Data management in the modern structural biology and biomedical research environment

Modern high-throughput structural biology laboratories produce vast amounts of raw experimental data. The traditional method of data reduction is very simple-results are summarized in peer-reviewed publications, which are hopefully published in high-impact journals. By their nature, publications include only the most important results derived from experiments that may have been performed over the course of many years. The main content of the published paper is a concise compilation of these data, an interpretation of the experimental results, and a comparison of these results with those obtained by other scientists.Due to an avalanche of structural biology manuscripts submitted to scientific journals, in many recent cases descriptions of experimental methodology (and sometimes even experimental results) are pushed to supplementary materials that are only published online and sometimes may not be reviewed as thoroughly as the main body of a manuscript. Trouble may arise when experimental results are contradicting the results obtained by other scientists, which requires (in the best case) the reexamination of the original raw data or independent repetition of the experiment according to the published description of the experiment. There are reports that a significant fraction of experiments obtained in academic laboratories cannot be repeated in an industrial environment (Begley CG & Ellis LM, Nature 483(7391):531-3, 2012). This is not an indication of scientific fraud but rather reflects the inadequate description of experiments performed on different equipment and on biological samples that were produced with disparate methods. For that reason the goal of a modern data management system is not only the simple replacement of the laboratory notebook by an electronic one but also the creation of a sophisticated, internally consistent, scalable data management system that will combine data obtained by a variety of experiments performed by various individuals on diverse equipment. All data should be stored in a core database that can be used by custom applications to prepare internal reports, statistics, and perform other functions that are specific to the research that is pursued in a particular laboratory.This chapter presents a general overview of the methods of data management and analysis used by structural genomics (SG) programs. In addition to a review of the existing literature on the subject, also presented is experience in the development of two SG data management systems, UniTrack and LabDB. The description is targeted to a general audience, as some technical details have been (or will be) published elsewhere. The focus is on "data management," meaning the process of gathering, organizing, and storing data, but also briefly discussed is "data mining," the process of analysis ideally leading to an understanding of the data. In other words, data mining is the conversion of data into information. Clearly, effective data management is a precondition for any useful data mining. If done properly, gathering details on millions of experiments on thousands of proteins and making them publicly available for analysis-even after the projects themselves have ended-may turn out to be one of the most important benefits of SG programs.

Phytotracker, an information management system for easy recording and tracking of plants, seeds and plasmids

BACKGROUND

A large number of different plant lines are produced and maintained in a typical plant research laboratory, both as seed stocks and in active growth. These collections need careful and consistent management to track and maintain them properly, and this is a particularly pressing issue in laboratories undertaking research involving genetic manipulation due to regulatory requirements. Researchers and PIs need to access these data and collections, and therefore an easy-to-use plant-oriented laboratory information management system that implements, maintains and displays the information in a simple and visual format would be of great help in both the daily work in the lab and in ensuring regulatory compliance.

RESULTS

Here, we introduce 'Phytotracker', a laboratory management system designed specifically to organise and track plasmids, seeds and growing plants that can be used in mixed platform environments. Phytotracker is designed with simplicity of user operation and ease of installation and management as the major factor, whilst providing tracking tools that cover the full range of activities in molecular genetics labs. It utilises the cross-platform Filemaker relational database, which allows it to be run as a stand-alone or as a server-based networked solution available across all workstations in a lab that can be internet accessible if desired. It can also be readily modified or customised further. Phytotracker provides cataloguing and search functions for plasmids, seed batches, seed stocks and plants growing in pots or trays, and allows tracking of each plant from seed sowing, through harvest to the new seed batch and can print appropriate labels at each stage. The system enters seed information as it is transferred from the previous harvest data, and allows both selfing and hybridization (crossing) to be defined and tracked. Transgenic lines can be linked to their plasmid DNA source. This ease of use and flexibility helps users to reduce their time needed to organise their plants, seeds and plasmids and to maintain laboratory continuity involving multiple workers.

CONCLUSION

We have developed and used Phytotracker for over five years and have found it has been an intuitive, powerful and flexible research tool in organising our plasmid, seed and plant collections requiring minimal maintenance and training for users. It has been developed in an Arabidopsis molecular genetics environment, but can be readily adapted for almost any plant laboratory research.

Quantitative and automated high-throughput genome-wide RNAi screens in C. elegans

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. elegans, gene function can be knocked down simply and efficiently by feeding worms with bacteria expressing a dsRNA corresponding to a specific gene (1). While the creation of libraries of RNAi clones covering most of the C. elegans genome (2,3) opened the way for true functional genomic studies (see for example (4-7)), most established methods are laborious. Moy and colleagues have developed semi-automated protocols that facilitate genome-wide screens (8). The approach relies on microscopic imaging and image analysis. Here we describe an alternative protocol for a high-throughput genome-wide screen, based on robotic handling of bacterial RNAi clones, quantitative analysis using the COPAS Biosort (Union Biometrica (UBI)), and an integrated software: the MBioLIMS (Laboratory Information Management System from Modul-Bio) a technology that provides increased throughput for data management and sample tracking. The method allows screens to be conducted on solid medium plates. This is particularly important for some studies, such as those addressing host-pathogen interactions in C. elegans, since certain microbes do not efficiently infect worms in liquid culture. We show how the method can be used to quantify the importance of genes in anti-fungal innate immunity in C. elegans. In this case, the approach relies on the use of a transgenic strain carrying an epidermal infection-inducible fluorescent reporter gene, with GFP under the control of the promoter of the antimicrobial peptide gene nlp 29 and a red fluorescent reporter that is expressed constitutively in the epidermis. The latter provides an internal control for the functional integrity of the epidermis and nonspecific transgene silencing(9). When control worms are infected by the fungus they fluoresce green. Knocking down by RNAi a gene required for nlp 29 expression results in diminished fluorescence after infection. Currently, this protocol allows more than 3,000 RNAi clones to be tested and analyzed per week, opening the possibility of screening the entire genome in less than 2 months.

The Center for Eukaryotic Structural Genomics

The Center for Eukaryotic Structural Genomics (CESG) is a "specialized" or "technology development" center supported by the Protein Structure Initiative (PSI). CESG's mission is to develop improved methods for the high-throughput solution of structures from eukaryotic proteins, with a very strong weighting toward human proteins of biomedical relevance. During the first three years of PSI-2, CESG selected targets representing 601 proteins from Homo sapiens, 33 from mouse, 10 from rat, 139 from Galdieria sulphuraria, 35 from Arabidopsis thaliana, 96 from Cyanidioschyzon merolae, 80 from Plasmodium falciparum, 24 from yeast, and about 25 from other eukaryotes. Notably, 30% of all structures of human proteins solved by the PSI Centers were determined at CESG. Whereas eukaryotic proteins generally are considered to be much more challenging targets than prokaryotic proteins, the technology now in place at CESG yields success rates that are comparable to those of the large production centers that work primarily on prokaryotic proteins. We describe here the technological innovations that underlie CESG's platforms for bioinformatics and laboratory information management, target selection, protein production, and structure determination by X-ray crystallography or NMR spectroscopy.