Sun Lab on genomic ITH

Translating Intra Tumor Heterogeneity


The Sun lab innovates computational algorithms to quantify and model intra tumor heterogeneity (ITH) from next generation sequencing (NGS) data, and leverages patterns of ITH to statistically infer the dynamics of cancer.

  • Quantifying ITH: We develop statistical methods to achieve a bias-minimized, high-fidelity quantification of (epi)genomic ITH from tumor sequencing data. Such data involve tissue sampling at increasing granularity, i.e., single bulk, longitudinal, multi-region, and single cell sampling. Both genomic ITH (copy number, structural and small variants) and epigenetic ITH (expression level, chromatin states) are of our interest.
  • Modelling ITH: We use mathematical and computational models to interpret the observed ITH from patient data. Hypotheses regarding genome instability, clonal dynamics and tumor microenvironments are tested by comparing ITH between the model and patient data. By learning patient specific model parameters, we increase ITH’s explanatory value for tumor dynamics as well as its predictive power for tumor evolvability.

As an emerging field that encourages interdisciplinary cooperation, we maintain vigorous cooperative research projects with clinicians, biologists, mathematicians, engineers, and physicists to bridge the scales among (epi)genetic variations, cellular behavior and tumor tissue patterns.


Open Post-Doctoral Associate Position:

The Sun lab is seeking a highly-motivated postdoc with a passion to innovate and discover in cancer data science. Our lab studies the dynamics of cancer by quantifying and modeling intratumor heterogeneity (ITH) from high-throughput sequencing data. We design computational algorithms to facilitate the translation of tumor heterogeneity into its evolvability. The potential research directions are listed below:

{Methods} for quantifying and visualizing intratumor heterogeneity
from NGS data;
{Computational and Mathematical modeling} of the spatial/temporal
tumor (epi)genetic heterogeneity, in consideration of tumor
{Copy number evolution} in association with clonal dynamics.

The person who holds this position will receive hands-on mentoring of analytic and programming skills in advanced cancer genomics/bioinformatics and tailored training for career development. While the successful candidate is expected to get involved in the aforementioned research topics, his/her own research interests will be encouraged and promoted under the general scope of the lab. For more information on the position, applicants may directly email Ruping Sun (ruping at umn dot edu) with your CV.


  • A doctoral degree in bioinformatics, genetics, computer science, biostatistics, biomedical engineering, applied mathematics, biophysics, or other appropriate areas;
  • Script language programming skills;
  • Excellent written and verbal communication skills.
  • Experience in cancer genetics, genomics and/or mathematical modeling (preferred).
  • A record of publication and/or conference presentations.

Other Positions:

We welcome students and interns. Join us if

  • you have good programming skills and want to step into the Cancer Genomics and Bioinformatics field to make an impact.
  • you are trained in biostatistics, mathematics or biophysics and want to expand the potential of your work in the context of Cancer Genomics and Evolution.
  • you are from biology background and like to learn more about bioinformatics in Cancer.
  • you want to cross disciplines to increase your scientific skills.
Folks interested to work in the group directly email Ruping Sun (ruping at umn dot edu).