'PACLIMS': A component LIM system for high-throughput functional genomic analysis

BACKGROUND: Recent advances in sequencing techniques leading to cost reduction have resulted in the generation of a growing number of sequenced eukaryotic genomes. Computational tools greatly assist in defining open reading frames and assigning tentative annotations. However, gene functions cannot be asserted without biological support through, among other things, mutational analysis. In taking a genome-wide approach to functionally annotate an entire organism, in this application the ~11,000 predicted genes in the rice blast fungus (Magnaporthe grisea), an effective platform for tracking and storing both the biological materials created and the data produced across several participating institutions was required. RESULTS: The platform designed, named PACLIMS, was built to support our high throughput pipeline for generating 50,000 random insertion mutants of Magnaporthe grisea. To be a useful tool for materials and data tracking and storage, PACLIMS was designed to be simple to use, modifiable to accommodate refinement of research protocols, and cost-efficient. Data entry into PACLIMS was simplified through the use of barcodes and scanners, thus reducing the potential human error, time constraints, and labor. This platform was designed in concert with our experimental protocol so that it leads the researchers through each step of the process from mutant generation through phenotypic assays, thus ensuring that every mutant produced is handled in an identical manner and all necessary data is captured. CONCLUSION: Many sequenced eukaryotes have reached the point where computational analyses are no longer sufficient and require biological support for their predicted genes. Consequently, there is an increasing need for platforms that support high throughput genome-wide mutational analyses. While PACLIMS was designed specifically for this project, the source and ideas present in its implementation can be used as a model for other high throughput mutational endeavors

\u27PACLIMS\u27: a component LIM system for high-throughput functional genomic analysis

BACKGROUND: Recent advances in sequencing techniques leading to cost reduction have resulted in the generation of a growing number of sequenced eukaryotic genomes. Computational tools greatly assist in defining open reading frames and assigning tentative annotations. However, gene functions cannot be asserted without biological support through, among other things, mutational analysis. In taking a genome-wide approach to functionally annotate an entire organism, in this application the approximately 11,000 predicted genes in the rice blast fungus (Magnaporthe grisea), an effective platform for tracking and storing both the biological materials created and the data produced across several participating institutions was required. RESULTS: The platform designed, named PACLIMS, was built to support our high throughput pipeline for generating 50,000 random insertion mutants of Magnaporthe grisea. To be a useful tool for materials and data tracking and storage, PACLIMS was designed to be simple to use, modifiable to accommodate refinement of research protocols, and cost-efficient. Data entry into PACLIMS was simplified through the use of barcodes and scanners, thus reducing the potential human error, time constraints, and labor. This platform was designed in concert with our experimental protocol so that it leads the researchers through each step of the process from mutant generation through phenotypic assays, thus ensuring that every mutant produced is handled in an identical manner and all necessary data is captured. CONCLUSION: Many sequenced eukaryotes have reached the point where computational analyses are no longer sufficient and require biological support for their predicted genes. Consequently, there is an increasing need for platforms that support high throughput genome-wide mutational analyses. While PACLIMS was designed specifically for this project, the source and ideas present in its implementation can be used as a model for other high throughput mutational endeavors

Laboratory Information Management Software for genotyping workflows: applications in high throughput crop genotyping

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 from

SNPLims: a data management system for genome wide association studies

<p>Abstract</p> <p>Background</p> <p>Recent progresses in genotyping technologies allow the generation high-density genetic maps using hundreds of thousands of genetic markers for each DNA sample. The availability of this large amount of genotypic data facilitates the whole genome search for genetic basis of diseases.</p> <p>We need a suitable information management system to efficiently manage the data flow produced by whole genome genotyping and to make it available for further analyses.</p> <p>Results</p> <p>We have developed an information system mainly devoted to the storage and management of SNP genotype data produced by the Illumina platform from the raw outputs of genotyping into a relational database.</p> <p>The relational database can be accessed in order to import any existing data and export user-defined formats compatible with many different genetic analysis programs.</p> <p>After calculating family-based or case-control association study data, the results can be imported in SNPLims. One of the main features is to allow the user to rapidly identify and annotate statistically relevant polymorphisms from the large volume of data analyzed. Results can be easily visualized either graphically or creating ASCII comma separated format output files, which can be used as input to further analyses.</p> <p>Conclusions</p> <p>The proposed infrastructure allows to manage a relatively large amount of genotypes for each sample and an arbitrary number of samples and phenotypes. Moreover, it enables the users to control the quality of the data and to perform the most common screening analyses and identify genes that become “candidate” for the disease under consideration.</p

eCOMPAGT – efficient Combination and Management of Phenotypes and Genotypes for Genetic Epidemiology

<p>Abstract</p> <p>Background</p> <p>High-throughput genotyping and phenotyping projects of large epidemiological study populations require sophisticated laboratory information management systems. Most epidemiological studies include subject-related personal information, which needs to be handled with care by following data privacy protection guidelines. In addition, genotyping core facilities handling cooperative projects require a straightforward solution to monitor the status and financial resources of the different projects.</p> <p>Description</p> <p>We developed a database system for an efficient combination and management of phenotypes and genotypes (eCOMPAGT) deriving from genetic epidemiological studies. eCOMPAGT securely stores and manages genotype and phenotype data and enables different user modes with different rights. Special attention was drawn on the import of data deriving from TaqMan and SNPlex genotyping assays. However, the database solution is adjustable to other genotyping systems by programming additional interfaces. Further important features are the scalability of the database and an export interface to statistical software.</p> <p>Conclusion</p> <p>eCOMPAGT can store, administer and connect phenotype data with all kinds of genotype data and is available as a downloadable version at <url>http://dbis-informatik.uibk.ac.at/ecompagt</url>.</p

