UCONN

HEALTH

Photo of Zhengqing  Ouyang

Zhengqing Ouyang

Assistant Professor, Genetics and Genome Sciences
Academic Office Location:
Jackson Labs
UConn Health
263 Farmington Avenue
Farmington, CT 06030
Phone: 860-856-2494
Email: zhengqing.ouyang@jax.org
Website(s):

Genetics & Developmental Biology Graduate Program

Ouyang Lab Page

Education
DegreeInstitutionMajor
B.S.Peking UniversityMechanics and Applied Mathematics
M.S.Peking UniversityBioinformatics
Ph.D.Stanford UniversityComputational Biology

Post-Graduate Training
TrainingInstitutionSpecialty
PostdoctoralStanford University School of MedicineGenomics and Epigenomics, Advisors: Howard Chang & Michael Snyder

Awards
Name of Award/HonorAwarding Organization
Young Investigators of the Year AwardGenome Technology Magazine
Predoctoral Training Grant, (2009-2010)California Institute for Regenerative Medicine
Innovation Award, the highest award for graduate research at Peking University Peking University
Outstanding Paper Award, Choong Shin-Piaw Physical Science ForumPeking University
World's Top 20, Certificate of DistinctionS'Star Bioinformatics Education
Best Graduate AwardBeijing City
Best Graduate AwardPeking University
Taizhao Grant, for selected undergraduate researchers at Peking University (2001-2002)Peking University
Hosogoe ScholarshipPeking University
Name & DescriptionCategoryRoleTypeScopeStart YearEnd Year
25th International Joint Conference on Artificial IntelligenceProgram Committee MemberExternalInternational2016
Invited Session “Innovative Statistical Methods in Genomics and Genetics” of the 24th Applied Statistics Symposium and 13th Graybill ConferenceOrganizerExternalInternational2015
Frontiers in Genetics and Bioengineering and BiotechnologyEditorial BoardEditorial Board MemberExternalInternational2015
Journal of Metabolomics and Systems BiologyEditorial BoardEditorial Board MemberExternalInternational2013
Biomedical Engineering and Computational BiologyEditorial BoardEditorExternalNational2011
Annals of Applied Statistics, BioinformaticsProfessional/Scientific JournalReviewerExternalNational
BMC Bioinformatics, Biomedical Engineering and Computational BiologyProfessional/Scientific JournalReviewerExternalNational
Cancer InformaticsProfessional/Scientific JournalReviewerExternalNational
Evolutionary BioinformaticsProfessional/Scientific JournalReviewerExternalNational
Molecular Biology and EvolutionProfessional/Scientific JournalReviewerExternalNational
Scientific Reports Professional/Scientific JournalReviewerExternalNational
Journal of the American Statistical AssociationReviewerExternalInternational
Annals of Applied StatisticsReviewerExternalInternational
Neural Computing and ApplicationsReviewerExternalInternational
Nucleic Acids ResearchReviewerExternalInternational
Genome BiologyReviewerExternalInternational
PLoS Computational BiologyReviewerExternalInternational
BioinformaticsReviewerExternalInternational
Nature ProtocolsReviewerExternalInternational
MethodsReviewerExternalInternational
WIREs Systems Biology and MedicineReviewerExternalInternational

The Ouyang Lab is focused on the development and application of statistical and computational methodologies in genomics. We design and develop methods and tools for analyzing data from next generation sequencing and other genomic technologies. We dissect the structure, function, and variation of chromatin, RNA, and regulatory networks. We identify genetic and epigenetic mechanisms that are predictive of complex phenotypes. Our research builds infrastructure for data science in precision genomics with applications in human health and diseases. 


Chromatin


With the vast development of next generation sequencing technologies, quantitative modeling of gene regulation at the genome-level is becoming intriguing. To understand how much gene expression variation across the genome is explained by transcription factor binding, we developed the first integrative model for joint analysis of ChIP-Seq and RNA-Seq data (Ouyang Z, Zhou Q, and Wong WH, PNAS 2009). The TF-gene association strength was defined by summing the binding peaks weighted by their intensities and the distances to transcription start sites. We then used principal component analysis and variable selection to predict genome-wide gene expression using combination of TFs. The model effectively captures combinatorial relationships among TFs. Applying the model to mouse embryonic stem cells, for the first time, we found the binding signals of 12 sequence-specific TFs have remarkably high predictive power on absolute mRNA abundance measured from RNA-Seq (r = 0.806). The model revealed combinatorial gene regulation, with some TFs acting mainly as activators, while others acting as either activators or repressors depending on the context. Ongoing research includes developing a more comprehensive framework for transcription regulation by integrative statistical modeling. 


