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research.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
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<meta name="description" content="">
<meta name="author" content="">
<title>Research</title>
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<div class="col-12">
<h1 class="page-header">CSBIO Research
</h1>
</div>
</div>
<hr class="large-break">
<!-- Project One -->
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<div class="col-5">
<br>
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<br>
<a href="images/ChadGI.png">
<img class="img-fluid img-hover" src="images/ChadGI.png" alt="">
</a>
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<div class="col-7">
<h3>Genetic interactions</h3>
<p> We are developing computational methods for mapping and interpreting large-scale genetic interaction
networks in yeast and in human cells. A powerful approach to characterizing functional organization of genomes
is combinatorial genetic perturbation. While the disruption of individual genes only rarely leads to strong
phenotypes (e.g. only ~10% of the genome is essential in human cells), simultaneous disruption of combinations
of genes can often result in interpretable phenotypes. These instances, where combined disruption or mutation
of two genes leads to an unexpected phenotype, are called genetic interactions. Because of its genetic
tractability and a rich set of existing experimental tools, yeast is the premier model system for analysis of
genetic interactions. Working with the <a href="http://sites.utoronto.ca/boonelab/">Boone</a> and <a
href="http://sites.utoronto.ca/andrewslab/">Andrews</a> labs, we are developing computational methods for
mapping and analyzing global genetic interaction networks in yeast. We have recently completed the first
complete genetic interaction network for any species <a
href="http://science.sciencemag.org/content/353/6306/aaf1420.full">(Costanzo 2016)</a> and are currently
focused on mapping genetic interactions across diverse environmental conditions. </p>
<!--<a class="btn btn-primary" href="#">View Project</i></a>-->
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<hr class="small-break">
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<a href="images/CRISPR_GI.png">
<img class="img-fluid img-hover" src="images/CRISPR_GI.png" alt=" " style="max-height: 300px">
</a>
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<div class="col-7">
<h3>Genetic interaction analysis in human cells</h3>
<p>We work towards a global reference map of human gene function. In model organisms, genetic interaction
mapping and network analysis can elucidate the function for every gene in the genome. Together with the <a
href="http://moffatlab.ccbr.utoronto.ca/index.html">Moffat</a>, <a
href="http://sites.utoronto.ca/boonelab/">Boone</a> and <a
href="http://sites.utoronto.ca/andrewslab/">Andrews</a> labs at the Donnelly Centre for Cellular and
Biomolecular Research in Toronto, we generate a genome-wide map of genetic interactions in a human cell line.
Using CRISPR/Cas9 screening, we test effects of simultaneous mutations in two genes across millions of gene
pairs. To represent genetic interaction information of all possible ~180,000,000 combinations, we develop
methods to predict and screen gene pairs that have the best chance of interacting. We also develop methods to
identify genetic interactions from CRISPR/Cas9 screening data and integrate other genomic data to generate a
global atlas of human gene function.</p>
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<a href="images/BRIDGE.png">
<img class="img-fluid img-hover" src="images/BRIDGE.png" alt="">
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<h3>Genetic interactions in complex human diseases</h3>
<p>Almost all complex human diseases have a genetic component. Understanding how genetic variants contribute to
the development of these diseases can be invaluable to our society. It can provide guidance to disease
diagnosis, inform genetic-centric strategies for disease prevention, and stimulate the development of new and
more effective drugs. Although our knowledge and understanding of the genetics that underlie complex diseases
has advanced substantially in recent years, due to the complexity of most human diseases, the precise genetic
causes of the large majority of diseases are still unclear. For instance, in most diseases, the discovered
single genetic variants identified by genome-wide association studies (GWAS) only explain a small fraction of
the estimated total heritable disease risk derived from familial aggregation studies, suggesting there are
still genetic factors that are not well-understood. Genetic interactions have been reported to underlie
phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans is
significantly understudied. This is mainly because existing methods for identifying them focus on testing
individual locus pairs, which undermines statistical power. We are working on leveraging insights from genetic
interaction screens in model organism to develop methods to discover interactions from human population
genetic data. More specifically, we developed a computational approach called BridGE that identifies pathways
connected by genetic interactions from GWAS data. We examined this method with many complex human diseases and
showed it can be used as a general framework for mapping complex genetic networks underlying human disease
from genome-wide genotype data.</p>
<!--<a class="btn btn-primary" href="#">View Project</i></a>-->
</div>
</div>
<hr class="small-break">
<!-- Project Two -->
<div class="row">
<div class="col-5">
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<a href="images/compartmental.png">
<img class="img-fluid img-hover" src="images/compartmental.png" alt="">
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<div class="col-7">
<h3>Modeling plant immune response</h3>
<p>In collaboration with the Katagiri lab here at UMN, we are developing novel computational approaches to
interpret dynamic transcriptome data in Arabidopsis generated during the plant’s immune response. Although
clustering methods can help group genes based on the profile similarity, they fail to provide a mechanistic
understanding of the gene clusters thus undermine the information within time-series data. In our research, we
use multi-compartment models to generate dynamic profiles with fast and slow response patterns. Based on how
well the time-series transcript data of a gene are fit, we can assign the gene to the compartment with the
optimal response pattern. We find gene clusters enriched for transcription factor motifs that regulate early
and late immune response. Our approach provides a mechanistic way to classify immune response genes and allows
us to better interpret Arabidopsis transcriptome dynamics.
</p>
</div>
</div>
<hr class="small-break">
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<div class="row">
<div class="col-5">
<br>
<a href="images/chemical_genomics_press.jpg">
<img class="img-fluid img-hover" src="images/chemical_genomics_press.jpg" alt="">
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<h3>Chemical genomics</h3>
<p>We are screening chemical libraries against the Saccharomyces Cerevisiae mutant collections in order to
understand the mode-of-action of compounds. This research reduces the time and expense required for the
creation of clinically relevant compounds. With our collaborators in the Boone Lab at the University of
Toronto and at RIKEN in Japan, we have developed an ultra high-throughput chemical-genomics assay that allows
the prediction of a compound’s gene- and process-level targets across the entire genome, filling a critical
gap in the way compounds are screened for bioactivity. This methodology was applied to screen more than 13,000
compounds within yeast with diverse origins. From these data, we identified compounds with novel targets as
well as the general cellular functions that tend to be disrupted by the compounds from diverse collections.
In collaboration with the Bielinsky Lab, we are translating this chemical genomics work from yeast to human
cells. CRISPR-Cas9 technology enables chemical-genetic screening in human cell lines, allowing us to
interrogate chemical-genetic interactions across a library of defined deletion mutants. Current work focuses
on developing analyses pipelines to identify chemical-genetic interactions and predict genetic targets or
bioprocesses perturbed by a chemical compound. Using these screens, we can more efficiently identify candidate
drug therapeutics and build a comprehensive drug library to help realize precision medicine.</p>
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