Scientific Computing

It is apparent that the challenges facing scientific software developers are quite different from those facing their commercial counterparts. Among these differences are the challenges posed by the complex and uncertain nature of the science. There is also the fact that many scientists have experience of developing their own software, albeit in a very restricted setting, leading them to have unrealistic expectations about software development in a different setting. In this paper, we explore the challenges facing scientific software developers focusing especially on molecular biology. We claim that the nature and practice of molecular biology is quite different from that of the physical sciences and pose different problems to software developers. We do not claim that this paper is the last word on the topic but hope that it serves as the inspiration for further debate.

The techniques used in protein production and structural biology have been developing rapidly, but techniques for recording the laboratory information produced have not kept pace. One approach is the development of laboratory information-management systems (LIMS), which typically use a relational database schema to model and store results from a laboratory workflow. The underlying philosophy and implementation of the Protein Information Management System (PiMS), a LIMS development specifically targeted at the flexible and unpredictable workflows of protein-production research laboratories of all scales, is described. PiMS is a web-based Java application that uses either Postgres or Oracle as the underlying relational database-management system. PiMS is available under a free licence to all academic laboratories either for local installation or for use as a managed service.

A major advance in protein structure determination has been the advent of nanolitre-scale crystallization and (in a high-throughput environment) the development of robotic systems for storing and imaging crystallization trials. Most of these trials are carried out in 96-well (or higher density) plates and managing them is a significant information management challenge. We describe xtalPiMS, a web-based application for the management and monitoring of crystallization trials. xtalPiMS has a user-interface layer based on the standards of the Protein Information Management System (PiMS) and a database layer which links the crystallization trial images to the meta-data associated with a particular crystallization trial. The user interface has been optimized for the efficient monitoring of high-throughput environments with three different automated imagers and work to support a fourth imager is in progress, but it can even be of use without robotics. The database can either be a PiMS database or a legacy database for which a suitable mapping layer has been developed.

Background / Purpose: The amount of data produced by biological sciences experiments are being referred to as data deluge or data flood. The need for integrating data produced from fragmented sources has never been more important and in focus. Main conclusion: BioMedBridges, a project which intends to build bridges across data sources across Europe, needs a holistic approach to integrate the dissimilar data. As mentioned earlier, the semantic approach makes real sense in integrating life sciences data. The experimental information stored in PiMS will be exposed to other research infrastructures in the form of RDF conforming to a vocabulary that is commonly agreed upon. This helps to inference and seamlessly integrate the flow of information across resources.

The West-Life project (https://about.west-life.eu/) is a Horizon 2020 project funded by the European Commission to provide data processing and data management services for the international community of structural biologists, and in particular to support integrative experimental approaches within the field of structural biology. It has developed enhancements to existing web services for structure solution and analysis, created new pipelines to link these services into more complex higher-level workflows, and added new data management facilities. Through this work it has striven to make the benefits of European e-Infrastructures more accessible to life-science researchers in general and structural biologists in particular.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier prob...