An integrated proteome database for two-dimensional electrophoresis data analysis and laboratory information management system

We describe an integrated proteome database, termed Yonsei Proteome Research Center Proteome Database (YPRC-PDB) which can store, retrieve and analyze various information including two-dimensional electrophoresis (2-DE) images and associated spot information that were obtained during studies of hepatocellular carcinoma (HCC). YPRC-PDB is also designed to perform as a laboratory information management system that manages sample information, clinical background, conditions of both sample preparation and 2-DE, and entire sets of experimental results. It also features query system and data-mining applications, which are amenable to automatically analyze expression level changes of a specific protein and directly link to clinical information. The user interface is web-based, so that the results from other laboratories can be shared effectively. In particular, the master gel image query is equipped with a graphic tool that can easily identify the relationship between the specific pathological stage of HCC and expression levels of a potential marker protein on the master gel image. Thus, YPRC-PDB is a versatile integrated database suitable for subsequent analyses. The information in YPRC-PDB is updated easily and it is available to authorized users on the World Wide Web (http://yprcpdb.proteomix.org/ approximately damduck/).     

Development of an open source laboratory information management system for 2-D gel electrophoresis-based proteomics workflow

Background  

In the post-genome era, most research scientists working in the field of proteomics are confronted with difficulties in management of large volumes of data, which they are required to keep in formats suitable for subsequent data mining. Therefore, a well-developed open source laboratory information management system (LIMS) should be available for their proteomics research studies.     

实验用小型猪信息管理系统开发

为满足小型猪培育数字化管理需要开发的"中国实验用小型猪信息管理系统"由育种管理、饲养管理、群体监测和操作规程等14个模块组成,其中最具特色的部分是育种管理和群体监测模块。为确保数据库质量,育种管理模块创建了以级联表和单选框勾选、个体号和群体类型自动生成的个体信息录入方式;根据近交系和封闭群建系规则创建了自动配种模块,基于系谱生成配种计划表,结合发情记录、产仔记录等实现繁殖管理数字化。群体监测模块由生物学特性、微生物和寄生虫质量控制、遗传质量控制三个部分组成,是建立高品质实验用小型猪群体的信息保障。     

CytoAccess, a relational laboratory information management system for a clinical cytogenetics laboratory

We developed a CytoAccess laboratory management system based on the widely used Microsoft Access software to facilitate data processing, result reporting, and quality management for a full-service cytogenetics laboratory. The CytoAccess system consists of four functional modules. The data entry module is for logging in patient information. The result entry module is used to generate chromosome, fluorescent in situ hybridization (FISH), and array comparative genomic hybridization (aCGH) reports. The administrative module enables periodic monitoring of quality control and quality improvement (QA/QI) parameters and produces billing forms. The maintenance module allows users to update clinical demographics, report templates, code tables, and to refresh data links. We have integrated into this system over 15,000 chromosome and FISH results from prenatal, postnatal, and cancer cases for the past six years. This system is cost-effective, user-friendly, flexible in updating, and potentially adaptable for data mining.     

真菌检验信息系统软件的开发研究

随着信息化技术的不断提高,医院对于信息化建设的要求也越来越高,在为广大普通科室建立了如医院信息系统（HIS）、检验信息系统（LIS）、PACS等大型医疗信息系统之后,一些特殊科室单位就对应自己工作,提出了建设相对小型、具有特殊功能信息系统的要求。皮肤科真菌实验室,在医院里不仅是皮肤科临床工作的基础,而且还承担着全院真菌标本的检验和鉴定工作。真菌实验室检查在医院尤其是大中型医院的展开,对于提高真菌病的诊治水平,     

MendeLIMS: a web-based laboratory information management system for clinical genome sequencing

Background

Large clinical genomics studies using next generation DNA sequencing require the ability to select and track samples from a large population of patients through many experimental steps. With the number of clinical genome sequencing studies increasing, it is critical to maintain adequate laboratory information management systems to manage the thousands of patient samples that are subject to this type of genetic analysis.

Results

To meet the needs of clinical population studies using genome sequencing, we developed a web-based laboratory information management system (LIMS) with a flexible configuration that is adaptable to continuously evolving experimental protocols of next generation DNA sequencing technologies. Our system is referred to as MendeLIMS, is easily implemented with open source tools and is also highly configurable and extensible. MendeLIMS has been invaluable in the management of our clinical genome sequencing studies.

Conclusions

We maintain a publicly available demonstration version of the application for evaluation purposes at http://mendelims.stanford.edu. MendeLIMS is programmed in Ruby on Rails (RoR) and accesses data stored in SQL-compliant relational databases. Software is freely available for non-commercial use at http://dna-discovery.stanford.edu/software/mendelims/.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-290) contains supplementary material, which is available to authorized users.     

