Registration
Attendance to tutorials for workshop participants is free, but subject to the limitations listed below. Workshop participants must specify their interest for one or more tutorials in the workshop registration form.
For Tutorial 3, register as well through the Application form, even if you plan to register, or have already registered, for the Workshop. For this tutorial, see also the detailed programme.
In order to register for Tutorials 1 or 2 without attending the workshop, you must send an email to nettab.workshops@gmail.com.
Please note that for some tutorials a limited number of attendees will be accepted. Priority will be given to workshop participants on a first-come/first-served policy.
List of tutorials
Tutorial 1, Monday October 16, 2017, 10:00 – 12:30
An introductory tour on Big Data, Big Data technologies, and Big Data applications in Biology and Medicine
Simona Rombo, University of Palermo, Italy
Tutorial offered by INdAM – GNCS Project 2017: “Efficient Algorithms and Techniques for the Organization, Management and Analysis of Biological Big Data”
Tutorial 2, Monday October 16, 2017, 10:00 – 12:30
Biological network-based analysis of "-omics" for precision medicine: overview, interaction databases and network diffusion approaches
Ettore Mosca, Institute of Biomedical Technologies / CNR, Segrate (MI), Italy
Tutorial offered by CNR InterOmics Flagship project
Tutorial 3, Wednesday October 18, 15:00-18:00 – Thursday October 19, 09:00-17:00
Biological Networks: data analysis, visualisation and medical application (provisional title)
Alberto Calderone, ELIXIR-IIB, Italy
Inna Kuperstein, Institut Curie, Paris, France
Luana Licata, ELIXIR-IIB, Italy
Tutorial offered by ELIXIR-IIB, the Italian ELIXIR node.
Tutorial 1, Monday October 16, 2017, 10:00 – 12:30
Tutorial offered by INdAM – GNCS Project 2017: “Efficient Algorithms and Techniques for the Organization, Management and Analysis of Biological Big Data”
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Simona Rombo, University of Palermo, Italy |
Simona E. Rombo is Assistant Professor at the Department of Mathematics and Computer Science (DMI) of the University of Palermo (Palermo, Italy) since September 2012. Her main research activities focus on Bioinformatics, Algorithms and Data Structures, Data Mining. She co-authored more than 50 scientific publications appearing in established journals and conferences proceedings in these fields. In particular, she worked on local/global alignment and querying of biological networks, clustering of protein-protein interaction networks, analysis of biological sequences and 2D arrays, analysis and management of biological big data. She contributed to develop and/or coordinated the development of several software tools related to these topics. She serves as a member of the Program Committee for several international conferences (e.g., GECCO, EvoBio, Bioinformatics) and she is on the Editorial Board of Journal of Big Data, The Open Bioinformatics Journal and others. She was invited lecturer and/or visiting scientist at several prestigious institutes, such as Oxford University (Oxford, UK), National Institute of Health (Bethesda, USA), College of Computing of the Georgia Institute of Technology (Atlanta, USA), Purdue University (West Lafayette, USA). She contributed with her research to many national and international research grants.
An introductory tour on Big Data, Big Data technologies, and Big Data applications in Biology and Medicine
In this tutorial we aim at providing an overview on the Big Data technologies, by specifying some technical aspects and by illustrating how these technologies may be applied in the bioinformatics/biomedical context.
In particular, the first part of the tutorial will focus on introducing the audience to the notion of "Big Data". Then the Map-Reduce paradigm will be explained, by also discussing how it has been implemented on the Apache Hadoop and Spark frameworks. Moreover, the two frameworks will be compared in terms of their advantages and drawbacks, by explaining when each of them is more suitable to be applied than the other one.
In the second part of the tutorial, it will be explained why the relational model cannot be applied for database design in the Big Data era, and the notion of NoSQL databases will be illustrated as well as some of the most famous examples, such as Cassandra, MongoDB, HBase, etc.. Finally, some applications of Big Data technologies in the context of Biology and Medicine will be showed.
Tutorial 2, Monday October 16, 2017, 10:00 – 12:30
Tutorial offered by CNR InterOmics Flagship project
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Ettore Mosca, Institute of Biomedical Technologies / CNR, Milan, Italy |
Ettore Mosca is a permanent researcher at the Institute of Biomedical Technologies of the National Research Council of Italy since March 2014. He received the BS in Molecular Biotechnologies, MS in Bioinformatics and PhD in Computer Science from the University of Milan-Bicocca. His main research activities focus on "-omics" data analysis and biological networks.
In particular, he worked on the analysis of different types of "-omics" (exome sequencing, SNP arrays, gene expression microarrays, RNA-seq, small RNA-seq and protein expression data), biological network-based and pathway-based methods, multi-omics data integration, modeling of biological pathways as dynamical systems, implementation of software packages.
He spent a few months as visiting scientist at the University of Edinburgh and at the University of Cambridge. He has been involved in several Italian and European projects (BIOINFOGRID, EGEE, NET2DRUG, MIMOMICS). He trained students of Biotechnologies, Biomedical Engineering and Applied Physics in collaboration with Italian Universities. He is a full member of the Bioinformatics Italian Society (BITS) and National Association of Italian Biotechnologists (ANBI). He is the author of more than 30 papers in peer-reviewed international scientific journals and several speeches at scientific conferences.
