Peng Jiang, PhD
 Title: Assistant Professor
 Dept: Biological, Geological and Environmental Sciences
 Address: 2121 Euclid Ave. , Cleveland, OH 44115

Courses Taught


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Research Keywords:
# Data Science Computational Biology, Bioinformatics, Biostatistics, Machine Learning, Software Development # Molecular Biology Alternative splicing, Gene regulation, RNA-seq, CLIP-seq # Developmental & Regenerative Biology Muscle & Bone regeneration
Ph.D., Bioinformatics/Biostatistics, Southeast University, China, 2007
B.S., Biomedical Engineering, Southeast University, China, 2003
Brief Bio:
My research is centered on developing statistical methods and data-driven approaches to leverage massive genomic and transcriptomic data to investigate the temporal-spatial variation and dynamics of gene regulation, understanding and tackling the heterogeneity of cellular populations, complex traits and diseases. I have authored 41 papers (17 corresponding or first authors) and served as Principal Investigator or Co-Investigator leading the bioinformatic and statistical methods development and applications in numerous federally funded research projects, including Defense Advanced Research Projects Agency (DARPA) and NIH.
Research Interests:
My long-term goal is to develop computational algorithms and tools to model multi-omics data (e.g., genomics, transcriptomics, and epigenetics) and discover temporal-spatial patterns that are of interest to gene regulation in development, regenerative biology, and human diseases. I have developed multiple bioinformatic and statistical methods/tools to study transcriptomic patterns, gene regulation, and cellular heterogeneity, including but not limited to: (a) TimeMeter: a dynamic time warping based statistical framework and R package for comparative time-series gene expression data analysis; (b) SinQC2: a statistical method and tool to detect technical artifacts from heterogeneous single-cell RNA-seq data; (c) a comparative RNA-seq pipeline to study gene regulation for species lacking sequenced genomes or with poorly annotated genes with applications to study axolotl early development3, Nile rat transcriptome (collaboration with Dr. Huishi Toh, UCSB), and reindeer antler regeneration (collaboration with Dr. Jeff Biernaskie, University of Calgary); (d) MPBind: a meta-motif based statistical framework and tool to predict whole cell or DNA/RNA-binding protein binding aptamers (an potential alternative to antibody) from systematic evolution of ligands by exponential enrichment (SELEX) -seq data; (e) a negative binomial distribution based meta-statistical framework for CLIP-seq peak calling with replicates5; and (f) several machine learning prediction models (e.g. MiPred6, RF-DYMHC7 and RFRCDB-siRNA) for large-scale genomic and transcriptomic data. I also led several bioinformatics and statistical studies (as first/co-first author or corresponding author) for human pluripotent stem cell differentiation (retinal cells lineage9), amphibian early development3, the contribution of Alu repeat elements to the human proteome, neural toxicity prediction, the role and transcriptomic landscape of ESRP regulated epithelial┬┐mesenchymal transition mechanism, context-dependent human genetic variations, and the impact of diet during early development for life-long type 2 diabetic risk. In addition to my contribution to the basic science, I am thrilled to develop novel technologies, methods and software that either can be contributed to translational science or have the potential to be commercialized. For example, I co-invented the Ligation-Mediated Sequencing (LM-Seq), which is a low-cost, strand-specific, and low input starting RNAs sequencing sample preparation protocol, to enable large-scale comparative gene expression profiling.
Teaching Areas:
Computational Biology (BIO 493, 494, 593, 594)
Molecular Biology (BIO 610, 810)
Professional Service:
Associate Editor: Frontiers in Genetics
Reviewer for Journals: Nature Communications, Nucleic Acids Research, Genome Biology, Bioinformatics, Toxicological Sciences, BMC Genomics, Biomedical Engineering and Computational Biology, International Journal of Biometrics and Bioinformatics, Frontiers in Bioinformatics and Computational Biology, Advances and Applications in Bioinformatics and Chemistry, Biomarker Insights, The Application of Clinical Genetics, Bioinformatics and Biology Insights, Current Biomarker Findings, Genomics, Proteomics & Bioinformatics (GPB)
Research Grants:

1. DARPA            D20AC00002              2/26/2020 - 5/30/2024
Defense Advanced Research Projects Agency (DARPA)
Role: Principal Investigator (Sub-award)
This is a Multi-Institutional/Collaborative Research Project with total budget $22 Million. The overall objective of REPAIR program is to engineer a technology which will dramatically improve the speed and functional outcome of wound healing.

2. NHLBI/NIH  Progenitor Cell Translational Consortium (PCTC) Sub-award (MIRC-002500: Cleveland State University) 9/1/2021 - 5/31/2022
Role: Principal Investigator