Mattia Pelizzola's foto

Mattia Pelizzola

Team Leader

IIT@SEMM

Address Via Adamello 16
City Milano
ZIP Code 20139
Country Italy
Phone 0039-02-574303022

Bio

Education

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.

Working experience

2011 Team Leader. Italian Institute of Technology (IIT) in the Center for Genomic Science of IIT@SEMM.

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).

Other Publications

Selected publications:

  1. "Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells". Lister R*, Pelizzola M*, Kida YS, Hawkins RD, Nery JR, Hon G, Antosiewicz-Bourget J, O'Malley R, Castanon R, Downes M, Yu R, Stewart R, Ren B, Thomson JA, Evans RM and Ecker JR. Nature 2011.
  2. "The DNA methylome". Pelizzola M and Ecker JR. FEBS Lett 2010.
  3. "Human DNA methylomes at base resolution show widespread epigenomic differences". Lister R*, Pelizzola M*, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM, Edsall L, Antosiewicz-Bourget J, Stewart R, Ruotti V, Millar AH, Thomson JA, Ren B, Ecker JR. Nature 2009.
  4. "Genome-wide screen of promoter methylation identifies novel markers in melanoma". Koga Y*, Pelizzola M*, Cheng E, Krauthammer M, Sznol M, Ariyan S, Narayan D, Molinaro AM, Halaban R, Weissman SM. Genome Res 2009.
  5. "MEDME: an experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment". Pelizzola M*, Koga Y*, Urban AE, Krauthammer M, Weissman S, Halaban R, Molinaro AM. Genome Research 2008.
  6. "AMDA: an R package for the automated microarray data analysis". Pelizzola M, Pavelka N, Foti M, Ricciardi-Castagnoli P. BMC Bioinformatics 2006.
  7. "A power law global error model for the identification of differentially expressed genes in microarray data". Pavelka N*, Pelizzola M*, Vizzardelli C*, Capozzoli M, Splendiani A, Granucci F, Ricciardi-Castagnoli P. BMC Bioinformatics 2004.

* These authors equally contributed.

For a complete list of publications click here.

Projects

Epigenomics

·       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).

·       MethylPipe: a R package for the analysis of base-resolution DNA methylation data and their integration with heterogeneous data types.

·       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).

·       Profiling of promoter DNA methylation in human melanoma allowed the identification of differentially methylated markers (Koga Y, Pelizzola M et al, Genome Res 2009).

·       Modeling the effect of promoter methylation to the transcriptional activity: the importance of considering the distance of mC from the TSS, and the overall promoter CpG content, when predicting the transcriptional repression of downstream genes (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).

Integrative genomics

·       MethylPipe: a R package for the analysis of base-resolution DNA methylation data and their integration with heterogeneous data types (Pelizzola M, Lister M, Ecker JR, unpublished).

·       A method for the identification of regulatory modules in higher eukaryotes (TF and target genes). This method is based on the evaluation of the tissue-specific co-expression of genes with similar regulatory regions. Resulting modules are also evaluated for dysregulation of the target genes in cancer (Pelizzola M and Caselle M, unpublished).

Microarray data analysis

·       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 (Pelizzola M, Pavelka N et al, BMC Bioinformatics 2004; Pavelka N et al, Mol Cell Proteomics 2006).

·       Collaboration for the data analysis with several research groups, with particular focus on datasets relevant for the study of immune responses.