Computational Neuropathology & Oncology (CNO) Research Group

Freie Universität Berlin, Germany Charité-Universitätsmedizin Berlin
Dep. of Mathematics and Computer Science Institute of Medical Genetics
Institute of Computer Science and Human Genetics
and Institute of Bioinformatics
Group website @ FU


News

We offer projects for Master's and Bachelor degree students at Freie Universität Berlin!

2017-08-10

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.

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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) 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.


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People

(see also CNO members @ FU)

Principal Investigator:

Rosario M. Piro, PhD
Assistant Professor (Juniorprofessor) of Bioinformatics

E-mail: r.piro -AT- fu-berlin.de
Homepage: http://www.rmpiro.net


PhD Students:

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

E-mail:
Homepage: Verônica@MPIMG


Students:

Rafael Alberdi Vallejo
M.Sc. student

E-mail:
Homepage:

Jan Hoffschulte
B.Sc. student

E-mail:
Homepage:

Sandra Krüger
B.Sc. student

E-mail:
Homepage:

Sharen Nicole Heinig
B.Sc. student

E-mail:
Homepage:


Alumni:

Calvinna Caswara
B.Sc. student

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

Marc Horlacher
B.Sc. student

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

Bonian Riebe
B.Sc. student

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

Anja Seidel
B.Sc. student

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


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Publications

2017

  • Computational Exome and Genome Analysis (book)
    Authors: Robinson PN, Piro RM, Jäger M.
    Chapman & Hall/CRC Mathematical and Computational Biology series
    CRC Press (Taylor & Francis Group), Boca Raton, FL, USA, 2017. [available at: CRC Press]

  • Molecular transition of an adult low-grade brain tumor to an atypical teratoid/rhabdoid tumor over a time-course of 14 years
    Authors: Schweizer Y, Meszaros Z, Jones DTW, Koelsche C, Boudalil M, Fiesel P, Schrimpf D, Piro RM, Brehmer S, von Deimling A, Kerl U, Seiz-Rosenhagen M, Capper D.
    Journal of Neuropathology & Experimental Neurology 76(8):655-664, 2017. [available at: Oxford Academic]

2016

  • No correlation between NF1 mutation position and risk of optic pathway glioma in 77 unrelated NF1 patients
    Authors: Hutter S, Piro RM, Waszak SM, Kehrer-Sawatzki H, Friedrich RE, Lassaletta A, Witt O, Korbel JO, Lichter P, Schuhmann MU, Pfister SM, Tabori U, Mautner VF, Jones DTW.
    Human Genetics 135(5):469-475, 2016. [available at: SpringerLink]

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Downloads

PathWave:

PathWave enables the identification of disease-specific regulation patterns by combining gene expression data and network topology. By analyzing regulatory patterns at multiple scales, PAthWave is capabale to detect both global deregulation of pathways and localized regulatory switches that may still have a singificant impact on metabolic fluxes. PathWave is available here, or from our collaboration partners at Hans Knöll Institute (HKI), Jena:

CITING PATHWAVE: please cite Piro et al. (2014) for PathWave version 2.1, Schramm et al. (2010) for PathWave version 1.0, and both papers (Schramm et al. 2010; Piro et al. 2014) for PathWave in general.

  1. Schramm G, et al. (2010) PathWave: Discovering patterns of differentially regulated enzymes in metabolic pathways. Bioinformatics 26(9):1225-1231.
  2. Piro RM, et al. (2014) Network topology-based detection of differential gene regulation and regulatory switches in cell metabolism and signaling. BMC Systems Biology 8:56.

CGPACE:

CGPACE (Candidate Gene Prioritization by Analysis of Co-Expression) is the latest version of the software developed and continusously improved for the following papers:

  1. Piro RM, et al. (2010) Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR. Bioinformatics 26(18):i618-624.
  2. Piro RM, et al. (2011) Evaluation of candidate genes from orphan FEB and GEFS+ loci by analysis of human brain gene expression atlases. PLoS ONE 6(8):e23149.
  3. Piro RM, et al. (2013) Disease-gene discovery by integration of 3D gene expression and transcription factor binding affinities. Bioinformatics 29(4):468-475.

Note: despite its name, CGPACE can be used for data other than gene expression profiles (e.g. total binding affinities/TBAs in paper 3).

Please cite paper 1 when using CGPACE with a Pearson correlation coefficient and paper 3 when using it with the Relative Intensity Overlap (RIO), the use for TBAs and/or a noisy-OR gate for data integration.

See the publications for descriptions of the algorithm, the RIO, the noisy-OR and the use for TBAs; contact me in case of further questions.

CGPACE is released under the terms of the GNU General Public License (Version 2 or later).

Stable releases:
CGPACE-5-2012-12-03.tgz [source code, 57kB]; date: December, 2012.

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Contact

For contacting us, please write to:

Prof. Dr. Rosario M. Piro

Dep. of Mathematics and Computer Science
Institute of Computer Science
Freie Universität Berlin
Takustr. 9
14195 Berlin, Germany
Phone: +49 (0)30 838 55114

E-mail: r.piro -AT- fu-berlin.de
Group website: CNO @ FU-Mathematik und Informatik

or:
Institute of Medical and Human Genetics
Charité-Universitätsmedizin Berlin
Augustenburger Platz 1
13353 Berlin, Germany

Impressum/Legal notice according to German legislation

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. Dr. Rosario M. Piro
Dep. of Mathematics and Computer Science
Freie Universität Berlin
Takustr. 9
14195 Berlin, Germany
E-mail: r.piro -AT- fu-berlin.de

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.

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