Sara Mostafavi

Sara Mostafavi

Visit Dr. Mostafavi's UBC Statistics department website here.

"Research at the Mostafavi lab focuses on developing computational and statistical approaches for integrating and interpreting diverse types of genomics data, with the ultimate goal of disentangling meaningful molecular associations for common and complex pathologies, such as neurodegenerative and psychiatric disorders."

The availability of a wide range of high-throughput biological assays enable us to measure parts of biological systems at various cellular resolutions—for example, at the genome, epigenome, transcriptome, and proteome levels. The ability to capture several views of biological systems provides a unique opportunity to derive interpretable, cascading models that link the genomic sequence to gene expression, and then to more downstream cellular phenotypes that govern cell behaviour. At the same time, using these multiple resolutions, we also have an opportunity to disentangle various factors that underlie a given disease. For example, the combination of genotyping and epigenomic data can summarize the effect of genetic factors, environmental factors, and interactions between the genetic and the environmental factors that act on a cellular or organismal level.

However, interpreting these high-throughput data in the context of a given disease requires addressing significant computational and statistical challenges that stem from scale, heterogeneity, and the abundance of technological and biological confounding factors. In this context, our lab is interested in developing and applying scalable machine learning approaches for integrating heterogeneous data types, while modeling and removing the effect of known and hidden confounding factors. This work is supported by our parallel efforts in constructing gene regulatory networks and predicting gene function, enabling systematic interpretation of disease associated genetic disruptions.

MAJOR ACHIEVEMENTS & PUBLICATIONS

Canada Research Chair II 2015-2020

Canadian Institute for Advanced Research Scholar Award – 2015

Mostafavi S, Yoshida H, Moodley D, LeBoité H, Rothamel K, Raj T, Ye CJ, Chevrier N, Zhang SY, Feng T, Lee M, Casanova JL, Clark JD, Hegen M, Telliez JP, Hacohen N, De Jager PL, Regev A, Mathis D, Benoist C and the Immunological Genome Project Consortium. Parsing the interferon transcriptional network and its disease associations. Cell. 28;164(3):564-78.

Pierson E; GTEx Consortium, Koller D, Battle A, Mostafavi S. Sharing and Specificity of Co-expression Networks across 35 Human Tissues. PLoS Comput Biol. 11(5):e1004220.

Mostafavi S, Battle A, Zhu X, Urban AE, Levinson D, Montgomery SB, Koller D. (2013). Normalizing RNA-sequencing data by modeling hidden covariates with prior knowledge. PLoS ONE. 8 (7): e68141.

Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MW, Mc-Cormick C, Haudenschild CD, Beckman K, Shi J, Mei R, Urban AE, Montgomery SB, Levinson D, Koller D. (2013). Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Research. gr.155192.113.

Raj T, Rothamel K, Mostafavi S, Ye C, Lee M, Replogle J, Von Korff A, Imboya S, McCabe C, Okada Y, Patsapolous N, Lee M, Wood I, Mathis D, Hafler D, Koller D, Regev A, Hacohen N, Benoist C*, Stranger BE*, De Jager PL*. (2014) Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344(6183).

Mostafavi S, Battle A, Zhu X, Potash JB, Weissman MW, Shi J, Beckman K, Haudenschild C, McCormick C, Mei R, Gameroff MJ, Gindes H, Adams P, Goes FS, Mondimore FM, MacKinnon D, Notes L, Schweizer B, Furman D, Montgomery SB, Urban AE, Koller D, Levinson D. (2013). Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing. Molecular Psychiatry. mp.2013.161

Mostafavi S, Goldenberg A, Morris Q. (2012). Labeling nodes using three degrees of propagation. PLoS ONE 7(12): e51947.

Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q. (2008) GeneMANIA: A real-time multiple association network integration algorithm for predicting gene function. Genome Biology. 9 (Suppl 1):S4.