Rendong Yang, PhD

Assistant Professor
Computational Cancer Genomics

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Dr. Yang is an assistant professor and section leader for cancer genomics at the Hormel Institute. Dr. Yang obtained his PhD in China Agricultural University, where his work involved the topic of microarray data analysis. Briefly he developed two statistical models, called ARSER and LSPR, to detect periodically expressed transcripts from evenly or unevenly sampled temporal microarray gene expression profiles respectively. By applying these algorithms to Arabidopsis and rice transcriptome, a list of novel clock-controlled genes that regulating plant circadian rhythm were identified. Dr. Yang finished his postdoctoral training at Emory University, where his research switched to cancer genomics and epigenomics. Working with researchers in Winship Cancer Institute, he developed a bioinformatics pipeline to analyze the whole genome mate-pair and pair-end sequencing and RNA-seq data from three tumor cells in multiple myeloma, which leads to discovering a novel SPI-ZNF287 t(11;17) translocation. After postdoctoral training, Dr. Yang joined Supercomputing Institute at University of Minnesota as a Bioinformatics Analyst working on both clinical genomics and prostate cancer research to define and characterize AR gene rearrangements from DNA-seq data, and also to interrogate genome-wide binding profiles of AR and AR variants in prostate cancer cells and tissues.


Emory University
Bioinformatics, China Agricultural University
Computer Science, China Agricultural University

Professional memberships

International Society for Computational Biology

Research Interests

• Data analysis and algorithms design on next-generation sequencing data
• Cancer genomics and epigenomics
• Gene regulation and system biology
• Machine learning and large-scale data mining


  1. Etten JLV, Nyquist M, Li Y, Yang R, Ho Y, Johnson R, Voytas D, Henzler CM, and Dehm SM. (2017) Targeting a single alternative polyadenylation site coordinately blocks expression of androgen receptor mRNA splice variants in prostate cancer. Cancer Research, doi:10.1158/0008-5472.CAN- 17-0320.
  2. Dalal K, Che M, Que NS, Sharma A, Yang R, Lallous N, Borgmann H, Ozistanbullu D, Tse R, Ban F, Li H, Tam KJ, Roshan-Moniri M, Leblanc E, Gleave ME, Gewirth DT, Dehm SM, Cherkasov A, Rennie PS. (2017) Bypassing drug-resistance mechanisms of prostate cancer with small-molecules that target androgen receptor chromatin interactions. Molecular Cancer Therapeutics, doi: 10.1158/1535-7163.MCT-17-0259.
  3. Kohli M, Ho Y, Hillman DW, Etten JLV, Henzler CM, Yang R, Li Y, Tseng E, Hon T, Clark T, Tan W, Carlson RE, Wang L, Sicotte H, Thai H, Jimenez R, Huang H, Vedell PT, Eckloff B, Quevedo F, Pitot HC, Costello B, Jen J, Wieben ED, Silverstein K, Wang L, and Dehm SM. (2017) Androgen receptor variant AR-V9 is co-expressed with AR-V7 in prostate cancer metastases and predicts abiraterone resistance. Clinical Cancer Research, 23(16), 4704-4715.
  4. Katerndahl CDS, Heltemes-Harris LM, Willette MJL, Henzler CM, Frietze S, Yang R, Schjerven H, Silverstein KAT, Ramsey LB, Hubbard G, Wells AD, Kuiper RP, Scheijen B, van Leeuwen FN, Muschen M, Kornblau SM and Farrar MA. (2017) STAT5 antagonism of B cell enhancer networks drives leukemia and poor patient survival. Nature Immunology. 18(6), 694-704.
  5. Henzler CM∗, Li Y∗, Yang R∗, McBride T, Ho Y, Sprenger C, Liu G, Coleman I, Lakely B, Li R, Ma S, Landman SR, Kumar V, Hwang TH, Raj GV, Morrissey C, Nelson PS, Plymate SR and Dehm SM. (2016) Truncation and constitutive activation of the androgen receptor by diverse genomic rearrangements in prostate cancer. Nature Communications, 7:13668.
  6. Yang R#, Nelson AC, Henzler CM, Thyagarajan B and Silverstein KA. (2015) ScanIndel: a hybrid framework for indel detection via gapped alignment, split reads and de novo assembly. Genome medicine. 7(1):1.
  7. Yang R, Chen L, Newman S, Gandhi K, Doho G, Moreno CS, Vertino PM, Bernal-Mizarchi L, Sagar L, Boise L, Rossi M, Kowalski J and Qin Z. (2014) Integrated analysis of whole genome paired-end and mate-pair sequencing data for identifying genomic structural variations in multiple myeloma. Cancer Informatics, 13(Suppl 2), 49-53.
  8. Yang R, Bai Y, Qin Z and Yu T. (2014) EgoNet: identification of human disease ego-network modules with application to breast cancer. BMC Genomics, 15(1), 314.
  9. Asangani IA, Dommeti L, Wang X, Malik R, Cieslik M, Yang R, Escara-Wilke J, Wilder-Romans K, Dhanireddy S, Engelke C, Iyer MK, Jing X, Wu Y, Cao X, Qin ZS, Wang S, Feng FY and Chinnaiyan AM. (2014) Therapeutic Targeting of BET Bromodomain Proteins in Castration-Resistant Prostate Cancer. Nature, 510(7504), 278-282.
  10. Yang R, Zhang C and Su Z. (2011) LSPR: an integrated periodicity detection algorithm for unevenly sampled temporal microarray data. Bioinformatics, 27(7), 1023-1025.
  11. Yang R and Su Z. (2010) Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation. Bioinformatics, 26(12), i168-i174.

