Biostatistics and Bioinformatics Core
Single-cell RNAseq (scRNA); JCI collaboration with Sumera I. Ilyas, M.B.B.S.
The Biostatistics and Bioinformatics Core provides statistical, bioinformatics, computational biology collaboration, and data management support for Hepatobiliary SPORE research projects and cores.
The core also provides data management for clinical trials, monitors adverse events and prepares data summaries for manuscript preparation. The Biostatistics and Bioinformatics Core works closely with the Hepatobiliary SPORE's other cores to ensure a smooth continuum of data flow for clinical trials.
The Biostatistics and Bioinformatics Core:
- Provides SPORE investigators access to expertise in collaborative development of study designs and analysis plans, state-of-the-art data analysis and interpretation, data management resources, sequencing alignment, variant calling, data analysis and quality assurance, pathway analysis, and access to and analysis of data in public databases.
- Provides management and integration of existing and newly collected data through consistent and compatible data handling.
- Supports the Biospecimen and Pathology Core by providing resources to the research community and a centralized system to facilitate collaboration and data reuse.
TCGA work and machine learning
Source: Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, Fan H, Shen H, Way GP, Greene CS, Liu Y, Akbani R, Feng B, Donehower LA, Miller C, Shen Y, Karimi M, Chen H, Kim P, Jia P, Shinbrot E, Zhang S, Liu J, Hu H, Bailey MH, Yau C, Wolf D, Zhao Z, Weinstein JN, Li L, Ding L, Mills GB, Laird PW, Wheeler DA, Shmulevich I; Cancer Genome Atlas Research Network, Monnat RJ Jr, Xiao Y, Wang C. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Rep. 2018 Apr 3;23(1):239-254.e6. doi: 10.1016/j.celrep.2018.03.076. PMID: 29617664; PMCID: PMC5961503.
Mutational signature inference
Source: Jessen, E., Liu, Y., Davila, J. et al. Determining mutational burden and signature using RNA-seq from tumor-only samples. BMC Med Genomics 14, 65 (2021).
PDX and patient RNAseq pipeline
Source: Liu, Y., Chanana, P., Davila, J.I. et al. Gene expression differences between matched pairs of ovarian cancer patient tumors and patient-derived xenografts. Sci Rep 9, 6314 (2019).
Single-cell RNAseq (scRNA)
Source: J Clin Invest. 2020;130(10):5380-5396. 2020, American Society for Clinical Investigation.
Core leaders
Chen Wang, Ph.D.
Director, Bioinformatics
Qian Shi, Ph.D.
Director, Biostatistics
Zachary C. Fogarty
Staff Member, Bioinformatics
Erik Jessen, Ph.D.
Staff Member, Bioinformatics
Chantal E. McCabe, Ph.D.
Staff Member, Bioinformatics
Daniel R. O'Brien
Staff Member, Bioinformatics
Joseph J. Larson, M.S.
Statistician