Alun Ashton

Data Analysis WorkbeNch (DAWN)

DAWN is a generic data analysis software platform that has been developed for use at synchrotron beamlines for data visualization and analysis. Its generic design makes it suitable for use in a range of scientific and engineering applications.

The new CCP4 Coordinate Library as a toolkit for the design of coordinate-related applications in protein crystallography

The new CCP4 Coordinate Library is a development aiming to provide a common layer of coordinate-related functionality to the existing applications in the CCP4 suite, as well as a variety of tools that can simplify the design of new applications where they relate to atomic coordinates. The Library comprises a wide spectrum of useful functions, ranging from parsing coordinate formats and elementary editing operations on the coordinate hierarchy of biomolecules, to high-level functionality such as calculation of secondary structure, interatomic bonds, atomic contacts, symmetry transformations, structure superposition and many others. Most of the functions are available in a C++ object interface; however, a Fortran interface is provided for compatibility with older CCP4 applications. The paper describes the general principles of the Library design and the most important functionality. The Library, together with documentation, is available under the LGPL license from the CCP4 suite version 5.0 and higher.

High‐speed crystal detection and characterization using a fast‐readout detector

A grid-scan tool that enables rapid characterization of large sample volumes using a microfocused X-ray beam and a fast-readout detector is reported.

Ongoing developments in CCP4 for high-throughput structure determination

Collaborative Computational Project Number 4 (CCP4) was established in 1979 to promote collaboration between UK groups writing software for protein crystallography. From these beginnings, CCP4 now distributes a large software suite and is active in developing new software. In this article, an overview is given of recent and ongoing developments in the CCP4 software suite, in particular as they pertain to high-throughput studies. Developments in individual programs are discussed first, although these are covered in more detail elsewhere. The bulk of the article focuses on the infrastructure of the software suite which allows the user to move effortlessly between different programs or to create automated schemas. Major changes to the software library at the heart of the CCP4 suite, developments in the CCP4 graphical user interface, and data management within CCP4 are discussed. The latter is crucial to high-throughput studies, where a large number of data are imported, created and finally archived.

A 3D cellular context for the macromolecular world

We report the outcomes of the discussion initiated at the workshop entitled A 3D Cellular Context for the Macromolecular World and propose how data from emerging three-dimensional (3D) cellular imaging techniques—such as electron tomography, 3D scanning electron microscopy and soft X-ray tomography—should be archived, curated, validated and disseminated, to enable their interpretation and reuse by the biomedical community.

Design of a data model for developing laboratory information management and analysis systems for protein production

Data management has emerged as one of the central issues in the high‐throughput processes of taking a protein target sequence through to a protein sample. To simplify this task, and following extensive consultation with the international structural genomics community, we describe here a model of the data related to protein production. The model is suitable for both large and small facilities for use in tracking samples, experiments, and results through the many procedures involved. The model is described in Unified Modeling Language (UML). In addition, we present relational database schemas derived from the UML. These relational schemas are already in use in a number of data management projects. Proteins 2005.

SuRVoS: Super-Region Volume Segmentation workbench

Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic segmentation of biological volumes is still a very challenging research field, and current methods usually require a large amount of manually-produced training data to deliver a high-quality segmentation. However, the complex appearance of cellular features and the high variance from one sample to another, along with the time-consuming work of manually labelling complete volumes, makes the required training data very scarce or non-existent. Thus, fully automatic approaches are often infeasible for many practical applications. With the aim of unifying the segmentation power of automatic approaches with the user expertise and ability to manually annotate biological samples, we present a new workbench named SuRVoS (Super-Region Volume Segmentation). Within this software, a volume to be segmented is first partitioned into hierarchical segmentation layers (named Super-Regions) and is then interactively segmented with the users knowledge input in the form of training annotations. SuRVoS first learns from and then extends user inputs to the rest of the volume, while using Super-Regions for quicker and easier segmentation than when using a voxel grid. These benefits are especially noticeable on noisy, low-dose, biological datasets.

dxtbx: the diffraction experiment toolbox

A Python/C++ library for reading image data and experimental geometry for X-ray diffraction experiments from arbitrary data sources is presented.

MOLE: a data management application based on a protein production data model.

MOLE (mining, organizing, and logging experiments) has been developed to meet the growing data management and target tracking needs of molecular biologists and protein crystallographers. The prototype reported here will become a Laboratory Information Management System (LIMS) to help protein scientists manage the large amounts of laboratory data being generated due to the acceleration in proteome research and will furthermore facilitate collaborations between groups based at different sites. To achieve this, MOLE is based on the data model for protein production devised at the European Bioinformatics Institute (Pajon A, et al., Proteins in press). Proteins 2005.

Application of in situ diffraction in high-throughput structure determination platforms.

Macromolecular crystallography (MX) is the most powerful technique available to structural biologists to visualize in atomic detail the macromolecular machinery of the cell. Since the emergence of structural genomics initiatives, significant advances have been made in all key steps of the structure determination process. In particular, third-generation synchrotron sources and the application of highly automated approaches to data acquisition and analysis at these facilities have been the major factors in the rate of increase of macromolecular structures determined annually. A plethora of tools are now available to users of synchrotron beamlines to enable rapid and efficient evaluation of samples, collection of the best data, and in favorable cases structure solution in near real time. Here, we provide a short overview of the emerging use of collecting X-ray diffraction data directly from the crystallization experiment. These in situ experiments are now routinely available to users at a number of synchrotron MX beamlines. A practical guide to the use of the method on the MX suite of beamlines at Diamond Light Source is given.