The LabBase system for data management in large scale biology research laboratories

A brief history of bioinformatics

It is easy for today’s students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of ‘Big Data’ has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology.

Design and implementation of a generalized laboratory data model

Background

Investigators in the biological sciences continue to exploit laboratory automation methods and have dramatically increased the rates at which they can generate data. In many environments, the methods themselves also evolve in a rapid and fluid manner. These observations point to the importance of robust information management systems in the modern laboratory. Designing and implementing such systems is non-trivial and it appears that in many cases a database project ultimately proves unserviceable.

Results

We describe a general modeling framework for laboratory data and its implementation as an information management system. The model utilizes several abstraction techniques, focusing especially on the concepts of inheritance and meta-data. Traditional approaches commingle event-oriented data with regular entity data in ad hoc ways. Instead, we define distinct regular entity and event schemas, but fully integrate these via a standardized interface. The design allows straightforward definition of a "processing pipeline" as a sequence of events, obviating the need for separate workflow management systems. A layer above the event-oriented schema integrates events into a workflow by defining "processing directives", which act as automated project managers of items in the system. Directives can be added or modified in an almost trivial fashion, i.e., without the need for schema modification or re-certification of applications. Association between regular entities and events is managed via simple "many-to-many" relationships. We describe the programming interface, as well as techniques for handling input/output, process control, and state transitions.

Conclusion

The implementation described here has served as the Washington University Genome Sequencing Center's primary information system for several years. It handles all transactions underlying a throughput rate of about 9 million sequencing reactions of various kinds per month and has handily weathered a number of major pipeline reconfigurations. The basic data model can be readily adapted to other high-volume processing environments.

'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 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.

Intelligent software for laboratory automation

The automation of laboratory techniques has greatly increased the number of experiments that can be carried out in the chemical and biological sciences. Until recently, this automation has focused primarily on improving hardware. Here we argue that future advances will concentrate on intelligent software to integrate physical experimentation and results analysis with hypothesis formulation and experiment planning. To illustrate our thesis, we describe the 'Robot Scientist' - the first physically implemented example of such a closed loop system. In the Robot Scientist, experimentation is performed by a laboratory robot, hypotheses concerning the results are generated by machine learning and experiments are allocated and selected by a combination of techniques derived from artificial intelligence research. The performance of the Robot Scientist has been evaluated by a rediscovery task based on yeast functional genomics. The Robot Scientist is proof that the integration of programmable laboratory hardware and intelligent software can be used to develop increasingly automated laboratories.

A Flexible Data Analysis Tool for Chemical Genetic Screens

High-throughput assays generate immense quantities of data that require sophisticated data analysis tools. We have created a freely available software tool, SLIMS (Small Laboratory Information Management System), for chemical genetics which facilitates the collection and analysis of large-scale chemical screening data. Compound structures, physical locations, and raw data can be loaded into SLIMS. Raw data from high-throughput assays are normalized using flexible analysis protocols, and systematic spatial errors are automatically identified and corrected. Various computational analyses are performed on tested compounds, and dilution-series data are processed using standard or user-defined algorithms. Finally, published literature associated with active compounds is automatically retrieved from Medline and processed to yield potential mechanisms of actions. SLIMS provides a framework for analyzing high-throughput assay data both as a laboratory information management system and as a platform for experimental analysis.

Mapping by sequencing the Pneumocystis genome using the ordering DNA sequences V3 tool

A bioinformatics tool called ODS3 has been created for mapping by sequencing. The tool allows the creation of integrated genomic maps from genetic, physical mapping, and sequencing data and permits an integrated genome map to be stored, retrieved, viewed, and queried in a stand-alone capacity, in a client/server relationship with the Fungal Genome Database (FGDB), and as a web-browsing tool for the FGDB. In that ODS3 is programmed in Java, the tool promotes platform independence and supports export of integrated genome-mapping data in the extensible markup language (XML) for data interchange with other genome information systems. The tool ODS3 is used to create an initial integrated genome map of the AIDS-related fungal pathogen, Pneumocystis carinii. Contig dynamics would indicate that this physical map is approximately 50% complete with approximately 200 contigs. A total of 10 putative multigene families were found. Two of these putative families were previously characterized in P. carinii, namely the major surface glycoproteins (MSGs) and HSP70 proteins; three of these putative families (not previously characterized in P. carinii) were found to be similar to families encoding the HSP60 in Schizosaccharomyces pombe, the heat-shock psi protein in S. pombe, and the RNA synthetase family (i.e., MES1) in Saccharomyces cerevisiae. Physical mapping data are consistent with the 16S, 5.8S, and 26S rDNA genes being single copy in P. carinii. No other fungus outside this genus is known to have the rDNA genes in single copy.