Implementation Of Allotrope Framework
Laboratories are able to generate large amounts of data through a variety sources including software, instruments and human input. Since ages scientists/technicians in the lab have been spending a long time to maintain experiment data related papers and they can seem very productive with these papers. Labs must be well organized and kept up to date for a variety of reasons. Any scientist is concerned with the biological linkage of a sample and its traceability to its source. During every phase of an experiment, data are generated. Data is generated at every stage of an experiment, e.g. Immediately after an experiment, these data should be available to analyze. Each observation is important, as it could lead to a breakthrough innovation. The software used to enter data is available from many companies. However, the data’s value lies in its output. Data interchangeability and integration between systems are made difficult due to the proprietary formats of each instrument. The integration of all information, even meta-data is difficult because there is no comprehensive option. Scientists are reluctant to abandon paper because of two reasons: paper-based procedures and the lack of integrated systems. To move towards a paperless laboratory, the ideal thing to do is modify processes in order to ensure less dependence on paper. To achieve a paperless laboratories, it is not enough to modify paper-based procedures. Organisations must also address the second major cause – the absence of a unified system.
Laboratory Information Management Systems, or LIMS, are the most common software used in laboratories. In most laboratories, LIMS is not the only software used. The output of the sample analysis is done through instrument interfaces. LIMS is required to integrate with other enterprise-wide software to help achieve “Paperless Flow”. This includes enterprise resource planning, electronic lab notebooks (ELN), scientific database management systems (SDMS), chromatographic databases (CDS), stock management, training management, statistical packages, and more. Although the goal is seamless integration between these systems, manual operations are still prevalent. Oft, the data entry and workflow is performed by non-technical staff or nonscientists. The people working at the umbrella level don’t realize that the metadata and data generated by the support processes are not available for query. Data entry is not synchronized with data mining. The majority of organizations are trying to reduce manual operations in order to move closer to a paperless lab.
You will find embedded software in all the analytical and instrumentation technologies available on the market. The pharmaceutical industry is becoming increasingly networked. U.S. Food and Drug Administration regulation, for example requires that these devices be closely monitored and subject to audits. In other words, software is as important in instrumentation as hardware. Interconnectedness, collaboration and analytics are essential when research is global. Regulatory compliance and objectives for business transformation are driving forces behind a paperless environment.
Data repositories that are effective, efficient and able to transfer data effectively between applications will be able to deliver the paperless laboratories for any organization.
Industry players are increasingly concerned about the issue of standardizing scientific data. There are many initiatives like the SiLA consortium and the AnIML language (Analytical Information Markup Language), the Allotrope Foundation Framework (ADF Framework), the Pistoia Alliance, to develop standards that will benefit the entire community.
Allotrope Foundation consists of a group of multinational pharmaceutical and biopharmaceutical corporations with the common goal to develop new technologies and standards for R&D data handling, initially focusing on analytical chemical research. Senior industry participants are emphasizing that the Allotrope Foundation is aligned with FDA’s lab regulations in its efforts to create an instrument- and vendor-agnostic common data format for labs. The Allotrope Framework is made up of the Allotrope Data Format. The ADF is an open format for storing data sets with unlimited size. They are stored in a file that can be organized into n-dimensional arrays and stored metadata. The Framework allows cross-platform transfers of data and data sharing. It also makes it easier to use. This project is funded entirely by the Allotrope Foundation’s members such as Amgen Bayer Biogen Pfizer Baxter. It is progressing rapidly in achieving its goals, which include reducing wasted efforts, improving the integrity of data and realizing value from analytical data.
The Framework is a software toolkit that facilitates the consistent usage of standards & meta data in software developments. Currently, it consists of 3 components.
