Computational Neuropathology & Oncology

Research Group

Politecnico di Milano

Computational Neuropathology & Oncology (CNO) Research Group

Politecnico di Milano
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB)
Via Ponzio 34/5, 20133 Milano, Italy


Research

Basic medical research frequently involves massive data production, such as the determination of genomic sequences and genome-wide gene expression levels by high throughput sequencing. Consequently, the importance of computational biology for medical research has constantly increased over the last decades. Both the analysis of medical data and the development of novel computational methods for this purpose are essential to keep pace with fast evolving experimental techniques and their application to growing numbers of patient samples.


Our Computational Neuropathology & Oncology (CNO) research group (established in March 2015 at Freie Universität Berlin and moved to Polictecnico di Milano in February 2020) develops novel computational approaches and applies existing methods with a primary focus on computational neuropathology, including cancer genetics/genomics and regulatory changes in metabolic and signaling networks of brain tumors, as well as topics related to the identification and the prioritization of candidate genes for human hereditary disorders of the central nervous system. Our research interest further extends from brain cancer to computational oncology in general, including other solid tumors and leukemias.



People

Principal Investigator:

Rosario M. Piro, PhD
Associate Professor of Bioinformatics

E-mail: rosariomichael.piro -AT- polimi.it
Homepage: http://www.rmpiro.net

PhD Students:




Students:




Alumni - Politecnico di Milano:




Alumni - Freie Universität Berlin:

Verônica Rodrigues de Melo Costa
PhD student (in collaboration with Freie Universität Berlin and the Long non-coding RNA research group at MPI for Molecular Genetics, Berlin)

E-mail:
Homepage:

Rafael Alberdi Vallejo
M.Sc. student (2018)

Thesis: Vorhersagen von krankheitsassoziierten Genen mittels hochauflösenden Genexpressionsprofilen des Gehirns

Tobias Balzer
B.Sc. student (2019)

Thesis: Normalisation of mutational signatures for the application on specific genomic sites

Calvinna Caswara
B.Sc. student (2016/2017)

Thesis: Evaluating voting-based consensus clustering of tumor samples using expression patterns from individual metabolic pathways

Willie Ekaputra
B.Sc. student (2019)

Thesis: Analysis of the differential co-expression of protein complexes in breast cancer

Franziska Fritz
M.Sc. student (2020)

Thesis: Relationship between recurrently mutated tumor suppressor genes and mutational processes

Sharen Nicole Heinig
B.Sc. student (2017/2018)

Thesis: Mutability of oncogenes and tumor suppressor genes by different mutational processes

Jan Hoffschulte
B.Sc. student (2017)

Thesis: Evaluierung eines Peak Calling basierten Ansatzes zur Identifizierung intronischer Transkription in RNA-Seq Datensätzen

Marc Horlacher
B.Sc. student (2016/2017)

Thesis: Analyse viraler Proteine im Kontext des humanen Protein-Interaktions-Netzwerks

Jacqueline Krohn
B.Sc. student (2018)

Thesis: Evaluierung der Krankheitsvorhersage mittels 3D-Genexpressionsdaten durch Unterscheidung einzelner Gehirnregionen

Sandra Krüger
B.Sc. student (2017) & M.Sc. student (2019)

Thesis (B.Sc.): Identifizierung von Mutationssignaturen in einzelnen Tumoren
Thesis (M.Sc.): The reimplementation of PathWave and a new approach to combine network topology and gene expression data

Nina Kubiessa
B.Sc. student (2017/2018)

Thesis: Verbesserung von Krankheitsgenvorhersagen basiert auf 3D-Genexpressionsprofilen

Nathaly Reimert
B.Sc. student (2018)

Thesis: Impact of mutational processes on the mutation frequency of tumor suppressors

Bonian Riebe
B.Sc. student (2016)

Thesis: Evaluierung einer Clustering-basierten Pathway-Analyse für mehrere Tumorklassen

Anja Seidel
B.Sc. student (2016)

Thesis: Evaluierung der Haar-Wavelet-Transformation für den Vergleich von 3D-Genexpressionsprofilen zur Vorhersage von krankheitsassoziierten Genen

Arsene Martinien Wabo Ngouambe
M.Sc. student (2018/2019)

Thesis: Identifizierung von rekurrenten somatischen Mutationen in kurzen Introns


Publications

(CNO group members are highlighted)


Highlights

Exome book Exome book

2021


2020


2019


2018

  • [Research article] Gröbner SN, Worst BC, Weischenfeldt J, Buchhalter I, Kleinheinz K, Rudneva VA, Johann PD, Balasubramanian GP, Segura-Wang M, Brabetz S, Bender S, Hutter B, Sturm D, Pfaff E, Hübschmann D, Zipprich G, Heinold M, Eils J, Lawerenz C, Erkek S, Lambo S, Waszak S, Blattmann C, Borkhardt A, Kuhlen M, Eggert A, Fulda S, Gessler M, Wegert J, Kappler R, Baumhoer D, Burdach S, Kirschner-Schwabe R, Kontny U, Kulozik AE, Lohmann D, Hettmer S, Eckert C, Bielack S, Nathrath M, Niemeyer C, Richter GH, Schulte J, Siebert R, Westermann F, Molenaar JJ, Vassal G, Witt H, Burkhardt B, Kratz CP, Witt O, van Tilburg CM, Kramm CM, Fleischhack G, Dirksen U, Rutkowski S, Frühwald M, von Hoff K, Wolf S, Klingebiel T, Koscielniak E, Landgraf P, Koster J, Resnick AC, Zhang J, Liu Y, Zhou X, Waanders AJ, Zwijnenburg DA, Raman P, Brors B, Weber UD, Northcott PA, Pajtler KW, Kool M, Piro RM, Korbel JO, Schlesner M, Eils R, Jones DTW, Lichter P, Chavez L, Zapatka M, Pfister SM; ICGC PedBrain-Seq Project; ICGC MMML-Seq Project.
    The landscape of genomic alterations across childhood cancers.
    Nature 555(7696):321-327, 2018.