RNA


High-throughput technologies are greatly advancing our understanding on the regulation of RNAs, especially for the large set of functionally uncharacterized noncoding RNAs. RNA regulatory information is embedded not only in the primary sequences, but also within their structures. High-throughput sequencing coupled with nuclease digestion is emerging to dissect the structures of thousands of RNAs simultaneously. We developed a computational method for genome-scale reconstruction of RNA structure integrating sequencing data (Ouyang Z, Snyder MP and Chang HY, Genome Research 2012). It incorporates sequencing signals in a high-dimensional classification framework to select stable structure models from the Boltzmann ensemble. Testing over a wide range of mRNAs and noncoding RNAs, our method was demonstrated to be more accurate and robust than traditional approaches based on free energy minimization. This was the first time that high-throughput sequencing was proved to be useful for accurate RNA structure reconstruction. Using the reconstructed RNA structure models of yeast and mammalian transcriptomes, we uncovered the diverse impact of RNA structure on translation efficiency, transcription initiation, and protein-RNA interactions. We are further investigating RNA regulation using sequence and structure information systematically.


Network


Cell fate maintenance and transition are controlled by complex gene interactions. Cell-type specific gene expression patterns suggest the dynamics of gene regulatory networks. The increased depth of genomic profiling provides opportunities to more comprehensively reconstruct gene regulatory networks and study their dynamic properties. We are interested in quantitative description and statistical inference of gene regulatory networks from high-throughput genomic data. We are also interested in gene regulatory networks at different layers, such as chromatin and epigenetic regulation. We are developing and applying methods to infer gene regulatory networks in model systems.

Accepting students for Lab Rotations: Summer '17, Fall '17, Spring '18


We welcome highly motivated rotation students joining the lab with a focus on bioinformatics and computational genomics. Please discuss with me on possible projects.


 


 


 

Journal Articles

Dissertations

  • Integrative modeling of genome-wide regulation of gene expression.
    Ouyang Z 2010 Jan;
Title or AbstractTypeSponsor/EventDate/YearLocation
Statistical modeling of RNA structurome from next generation sequencing,Statistics Colloquium, Department of Statistics2016University of Connecticut, Storrs
Statistical modeling of RNase-seq for genome-wide inference of RNA structureStatistical Modeling of Epigenomics and Gene Regulation2015Harvard University
Statistical modeling of RNase-seq for genome-wide inference of RNA structure17th Meeting of New Researchers in Statistics and Probability2015Seattle, WA
Statistical modeling of RNase-seq for genome-wide inference of RNA structure24th Applied Statistics Symposium and 13th Graybill Conference2015Fort Collins, CO
From folding to interaction: A tale of two chainsInstitute for Systems Genomics Networking Workshop2014Farmington, CT
Decoding RNA regulationTsinghua University2014Beijing, China
Decoding RNA regulationFirst International Young Scholars Systems and Synthetic Biology Symposium2014Peking University, Beijing, China
Reconstructing and interpreting the RNA structuromeDepartmental Seminar, Department of Pathobiology and Veterinary Science2014University of Connecticut, Storrs
Reconstruct the RNA structurome from sequencing dataStatistics Seminar Series, Dept of Math. Sci & Cntr for Applied Math & Stat2013New Jersey Institute of Technology
RNA genomics: A structural and sequencing overviewBME Seminar, Department of Biomedical Engineering2013University of Connecticut, Storrs
Integrating high-throughput sequencing data into RNA secondary structure reconstructionBME Seminar, Department of Biomedical Engineering2013University of Connecticut, Storrs
Statistical reconstruction of RNA secondary structure from high-throughput sequencing dataJnt Stat Conf: Intl Chinese Statistical Assoc & Intl Soc for Biopharm Stats2013Washington, D.C.
SeqFold: Accurate genome-scale RNA structure reconstruction integrating experimental measurements provides insights into gene regulationLecture20th Annual International Conference on Intelligent Systems for Molecular2012Long Beach, California
SeqFold: Accurate genome-scale RNA structure reconstruction integrating experimental measurements provides insights into gene regulationLectureBiomedical Computation at Stanford2011Stanford University, Stanford, California
Modeling combinatorial gene regulation in embryonic stem cells from high-throughput sequencingLectureCalifornia Institute of Regenerative Medicine Grantee Meeting2010San Francisco, California
Modeling combinatorial gene regulation in embryonic stem cells from high-throughput sequencingLectureConference of Stem Cell Biology and Regenerative Medicine2009Stanford, California
Predicting digital gene expression from ChiP-Seq in embryonic stem cellsLectureMolecular Profiling Colloquium2009Stanford University, Stanford, California
Statistical analysis of gene regulation from high-throughput genomic dataLectureInternational Workshop on Probability Theory, Statistics and Their Applicat2009Beijing, China
Modeling gene regulation from next generation sequencingLectureInternational Chinese Statistical Association2009San Francisco, California
Regulatory networks of embryonic stem cell pluripotency and differentiationLectureThe Feldman Seminar2008Stanford University, Stanford, California