Biological network-based analysis of "-omics" for precision medicine: overview, interaction databases and network diffusion approaches
This tutorial aims at illustrating how the known molecular interactions can be useful in the interpretation of "-omics" data for precision medicine, with a main focus on data referable to genes.
In the first part, we will introduce broad objectives and principles of biological network-based analyses of "-omics" data. Then, we will overview the state-of-the art of current datasets from which networks can be defined, their completeness in relation to the coverage of high-throughput technologies and the issues in mapping "-omics" data to biological networks.
In the second part, we will focus on network diffusion-based approaches and illustrate how the principles behind this class of methods have been recently applied to several problems, including patient stratification and gene module extraction.
Throughout the tutorial, we will underline benefits, limitations and open issues in the field of network-based analysis, including also software availability and computational cost.
Tutorial 3, Wednesday October 18, 15:00 – Thursday October 19, 17:00
Tutorial offered by ELIXIR-IIB, the Italian ELIXIR node.
(See detailed programme)
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Alberto Calderone, ELIXIR-IIB, Italy |
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Inna Kuperstein, Institut Curie, Paris, France |
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Luana Licata, ELIXIR-IIB, Italy |
Inna (Faina) Kuperstein is a scientist at Institut Curie in Paris, working on systems biology of cancer and other human diseases (https://sysbio.curie.fr/). She obtained her PhD in neurochemistry at the Weizmann institute of science, Israel, where she studied molecular mechanisms of stroke and oxidative stress. Then she did a postdoctoral research in the Center for the Biology of Disease at the Flanders institute of biotechnology (VIB) in Leuven, Belgium in the field of molecular mechanisms of synaptic and neuronal toxicity in Alzheimer‘s disease.
Molecular and cell biologist with wide experience in signal transduction research, since 2009 she is applying her biological knowledge in the field of computational systems biology in the group of Computational Systems Biology of Cancer, Institut Curie. She participates in multidisciplinary projects with pharmaceutical companies, biologists, mathematicians, systems biologists and clinicians. In particular, she specialises in construction of comprehensive cell signaling maps, their applications for analysis with high-throughput data and results interpretation.
Among others, she has initiated and is currently involved in development of web-based tool NaviCell (https://navicell.curie.fr/) dedicated for manipulations with big signalling maps. This Google Maps engine-based tool allows user-friendly navigation through big molecular map as well as integration and visualisation of high-throughput data in the context of those maps. In addition, she has initiated and is now leading the Atlas of Cancer Signalling Network (ACSN) project (https://acsn.curie.fr/). This project is dedicated to systematic and detailed representation of molecular mechanisms involved in cancer. ACSN maps are applied for network analysis and modelling new synthetic interactions between genes in cancer, for predicting drug response and resistance mechanisms in patients.
Luana Licata acquired her competencies in over 15 years of professional involvement in various aspects of applied life sciences. She has a Master’s Degree in Biology and obtained her Ph.D. in biochemistry and structural biology at the Max-Planck-Institute of Biophysics in Frankfurt am Main in 2004. Since 2006, she has been working as researcher and biocurator in the Bioinformatics and Computational Biology Unit of the Molecular Genetics Laboratory led by prof. Gianni Cesareni at the Tor Vergata University of Rome, Italy.
Her main research activity as MINT Database curator (Molecular INTeraction database) includes the curation of molecular biology and molecular medicine papers with specific focus on biological pathways and protein interactions, and the integration and validation of heterogeneous biological data. More recently she has been involved in the coordination and curation of VirusMentha, which integrates data from the major databases and the SIGNOR resource for signalling information.
Alberto Calderone is a computer scientist with a Master’s Degree in bioinformatics. After working as IT consultant, he obtained his PhD in Bioinformatics under the supervision of prof. Gianni Cesareni, working in close contact with biologists and applying dynamical models, graph theory and machine learning to data analysis and processing.
His research interests include Networks and Computer Science, Data Analysis, Systems Biology, Computational Biology and Bioinformatics. He was an invited trainer in graph theory and analysis at the ELIXIR course “Protein Networks and Systems Biology” held in Bologna. He gave invited seminars on Systems Biology at the Sapienza and Tor Vergata Universities of Rome. He assists in practical training sessions on Computational Systems Biology analysis techniques at the University of Rome Tor Vergata. Currently, he is a Post-Doctoral Research Fellow in the Bioinformatics and Computational Biology Unit of the Molecular Genetics Laboratory led by prof. Gianni Cesareni (Tor Vergata University of Rome, IT).
Biological Networks: data analysis, visualisation and medical application (provisional title)
(See detailed programme)
The goal of this tutorial is to provide an overview on network construction, data analysis and modelling in bio-medical research. The participants will be introduced to molecular and causal interactions field, how these data are curated and which are the international standards and the ontologies adopted to described these data. A large number of resources and methodologies will be introduced.
We will provide hands-on session where participants will be asked to analyse a dataset in the context of network analysis and modelling. Moreover, available methods and resources for integrated analysis and visualization of different multi-omics data will be shown.
Participants will develop an understanding not only on static networks but also on discrete and continuous dynamical networks and how to simulate basic systems in order to derive meaningful information related to cancer.
