Dr. Matteo Pellegrini’s lab is interested in the development of computational approaches to interpret genomic data. These methodologies allow his lab to develop large-scale models of transcriptional and epigenetic regulation as well as signal transduction. Pelligrini’s approach is to build models that integrate varied data that shed light on these phenomena. The data is produced using the latest generation of high throughput sequencers, tiling and expression arrays along with mass spectrometry. His lab’s research focuses on the development of both low- and high-level analyses. For instance, they are developing suites of tools for the analysis of high throughput sequencing data, as well as tools that combine multiple data types to infer transcriptional regulatory mechanisms.
Selected Cancer-Related Publications:
Das D, Pellegrini M, Gray JW. A primer on regression methods for decoding cis-regulatory logic. PLoS Comput Biol. 2009 Jan;5(1):e1000269. Epub 2009 Jan 30.
Conway AE, Lindgren A, Galic Z, Pyle AD, Wu H, Zack JA, Pelligrini M, Teitell MA, Clark A. A Pluripotency and Self-Renewal Program Controls the Expansion of Genetically Unstable Cancer Stem Cells in Pluripotent Stem Cell-Derived Tumors. Stem Cells. 2008 Oct 2. [Epub ahead of print]
Pellegrini M, Cheng JC, Voutila J, Judelson D, Taylor J, Nelson SF, Sakamoto KM. Expression profile of CREB knockdown in myeloid leukemia cells. BMC Cancer. 2008 Sep 18;8:264.
Ferrari R, Pellegrini M, Horwitz GA, Xie W, Berk AJ, Kurdistani SK. Epigenetic reprogramming by adenovirus e1a. Science. 2008 Aug 22;321(5892):1086-8.
Levine DM, Haynor DR, Castle JC, Stepaniants SB, Pellegrini M, Mao M, Johnson JM. Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways. Genome Biol. 2006;7(10):R93. Epub 2006 Oct 17.