Developing computational methods for systems biology of cancerAchieving the goals specified by our team is possible only with using the up-to-date computational methodology: methods for network analysis and use, integrative data analysis methods, statistical and machine learning methods. In order to keep the competitive positions in the field, our group maintains a level of expertise allowing not only assessing existing methods and selecting the most suitable ones, but also adapt them to existing projects and, whenever necessary, suggest novel methodological developments. The group is able to package the developed methodology such that it can be used by the rest of the computational biology community.
The omics data coming from application of modern biotechnologies is characterized by the complexity which was not faced before. In particular, the single cell data reflects at a much fine-grained level than before the genetic and epigenetic tumoral heterogeneity, shaped by the action of cell fate decision transcriptional programs within the cell and affected by the environment. Visualizing and understanding this heterogeneity requires application of novel generation of computational methods which allows capturing strong non-linearities and non- Gaussian multidimensional distributions in the space of cell omics profiles. A number of new methods (tSNE, topological data analysis) and concepts (pseudo-time, branching cell trajectories, etc.) have emerged in the field for this reason. We are developing computational methods for advanced dimension reduction of omics data, based on construction of principal graphs, or application of omics deconvolution methods such as Independent Component Analysis (ICA).
Our group has developed a number of advanced methods for NGS data analysis helping better interpretation of the sequencing data in cancer biology. Control-FREEC is a continuation of the successful FREEC pipeline for assessing the copy number profiles, included the detection of LOH profiles from the sequencing data (Boeva et al, Bioinformatics, 2012a). Nebula web-server based on Galaxy open source network was developed for user-friendly analysis of CHiP-Seq data including using de novo discovery of sequence motifs (Boeva et al, Bioinformatics, 2012b). SV-Bay tool was developed for the analysis of paired-end data in order to detect structural variants in the genome taking into account copy number changes (Iakovishina et al, Bioinformatics, 2016). HMCan and HMCan-diff tools were developed in order to quantify the chromatin modifications in cancer taking into account the copy number changes (Ashoor et al, Bioinformatics, 2013; Ashoor et al, Nucleic Acids Res., 2017).
We are developing advanced tools for analysis of biological networks together with omics data. Several Cytoscape plugins has been developed in the past (BiNoM, OCSANA, DeDaL). We have developed Google Maps-based user-friendly interfaces for visualization of omics data on top of the large and complex biological networks, based on NaviCell technology. NaviCom web portal connects the Atlas of Cancer Signaling Network with cBioPortal, the major source of high-througput data in cancer biology.
Finally, the group has invested a lot into the methodology and applications of discrete modeling of biological networks.
Martignetti L, Calzone L, Bonnet E, Barillot E, Zinovyev A. ROMA: Representation and Quantification of Module Activity from Target Expression Data. Front Genet. 2016. 7:18.
February 19, 2016
Gorban AN, Mirkes EM, Zinovyev A. Piece-wise quadratic approximations of arbitrary error functions for fast and robust machine learning. Neural Netw. 2016, 84:28-38.
October 24, 2016 | Accelerating machine learning methods
Czerwinska U, Calzone L, Barillot E, Zinovyev A. DeDaL: Cytoscape 3 app for producing and morphing data-driven and structure-driven network layouts. BMC Syst Biol. 2015. 9:46.
August 14, 2015 | Data-driven network layouting
Remy E, Rebouissou S, Chaouiya C, Zinovyev A, Radvanyi F, Calzone L. A Modeling Approach to Explain Mutually Exclusive and Co-Occurring Genetic Alterations in Bladder Tumorigenesis.Cancer Res. 2015. 75(19):4042-52.
October 1, 2015 | Mechanistical model of mutual exclusive genome alterations
Bonnet E, Viara E, Kuperstein I, Calzone L, Cohen DP, Barillot E, Zinovyev A. NaviCell Web Service for network-based data visualization 2015. Nucleic Acids Res. 43(W1):W560-5.
Biton A., Bernard-Pierrot I., Lou Y., Krucker C., Chapeaublanc E., Rubio Perez C., Lopez Bigas N., Kamoun A., Neuzillet Y., Gestraud P., Grieco G., Rebouissou S., de Reyniès A., Benhamou S., Lebret T., Southgate J., Barillot E., Allory Y., Zinovyev A., Radvanyi F. Independent component analysis uncovers the landscape of the bladder tumor transcriptome and reveals insights into luminal and basal subtypesCell Reports. 2014. 9(4):1235-45.
November 2014 | ~7000 tumoral transcriptomes analysed using ICA
Vera-Licona P, Bonnet E, Barillot E, Zinovyev A.OCSANA: Optimal Combinations of Interventions from Network Analysis. 2013. Bioinformatics 15:29: 1571-1573.
April 2013 | Minimal cut set analysis for networks
Bonnet E, Calzone L, Rovera D, Stoll G, Barillot E, Zinovyev A.BiNoM 2.0, a Cytoscape plugin for accessing and analyzing pathways using standard systems biology formats 2013. BMC Syst Biol. 7(1):18.
March 2013 | BiNoM 2.0 publication
Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, Allison KR; The DREAM5 Consortium, Kellis M, Collins JJ, Stolovitzky G. Wisdom of crowds for robust gene network inference 2012. Nat Methods. 9(8):796-804
July 2012| DREAM winner publication
Barillot E., Calzone L., Hupe P., Vert J.-P., Zinovyev A. Computational Systems Biology of Cancer Chapman & Hall, CRC Mathematical & Computational Biology, 2012, 452 p.
August 2012 | Book written from the team experience
Boeva V, Zinovyev A, Bleakley K, Vert JP, Janoueix-Lerosey I, Delattre O, Barillot E. Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization. Bioinformatics. 2011 Jan 15;27(2):268-9.
January 2011 | >100 citations
Gorban A, Kegl B, Wunch D, Zinovyev A. (eds.) Principal Manifolds for Data Visualisation and Dimension Reduction. 2008. Lecture Notes in Computational Science and Engeneering 58, p.340.
November, 2008 | >200 citations
Rapaport F., Zinovyev A., Dutreix M., Barillot E., Vert J.-P.Classification of microarray data using gene networks. 2007. BMC Bioinformatics 8:35.
February 2007 | >200 citations