Mattia Pelizzola

About

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: MSc “Industrial Biotechnologies”. University of Milano-Bicocca.

Working experience

2011 Researcher. Team leader of the Epigenomics and Transcriptional Regulation 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.

2001 Computational biologist. Genopolis Consortium (University of Milano-Bicocca), Italy.

1999 Molecular biologist. University of Milano-Bicocca, Italy.

 

Interests

epigenomics transcriptional regulation DNA and RNA methylation RNA metabolism next generation sequencing integrative genomics

Projects

Current research activities

1. Myc-dependent dynamics of transcriptional and epigenetic regulation

2. Genomic and epigenomic determinants of RNA methylation (EPITRAN COST Action - European Epitranscriptomics Network)

3. Development of tools for the integrative analysis of public epigenomic HTS datasets

    

Past Research activities

Epigenomics

·     Development of methods for the integrative analysis of heterogeneous HTS epigenomics datasets (Galeota E et al. Briefings in Bioinformatics 2016).

·     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).

·     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).

Transcriptional Regulation

·      Integrative analysis of the dynamics of transcriptional regulation following the MYC activation (de Pretis S et al. Genome Research 2017).

·      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).

 

Software Development

·     OnASSIs: Ontology Annotation and Semantic SImilarity software

·     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:

1. “m6A-Dependent RNA Dynamics in T Cell Differentiation”. Furlan M, Galeota E, de Pretis S, Caselle M, Pelizzola M. Genes. 2019.
2. “Integrative analysis of RNA Polymerase II and transcriptional dynamics upon MYC activation”. de Pretis S, Kress T, Morelli MJ, Sabo A, Locarno C, Verrecchia A, Doni M, Campaner S, Amati B, Pelizzola M. Genome Research 2017.
3. “Identification of differentially methylated regions in the genome of Arabidopsis thaliana”. Kishore K, Pelizzola M. Methods in Molecular Biology, Issue Plant Chromatin Dynamics. Springer NY 2017.
4. “Computational Epigenomics”. Pelizzola M. Handbook of Neurobehavioral Genetics and Phenotyping. Wiley-Blackwell 2017.
5. “Integrated systems for NGS data management and analysis: open issues and available solutions”. Bianchi V, Ceol A, Ogier AGE, de Pretis S, Galeota E, Kishore K, Bora P, Croci O, Campaner S, Amati B, Morelli MJ, Pelizzola M. Frontiers in Genetics 2016.
6. "Ontology based annotations and semantic relations in large scale (epi)genomics data". Galeota E and Pelizzola M. Briefings in Bioinformatics 2016.
7. "methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data". Kishore K, de Pretis S, Lister R, Morelli MJ, Bianchi V, Amati B, Ecker JR*, Pelizzola M*. BMC Bioinformatics 2015.
8. "INSPEcT: a Computational Tool to Infer mRNA Synthesis, Processing and Degradation Dynamics from RNA- and 4sU-seq Time Course Experiments". de Pretis S, Kress T, Morelli MJ, Melloni GEM, Riva L, Amati B, Pelizzola M. Bioinformatics 2015.
9. "Computational epigenomics: challenges and opportunities". Robinson MD, and Pelizzola M. Front. Genet. 2015.
10. "Computational and experimental methods to decipher the epigenetic code". de Pretis S, and Pelizzola M. Front Genet. 2014.

 

Selected publications, first (co)authorship:

1. "Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis". Sabò A*, Kress TR*, Pelizzola M*, de Pretis S, Gorski MM, Tesi A, Morelli MJ, Bora P, Doni M, Verrecchia A, Tonelli C, Fagà G, Bianchi V, Ronchi A, Low D, Müller H, Guccione E, Campaner S, Amati B. Nature 2014.
2. "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.
3. "The DNA methylome". Pelizzola M and Ecker JR. FEBS Lett 2010.
4. "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.
5. "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.
6. "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.
7. "AMDA: an R package for the automated microarray data analysis". Pelizzola M, Pavelka N, Foti M, Ricciardi-Castagnoli P. BMC Bioinformatics 2006.
8. "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.

1. * These authors equally contributed.
2. You can find here bibliometric infos and the complete Pubmed list of publications.

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