CcpNmr AnalysisAssign: a flexible platform for integrated NMR analysis.

NMR spectroscopy is an indispensably powerful technique for the analysis of biomolecules under ambient conditions, both for structural- and functional studies. However, in practice the complexity of the technique has often frustrated its application by non-specialists. In this paper, we present CcpNmr version-3, the latest software release from the Collaborative Computational Project for NMR, for all aspects of NMR data analysis, including liquid- and solid-state NMR data.

Structure calculation, refinement and validation using CcpNmr Analysis.

CcpNmr Analysis provides a streamlined pipeline for both NMR chemical shift assignment and structure determination of biological macromolecules. In addition, it encompasses tools to analyse the many additional experiments that make NMR such a pivotal technique for research into complex biological questions. This report describes how CcpNmr Analysis can seamlessly link together all of the tasks in the NMR structure-determination process.

An overview of tools for the validation of protein NMR structures.

Biomolecular structures at atomic resolution present a valuable resource for the understanding of biology. NMR spectroscopy accounts for 11% of all structures in the PDB repository. In response to serious problems with the accuracy of some of the NMR-derived structures and in order to facilitate proper analysis of the experimental models, a number of program suites are available.

Structure simulation with calculated NMR parameters - integrating COSMOS into the CCPN framework.

The Collaborative Computing Project for NMR (CCPN) has build a software framework consisting of the CCPN data model (with APIs) for NMR related data, the CcpNmr Analysis program and additional tools like CcpNmr FormatConverter. The open architecture allows for the integration of external software to extend the abilities of the CCPN framework with additional calculation methods. Recently, we have carried out the first steps for integrating our software Computer Simulation of Molecular Structures (COSMOS) into the CCPN framework.

A software framework for analysing solid-state MAS NMR data.

Solid-state magic-angle-spinning (MAS) NMR of proteins has undergone many rapid methodological developments in recent years, enabling detailed studies of protein structure, function and dynamics. Software development, however, has not kept pace with these advances and data analysis is mostly performed using tools developed for solution NMR which do not directly address solid-state specific issues. Here we present additions to the CcpNmr Analysis software package which enable easier identification of spinning side bands, straightforward analysis of double quantum spectra, automatic consideration of non-uniform labelling schemes, as well as extension of other existing features to the needs of solid-state MAS data.

The CCPN Metabolomics Project: a fast protocol for metabolite identification by 2D-NMR.

Summary: We present here the freely available Metabolomics Project resource specifically designed to work under the CcpNmr Analysis program produced by CCPN (Collaborative Computing Project for NMR) (Vranken et al., 2005, The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins, 59, 687-696).

Straightforward and complete deposition of NMR data to the PDBe.

We present a suite of software for the complete and easy deposition of NMR data to the PDB and BMRB. This suite uses the CCPN framework and introduces a freely downloadable, graphical desktop application called CcpNmr Entry Completion Interface (ECI) for the secure editing of experimental information and associated datasets through the lifetime of an NMR project. CCPN projects can be created within the CcpNmr Analysis software or by importing existing NMR data files using the CcpNmr FormatConverter.

MEMOPS: data modelling and automatic code generation.

In recent years the amount of biological data has exploded to the point where much useful information can only be extracted by complex computational analyses. Such analyses are greatly facilitated by metadata standards, both in terms of the ability to compare data originating from different sources, and in terms of exchanging data in standard forms, e.g.

A nomenclature and data model to describe NMR experiments.

Despite ongoing efforts in organising NMR information, there is no consistent and well-described generic standard for naming NMR experiments. The main reason for the absence of a universal naming system is that the information content of the coherence pathways is difficult to describe in full detail. We propose a system that describes the common and generic elements of the coherence pathways produced by pulse sequences.

The CCPN data model for NMR spectroscopy: development of a software pipeline.

To address data management and data exchange problems in the nuclear magnetic resonance (NMR) community, the Collaborative Computing Project for the NMR community (CCPN) created a "Data Model" that describes all the different types of information needed in an NMR structural study, from molecular structure and NMR parameters to coordinates. This paper describes the development of a set of software applications that use the Data Model and its associated libraries, thus validating the approach. These applications are freely available and provide a pipeline for high-throughput analysis of NMR data.

A framework for scientific data modeling and automated software development.

Motivation: The lack of standards for storage and exchange of data is a serious hindrance for the large-scale data deposition, data mining and program interoperability that is becoming increasingly important in bioinformatics. The problem lies not only in defining and maintaining the standards, but also in convincing scientists and application programmers with a wide variety of backgrounds and interests to adhere to them.

Results: We present a UML-based programming framework for the modeling of data and the automated production of software to manipulate that data.