Bioinformatics Laboratory, University Magna Græcia of Catanzaro
Prof. Mario Cannataro (Associate Professor)
Dr. Pierangelo Veltri (PhD, Assistant Professor)
2 PhD Students in Informatics and Biomedical Engineering
Different graduate collaborators
bioinformatics and computational proteomics,
advanced electronic patient records,
grid problem solving environments and ontologies,
data mining applied to biomedical data.
Software Prototypes In the following, some main projects and software prototypes, mainly developed in collaboration with the University of Catanzaro Hospital, are described.
IMPRECO: A Framework to improve the Prediction of Protein Complexes.
Cellular processes are composed by a set of elementary events mediated by proteins. Identifying and analyzing all the possible physical interactions among proteins are important steps in studying cellular biology. Protein interactions are modeled as undirected graphs where vertices represent proteins and edges denote interactions among them. Resulting networks of interactions are thus studied by investigating graph properties, such as connectivity or degree distribution, or by individuating particular regions that encode relevant biological properties. Searching for small and highly interconnected regions is used for the prediction of protein complexes, i.e. a set of proteins assembled together playing a biological function. Currently, there exist different approaches for the prediction of complexes, that are based on a particular graph clustering schema. This scenario presents two main drawbacks: (i) currently available predictors may be only used by experts, (ii) results of different algorithms can not easily be integrated.
IMPRECO is a framework based on a novel algorithm that integrates results produced by different predictors to improve the prediction of complexes, and offers a graphical user interface to simplify the use. The proposed algorithm builds a metaclustering in three steps: (i) in the first step it considers the graphs commonly found by the different predictors, and then it finds both (ii) sub/supergraphs (ii), and (iii) overlapping graphs.
myMCL: a Web Portal Interface to MCL algorithm.
Interactomics is the study of the Interactome, i.e. the whole set of macromolecular interactions within a cell. Proteins interact among them and different interactions are represented as graphs named Protein to Protein Interaction (PPI) networks. The interest in analyzing PPI networks is related to the possibility of predicting PPI properties on the basis of global properties of the graph (e.g. verify if homology among species involves PPI similarity), or to find set of protein interactions that have a biological meaning. The prediction of protein complexes has been faced in the last years by using different clustering algorithms. The Markov Clustering algorithm (MCL) is a method that presents one of the best performance but is currently available only as a standalone application with a simple command-line interface.
Following a trend in bioinformatics, we provide a web portal allowing remote users to access MCL functions through the web. Link
SIGMCC: Querying Electronic Patient Record in a Peer-to-Peer Environment. The objective of SIGMCC is to build a meta Electronic Patient Record (metaEPR) to support cooperative work among healthcare centers (e.g. hospitals, primary doctors, etc.) through an hybrid Peer-to-Peer (P2P) network that allows the sharing of information about patients, that is the electronic patient records (EPRs).
Mario Cannataro, Domenico Talia, Giuseppe Tradigo, Paolo Trunfio, and Pierangelo Veltri, SIGMCC: a System for Sharing Meta Patient Records in a Peer-to-peer Environment, Future Generation Computer Systems, http://dx.doi.org/10.1016/j.future.2007.06.006, 2007
Mario Cannataro, Domenico Talia, Giuseppe Tradigo, Paolo Trunfio, Pierangelo Veltri, Giovanni Zarola, Sharing Electronic Patient Records Using A Peer-to-Peer Infrastructure, 11th World Congress on Internet in Medicine (MEDNET 2006), Toronto, October 13-20, 2006.
REVA, a system for the collection and analysis of voice signals currently under testing.
EIPEPTIDI. EIPeptiDi is a method and a tool for improving the data processing and peptide identification in sample sets subjected to ICAT labeling and LC-MS/MS analysis, based on cross validating MS/MS results. It boosts the ICAT data analysis software improving peptide identification throughout the input data set. Heavy/Light (H/L) pairs quantified but not identified by the MS/MS routine, are assigned to peptide sequences identified in other samples, by using similarity criteria based on chromatographic retention time and Heavy/Light mass attributes. EIPeptiDi significantly improves the number of identified peptides per sample, proving that the proposed method has a considerable impact on the protein identification process and, consequently, on the amount of potentially critical information in clinical studies.
Mario Cannataro, Giovanni Cuda, Marco Gaspari, Sergio Greco, Giuseppe Tradigo and Pierangelo Veltri, The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC MS/MS experiments, BMC Bioinformatics 2007, 8:255, http://dx.doi.org/10.1186/1471-2105-8-255, Published 15 July 2007
MS-ANALYZER. MS-Analyzer is a Grid-based Problem Solving Environment for the integrated management and processing of mass spectrometry proteomics data (MALDI-TOF, LC/MS/MS). It uses domain ontologies for modeling software tools and spectra data, and workflow techniques for designing data analysis applications. Moreover, SpecDB, a specialized spectra database, is used to manage and share experimental spectra data.
Mario Cannataro, Annalisa Barla, Roberto Flor, Alessandro Gallo, Giuseppe Jurman, Stefano Merler, Silvano Paoli, Giuseppe Tradigo, Pierangelo Veltri, Cesare Furlanello, A grid environment for high-throughput proteomics, IEEE Transaction on NanoBiosciences, 6(2): 117 - 123, June 2007, http://dx.doi.org/10.1109/TNB.2007.897495 2007.
M. Cannataro, P. Veltri, MS-Analyzer: Composing and Executing Preprocessing and Data Mining Services for Proteomics Applications on the Grid, Concurrency and Computation: Practice and Experience, Published Online: 19 Dec 2006. http://dx.doi.org/10.1002/cpe.1144, ISSN=1532-0626
M. Cannataro, P. Veltri, Sharing Mass Spectrometry Data in a Grid-based Distributed Proteomics Laboratory, Information Processing & Management, 43(3): 577-591, 2007, Elsevier. http://dx.doi.org/10.1016/j.ipm.2006.10.008, ISSN: 0306-4573
M. Cannataro, P. H. Guzzi, T. Mazza, G. Tradigo, P. Veltri, Managing Ontologies for Grid Computing, Multiagent and Grid Systems, Volume 2, Number 1, 2006, pp. 29-44. IOS Press. ISSN=1574-1702, http://iospress.metapress.com/openurl.asp?genre=article&issn=1574-1702&volume=2&issue=1&spage=29.
SpectraViewer. SpectraViewer is a software system for the visualization and management of spectra data. SpectraViewer currently supports MALDI-TOF and LC-MS/MS spectra data. It offers the followig functions: (i) loading of spectra, (ii) visualization of spectra (2D and 3D), (iii) management of spectra stored by using the HUPO-PSI mzData standard. SpectraViewer is dedeveloped by using the Java Web Start technology.
Cannataro, M., Cuda, G., Gaspari, M., Veltri, P.: An interactive tool for the management and visualization of mass-spectrometry proteomics data. In: Masulli, F., Mitra, S., Pasi, G. (eds.) WILF-07, LNCS (LNAI), vol. 4578, pp. 635–642. Springer, Heidelberg (2007)
JSSPRED is a software system for the protein secondary structure prediction developed in collaboration with the DEIS Department, University of Calabria.