MASTR-MS: a web-based collaborative laboratory information management system (LIMS) for metabolomics

Background

An increasing number of research laboratories and core analytical facilities around the world are developing high throughput metabolomic analytical and data processing pipelines that are capable of handling hundreds to thousands of individual samples per year, often over multiple projects, collaborations and sample types. At present, there are no Laboratory Information Management Systems (LIMS) that are specifically tailored for metabolomics laboratories that are capable of tracking samples and associated metadata from the beginning to the end of an experiment, including data processing and archiving, and which are also suitable for use in large institutional core facilities or multi-laboratory consortia as well as single laboratory environments.

Results

Here we present MASTR-MS, a downloadable and installable LIMS solution that can be deployed either within a single laboratory or used to link workflows across a multisite network. It comprises a Node Management System that can be used to link and manage projects across one or multiple collaborating laboratories; a User Management System which defines different user groups and privileges of users; a Quote Management System where client quotes are managed; a Project Management System in which metadata is stored and all aspects of project management, including experimental setup, sample tracking and instrument analysis, are defined, and a Data Management System that allows the automatic capture and storage of raw and processed data from the analytical instruments to the LIMS.

Conclusion

MASTR-MS is a comprehensive LIMS solution specifically designed for metabolomics. It captures the entire lifecycle of a sample starting from project and experiment design to sample analysis, data capture and storage. It acts as an electronic notebook, facilitating project management within a single laboratory or a multi-node collaborative environment. This software is being developed in close consultation with members of the metabolomics research community. It is freely available under the GNU GPL v3 licence and can be accessed from, https://muccg.github.io/mastr-ms/.

     

Automation of macromolecular crystallography beamlines

The production of three-dimensional crystallographic structural information of macromolecules can now be thought of as a pipeline which is being streamlined at every stage from protein cloning, expression and purification, through crystallisation to data collection and structure solution. Synchrotron X-ray beamlines are a key section of this pipeline as it is at these that the X-ray diffraction data that ultimately leads to the elucidation of macromolecular structures are collected. The burgeoning number of macromolecular crystallography (MX) beamlines available worldwide may be enhanced significantly with the automation of both their operation and of the experiments carried out on them. This paper reviews the current situation and provides a glimpse of how a MX beamline may look in the not too distant future.     

Doses for experiments with microbeams and microcrystals: Monte Carlo simulations in RADDOSE‐3D

Increasingly, microbeams and microcrystals are being used for macromolecular crystallography (MX) experiments at synchrotrons. However, radiation damage remains a major concern since it is a fundamental limiting factor affecting the success of macromolecular structure determination. The rate of radiation damage at cryotemperatures is known to be proportional to the absorbed dose, so to optimize experimental outcomes, accurate dose calculations are required which take into account the physics of the interactions of the crystal constituents. The program RADDOSE‐3D estimates the dose absorbed by samples during MX data collection at synchrotron sources, allowing direct comparison of radiation damage between experiments carried out with different samples and beam parameters. This has aided the study of MX radiation damage and enabled prediction of approximately when it will manifest in diffraction patterns so it can potentially be avoided. However, the probability of photoelectron escape from the sample and entry from the surrounding material has not previously been included in RADDOSE‐3D, leading to potentially inaccurate does estimates for experiments using microbeams or microcrystals. We present an extension to RADDOSE‐3D which performs Monte Carlo simulations of a rotating crystal during MX data collection, taking into account the redistribution of photoelectrons produced both in the sample and the material surrounding the crystal. As well as providing more accurate dose estimates, the Monte Carlo simulations highlight the importance of the size and composition of the surrounding material on the dose and thus the rate of radiation damage to the sample. Minimizing irradiation of the surrounding material or removing it almost completely will be key to extending the lifetime of microcrystals and enhancing the potential benefits of using higher incident X‐ray energies.     

RGMIMS: a web-based Laboratory Information Management System for plant functional genomics research

A robust Laboratory Information Management System (LIMS) is required for the efficient handling of data generated from large-scale insertional mutagenesis projects. The Rice Gene Machine Information Management System (RGMIMS), a web-based modular LIMS, developed in a rice functional genomics laboratory at CSIRO, currently has four core modules: Seed Management, Transformation Management, Plant/Progeny Management, Phenotype Management, and an ad hoc querying module. RGMIMS manages, preserves and tracks large inventories of transgenic germplasm and enables efficient and accurate record keeping of the large quantities of experimental data. RGMIMS automates and seamlessly integrates multi-step experimental processes. A web user interface, incorporating barcoding utilities, enables rapid data capture and tracking of biological resources. Ontologies from Gramene and Plant Ontology consortium are used to describe mutant phenotypes. RGMIMS supports generic research processes in plant mutagenesis and could readily be adapted to general LIMS for high-throughput plant research.     

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).     

Laboratory information management system for membrane protein structure initiative – from gene to crystal

Membrane Protein Structure Initiative (MPSI) exploits laboratory competencies to work collaboratively and distribute work among the different sites. This is possible as protein structure determination requires a series of steps, starting with target selection, through cloning, expression, purification, crystallization and finally structure determination. Distributed sites create a unique set of challenges for integrating and passing on information on the progress of targets. This role is played by the Protein Information Management System (PIMS), which is a laboratory information management system (LIMS), serving as a hub for MPSI, allowing collaborative structural proteomics to be carried out in a distributed fashion. It holds key information on the progress of cloning, expression, purification and crystallization of proteins. PIMS is employed to track the status of protein targets and to manage constructs, primers, experiments, protocols, sample locations and their detailed histories: thus playing a key role in MPSI data exchange. It also serves as the centre of a federation of interoperable information resources such as local laboratory information systems and international archival resources, like PDB or NCBI. During the challenging task of PIMS integration, within the MPSI, we discovered a number of prerequisites for successful PIMS integration. In this article we share our experiences and provide invaluable insights into the process of LIMS adaptation. This information should be of interest to partners who are thinking about using LIMS as a data centre for their collaborative efforts.     

Laboratory information management system for membrane protein structure initiative--from gene to crystal

Membrane Protein Structure Initiative (MPSI) exploits laboratory competencies to work collaboratively and distribute work among the different sites. This is possible as protein structure determination requires a series of steps, starting with target selection, through cloning, expression, purification, crystallization and finally structure determination. Distributed sites create a unique set of challenges for integrating and passing on information on the progress of targets. This role is played by the Protein Information Management System (PIMS), which is a laboratory information management system (LIMS), serving as a hub for MPSI, allowing collaborative structural proteomics to be carried out in a distributed fashion. It holds key information on the progress of cloning, expression, purification and crystallization of proteins. PIMS is employed to track the status of protein targets and to manage constructs, primers, experiments, protocols, sample locations and their detailed histories: thus playing a key role in MPSI data exchange. It also serves as the centre of a federation of interoperable information resources such as local laboratory information systems and international archival resources, like PDB or NCBI. During the challenging task of PIMS integration, within the MPSI, we discovered a number of prerequisites for successful PIMS integration. In this article we share our experiences and provide invaluable insights into the process of LIMS adaptation. This information should be of interest to partners who are thinking about using LIMS as a data centre for their collaborative efforts.     

A Laboratory Information Management System (LIMS) for a high throughput genetic platform aimed at candidate gene mutation screening

High throughput mutation screening in an automated environment generates large data sets that have to be organized and stored reliably. Complex multistep workflows require strict process management and careful data tracking. We have developed a Laboratory Information Management Systems (LIMS) tailored to high throughput candidate gene mutation scanning and resequencing that respects these requirements. Designed with a client/server architecture, our system is platform independent and based on open-source tools from the database to the web application development strategy. Flexible, expandable and secure, the LIMS, by communicating with most of the laboratory instruments and robots, tracks samples and laboratory information, capturing data at every step of our automated mutation screening workflow. An important feature of our LIMS is that it enables tracking of information through a laboratory workflow where the process at one step is contingent on results from a previous step. AVAILABILITY: Script for MySQL database table creation and source code of the whole JSP application are freely available on our website: http://www-gcs.iarc.fr/lims/. SUPPLEMENTARY INFORMATION: System server configuration, database structure and additional details on the LIMS and the mutation screening workflow are available on our website: http://www-gcs.iarc.fr/lims/     

STATegra EMS: an Experiment Management System for complex next-generation omics experiments

High-throughput sequencing assays are now routinely used to study different aspects of genome organization. As decreasing costs and widespread availability of sequencing enable more laboratories to use sequencing assays in their research projects, the number of samples and replicates in these experiments can quickly grow to several dozens of samples and thus require standardized annotation, storage and management of preprocessing steps. As a part of the STATegra project, we have developed an Experiment Management System (EMS) for high throughput omics data that supports different types of sequencing-based assays such as RNA-seq, ChIP-seq, Methyl-seq, etc, as well as proteomics and metabolomics data. The STATegra EMS provides metadata annotation of experimental design, samples and processing pipelines, as well as storage of different types of data files, from raw data to ready-to-use measurements. The system has been developed to provide research laboratories with a freely-available, integrated system that offers a simple and effective way for experiment annotation and tracking of analysis procedures.     

A Perl toolkit for LIMS development

Background  

High throughput laboratory techniques generate huge quantities of scientific data. Laboratory Information Management Systems (LIMS) are a necessary requirement, dealing with sample tracking, data storage and data reporting. Commercial LIMS solutions are available, but these can be both costly and overly complex for the task. The development of bespoke LIMS solutions offers a number of advantages, including the flexibility to fulfil all a laboratory's requirements at a fraction of the price of a commercial system. The programming language Perl is a perfect development solution for LIMS applications because of Perl's powerful but simple to use database and web interaction, it is also well known for enabling rapid application development and deployment, and boasts a very active and helpful developer community. The development of an in house LIMS from scratch however can take considerable time and resources, so programming tools that enable the rapid development of LIMS applications are essential but there are currently no LIMS development tools for Perl.     

Recording information on protein complexes in an information management system

The Protein Information Management System (PiMS) is a laboratory information management system (LIMS) designed for use with the production of proteins in a research environment. The software is distributed under the CCP4 licence, and so is available free of charge to academic laboratories. Like most LIMS, the underlying PiMS data model originally had no support for protein-protein complexes. To support the SPINE2-Complexes project the developers have extended PiMS to meet these requirements. The modifications to PiMS, described here, include data model changes, additional protocols, some user interface changes and functionality to detect when an experiment may have formed a complex. Example data are shown for the production of a crystal of a protein complex. Integration with SPINE2-Complexes Target Tracker application is also described.     

Detecting overlapping coding sequences in virus genomes

Background

With the advances in DNA sequencer-based technologies, it has become possible to automate several steps of the genotyping process leading to increased throughput. To efficiently handle the large amounts of genotypic data generated and help with quality control, there is a strong need for a software system that can help with the tracking of samples and capture and management of data at different steps of the process. Such systems, while serving to manage the workflow precisely, also encourage good laboratory practice by standardizing protocols, recording and annotating data from every step of the workflow.

Results

A laboratory information management system (LIMS) has been designed and implemented at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) that meets the requirements of a moderately high throughput molecular genotyping facility. The application is designed as modules and is simple to learn and use. The application leads the user through each step of the process from starting an experiment to the storing of output data from the genotype detection step with auto-binning of alleles; thus ensuring that every DNA sample is handled in an identical manner and all the necessary data are captured. The application keeps track of DNA samples and generated data. Data entry into the system is through the use of forms for file uploads. The LIMS provides functions to trace back to the electrophoresis gel files or sample source for any genotypic data and for repeating experiments. The LIMS is being presently used for the capture of high throughput SSR (simple-sequence repeat) genotyping data from the legume (chickpea, groundnut and pigeonpea) and cereal (sorghum and millets) crops of importance in the semi-arid tropics.

Conclusion

A laboratory information management system is available that has been found useful in the management of microsatellite genotype data in a moderately high throughput genotyping laboratory. The application with source code is freely available for academic users and can be downloaded fromhttp://www.icrisat.org/gt-bt/lims/lims.asp.     

