I was trained as a computational biologist with expertise in biology and computer science. At the University of California, Davis I was first exposed to 454-sequencing, to identify genetic variants, and the revolution of Next-generation Sequencing (NGS) technologies and large-scale data analysis. I quickly became aware of the amount of data that would be generated and the need for a robust and reproducible analysis pipelines. As I developed software and algorithms I also began to see the power of data integration for the purpose of elucidating biological mechanisms as well as the need of public data resources. Before re-entering bioinformatics, I spent two years as a software developer with Infosys. I developed and maintained a suite of large-scale customer relational management tools. This experience gave me insight into industry standards of software design and implantation as well as into data management. It also inspired me to apply these same standards to biological databases in research settings. Large quantitative datasets using global studies extend our knowledge of genes, their products and their interactions. By integrating quantitative datasets with curated, focused experimental and clinical data creates unique comprehensive databases. I have been involved in the design and implementation of databases, ENCODE (Encyclopedia of DNA Elements) and UCSC Genome Browser Projects, integrating scientific information into encyclopedic databases essential for investigation. While on these projects, I also implemented genomic analysis pipelines to facilitate reproducible data analysis in the Amazon Cloud (AWS). Using these approaches, I have been focused on the implementing, optimizing and distributing genomic analysis pipelines to facilitate reproducible data analysis.


In 2014, I moved to UT Southwestern Medical Center (UTSW) and took the opportunity of my position first as a computational biologist in the Green Center for Reproductive Biological Sciences and then in the Bioinformatics Core Facility to further understanding of the human genome by integration of large-scale functional and comparative genomics datasets in cancer. Specifically, in the Lonestar Oncology Network for Epigenetics Therapy and Research (LONESTAR) Consortium, I developed a multi-omics integration pipeline that identifies breast cancer subtype-specific transcription factors (TFs) bound at active enhancers that regulate gene expression patterns determining growth and clinical outcomes. I applied these approaches, in collaboration with Dr. Ping Mu (prostate cancer cell biology), to identify the enhancer landscape and key TFs driving prostate cancer resistance leading to new clinical targets. Currently, I am working on integrating multiple -omics assays to understand transcription factors driving gene regulatory networks in human cancers.

As the co-lead of the Data Analytics Core of the UT Southwestern Kidney Cancer SPORE (Dr. James Brugarolas), I developed the Kidney Cancer Explorer (KCE), facilitate hypothesis generation from clinical and genomic data. Using this framework for KCE, we aim at expanding this project to create a pan-cancer data commons (PC-DC), which will allow researchers to build patient cohorts based on clinical, pathological and genomic information, allowing researchers to identify molecular treads with clinical attributes or vice-versa.



(2008), Biology
Graduate School
Johns Hopkins University (2011), Biology

Research Interest

  • Chromatin structure and gene regulation
  • Comprehensive Scientific Databases
  • Data integration
  • Reproducibility and Open Science


Featured Publications LegendFeatured Publications

Activation of PARP-1 by snoRNAs Controls Ribosome Biogenesis and Cell Growth via the RNA Helicase DDX21.
Kim DS, Camacho CV, Nagari A, Malladi VS, Challa S, Kraus WL, Mol. Cell 2019 Sep 75 6 1270-1285.e14
Cardiac Reprogramming Factors Synergistically Activate Genome-wide Cardiogenic Stage-Specific Enhancers.
Hashimoto H, Wang Z, Garry GA, Malladi VS, Botten GA, Ye W, Zhou H, Osterwalder M, Dickel DE, Visel A, Liu N, Bassel-Duby R, Olson EN, Cell Stem Cell 2019 Jul 25 1 69-86.e5