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D. R. Mani

Publications

Mertins P, Mani DR, Ruggles KV, et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 2016;534(7605):55–62.

Mani DR, Abbatiello SE, Carr SA. Statistical characterization of multiple-reaction monitoring mass spectrometry (MRM-MS) assays for quantitative proteomics. BMC Bioinformatics 2012;13(Suppl 16):S9.

Abbatiello SE, Mani DR, Keshishian H, Carr SA. Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry. Clin Chem. 2010;56(2):291–305.

Fusaro VA, Mani DR, Mesirov JP, Carr SA. Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotech. 2009;27(2):190–198.
 

D. R. Mani , Ph.D.

D. R. Mani is a principal computational scientist in the Proteomics Platform at the Broad Institute of MIT and Harvard under the direction of Steven Carr. His work focuses on the application of computational methods to the analysis of proteomics data, ranging from proteogenomics and biomarker discovery to the targeted measurement and quantification of specific proteins.

For over a decade, he has been applying pattern recognition, machine learning, signal processing, and statistical algorithms to the analysis of large-scale data generated from a wide range of bio-assays including mass spectrometry based proteomics, next-generation sequencing and gene expression profiling. His recent research has focused on the design and implementation of innovative algorithms to enable proteogenomic data analysis, pattern-based discovery of proteomic biomarker candidates, evaluation of data quality, assessment of variability and reproducibility in mass spectrometry based assays, and data visualization.

He has also been leading statistical data analysis for the Broad Institute’s Proteome Characterization Center and Proteogenomic Data Analysis Center established under the National Cancer Institute Clinical Proteomics Tumor Analysis Consortium (CPTAC), focusing on proteogenomic analysis of proteomic, phosphoproteomic, and genomic data derived from cancer samples.

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 manidr@broadinstitute.org.
 

April 2017