Validation of macromolecular flexibility in solution by small-angle X-ray scattering (SAXS)

The dynamics of macromolecular conformations are critical to the action of cellular networks. Solution X-ray scattering studies, in combination with macromolecular X-ray crystallography (MX) and nuclear magnetic resonance (NMR), strive to determine complete and accurate states of macromolecules, providing novel insights describing allosteric mechanisms, supramolecular complexes, and dynamic molecular machines. This review addresses theoretical and practical concepts, concerns, and considerations for using these techniques in conjunction with computational methods to productively combine solution-scattering data with high-resolution structures. I discuss the principal means of direct identification of macromolecular flexibility from SAXS data followed by critical concerns about the methods used to calculate theoretical SAXS profiles from high-resolution structures. The SAXS profile is a direct interrogation of the thermodynamic ensemble and techniques such as, for example, minimal ensemble search (MES), enhance interpretation of SAXS experiments by describing the SAXS profiles as population-weighted thermodynamic ensembles. I discuss recent developments in computational techniques used for conformational sampling, and how these techniques provide a basis for assessing the level of the flexibility within a sample. Although these approaches sacrifice atomic detail, the knowledge gained from ensemble analysis is often appropriate for developing hypotheses and guiding biochemical experiments. Examples of the use of SAXS and combined approaches with X-ray crystallography, NMR, and computational methods to characterize dynamic assemblies are presented.     

High-pressure macromolecular crystallography (HPMX): status and prospects

Recent technical developments, achievements and prospects of high-pressure (HP) macromolecular crystallography (MX) are reviewed. Technical difficulties associated with this technique have been essentially solved by combining synchrotron radiation of ultra-short wavelength, large-aperture diamond anvil cells and new sample-mounting techniques. The quality of diffraction data collected at HP can now meet standards of conventional MX. The exploitation of the potential of the combination of X-ray diffraction and high-pressure perturbation is progressing well. The ability of pressure to shift the population distribution of conformers in solution, which is exploited in particular by NMR, can also be used in the crystalline state with specific advantages. HPMX has indeed bright prospects, in particular to elucidate the structure of higher-energy conformers that are often of high biological significance. Furthermore, HPMX may be of interest for conventional crystallographic studies, as pressure is a fairly general tool to improve order in pre-existing crystals with minimal perturbation of the native structure.     

New York-Structural GenomiX Research Consortium (NYSGXRC): A Large Scale Center for the Protein Structure Initiative

Structural GenomiX, Inc. (SGX), four New York area institutions, and two University of California schools have formed the New York Structural GenomiX Research Consortium (NYSGXRC), an industrial/academic Research Consortium that exploits individual core competencies to support all aspects of the NIH-NIGMS funded Protein Structure Initiative (PSI), including protein family classification and target selection, generation of protein for biophysical analyses, sample preparation for structural studies, structure determination and analyses, and dissemination of results. At the end of the PSI Pilot Study Phase (PSI-1), the NYSGXRC will be capable of producing 100–200 experimentally determined protein structures annually. All Consortium activities can be scaled to increase production capacity significantly during the Production Phase of the PSI (PSI-2). The Consortium utilizes both centralized and de-centralized production teams with clearly defined deliverables and hand-off procedures that are supported by a web-based target/sample tracking system (SGX Laboratory Information Data Management System, LIMS, and NYSGXRC Internal Consortium Experimental Database, ICE-DB). Consortium management is provided by an Executive Committee, which is composed of the PI and all Co-PIs. Progress to date is tracked on a publicly available Consortium web site (http://www.nysgxrc.org) and all DNA/protein reagents and experimental protocols are distributed freely from the New York City Area institutions. In addition to meeting the requirements of the Pilot Study Phase and preparing for the Production Phase of the PSI, the NYSGXRC aims to develop modular technologies that are transferable to structural biology laboratories in both academe and industry. The NYSGXRC PI and Co-PIs intend the PSI to have a transforming effect on the disciplines of X-ray crystallography and NMR spectroscopy of biological macromolecules. Working with other PSI-funded Centers, the NYSGXRC seeks to create the structural biology laboratory of the future. Herein, we present an overview of the organization of the NYSGXRC and describe progress toward development of a high-throughput Gene→Structure platform. An analysis of current and projected consortium metrics reflects progress to date and delineates opportunities for further technology development.     

