Ding Lab

Schematic of blood stem cell mutation accumulation and evolution to cancer.

The Ding Lab was established in 2005. Our mission is to discover genetic alterations relevant to human disease - particularly in cancer - and translate these genomic findings into medically actionable information.

Our research in the field of medical genomics has led to pioneering new software methods that work with state-of-the-art sequencing technology to identify variants that contribute to disease. These events include mutations, gene expression changes, epigenetic modifications, copy number alterations, and structural variations. We often extend such analyses using animal models and high throughput in vitro systems. The resulting information, collected from thousands of tumors across many forms of cancer, allows us to address biological questions that were previously unanswerable regarding the initiation, progression, metastasis, and treatment of cancer at the level of the gene, the pathway, and the cell. Ultimately, this work will contribute to the understanding and treatment of cancer at its most fundamental level.

Our lab is an interdisciplinary team of bioinformatics analysts, research biologists, mathematicians, and computer scientists. We also collaborate extensively with other biomedical researchers and clinicians both at Washington University and elsewhere, on large-scale cancer projects and their associated biological and bioinformatic methodologies.


Cancer Genomics and Proteomics Projects

  • The Cancer Genome Atlas (TCGA): A national initiative to catalog the genetic changes found in cancer (more info)
  • Pediatric Cancer Genome Project (PCGP): A collaboration between St. Jude Children's Research Hospital and Washington University. It is aimed at understanding the genetic origins of childhood cancers (more info)
  • International Cancer Genome Consortium (ICGC): To obtain a comprehensive description of genomic, transcriptomic and epigenomic changes in 50 different tumor types and/or subtypes which are of clinical and societal importance across the globe (more info)
  • Clinical Proteomic Tumor Analysis Consortium (CPTAC): A comprehensive and coordinated effort to accelerate the understanding of the molecular basis of cancer through the application of robust, quantitative, proteomic technologies and workflows (more info)
  • Washington University Cancer Genome Initiative (WUCGI): This projects aims to sequence the tumor and normal genomes from hundreds of patients with cancers such as leukemia, breast, lung, cervical, brain and others. It also focuses on mouse models of cancer (more info)

Computation Methods Projects

The following is a list of software this group has contributed. Many of these are available from the McDonnell Genome Institute's suite of Genome Modeling Tools

  • MuSiC: The MuSiC suite is a set of tools aimed at determining the significance of somatic mutations discovered within a given cohort of cancer samples, incorporating the cohort's alignment data, variant lists and any relevant clinical data (pubmed) (download)
  • Genome Model System (GMS): A turnkey system for variant discovery and interpretation (download)
  • BreakDancer: WashU structural variant (SV) detection algorithm for paired-end data (pubmed) (download)
  • Pindel: Indel and structural variant detection (pubmed) (download)
  • PathScan: WashU algorithm that assesses significance of gene groups by Boolean tagging each gene as mutated or not mutated (pubmed)
  • CMDS: WashU algorithm for identifying recurrent DNA copy number changes (pubmed)
  • Pairoscope: WashU tool for visualizing SVs from Illumina/Solexa paired-end reads (download)
  • PolyScan: Automatic indel and SNP detection to the analysis of human re-sequencing data (pubmed)
  • SomaticSniper: WashU somatic SNV detection algorithm for whole genome resequencing data (pubmed) (download)
  • TIGRA_SV: Tigra_sv is a program that conducts targeted local assembly of structural variation (SV) using the iterative graph routing assembly (TIGRA) algorithm (download)
  • VarScan: Somatic and germline variant detection for massively parallel sequencing (pubmed) (download)
  • BreakFusion: Targeted Assembly-based Identification of Gene Fusions in Whole Transcriptome Paired-end Sequencing Data (pubmed)
  • MSIsensor: WashU algorithm for microsatellite instability detection using paired tumor-normal sequence data (pubmed) (download)

For more information on these tools, please visit the Turnkey Variant Analysis Project (TVAP) page.