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.

Abstract. The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed.

Abstract. 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 user's and/or application supplied information, performance and scalability. The iGrid Information Service has been specified and carefully designed to meet these requirements.

Increasingly, complex scientific applications are structured in terms of workflows. These applications are usually computationally and/or data intensive and thus are well suited for execution in grid environments. Distributed, geographically spread computing and storage resources are made available to scientists belonging to virtual organizations sharing resources across multiple administrative domains through established service-level agreements. Grids provide an unprecedented opportunity for distributed workflow execution; indeed, many applications are well beyond the capabilities of a single computer, and partitioning the overall computation on different components whose execution may benefit from runs on different architectures could provide better performances. In this paper we describe the design and implementation of the Grid Resource Broker (GRB) workflow engine. M. CAFARO ET AL.unprecedented opportunity for distributed workflow execution; indeed, many applications are well beyond the capabilities of a single computer, and partitioning the overall computation on different components whose execution may benefit from runs on different architectures could provide better performances. Workflow management is emerging as one of the most important services of GCEs; it captures the linkage of its constituent services to build a larger composite service. An important step of workflow execution is verification and validation [3,4]. Conceptually, a workflow is a set of tasks with strict data dependencies. In its simplest form, a workflow is modelled as a directed acyclic graph (DAG), whose vertices represent computational tasks; the edges in the graph model the data dependencies among the tasks. Execution of a task in the workflow may begin only when all of its inputs are made available to the task and, upon task completion, the output produced must be transferred to the child tasks, i.e. to the adjacent vertices. More complex workflow models are possible and desirable. In particular, many advanced applications would not be possible without allowing for the possibility of cycles and/or condition vertices in the graph underlying the workflow. Cycles capture the essence of complex applications requiring repeated execution of some steps and condition vertices provide the means to enhance a workflow by adding a control flow based on the truth of expressions related to conditions specified by the user. This mechanism allows for conditional branching, since in this case the execution of a particular task does not necessarily trigger the execution of the set of all of its adjacent tasks (as in the case of DAGs). In this paper we describe the design and implementation of the Grid Resource Broker (GRB) [5,6] workflow engine, which deals with workflows described by arbitrary graphs and handles both cycle and condition vertices; an important feature provided is recursive composition, i.e. the possibility to define a workflow vertex as a sub-workflow or parameter sweep vertex instead of a batch task. This paper is organized as ...