapid advancement of next generation sequencing technology and reduction of sequencing cost, we envision that tens of thousands or even millions of human genome sequence will become available in the next years. Fast, efficient, and accurate computational tools will be needed to analyze these genomes and identify actionable mutations. The methodology that we are developing will help to fill these needs., program purpose french : The human genome contains over 22,000 genes, which remarkably is not much more than the single cell budding yeast or a fruit fly. So what makes humans so much more complex? The answer lies in the immensely complex and delicate ways by which the human genes are regulated or turned on and off. Glitches in these regulations often result in protein products being made at a wrong amount or at wrong time or wrong place, which in turn can cause diseases such as cancer. In order to better understand how human cells work and function, it is crucial to understand how each of the 22,000 genes is regulated and how groups of genes are turned on and off to fulfill the required cellular task. 
 
Supported by a previous Discovery Grant, my students and I have made significant progress in developing computing methods in studying gene regulation and genetic variations. For example, we developed a suite of software tools that can accurately predict microRNA target genes and identify miRNA regulations that are important in human diseases. We also developed algorithms that can integrate transcription factors (TFs), microRNAs and other regulation elements into a comprehensive regulatory network, and computational tools for analyzing such network. 
 
In the next funded period, we will further improve and extend these algorithms so that they can run efficiently and quickly on a genomic scale. Our software tool will be able to quickly scan genome sequences and identify mutations that disrupt regulatory elements. These tools will be able to integrate different type of regulatory elements, such as microRNAs, transcription factors (TF), RNA binding proteins (RBP), RNA structure, and DNA or RNA methylation, into one comprehensive network model. Interestingly many of these regulatory mechanisms can cross-talk to each other. For example, microRNAs and RBPs can compete for the same target position in the 3&rsquo;UTR, and methylation in these target sites can mask the binding by microRNAs or RBPs. Our integrated regulatory network that will help researchers to quickly and accurately identify and prioritize mutations or mutated regulations. 
 
With the rapid advancement of next generation sequencing technology and reduction of sequencing cost, we envision that tens of thousands or even millions of human genome sequence will become available in the next years. Fast, efficient, and accurate computational tools will be needed to analyze these genomes and identify actionable mutations. The methodology that we are developing will help to fill these needs., agreement title english : Computational Modeling of Gene Regulation, agreement title french : Computational Modeling of Gene Regulation, agreement number : RGPIN, agreement value : 130000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada