IceBear: an intuitive and versatile web application for research-data tracking from crystallization experiment to PDB deposition.

The web-based IceBear software is a versatile tool to monitor the results of crystallization experiments and is designed to facilitate supervisor and student communications. It also records and tracks all relevant information from crystallization setup to PDB deposition in protein crystallography projects. Fully automated data collection is now possible at several synchrotrons, which means that the number of samples tested at the synchrotron is currently increasing rapidly. Therefore, the protein crystallography research communities at the University of Oulu, Weizmann Institute of Science and Diamond Light Source have joined forces to automate the uploading of sample metadata to the synchrotron. In IceBear, each crystal selected for data collection is given a unique sample name and a crystal page is generated. Subsequently, the metadata required for data collection are uploaded directly to the ISPyB synchrotron database by a shipment module, and for each sample a link to the relevant ISPyB page is stored. IceBear allows notes to be made for each sample during cryocooling treatment and during data collection, as well as in later steps of the structure determination. Protocols are also available to aid the recycling of pins, pucks and dewars when the dewar returns from the synchrotron. The IceBear database is organized around projects, and project members can easily access the crystallization and diffraction metadata for each sample, as well as any additional information that has been provided via the notes. The crystal page for each sample connects the crystallization, diffraction and structural information by providing links to the IceBear drop-viewer page and to the ISPyB data-collection page, as well as to the structure deposited in the Protein Data Bank.

How best to use photons.

Strategies for collecting X-ray diffraction data have evolved alongside beamline hardware and detector developments. The traditional approaches for diffraction data collection have emphasised collecting data from noisy integrating detectors (i.e. film, image plates and CCD detectors). With fast pixel array detectors on stable beamlines, the limiting factor becomes the sample lifetime, and the question becomes one of how to expend the photons that your sample can diffract, i.e. as a smaller number of stronger measurements or a larger number of weaker data. This parameter space is explored via experiment and synthetic data treatment and advice is derived on how best to use the equipment on a modern beamline. Suggestions are also made on how to acquire data in a conservative manner if very little is known about the sample lifetime.

Where is crystallography going?

Macromolecular crystallography (MX) has been a motor for biology for over half a century and this continues apace. A series of revolutions, including the production of recombinant proteins and cryo-crystallography, have meant that MX has repeatedly reinvented itself to dramatically increase its reach. Over the last 30 years synchrotron radiation has nucleated a succession of advances, ranging from detectors to optics and automation. These advances, in turn, open up opportunities. For instance, a further order of magnitude could perhaps be gained in signal to noise for general synchrotron experiments. In addition, X-ray free-electron lasers offer to capture fragments of reciprocal space without radiation damage, and open up the subpicosecond regime of protein dynamics and activity. But electrons have recently stolen the limelight: so is X-ray crystallography in rude health, or will imaging methods, especially single-particle electron microscopy, render it obsolete for the most interesting biology, whilst electron diffraction enables structure determination from even the smallest crystals? We will lay out some information to help you decide.

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench.

Segmentation is the process of isolating specific regions or objects within an imaged volume, so that further study can be undertaken on these areas of interest. When considering the analysis of complex biological systems, the segmentation of three-dimensional image data is a time consuming and labor intensive step. With the increased availability of many imaging modalities and with automated data collection schemes, this poses an increased challenge for the modern experimental biologist to move from data to knowledge. This publication describes the use of SuRVoS Workbench, a program designed to address these issues by providing methods to semi-automatically segment complex biological volumetric data. Three datasets of differing magnification and imaging modalities are presented here, each highlighting different strategies of segmenting with SuRVoS. Phase contrast X-ray tomography (microCT) of the fruiting body of a plant is used to demonstrate segmentation using model training, cryo electron tomography (cryoET) of human platelets is used to demonstrate segmentation using super- and megavoxels, and cryo soft X-ray tomography (cryoSXT) of a mammalian cell line is used to demonstrate the label splitting tools. Strategies and parameters for each datatype are also presented. By blending a selection of semi-automatic processes into a single interactive tool, SuRVoS provides several benefits. Overall time to segment volumetric data is reduced by a factor of five when compared to manual segmentation, a mainstay in many image processing fields. This is a significant savings when full manual segmentation can take weeks of effort. Additionally, subjectivity is addressed through the use of computationally identified boundaries, and splitting complex collections of objects by their calculated properties rather than on a case-by-case basis.

Best practices for managing large CryoEM facilities.

This paper provides an overview of the discussion and presentations from the Workshop on the Management of Large CryoEM Facilities held at the New York Structural Biology Center, New York, NY on February 6-7, 2017. A major objective of the workshop was to discuss best practices for managing cryoEM facilities. The discussions were largely focused on supporting single-particle methods for cryoEM and topics included: user access, assessing projects, workflow, sample handling, microscopy, data management and processing, and user training.

Electron Bio-Imaging Centre (eBIC): the UK national research facility for biological electron microscopy.

The recent resolution revolution in cryo-EM has led to a massive increase in demand for both time on high-end cryo-electron microscopes and access to cryo-electron microscopy expertise. In anticipation of this demand, eBIC was set up at Diamond Light Source in collaboration with Birkbeck College London and the University of Oxford, and funded by the Wellcome Trust, the UK Medical Research Council (MRC) and the Biotechnology and Biological Sciences Research Council (BBSRC) to provide access to high-end equipment through peer review. eBIC is currently in its start-up phase and began by offering time on a single FEI Titan Krios microscope equipped with the latest generation of direct electron detectors from two manufacturers. Here, the current status and modes of access for potential users of eBIC are outlined. In the first year of operation, 222 d of microscope time were delivered to external research groups, with 95 visits in total, of which 53 were from unique groups. The data collected have generated multiple high- to intermediate-resolution structures (2.8-8 Å), ten of which have been published. A second Krios microscope is now in operation, with two more due to come online in 2017. In the next phase of growth of eBIC, in addition to more microscope time, new data-collection strategies and sample-preparation techniques will be made available to external user groups. Finally, all raw data are archived, and a metadata catalogue and automated pipelines for data analysis are being developed.

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 user's 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.

Automatic processing of multimodal tomography datasets.

With the development of fourth-generation high-brightness synchrotrons on the horizon, the already large volume of data that will be collected on imaging and mapping beamlines is set to increase by orders of magnitude. As such, an easy and accessible way of dealing with such large datasets as quickly as possible is required in order to be able to address the core scientific problems during the experimental data collection. Savu is an accessible and flexible big data processing framework that is able to deal with both the variety and the volume of data of multimodal and multidimensional scientific datasets output such as those from chemical tomography experiments on the I18 microfocus scanning beamline at Diamond Light Source.

Data Analysis WorkbeNch (DAWN).

Synchrotron light source facilities worldwide generate terabytes of data in numerous incompatible data formats from a wide range of experiment types. The Data Analysis WorkbeNch (DAWN) was developed to address the challenge of providing a single visualization and analysis platform for data from any synchrotron experiment (including single-crystal and powder diffraction, tomography and spectroscopy), whilst also being sufficiently extensible for new specific use case analysis environments to be incorporated (e.g. ARPES, PEEM). In this work, the history and current state of DAWN are presented, with two case studies to demonstrate specific functionality. The first is an example of a data processing and reduction problem using the generic tools, whilst the second shows how these tools can be targeted to a specific scientific area.

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.

dxtbx: the diffraction experiment toolbox.

Data formats for recording X-ray diffraction data continue to evolve rapidly to accommodate new detector technologies developed in response to more intense light sources. Processing the data from single-crystal X-ray diffraction experiments therefore requires the ability to read, and correctly interpret, image data and metadata from a variety of instruments employing different experimental representations. Tools that have previously been developed to address this problem have been limited either by a lack of extensibility or by inconsistent treatment of image metadata. The dxtbx software package provides a consistent interface to both image data and experimental models, while supporting a completely generic user-extensible approach to reading the data files. The library is written in a mixture of C++ and Python and is distributed as part of the cctbx under an open-source licence at http://cctbx.sourceforge.net.

ISPyB: an information management system for synchrotron macromolecular crystallography.

MOTIVATION: Individual research groups now analyze thousands of samples per year at synchrotron macromolecular crystallography (MX) resources. The efficient management of experimental data is thus essential if the best possible experiments are to be performed and the best possible data used in downstream processes in structure determination pipelines. Information System for Protein crystallography Beamlines (ISPyB), a Laboratory Information Management System (LIMS) with an underlying data model allowing for the integration of analyses down-stream of the data collection experiment was developed to facilitate such data management. RESULTS: ISPyB is now a multisite, generic LIMS for synchrotron-based MX experiments. Its initial functionality has been enhanced to include improved sample tracking and reporting of experimental protocols, the direct ranking of the diffraction characteristics of individual samples and the archiving of raw data and results from ancillary experiments and post-experiment data processing protocols. This latter feature paves the way for ISPyB to play a central role in future macromolecular structure solution pipelines and validates the application of the approach used in ISPyB to other experimental techniques, such as biological solution Small Angle X-ray Scattering and spectroscopy, which have similar sample tracking and data handling requirements.

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.

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).