SPINE 2: a system for collaborative structural proteomics within a federated database framework

We present version 2 of the SPINE system for structural proteomics. SPINE is available over the web at http://nesg.org. It serves as the central hub for the Northeast Structural Genomics Consortium, allowing collaborative structural proteomics to be carried out in a distributed fashion. The core of SPINE is a laboratory information management system (LIMS) for key bits of information related to the progress of the consortium in cloning, expressing and purifying proteins and then solving their structures by NMR or X-ray crystallography. Originally, SPINE focused on tracking constructs, but, in its current form, it is able to track target sample tubes and store detailed sample histories. The core database comprises a set of standard relational tables and a data dictionary that form an initial ontology for proteomic properties and provide a framework for large-scale data mining. Moreover, SPINE sits at the center of a federation of interoperable information resources. These can be divided into (i) local resources closely coupled with SPINE that enable it to handle less standardized information (e.g. integrated mailing and publication lists), (ii) other information resources in the NESG consortium that are inter-linked with SPINE (e.g. crystallization LIMS local to particular laboratories) and (iii) international archival resources that SPINE links to and passes on information to (e.g. TargetDB at the PDB).     

SPINS: a laboratory information management system for organizing and archiving intermediate and final results from NMR protein structure determinations

Recent technological advances and experimental techniques have contributed to an increasing number and size of NMR datasets. In order to scale up productivity, laboratory information management systems for handling these extensive data need to be designed and implemented. The SPINS (Standardized ProteIn Nmr Storage) Laboratory Information Management System (LIMS) addresses these needs by providing an interface for archival of complete protein NMR structure determinations, together with functionality for depositing these data to the public BioMagResBank (BMRB). The software tracks intermediate files during each step of an NMR structure-determination process, including: data collection, data processing, resonance assignments, resonance assignment validation, structure calculation, and structure validation. The underlying SPINS data dictionary allows for the integration of various third party NMR data processing and analysis software, enabling users to launch programs they are accustomed to using for each step of the structure determination process directly out of the SPINS user interface.     

Analytical ultracentrifugation combined with X-ray and neutron scattering: Experiment and modelling

Analytical ultracentrifugation and solution scattering provide different multi-parameter structural and compositional information on proteins. The joint application of the two methods supplements high resolution structural studies by crystallography and NMR. We summarise the procedures required to obtain equivalent ultracentrifugation and X-ray and neutron scattering data. The constrained modelling of ultracentrifugation and scattering data is important to confirm the experimental data analysis and yields families of best-fit molecular models for comparison with crystallography and NMR structures. This modelling of ultracentrifugation and scattering data is described in terms of starting models, their conformational randomisation in trial-and-error fits, and the identification of the final best-fit models. Seven applications of these methods are described to illustrate the current state-of-the-art. These include the determination of antibody solution structures (the human IgG4 subclass, and oligomeric forms of human IgA and its secretory component), the solution structures of the complement proteins of innate immunity (Factor H and C3/C3u) and their interactions with macromolecular ligands (C-reactive protein), and anionic polysaccharides (heparin). Complementary features of joint ultracentrifugation and scattering experiments facilitate an improved understanding of crystal structures (illustrated for C3/C3u, C-reactive protein and heparin). If a large protein or its complex cannot be crystallised, the joint ultracentrifugation-scattering approach provides a means to obtain an overall macromolecular structure.     

生物实验信息管理模块建模技术研究

实验信息管理模块是生物实验室信息管理系统的核心,其设计的好坏直接关系到整个系统建设的成败。针对生物实验室实验流程灵活,数据流网络复杂等特点,研究设计了一套实验信息管理模块的模型,并将研究的模型用于基因测序实验室信息管理系统的开发并取得了成功。实践证明,该模型为实验室信息系统(LIMS)中实验管理模块的开发提供了理论支撑,并且提高了LIMS的开发效率。