Maria Mirto

Blurring contact maps of thousands of proteins: what we can learn by reconstructing 3D structure

BackgroundThe present knowledge of protein structures at atomic level derives from some 60,000 molecules. Yet the exponential ever growing set of hypothetical protein sequences comprises some 10 million chains and this makes the problem of protein structure prediction one of the challenging goals of bioinformatics. In this context, the protein representation with contact maps is an intermediate step of fold recognition and constitutes the input of contact map predictors. However contact map representations require fast and reliable methods to reconstruct the specific folding of the protein backbone.MethodsIn this paper, by adopting a GRID technology, our algorithm for 3D reconstruction FT-COMAR is benchmarked on a huge set of non redundant proteins (1716) taking random noise into consideration and this makes our computation the largest ever performed for the task at hand.ResultsWe can observe the effects of introducing random noise on 3D reconstruction and derive some considerations useful for future implementations. The dimension of the protein set allows also statistical considerations after grouping per SCOP structural classes.ConclusionsAll together our data indicate that the quality of 3D reconstruction is unaffected by deleting up to an average 75% of the real contacts while only few percentage of randomly generated contacts in place of non-contacts are sufficient to hamper 3D reconstruction.

iGrid, a novel grid information service

In this paper we describe iGrid, a novel Grid Information Service based on the relational model. iGrid is developed within the European GridLab project by the ISUFI Center for Advanced Computational Technologies (CACT) of the University of Lecce, Italy. Among iGrid requirements there are security, decentralized control, support for dynamic data and the possibility to handle users and/or application supplied information, performance and scalability. The iGrid Information Service has been specified and carefully designed to meet these requirements.

The grid relational catalog project

Today many DataGrid applications need to manage and process a very large amount of data distributed across multiple grid nodes and stored into heterogeneous databases. Grids encourage and promote the publication, sharing and integration of scientifica data (distributed across several Virtual Organizations) in a more open manner than is currently the case, and many e-Science pojects have an urgent need to interconnect legacy and independently operated databases through a set os data access and integration services. The complexity of data management within a Computational Grid comes from the distribution, scale and heterogeneity of data sources. A set of dynamic and adaptive services could address specific issues related to automatic data management providing high performance and transparency as well as fully exploiting a grid infrastructure. These services should involved data migration and integration, discovery of data sources and so on, providing a transparent and dynamic layer of data virtualization. In this pape we introduce the Grid-DBMS concept, a framework for dynamic data management in a grid enviroment, highlighting its requirements, architecture, components and services. We also present an overview about the Grid Relational Catalog Project (GRelC) developed at the CACT/ISUFI of the University of Lecce, which represents a partial implementation of a Grid-DBMS for the Globus Community.

Resource and service discovery in the igrid information service

In this paper we describe resource and service discovery mechanisms available in iGrid, a novel Grid Information Service based on the relational model. iGrid is developed within the GridLab project by the ISUFI Center for Advanced Computational Technologies (CACT) at the University of Lecce, Italy and it is deployed on the European GridLab testbed. The GridLab Information Service provides fast and secure access to both static and dynamic information through a GSI enabled web service. Besides publishing system information, iGrid also allow publication of users or service supplied information. The adoption of the relational model provides a flexible model for data, and the hierarchical distributed architecture provides scalability and fault tolerance.

ProGenGrid: a workflow service infrastructure for composing and executing bioinformatics grid services

We describe the ProGenGrid (Proteomics and Genomics Grid) Workflow system, developed by the CACT/ISUFI at the University of Lecce which aims at providing a tool that e-scientists can utilize to simulate biological experiments, compose existing analysis and visualization tools, monitor their execution, store the intermediate and final output and finally, if needed, save the model of the experiment for updating or reproducing it. The tools that we are considering are software components wrapped as Web services and composed through a workflow. Since bioinformatics applications need to use high performance machines or many workstations to reduce the computational time, we are exploiting a Grid infrastructure for interconnecting wide-spread tools and hardware resources.

Advanced grid database management with the GRelC data access service

Many data grid applications manage and process huge datasets distributed across multiple grid nodes and stored into heterogeneous databases; e-Science projects need to access widespread databases within a computational grid environment, through a set of secure, interoperable and efficient data grid services. In the data grid management area several projects aims at addressing these issues providing different solutions and proposing a set of data access and integration/federation services. In this paper we present the GRelC Data Access, a WS-I based data grid access service developed by SPACI Consortium and the University of Salento. Its architectural design is discussed and experimental results related to an European testbed are also reported.

Preference---Based Matchmaking of Grid Resources with CP---Nets

We deal with the problem of preference-based matchmaking of computational resources belonging to a Grid. We introduce CP–Nets, a recent development in the field of Artificial Intelligence, as a means to deal with user’s preferences in the context of Grid scheduling. We discuss CP–Nets from a theoretical perspective and then analyze, qualitatively and quantitatively, their impact on the matchmaking process, with the help of a Grid simulator we developed for this purpose. Many different experiments have been setup and carried out, and we report here our main findings and the lessons learnt.

The GRelC library: a basic pillar in the grid relational catalog architecture

Today many data grid applications need to manage and process a very large amount of data distributed across multiple grid nodes and stored in relational databases. The Grid Relational Catalog Project (GRelC) developed at the CACT/ISUFI of the University of Lecce, represents an attempt to design and deploy a grid-DBMS for the Globus Community. In this paper, after defining the grid-DBMS concept, we describe the GRelC library which is layered on top of the Globus Toolkit. The user can build client applications on top of it that can easily get access to and interact with data resources.

A Bioinfomatics Grid Alignment Toolkit

Even though many useful tools for sequence alignment are available, such as BLAST and PSI-BLAST by NCBI and FASTA by the University of Virginia, a key issue regarding sequence databases is their size, growing at an exponential rate. Grid and parallel computing are crucial techniques to maintain and improve the effectiveness of sequence comparison tools, whilst the Web Services approach may guarantee the interoperability among large collections of programs and data. This paper describes BioGAT, Bioinformatics Grid Alignment Toolkit, that offers optimized brokering and a data management system to exploit various bioinformatics alignment tools wrapped as Web Services in a Grid architecture.

The grid-DBMS: towards dynamic data management in grid environments

Nowadays many data grid applications need to manage and process a huge amount of data distributed across multiple grid nodes and stored into heterogeneous databases. Grids encourage the publication of scientific data in a more open manner than is currently the case, and many e-Science projects have an urgent need to interconnect legacy, and independently operated databases through a set of data access and integration services. In the data grid area a set of dynamic and adaptive services could address specific issues related to automatic data management aiming at both providing high performance and fully exploiting a grid infrastructure. In this paper we introduce the grid-DBMS concept, a framework for dynamic data management in grid environments, highlighting its requirements, architecture, components and services.