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Mattia Pelizzola

Team Leader

Research Line

Genomic Science




Via Adamello 16
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2006: PhD “Complexity in post-genomic biology”. University of Torino, Advisor M. Caselle.

2003: MSBioinformatics”. 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 AssociateBiostatistic Division, Yale University, Advisor A. Molinaro.

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

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



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


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


·     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).


Selected Publications

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.


  • ERC Consolidator finalist, 2018
  • Best Paper by the International Medical Informatics Association, 2018
  • Editor at Epigenomes (MDPI, ISSN: 2075-4655), 2018-
  • Cover in the October issue of Genome Research (de Pretis et al., Genome Research, 2017)
  • Abilitazione Scientifica Nazionale (eligibility to Associate Professorship): 05/E1 (General Biochem- istry), 05/E2 (Molecular Biology), 05/F1 (Applied Biology), 05/I1 (Genetics), 2017-2023
  • ISCB (International Society for Computational Biology) membership, 2016
  • Associated Editor at Frontiers in Genetics - Bioinformatics and Computational Biology (ISSN: 1664-8021), 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)


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Accetta e chiudi

I numeri di IIT

L’Istituto Italiano di Tecnologia (IIT) è una fondazione di diritto privato - cfr. determinazione Corte dei Conti 23/2015 “IIT è una fondazione da inquadrare fra gli organismi di diritto pubblico con la scelta di un modello di organizzazione di diritto privato per rispondere all’esigenza di assicurare procedure più snelle nella selezione non solo nell’ambito nazionale dei collaboratori, scienziati e ricercatori ”.

IIT è sotto la vigilanza del Ministero dell'Istruzione, dell'Università e della Ricerca e del Ministero dell'Economia e delle Finanze ed è stato istituito con la Legge 326/2003. La Fondazione ha l'obiettivo di promuovere l'eccellenza nella ricerca di base e in quella applicata e di favorire lo sviluppo del sistema economico nazionale. La costruzione dei laboratori iniziata nel 2006 si è conclusa nel 2009.

Lo staff complessivo di IIT conta circa 1440 persone. L’area scientifica è rappresentata da circa l’85% del personale. Il 45% dei ricercatori proviene dall’estero: di questi, il 29% è costituito da stranieri provenienti da oltre 50 Paesi e il 16% da italiani rientrati. Oggi il personale scientifico è composto da circa 60 principal investigators, circa 110 ricercatori e tecnologi di staff, circa 350 post doc, circa 500 studenti di dottorato e borsisti, circa 130 tecnici. Oltre 330 posti su 1400 creati su fondi esterni. Età media 34 anni. 41% donne / 59 % uomini.

Nel 2015 IIT ha ricevuto finanziamenti pubblici per circa 96 milioni di euro (80% del budget), conseguendo fondi esterni per 22 milioni di euro (20% budget) provenienti da 18 progetti europei17 finanziamenti da istituzioni nazionali e internazionali, circa 60 progetti industriali

La produzione di IIT ad oggi vanta circa 6990 pubblicazioni, oltre 130 finanziamenti Europei e 11 ERC, più di 350 domande di brevetto attive, oltre 12 start up costituite e altrettante in fase di lancio. Dal 2009 l’attività scientifica è stata ulteriormente rafforzata con la creazione di dieci centri di ricerca nel territorio nazionale (a Torino, Milano, Trento, Parma, Roma, Pisa, Napoli, Lecce, Ferrara) e internazionale (MIT ed Harvard negli USA) che, unitamente al Laboratorio Centrale di Genova, sviluppano i programmi di ricerca del piano scientifico 2015-2017.

IIT: the numbers

Istituto Italiano di Tecnologia (IIT) is a public research institute that adopts the organizational model of a private law foundation. IIT is overseen by Ministero dell'Istruzione, dell'Università e della Ricerca and Ministero dell'Economia e delle Finanze (the Italian Ministries of Education, Economy and Finance).  The Institute was set up according to Italian law 326/2003 with the objective of promoting excellence in basic and applied research andfostering Italy’s economic development. Construction of the Laboratories started in 2006 and finished in 2009.

IIT has an overall staff of about 1,440 people. The scientific staff covers about 85% of the total. Out of 45% of researchers coming from abroad 29% are foreigners coming from more than 50 countries and 16% are returned Italians. The scientific staff currently consists of approximately 60 Principal Investigators110 researchers and technologists350 post-docs and 500 PhD students and grant holders and 130 technicians. External funding has allowed the creation of more than 330 positions . The average age is 34 and the gender balance proportion  is 41% female against 59% male.

In 2015 IIT received 96 million euros in public funding (accounting for 80% of its budget) and obtained 22 million euros in external funding (accounting for 20% of its budget). External funding comes from 18 European Projects, other 17 national and international competitive projects and approximately 60 industrial projects.

So far IIT accounts for: about 6990 publications, more than 130 European grants and 11 ERC grants, more than 350 patents or patent applications12 up start-ups and as many  which are about to be launched. The Institute’s scientific activity has been further strengthened since 2009 with the establishment of 11 research nodes throughout Italy (Torino, Milano, Trento, Parma, Roma, Pisa, Napoli, Lecce, Ferrara) and abroad (MIT and Harvard University, USA), which, along with the Genoa-based Central Lab, implement the research programs included in the 2015-2017 Strategic Plan.