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Sida Ye

Sida Ye

Position: Graudate student
Role: PhD Candidate in Computational Sciences, University of Massachusetts Boston

Education

  • Ph.D. in Computational Sciences (Sep 2021 – Present), University of Massachusetts Boston
    Advisor: Prof. Kourosh Zarringhalam
    Relevant Courses: Probability Models, Neural Networks, Statistical Machine Learning, Algorithms in Bioinformatics, Numerical Linear Algebra, Numerical Analysis, Computational Statistics, Biomedical Signal and Image Processing

  • M.S. in Botany and Molecular Biology (2014 – 2020), Peking University

  • Visiting Student (Sep 2017 – Dec 2017), Sainsbury Laboratory, University of Cambridge
    Advisor: Prof. Yjro Helariutta

  • B.S. in Plant Sciences and Technology (2010 – 2014), Huazhong Agricultural University

Research Interests

  • Mathematical modeling and bioinformatics
  • Genome-scale gene essentiality and fitness analysis
  • Transposon mutagenesis (TN-Seq) pipelines
  • Parasitology (Plasmodium, Babesia, etc.)
  • Single-cell RNA-Seq and multi-omics data integration
  • Epigenetic regulation (CUT&RUN, modifications)
  • Machine learning (CNN, GNN, Bayesian networks)

Contact

Biography

With 6 years of wet-lab experience and 4 years of computational research, Mr. Ye bridges experimental biology and advanced computational approaches. He has extensively studied the molecular mechanisms in plant development at Peking University and the University of Cambridge, and he is now focusing on parasitology and computational genomics at the University of Massachusetts Boston and Harvard T.H. Chan School of Public Health. His primary research areas involve developing transposon mutagenesis pipelines, modeling gene essentiality, and exploring epigenetic regulation mechanisms.

Research Focus

  • Transposon Mutagenesis (TN-Seq): Designing and optimizing pipelines for Babesia divergens, Plasmodium knowlesi, and Plasmodium falciparum to study gene essentiality, fitness, and drug resistance.
  • Multi-Omics Integration: Analyzing bulk RNA-Seq, single-cell RNA-Seq, and epigenomic data (CUT&RUN/ChIP-Seq) to understand gene regulation and pathway dynamics.
  • Computational Methods: Developing Bayesian network-based and Gaussian mixture models to quantify essential genes and detect structural variants.
  • Machine Learning: Applying neural networks (CNN, GNN), U-Net, TensorFlow, and PyTorch for image segmentation, gene prediction, and multi-omics data analysis.

Publications

  1. Elsworth B.#, Ye S.#, Dass S.# et al. “The essential genome of Plasmodium knowlesi reveals determinants of antimalarial susceptibility.” Science, 2025. PMID:39913579.

  2. Ye S.#, Dass S.#, Elsworth B.# et al. “Uncovering the essentialome of Babesia divergens by piggyBac transposon mutagenesis.” [Prepared, #co-first author]

  3. Kumar M., Ye S. et al. “Lactylation of epigenetic regulators controls gene expression linked to sexual commitment in Plasmodium falciparum.” [Prepared, #co-first author]

  4. Keroack C. D., Elsworth B., Tennessen J. A., Paul A. S., Hua R., Ramirez-Ramirez L., Ye S. et al. “Comparative chemical genomics in Babesia species identifies the alkaline phosphatase PhoD as a determinant of antiparasitic resistance.” PNAS, 2024. PMID: 38377214.

  5. Zarringhalam K., Ye S. et al. “Cell cycle-regulated ApiAP2s and parasite development: the Toxoplasma paradigm.” Current Opinion in Microbiology, 2023. PMID: 37898053.

  6. Wang D., Liu N., Ye S. et al. “Rice tapetum differentiation is sensitive to downregulation of OsUCH3, a ubiquitin C-terminal hydrolase.” Plant Biotechnology Journal, 2023. PMID: 37057895.

  7. Wei G., Li S., Ye S. et al. “High-resolution small RNAs landscape provides insights into alkane adaptation in the marine alkane-degrader Alcanivorax dieselolei B-5.” International Journal of Molecular Sciences, 2022. PMID: 36555635.

  8. Zheng Y., Wang D., Ye S. et al. “Auxin guides germ-cell specification in Arabidopsis anthers.” PNAS, 2021. PMID: 34031248.

Current Projects

  • Babesia divergens Transposon Mutagenesis: Generating large-scale mutant libraries, exploring gene essentiality under various conditions, and integrating single-cell datasets to understand cold-induced quiescence.
  • Epigenetic Regulation in Plasmodium falciparum: Profiling lactylation and acetylation marks to dissect mechanisms of drug resistance and sexual commitment.
  • Genome Structural Variants & lncRNA Discovery: Using bulk RNA-Seq and DNA-Seq data to identify novel lncRNAs and structural changes affecting parasite viability.

Selected Presentations

  • “Uncovering the essentialome of Babesia divergens by piggyBac transposon mutagenesis” at the Molecular Parasitology Meeting (2023).
  • “Single-cell transcriptomic analysis of cold-induced quiescence in Babesia divergens” at the Molecular Parasitology Meeting (2022).

Honors and Awards

  • Oracle Doctoral Research Fellowship, UMass Boston (2024–2025)
  • Founder Scholarship, Peking University (2019)
  • First-class Scholarship for Graduate Students, Peking University (2018)
  • Second-class Scholarship for Graduate Students, Peking University (2017)
  • Distinguished Poster Award, The Academic Week (Peking University, 2016)
  • Lv Yichang Scholarship, Peking University (2015)
  • Scholarship for Outstanding Students, Huazhong Agricultural University (2010–2014)
  • National Encouragement Scholarship, Ministry of Education in China (2011)

Software and Resources

  • PkEssenDB: Interactive database for Plasmodium knowlesi essentiality analysis
  • AI-driven Gene Essentiality Prediction: Using neural networks to identify essential genes in parasite genomes
  • Various R/Python Packages: In-house pipelines for TN-Seq analysis, single-cell RNA-Seq, and multi-omics integration

Skills

  • Programming Languages: R, Python, Linux Bash, C++
  • Bioinformatics Analysis: TN-Seq, bulk RNA-Seq, scRNA-Seq, scATAC-Seq, CUT&RUN/ChIP-Seq, lncRNA, genome structural variants
  • Machine Learning Models: CNN, GNN, U-Net, TensorFlow, PyTorch
  • Protein Structure: Rosetta, AutoDock, protein-ligand docking
  • Mathematical Modeling: Bayesian inference, Hidden Markov Models, Gaussian mixture models
  • Tools and Workflow: Plotly, Docker, Snakemake, Nextflow