Development and mapping of SSR markers for maize

Microsatellite or simple sequence repeat (SSR) markers have wide applicability for genetic analysis in crop plant improvement strategies. The objectives of this project were to isolate, characterize, and map a comprehensive set of SSR markers for maize (Zea mays L.). We developed 1051 novel SSR markers for maize from microsatellite-enriched libraries and by identification of microsatellite-containing sequences in public and private databases. Three mapping populations were used to derive map positions for 978 of these markers. The main mapping population was the intermated B73 × Mo17 (IBM) population. In mapping this intermated recombinant inbred line population, we have contributed to development of a new high-resolution map resource for maize. The primer sequences, original sequence sources, data on polymorphisms across 11 inbred lines, and map positions have been integrated with information on other public SSR markers and released through MaizeDB at URL:www.agron.missouri.edu. The maize research community now has the most detailed and comprehensive SSR marker set of any plant species.     

Purdue ionomics information management system. An integrated functional genomics platform

The advent of high-throughput phenotyping technologies has created a deluge of information that is difficult to deal with without the appropriate data management tools. These data management tools should integrate defined workflow controls for genomic-scale data acquisition and validation, data storage and retrieval, and data analysis, indexed around the genomic information of the organism of interest. To maximize the impact of these large datasets, it is critical that they are rapidly disseminated to the broader research community, allowing open access for data mining and discovery. We describe here a system that incorporates such functionalities developed around the Purdue University high-throughput ionomics phenotyping platform. The Purdue Ionomics Information Management System (PiiMS) provides integrated workflow control, data storage, and analysis to facilitate high-throughput data acquisition, along with integrated tools for data search, retrieval, and visualization for hypothesis development. PiiMS is deployed as a World Wide Web-enabled system, allowing for integration of distributed workflow processes and open access to raw data for analysis by numerous laboratories. PiiMS currently contains data on shoot concentrations of P, Ca, K, Mg, Cu, Fe, Zn, Mn, Co, Ni, B, Se, Mo, Na, As, and Cd in over 60,000 shoot tissue samples of Arabidopsis (Arabidopsis thaliana), including ethyl methanesulfonate, fast-neutron and defined T-DNA mutants, and natural accession and populations of recombinant inbred lines from over 800 separate experiments, representing over 1,000,000 fully quantitative elemental concentrations. PiiMS is accessible at www.purdue.edu/dp/ionomics.     

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.     

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/.

     

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.     

ZmDB,an integrated database for maize genome research

Zea mays DataBase (ZmDB) seeks to provide a comprehensive view of maize (corn) genetics by linking genomic sequence data with gene expression analysis and phenotypes of mutant plants. ZmDB originated in 1999 as the Web portal for a large project of maize gene discovery, sequencing and phenotypic analysis using a transposon tagging strategy and expressed sequence tag (EST) sequencing. Recently, ZmDB has broadened its scope to include all public maize ESTs, genome survey sequences (GSSs), and protein sequences. More than 170 000 ESTs are currently clustered into approximately 20 000 contigs and about an equal number of apparent singlets. These clusters are continuously updated and annotated with respect to potential encoded protein products. More than 100 000 GSSs are similarly assembled and annotated by spliced alignment with EST and protein sequences. The ZmDB interface provides quick access to analytical tools for further sequence analysis. Every sequence record is linked to several display options and similarity search tools, including services for multiple sequence alignment, protein domain determination and spliced alignment. Furthermore, ZmDB provides web-based ordering of materials generated in the project, including ESTs, ordered collections of genomic sequences tagged with the RescueMu transposon and microarrays of amplified ESTs. ZmDB can be accessed at http://zmdb.iastate.edu/.     

锡林河流域综合管理信息系统研发与应用

流域综合管理信息系统是集成多主题流域信息,服务流域综合管理决策的工具。本文基于GIS技术,设计了锡林河流域水质评价、流域分析和灌溉需水估算模型,研制了锡林河流域综合管理信息系统,并在系统中集成了数据管理、查询、更新、处理、模型分析和输出等多种功能,在栅格水平上实现了模型参数的输入、传递、计算及显示与分析。其中,水质评价模型以遥感影像为基本信息源,实现水质评价指标实时监测与估算,应用影响因子与水质评价指标的回归系数,分析影响水质变化的关键影响因素;流域分析模型具备确定流向、估算流域累积流量并判定流域分区等功能;农业灌溉需水计算模型实现了在已有降雨条件下锡林河流域年灌溉需水量的估算。锡林河流域综合管理信息系统为该流域现代化管理和综合治理提供了决策参考。     

