Mani DR, et al. Cancer proteogenomics: current impact and future prospects. Nat Rev Cancer. 1–16 (2022).
Mani DR, et al. PANOPLY: a cloud-based platform for automated and reproducible proteogenomic data analysis. Nat Methods. 1–3 (2021).
Gillette MA, Mani DR, Carr SA. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182, 200-225.e35 (2020).
Krug K, Mani DR. A curated resource for phosphosite-specific signature analysis. Molecular & Cellular Proteomics. 18, 576–593 (2019).
Mertins P, Mani DR, Ruggles KV, et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 2016;534(7605):55–62.
DR Mani, Ph.D.
DR Mani is director of computational proteomics in the Proteomics Platform at the Broad Institute of MIT and Harvard. For two decades, he has been applying computational pattern recognition, machine learning, signal processing, and statistical data analysis to the analysis of omics data generated from a wide range of bio-assays, including mass spectrometry-based proteomics and gene expression profiling. His research has focused on the design and implementation of innovative algorithms to enable proteogenomic analysis, immunopeptidomics, pattern-based discovery of proteomic biomarker candidates, evaluation of data quality, assessment of variability and reproducibility in mass spectrometry-based assays, and data visualization.
As director of computational proteomics and principal investigator for the National Cancer Institute Clinical Proteomics Tumor Analysis Consortium (NCI-CPTAC) Proteogenomic Data Analysis Center (PGDAC) at the Broad, he has been leading statistical and proteogenomic data analysis for almost all projects in the Broad Proteomics Platform. He has developed PANOPLY, a cloud-based automated computational pipeline that has been extensively used in the analysis of cancer cohorts across the CPTAC. This proteogenomic data analysis pipeline encapsulates existing and new algorithms — including the recently developed phosphosite-specific signature analysis, immune analysis, and the widely applied multi-omics clustering-based cancer subtyping algorithm — in an easy-to-use, scalable, shareable and reproducible computing platform. In addition, as part of earlier NCI-CPTAC initiatives, he has made significant contributions to the statistical analysis and quality control of targeted mass spectrometry data, including development of AuDIT for identifying interferences in multiple reaction monitoring mass spectrometry (MRM-MS) and QuaSAR, a collection of tools and algorithms for analysis and visualization of MRM-MS data. He also has a strong foundation in parallel computing and implementation of data mining algorithms for big data analysis.
Mani joined the Broad Institute in 2001 after having worked in industry as a big data and data mining research scientist. He has a Ph.D. in computer science from the University of Pennsylvania and a M.S. in biostatistics from the Harvard School of Public Health.
Contact Mani via email at firstname.lastname@example.org.