Olga G. Troyanskaya

Contact
ogt@princeton.eduResearch Area
Genetics & GenomicsResearch Focus
Bioinformatics and genomicsThe 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).
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Rationale and design of the Kidney Precision Medicine Project. Kidney Int. 2021 ;99(3):498-510. .
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Genome-wide landscape of RNA-binding protein target site dysregulation reveals a major impact on psychiatric disorder risk. Nat Genet. 2021 ;53(2):166-173. .
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Modeling molecular development of breast cancer in canine mammary tumors. Genome Res. 2020 ;. .
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Presenilin 1 phosphorylation regulates amyloid-β degradation by microglia. Mol Psychiatry. 2020 ;. .
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SARS-CoV-2 Transmission among Marine Recruits during Quarantine. N Engl J Med. 2020 ;383(25):2407-2416. .
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Genomic RNA Elements Drive Phase Separation of the SARS-CoV-2 Nucleocapsid. Mol Cell. 2020 ;80(6):1078-1091.e6. .
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SARS-CoV-2 receptor networks in diabetic and COVID-19-associated kidney disease. Kidney Int. 2020 ;98(6):1502-1518. .
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Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies. Cell Syst. 2020 ;11(3):215-228.e5. .
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Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis. Neuron. 2020 ;107(5):821-835.e12. .
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Genomic analyses implicate noncoding de novo variants in congenital heart disease. Nat Genet. 2020 ;52(8):769-777. .
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RNA Identification of PRIME Cells Predicting Rheumatoid Arthritis Flares. N Engl J Med. 2020 ;383(3):218-228. .
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Machine learning, the kidney, and genotype-phenotype analysis. Kidney Int. 2020 ;97(6):1141-1149. .
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Mapping the physiological and molecular markers of stress and SSRI antidepressant treatment in S100a10 corticostriatal neurons. Mol Psychiatry. 2020 ;25(5):1112-1129. .
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Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight. 2020 ;5(6). .
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Subtype-specific transcriptional regulators in breast tumors subjected to genetic and epigenetic alterations. Bioinformatics. 2020 ;36(4):994-999. .
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Lack of a site-specific phosphorylation of Presenilin 1 disrupts microglial gene networks and progenitors during development. PLoS One. 2020 ;15(8):e0237773. .
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Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development. PLoS Genet. 2019 ;15(9):e1008382. .
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Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nat Genet. 2019 ;51(6):973-980. .
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Selene: a PyTorch-based deep learning library for sequence data. Nat Methods. 2019 ;16(4):315-318. .
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A Computational Framework for Genome-wide Characterization of the Human Disease Landscape. Cell Syst. 2019 ;8(2):152-162.e6. .
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Organoid single cell profiling identifies a transcriptional signature of glomerular disease. JCI Insight. 2019 ;4(1). .
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Interpretation of an individual functional genomics experiment guided by massive public data. Nat Methods. 2018 ;15(12):1049-1052. .
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Enabling Precision Medicine through Integrative Network Models. J Mol Biol. 2018 ;430(18 Pt A):2913-2923. .
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Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet. 2018 ;50(8):1171-1179. .
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GIANT 2.0: genome-scale integrated analysis of gene networks in tissues. Nucleic Acids Res. 2018 ;46(W1):W65-W70. .
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GIANT API: an application programming interface for functional genomics. Nucleic Acids Res. 2016 ;44(W1):W587-92. .
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Probabilistic modelling of chromatin code landscape reveals functional diversity of enhancer-like chromatin states. Nat Commun. 2016 ;7:10528. .
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Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nat Neurosci. 2016 ;19(11):1454-1462. .
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A global genetic interaction network maps a wiring diagram of cellular function. Science. 2016 ;353(6306). .
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Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases. Immunity. 2015 ;43(3):605-14. .
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Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015 ;12(10):931-4. .
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Low-variance RNAs identify Parkinson's disease molecular signature in blood. Mov Disord. 2015 ;30(6):813-21. .
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Implications of Big Data for cell biology. Mol Biol Cell. 2015 ;26(14):2575-8. .
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FNTM: a server for predicting functional networks of tissues in mouse. Nucleic Acids Res. 2015 ;43(W1):W182-7. .