Implementation of a configurable laboratory information management system for use in cellular process development and manufacturing

Background aimsRegulatory requirements for the manufacturing of cell products for clinical investigation require a significant level of record-keeping, starting early in process development and continuing through to the execution and requisite follow-up of patients on clinical trials. Central to record-keeping is the management of documentation related to patients, raw materials, processes, assays and facilities.MethodsTo support these requirements, we evaluated several laboratory information management systems (LIMS), including their cost, flexibility, regulatory compliance, ongoing programming requirements and ability to integrate with laboratory equipment. After selecting a system, we performed a pilot study to develop a user-configurable LIMS for our laboratory in support of our pre-clinical and clinical cell-production activities. We report here on the design and utilization of this system to manage accrual with a healthy blood-donor protocol, as well as manufacturing operations for the production of a master cell bank and several patient-specific stem cell products.ResultsThe system was used successfully to manage blood donor eligibility, recruiting, appointments, billing and serology, and to provide annual accrual reports. Quality management reporting features of the system were used to capture, report and investigate process and equipment deviations that occurred during the production of a master cell bank and patient products.ConclusionsOverall the system has served to support the compliance requirements of process development and phase I/II clinical trial activities for our laboratory and can be easily modified to meet the needs of similar laboratories.     

PARPs database: A LIMS systems for protein-protein interaction data mining or laboratory information management system

Background  

In the "post-genome" era, mass spectrometry (MS) has become an important method for the analysis of proteins and the rapid advancement of this technique, in combination with other proteomics methods, results in an increasing amount of proteome data. This data must be archived and analysed using specialized bioinformatics tools.     

Association mapping in an elite maize breeding population

Association mapping (AM) is a powerful approach to dissect the genetic architecture of quantitative traits. The main goal of our study was to empirically compare several statistical methods of AM using data of an elite maize breeding program with respect to QTL detection power and possibility to correct for population stratification. These models were based on the inclusion of cofactors (Model A), cofactors and population effect (Model B), and SNP effects nested within populations (Model C). A total of 930 testcross progenies of an elite maize breeding population were field-evaluated for grain yield and grain moisture in multi-location trials and fingerprinted with 425 SNP markers. For grain yield, population stratification was effectively controlled by Model A. For grain moisture with a high ratio of variance among versus within populations, Model B should be applied in order to avoid potential false positives. Model C revealed large differences among allele substitution effects for trait-associated SNPs across multiple plant breeding populations. This heterogeneous SNP allele substitution effects have a severe impact for genomic selection studies, where SNP effects are often assumed to be independent of the genetic background.     

MOsDB: an integrated information resource for rice genomics

The MIPS Rice (Oryza sativa) database (MOsDB; http://mips.gsf.de/proj/rice) provides a comprehensive data collection dedicated to the genome information of rice. Rice (O. sativa L.) is one of the most important food crops for over half the world's population and serves as a major model system in cereal genome research. MOsDB integrates data from two publicly available rice genomic sequences, O. sativa L. ssp. indica and O. sativa L. ssp. japonica. Besides regularly updated rice genome sequence information, MOsDB provides an integrated resource for associated analysis data, e.g. internal and external annotation information as well as a complex characterization of all annotated rice genes. The MOsDB web interface supports various search options and allows browsing the database content. MOsDB is continuously expanding to include an increasing range of data type and the growing amount of information on the rice genome.     

Towards an integrated management system based on abstraction of heterogeneous virtual resources

Virtualization technology reduces the costs for server installation, operation, and maintenance and it can simplify development of distributed systems. Currently, there are various virtualization technologies such as Xen, KVM, VMware, and etc, and all these technologies support various virtualization functions individually on the heterogeneous platforms. Therefore, it is important to be able to integrate and manage these heterogeneous virtualized resources in order to develop distributed systems based on the current virtualization techniques. This paper presents an integrated management system that is able to provide information for the usage of heterogeneous virtual resources and also to control them. The main focus of the system is to abstract various virtual resources and to reconfigure them flexibly. For this, an integrated management system has been developed and implemented based on a libvirt-based virtualization API and data distribution service (DDS).     

The locust jump: an integrated laboratory investigation

The locust is well known for its ability to jump large distances to avoid predation. This class sets out a series of investigations into the mechanisms underlying the jump enabling students to bring together information from biomechanics, muscle physiology, and anatomy. The nature of the investigation allows it to be undertaken at a number of levels of complexity from relatively simple comparative observations to detailed analysis of the properties of the muscles and the energy storage systems involved in powering the jump. The relative size and robustness of the locust make it simple to handle and ideal for such investigations.