Areas of Research: Biomedical informatics, computational biology, systems biology and functional genomics; interfacing biomedical science with machine learning, algorithms, and systems development
- Computer Science and the Lewis-Sigler Institute for Integrative Genomics
242 Carl Icahn Laboratory
Bioinformatics and genomics
The new era of high-throughput experimental methods in molecular biology has created exciting challenges for computer science to develop novel algorithms for complex, accurate, and consistent interpretation of diverse biological information. In the next decades, large-scale explorations of complex molecular, cellular, and organismic systems at complementary levels of resolution will allow us to integrate our understanding of macroscopic physiology and microscopic biology. To realize the full potential of these developments, we need to develop sophisticated bioinformatics frameworks to integrate and synthesize diverse biological data produced by these methods.
The goal of the research in my laboratory is to bring the capabilities of computer science and statistics to the study of gene function and regulation in the biological networks through integrated analysis of biological data from diverse data sources--both existing and yet to come (e.g. from diverse gene expression data sets and proteomic studies). We are designing systematic and accurate computational and statistical algorithms for biological signal detection in high-throughput data sets. More specifically, our lab is interested in developing methods for better gene expression data processing and algorithms for integrated analysis of biological data from multiple genomic data sets and different types of data sources (e.g. genomic sequences, gene expression, and proteomics data).
My laboratory combines computational methods with an experimental component in a unified effort to develop comprehensive descriptions of genetic systems of cellular controls, including those whose malfunctioning becomes the basis of genetic disorders, such as cancer, and others whose failure might produce developmental defects in model systems. The experimental component the lab focuses on is S. cerevisiae (baker's yeast).
- Wong, AK, Krishnan, A, Troyanskaya, OG. (2018) GIANT 2.0: genome-scale integrated analysis of gene netwoks in tissues. Nucleic Acids Res, 2017 May 25. Pubmed
- Rangan, AV, et al. (2018) A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data. PLoS Comput Biol, 14(5): e1006105. Pubmed
- Clancy, T, Dannenfelser, R, Troyanskaya, O, Malmberg, KJ, Hovig, E, Kristensen, V. (2017) Bioinformatics approaches t profile the tumor microenvironment for immunotherapeutic discovery. Curr Pharm Des, 23(32): 4716-4725. Pubmed
- Dannenfelser, R, Nome M, Tahiri, A, Ursini-Siegel, J, Moen Vollan, Hk, Haakensen, VD, Helland, A, Naume, B, Caldas, C, Børresen-Dale, AL, Kristensen, VN, Troyanskaya,OG. (2017) Oncotarget, 8(34): 57121-57133. Pubmed
- Nirschi, CJ, et al. (2017) IFNy-dependent tissue-immune homeostasis is co-opted in the tumor microenvironment. Cell, 170(1): 127-141.e15. Pubmed
- Costanaz, M, et al. (2016) A global enetic interaction network maps a wiring diagram of cellular function. Science. 253(3306), piiaaf1420. Pubmed
- Krishnan, A, Zhang, R, Yao, V, Theesfeld, CL, Wong, AK, Tadych, A, Volfovsky, N, Packer, A, Lash, A, Troyanskaya, OG. (2016) Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nat. Neurosci. 2016 Aug 1 (Epub ahead of print} Pubmed
- Watson, E, Olin-Sandoval, V, Hoy, MJ, Li, CH, Louisse, T, Yao, V, Mori, A, Holdorf, AD, Troyanskaya, OG, Ralser,M, Walhout, AJ. (2016) Metabolic network rewiring of propionate flux compensates vitamin B12 deficiency in C. elegans. Elife. 5 pii: e17670. Pubmed
- Roberts, AM, Wong, AK, Fisk, I, Troyanskaya, OG. (2016) GIANT API: an application programming interface for functional genomics. Nucleic Acids Res. 44(W1): W587-92. Pubmed
- Zhou, J, Troyanskaya, OG. (2016) Probabilistic modelling of chromatin code landscape reveals functional diversity of enhancer-like chromatin states. Nat. Commun. 7: 10528. Pubmed
- Gorenshteyn, D, Zaslavsky, E, Fribourg, M, Park, CY, Wong, AK, Tadych, A, Hartman, BM, Albrecht, RA, Garcia-Sastre, A, Kleinstein, SH, Troyanskaya, OG, Sealfon, SC. (2015) Interactive big data resource to elucidate human immune pathways and diseases. Immunity. 43(3): 605-14. Pubmed
- Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 12(10): 931-4. Pubmed
- Dolinski K, Troyanskaya OG. (2015) Implications of Big Data for cell biology. Mol Biol Cell. 26(14): 2575-8. Pubmed
- Wong, AK, Krishnan A, Yao V, Tadych A, Troyanskaya OG. (2015) IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Res. pii: gkv486. Pubmed
- Goya, J, Wong AK, Yao V, Krishnan A, Homilius M, Troyanskaya OG. (2015) FNTM: a server for predicting functional networks of tissues in mouse. Nucleic Acids Res. pii: gkv443. Pubmed
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