Computer aided data acquisition tool for high-throughput phenotyping of plant populations

<p>Abstract</p> <p>Background</p> <p>The data generated during a course of a biological experiment/study can be sometimes be massive and its management becomes quite critical for the success of the investigation undertaken. The accumulation and analysis of such large datasets often becomes tedious for biologists and lab technicians. Most of the current phenotype data acquisition management systems do not cater to the specialized needs of large-scale data analysis. The successful application of genomic tools/strategies to introduce desired traits in plants requires extensive and precise phenotyping of plant populations or gene bank material, thus necessitating an efficient data acquisition system.</p> <p>Results</p> <p>Here we describe newly developed software "<b>PHENOME" </b>for high-throughput phenotyping, which allows researchers to accumulate, categorize, and manage large volume of phenotypic data. In this study, a large number of individual tomato plants were phenotyped with the "PHENOME" application using a Personal Digital Assistant (PDA) with built-in barcode scanner in concert with customized database specific for handling large populations.</p> <p>Conclusion</p> <p>The phenotyping of large population of plants both in the laboratory and in the field is very efficiently managed using PDA. The data is transferred to a specialized database(s) where it can be further analyzed and catalogued. The "PHENOME" aids collection and analysis of data obtained in large-scale mutagenesis, assessing quantitative trait loci (QTLs), raising mapping population, sampling of several individuals in one or more ecological niches etc.</p

Bioinformatics tools for crop research and breeding

Crop improvement has always been, but will be even more so in the twenty-first century, an information intensive process. For effective and efficient improvement, a range of activities from molecular biology to genetics to indirect selection must now be involved. The rate of progress made by any breeding programme depends as much on the efficient integration of information from these activities as it does on the activities themselves. Plant breeders are now realizing the importance of innovative approaches that include the use of a range of molecular methods and their outputs, and the possibilities of transferring this information from model species to cultivated crops. The use of these high throughput methods in model crops has already generated a large amount of public resources such as databases containing genetic resource, genomic and genetic information; tools for the effective analysis, data mining and visualization of such information; and semantic web resources for data integration. In this chapter, we highlight the role and contributions of bioinformatics to crop research and breeding by focusing on the bioinformatics resources that are available for crop science research and breeding, and indicating gaps that need to be bridged that will allow scientists to access, transfer and integrate data with eas

Software for supporting large scale data processing for High Throughput Screening

High Throughput Screening for is a valuable data generation technique for data driven knowledge discovery. Because the rate of data generation is so great, it is a challenge to cope with the demands of post experiment data analysis. This thesis presents three software solutions that I implemented in an attempt to alleviate this problem. The first is K-Screen, a Laboratory Information Management System designed to handle and visualize large High Throughput Screening datasets. K-Screen is being successfully used by the University of Kansas High Throughput Screening Laboratory to better organize and visualize their data. The next two algorithms are designed to accelerate the search times for chemical similarity searches using 1-dimensional fingerprints. The first algorithm balances information content in bit strings to attempt to find more optimal ordering and segmentation patterns for chemical fingerprints. The second algorithm eliminates redundant pruning calculations for large batch chemical similarity searches and shows a 250% improvement for the fastest current fingerprint search algorithm for large batch queries

LIMS Implementation in a Genotyping Study

Discovery laboratories are dealing with DNA sequencer-based technologies which have seen great advancement over the past decade, resulting in several steps of the genotyping process becoming automated. This, in turn, has led to increased throughput. Laboratory Information Management Systems (LIMS) are needed to organize data flow as large amounts of data are difficult to process by hand. A commercially developed LIMS was implemented at a Clinical Pharmacology Division laboratory of Indiana University, Indianapolis, during a P450 2D6 genotyping study. The LIMS application used was BiotrackerTM (Ocimum Biosolutions), and its modular design led users through each step of the genotyping process, from starting an experiment to the storing of output data from the genotype detection step. This ensured that every DNA sample was handled in an identical manner and all the necessary data were captured. The application helped design protocols and experiments, and manage different projects utilizing laboratory resources from the same inventory source, as in any typical laboratory. DNA samples, reagents, instruments, and generated data were also easily recorded and tracked. LIMS provide functions to trace back to protocols, inventories, projects, files or sample source for any genotype data. One of the features of LIMS that is not crucial to academic laboratories but was found useful during this project was the audit trail functionality, which allowed researchers to know who carried out what experiment at what time, and also to track inventories. Workflows of projects were also designed, and submitted for review and approval. Another aspect of this project was a survey to find out the knowledge and attitudes toward LIMS in academic research. It was observed that most academic researchers are not familiar with the total capabilities of LIMS, defined as special computer software that is used in the laboratory for the management of samples, inventories, laboratory users, instruments, standards and other laboratory functions such as invoicing, plate management, and work flow automation. However, several software technologies are employed but mostly for data storage and instrument integration, which normally come with vendor-specific instruments. Also, most respondents in laboratories conducting genotyping studies and DNA sequencing are more likely to use some form of LIMS. Lack of knowledge was cited as the most prevalent reason for not having used LIMS