Primary Research Areas

Developing novel algorithms for indel detection. Our most recent work has been in the development of clinical genomic variant detection pipelines for our customized oncology gene panels in the University of Minnesota Molecular Diagnostic Lab. Briefly, we developed new algorithm named ScanIndel to accurately detect insertion and deletion (indel) mutations in human genome from next generation sequencing data. In particular, ScanIndel reliably detects medium-size and large indels. With this method, indels contribute to pathogenesis of constitutional and somatic diseases can be identified quickly and accurately which is important for targeted therapy or patient prognosis

Detecting gene rearrangement, splicing and epigenetic regulator of AR in prostate cancer. We developed an integrated pipeline to detect structural rearrangements in the AR gene, which encodes the androgen receptor. Through this work, we identified diverse genomic-dependent and genomic-independent AR splicing variants expressed prostate cancer. Additionally, our early work focuses on prostate cancer eipgenomic studies by analyzing the ChIP-seq data of AR and BRD4 in prostate cancer VCaP cell line. Our study revealed the crosstalk between AR and BRD4 signaling in prostate cancer progression.

Developing algorithms for detecting cancer biomarkers and rhythmic gene expression patterns. We have been developing EgoNet algorithm by integrating gene expression microarray data and protein interaction networks to identify network modules that can distinguishing different breast cancer subtypes. We have also developed ARSER and LSRP algorithms to detect periodical gene expression pattern from evenly and unevenly sampled short time course gene expression profiles respectively. Our developed algorithms for analyzing high-throughput gene expression data have been largely used in the field of cancer and neuroscience research.

Research Specialties

  • Algorithms design on next-generation sequencing technologies, including ChIPdenovo for de novo ChIP-seq assembly and analysis and SVfinder for structural variation detection on cancer genome sequencing.
  • Developing machine learning approach to identify network modules associated with disease out- comes based on gene expression profiling and protein interaction networks.
  • Collaborative research on cancer biomarkers discovery and cancer epigenomics.


Prostate Cancer Foundation Young Investigator Award, 2015

PhRMA Foundation Research Starter grant, 2017

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