2017


2016



Downloads

decompTumor2Sig:

The decompTumor2Sig R package uses quadratic programming to decompose the mutation catalog from an individual tumor sample (or multiple individual tumor samples) into a set of given mutational signatures (either the Alexandrov model or the Shiraishi model), computing weights that reflect the contributions of the signatures to the mutation load of the tumor.

Citing decompTumor2Sig: Krüger S, Piro RM. decompTumor2Sig: identification of mutational signatures active in individual tumors. BMC Bioinformatics 20(Suppl 4):152, 2019.


MutViz 2.0:

MutViz is a web tool which helps to identify enrichments of somatic mutations (single nucelotide variants) falling on predefined or user-specified sets of small genomic regions, such as transcription factor binding sites, promoters and others. MutViz is developed within the Data-driven Genomic Computing (GeCo) project.

Citing MutViz 2.0: Gulino A, Stamoulakatou E, Piro RM. MutViz 2.0: visual analysis of somatic mutations and the impact of mutational signatures on selected genomic regions. NAR Cancer 3(2):zcab012, 2021.


SPLICE-q:

SPLICE-q is a Python tool for genome-wide SPLICing Efficiency quantification from RNA-seq data. I takes a BAM file of mapped, strand-specific RNA-seq reads and a GTF gene annotation file and computes one of two available measurements of splicing efficiency for individual introns.

Citing SPLICE-q: de Melo Costa VR, Pfeuffer J, Louloupi A, Ørom UAV, Piro RM. SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency. BMC Bioinformatics 22:368, 2021.


[For further downloads, see R.M.Piro → Downloads]



News

We offer thesis projects for M.Sc. students at Politecnico di Milano!


2021-04-09

Exome book
New research article published in NAR Cancer: MutViz 2.0: visual analysis of somatic mutations and the impact of mutational signatures on selected genomic regions.

2020-06-05

New review article/book chapter published in the Translational Epigenetics Series of Elsevier/Academic Press: "Sequencing technologies for epigenetics: from basics to applications".


2020-02-17

The group has officially moved from Freie Universität Berlin to the Department of Electronics, Information and Bioingeneering (DEIB) at Politecnico di Milano.


2019-05-03

Exome book
Our R package decompTumor2Sig has just been released with Bioconductor 3.9!

2020-01-03

New research article to be presented at the Pacific Symposium of Biocomputing 2020 in Hawaii, USA: "Impact of mutational signatures on microRNA and their response elements".


2019-10-28

New research article to be presented at the 19th IEEE International Conference on Bioinformatics and Bioengineering (BIBE) 2019 in Athens, Greece: "Deleterious impact of mutational processes on transcription factor binding sites in human cancer".


2019-04-18

New research article: "decompTumor2Sig: identification of mutational signatures active in individual tumors".


2019-01-12

New review article/book chapter: "Network-based methods and other approaches for predicting lncRNA functions and disease associations".


2017-08-10

Exome book
The CRC Press book Computational Exome and Genome Analysis is finally being printed!

2017-07-13

Our talk submitted to the NETTAB 2017 workshop has been accepted!


2017-01-12

Inivited talk at the IEEE conference Confluence-2017, Noida, India.


2015-03-16

CNO research group established at Freie Universität Berlin and Charité-Universitätsmedizin Berlin, Germany.



Contact

For contacting the research group, please write to:

Prof. Rosario M. Piro

Politecnico di Milano
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB)
Via Ponzio 34/5
20133 Milano, Italy
Phone: +39 02 2399 3538

E-mail: rosariomichael.piro -AT- polimi.it
Institutional website: Piro @ DEIB

Where to find us:



Impressum/Legal notice according to German legislation:

[This website is hosted in Germany...]

Responsible for the content of this website according to § 5 TMG (German Act for Telecommunications Media Services) and § 55 Abs. 2 RStV (German Interstate Broadcasting Agreement):

Prof. Rosario M. Piro
Politecnico di Milano
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB)
Via Ponzio 34/5
20133 Milano, Italy
E-mail: rosariomichael.piro -AT- polimi.it


Liability for the content of linked websites:

This website contains links to external websites on whose content I have no influence and for which I thus cannot take any responsibility. The responsibility for the content of linked websites lies with the providers or administrators of the respective websites. Linked websites have been checked for a possible infringement of laws upon creation of the link. No infringement could be identified when the links were created. A permanent control of the content of the linked websites is not reasonable without a tangible suspicion of the violation of a law. If I should be notified or otherwise become aware of violations of law by the content of a linked website, I will immediately remove the corresponding link.