2006: PhD “Complexity in post-genomic biology”. University of Torino, Advisor M. Caselle.
2003: MS “Bioinformatics”. University of Milano-Bicocca, Advisor P. Ricciardi-Castagnoli.
2001: BS “Industrial Biotechnologies”. University of Milano-Bicocca.
2011 Researcher. Team leader of the Computational epigenomics unit at the Center for Genomic Science, c/o IFOM-IEO Campus, Milano.
2009 Postdoctoral Research Associate. Genomic Analysis Lab, Salk Institute, Advisor J.R. Ecker.
2007 Postdoctoral Research Associate.Biostatistic Division, Yale University, Advisor A. Molinaro.
2003 Computational biologist. Genopolis Consortium, Milan, Italy.
2001 Computational biologist at Biopolo (Affymetrix service provider).
Current research activities
Myc-dependent dynamics of transcriptional and epigenetic regulation
Genomic and epigenomic determinants of RNA methylation
Development of tools for the integrative analysis of public epigenomic HTS datasets
see here for more details
Past Research activities
· Development of methods for the analysis of DNA methylation and epigenomics data: the methylPipe and compEpiTools R packages were developed for the integrative analysis of DNA methylation data and other epigenomics data; both packages are available in the Bioconductor project (Kishore K et al. BMC Bioinformatics 2015, accepted).
· Integrative analysis of transcriptional and epigenetic patterns associated with the modulation of Myc binding on the genome of mouse B-cells supports the notion that Myc acts primarily by regulating specific groups of genes, only indirectly leading to transcriptional amplification (Sabo A*, Kress TR*, Pelizzola M* et al. Nature 2014).
· Integrative analysis of the DNA methylomes of induced pluripotent stem cells (iPSC), stem cells, and somatic cells, reveal the presence of hotspots of aberrant epigenomic reprogramming in iPSC (Lister R*, Pelizzola M* et al, Nature 2010).
· The first single-base resolution maps of methylated cytosines in human embryonic stem cells and fetal fibroblasts (along with integrative analysis of mRNA, smallRNA, histone modifications, TFBS). Widespread differences were identified in the composition and patterning of cytosine methylation between the two genomes (Lister R*, Pelizzola M* et al, Nature 2009).
· Identification of DNA methylation markers in human melanoma samples (Koga Y*, Pelizzola M* et al. Genome Research 2009).
· Dissecting the relationship between promoter methylation and transcriptional activity: the role of the methyl-cytosines density, their distance from the TSS, and the promoter CpG content (Koga Y*, Pelizzola M* et al, Genome Res 2009).
· Modeling the relationship between MeDIP enrichment and the DNA methylation level: a model implemented in a Bioconductor library (MEDME) allows predicting absolute and relative methylation levels from MeDIP-chip enrichment values (Pelizzola M*, Koga Y* et al, Genome Res 2008).
· Modeling the dynamics of transcriptional regulation: we developed INSPEcT, a computational tool for the genome-wide inference of the rates of RNA synthesis, degradation and pre-mRNA processing (de Pretis S et al. Bioinformatics 2015).
· AMDA: a pipeline for the automatic analysis of Affymetrix microarray data, providing a PDF report inclusive of results and documentation with one click software (Pelizzola M et al, BMC Bioinformatics 2006).
· PLGEM: a Bioconductor library including a global error model and its application for the identification of differentially expressed features in transcriptomics and proteomics datasets (Pavelka N*, Pelizzola M* et al, BMC Bioinformatics 2004; Pavelka N et al, Mol Cell Proteomics 2006).
· INSPEcT: an R/Bioconductor package to study the dynamics of transcriptional regulation (de Pretis S et al. Bioinformatics 2015).
· compEpiTools: an R/Bioconductor package for the integrative analysis of epigenomics data (Kishore K et al. BMC Bioinformatics 2015).
· methylPipe: an R/Bioconductor package for the analysis of DNA methylation data (Kishore K et al. BMC Bioinformatics 2015).
· MEDME: an R/Bioconductor package for the analysis of MeDIP-chip data (Pelizzola M*, Koga Y* et al. Genome Research 2009).
· AMDA: an R package for the automatic microarray data analysis (Pelizzola M et al. BMC Bioinformatics 2006).
· PLGEM: an R/Bioconductor package for the identification of differentially epressed genes and proteins (Pavelka N*, Pelizzola M* et al. BMC Bioinformatics 2004).
Publications, last authorship:
Selected publications, first (co)authorship:
* These authors equally contributed.
· Frontiers in Genetics (Bioinformatics and Computational Biology), Associated Editor, 2015-current
· Epigenesys, Associate membership, 2013-2015
· Bioinformatic Italian Society (BITS) membership, 2012-current
· European FP7 collaborative grant RADIANT (Rapid development and distribution of statistical tools for high-throughput sequencing data), 2013-2015
· #4 hottest paper of 2011, ScienceWatch (Lister R, Pelizzola M et al, Nature 2011)
· Catharina Foundation Postdoctoral Fellowship Award, 2010
· #2 Scientific Discovery of the Year 2009, TIME Magazine (Lister R, Pelizzola M et al, Nature 2009)