# Core C: Computational Core

> **NIH NIH P01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2020 · $206,784

## Abstract

Abstract - Core C (Computational Core) 
The DNA and RNA next generation sequencing and experimental data generated by Projects 1 – 3 require 
trained bioinformaticians to process the data and conduct the downstream analysis to help the project 
investigators interpret the results and identify target genes for validation. Over the past years, the 
Computational Core has developed an infrastructure with powerful computing clusters, storage space, and 
well-established software to conduct the computations required. We have also been working closely with 
Project 1 and Core B (Preclinical Therapeutics Core) investigators to develop and improve the algorithms 
for tracking molecular barcodes in vivo in order to to accurately evaluate genes for their tumor initiating 
potentials and to identify targets for clinical treatments of pancreatic ductal adenocarcinoma. The following 
three specific aims are proposed: 
1. One significant contribution the Computational Core can make is to conduct integrative analysis 
leveraging readily available MDACC internal patients and external public data sources to seek for answers, 
formulate hypothesis, and validate the experimental results with dedicated computational biologists. We will 
mine the private and public databases to provide information needed to guide the experiments and the 
validation of the experimental results. Databases or archives containing data collected from TCGA, CCLE, 
COSMIC, and MD Anderson Cancer Center's patient data repositories have been built with interfaces and 
applications to facilitate exploration, and visualization have been developed. Data mining results will be 
communicated with project investigators. 
2. We will provide P01 investigators with statistical analysis of RNASeq gene expression data to identify 
differentially expressed genes between genomic groups, disease subtypes, or treatment conditions to 
identify biomarkers. A collection of publically available and internally developed tools have been configured 
to run on the computing cluster that can be selected based on the nature of the experiment and data 
structure. We will also further improve the algorithm we have developed for in vivo screening projects to 
identify genes as potential targets of treatments. 
3. We will process the next generation sequencing data following established standards and implement 
quality control measures at each step to produce high quality data that can be used for downstream 
analysis to identify target genes with clinical implications. A production level infrastructure has been 
established to ensure the achievement of this goal.

## Key facts

- **NIH application ID:** 9904489
- **Project number:** 5P01CA117969-15
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** PHILLIP ANDREW FUTREAL
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $206,784
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9904489

## Citation

> US National Institutes of Health, RePORTER application 9904489, Core C: Computational Core (5P01CA117969-15). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9904489. Licensed CC0.

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