# Core 2: Computational Core

> **NIH NIH U54** · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · 2020 · $312,008

## Abstract

COMPUTATIONAL SHARED RESEARCH CORE
Project Summary:
This Center will dissect the clonal heterogeneity of tumors by profiling their genotypes using whole genome
sequencing, whole exome sequencing, RNA sequencing, and single cell RNA sequencing; and also by
developing software for the analysis of their genotypes and phenotypes. Because both scientific projects will
require overlapping sets of data and software, unifying their management will provide efficiencies and enable a
greater ability to implement frameworks that enforce best practices. Therefore, we will create a Computational
Shared Research Core to enhance synergy among projects by managing the storage, analysis, and
dissemination of data. It will do this in a manner that is reproducible, secure, and maintains patient privacy. It
will also develop, archive, and disseminate novel computational tools based on Bayesian statistics,
mathematics, and computer science.
As we developed this Core, we agreed upon the following principles that will guide its missing. 1) The integrity,
security, and privacy of the data is paramount. 2) Ensuring the reproducibility of research requires an active
effort at all steps of the research process. 3) Centralization of preprocessing and standardized analyses can
accelerate research. And 4) public dissemination of data and code in a manner that is consistent with or
exceeds community standards is necessary to promote the progress of research. These values are encoded in
the five aims and operating procedures that we have established.
The specific aims for the core are: 1) Data management, storage, and distribution. We will maintain and
distribute the raw and processed genomic data generated by the cores and projects. 2) Data preprocessing.
We will standardize the procedures to check the quality of and preprocess the raw data (DNA-Seq, RNA-Seq,
and single cell RNA-Seq) into meaningful measures that can be analyzed in the projects. 3) Development and
application of novel computational methods. Our Core will develop novel tools that will be applied to both
projects. We will develop SNIPER (Structural Network Integrative PhEnotypeR), based on a Bayesian
structural network modeling approach, to integrate gene- and pathway-level information into phenotypic
signatures. We can then model the evolution of these phenotypes using Cancer IPM, a differential equation
model based on evolutionary theory. Finally, we will apply and optimize SuperSeeker, which interrogates tumor
subclone structure evolution and phylogeny. 4) Pipeline development. To process and analyze the data, we
will use an expert system we developed, BETSY, that creates pipelines. This both accelerates research and
promotes reproducibility by automating the analysis of the data. Further, BETSY automatically documents each
analysis in detail. 5) Standardization of computational environment. We will create and distribute
standardized computing environments in Docker containers.

## Key facts

- **NIH application ID:** 9959355
- **Project number:** 5U54CA209978-05
- **Recipient organization:** BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
- **Principal Investigator:** JEFFREY T CHANG
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $312,008
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9959355

## Citation

> US National Institutes of Health, RePORTER application 9959355, Core 2: Computational Core (5U54CA209978-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9959355. Licensed CC0.

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