ADF:
Allotrope Data Format ADF (Allotrope Data Format) is a versatile and vendor-independent data format for storing data files of any size. This data is easily stored, used and shared across all operating systems. The ADF includes a datacube for storing numerical information in n-dimensional Arrays, a Data Description Layer for storing context metadata in an RDF data model, as well as a package of data that acts like a virtual filesystem to store files associated with experiments. Allotrope Framework comes with class libraries for a consistent implementation of standards. The Foundation offers an ADF explorer, a tool that allows you to view data in any ADF (data description) file. An ADF file details:
Why the data were collected (samples or studies, purpose)
How to generate data (instrument or method)
How data is analysed (processing method)
The data format (dimensions, measurements, structure)
The ADF’s purpose is to facilitate real-time and rapid access to archived analytic data, as well as long-term stabilization. The ADF has been designed for performance and extensibility, to accommodate new technologies while still maintaining compatibility.
AFO:
The Allotrope Taxonomies and Ontology serve as the foundation for a controlled terminology that can be used to describe and execute tests or measurements, and then later interpret their data. The APN and member companies are working together to develop a standard language that describes equipment, processes and materials. This is based on real-world use cases and is extensible.
ADM:
Allotrope Data Models allow you to define standardized data structures that are used to describe ontologies. reproducible, predictable, verifiable) way.
Data Accessibility (Data Sharing) – By creating a data representation which allows for easy sharing, access, and exchange of data generated by any vendor’s laboratory equipment or software. It allows data that is incompatible proprietary formats, metadata and data stored in silos, to be accessed and shared instantly.
Data Integration – Allotrope Framework’s standard format data and metadata will enable compatibility in laboratory infrastructure and reduce the effort required to integrate workflows and applications. This will result in a higher level of automation for systems and processes.
Data Integrity. Allotrope Framework eliminates the need to convert files between formats or manually type data.
Regulatory compliance – Interoperability between laboratory infrastructures allows data to be linked for Quality Control (QC), as well as complete data traceability throughout its entire lifecycle. Allotrope Framework adopters can easily search, read, and share data, thereby addressing issues such as data integrity, regulatory compliance, and data sharing.
Scientific Reproducibility (Replicability) – The Framework provides a complete and accurate representation for the metadata necessary to document the experiments (methods/materials/conditions/results/algorithms), allowing reproducibility in a matter of clicks.
Improved Analytics – Allotrope Framework reduces the time to convert data across data sources and improves its quality. This enables the successful implementation and execution of a big data analytics strategy. ADF’s Data Description Layer uses an RDF-based data model which provides the ability to create business rules on top of standardized vocabulary.
Reduced Costs: The lack of need for software patches and customized solutions will result in a reduction of IT expenditures. Interoperability between software and instruments will reduce effort and costs for maintenance and support. Allotrope Framework adoption allows for increased laboratory automation. This improves overall operational efficiency. SME’s and Technology Partners
Members have been collaborating with vendor-partners to demonstrate the benefits of the framework, including how it facilitates cross platform data exchange, makes data easier to find, access and share, and allows greater automation for laboratory data flow. Allotrope Foundation launched the first phase for commercial use, and received a 2017 Bio IT World Best Practice award.
As part the Allotrope Foundation members are involved in working groups with specific roles, including teams that define technology-specific taxonomies or data models. Other teams include those that define governance, support and ontology processes, and also teams who define technical and ontological working groups. This collaboration, which includes >100 diverse professionals from Pharmaceutical, Biopharmaceutical, Crop Sciences, Instrument and Software companies in Analytical Sciences, Regulatory & Quality, Data Sciences, Information Technologies, and other industries, enables the industry to keep up with technological trends and business requirements.
Partners in the network include Abbott Informatics and Perkin Elmer. In addition to understanding the holistic picture, they can offer a wider standardization proposition. They also have a hand in the development of standardized frameworks that are practical. When data is shared, it has a much higher value than if the data was kept in isolation.
Agilent has been a member of Allotrope Framework since 2012. Allotrope has been a member of the Allotrope Framework for over a year.
Agilent’s contribution to Allotrope Foundation
Agilent chromatography software, such as Chemstation MassHunter and Chemstation, produces data in its proprietary format.
It is important to standardize data formats for integration when migrating from Chemstation MassHunter.
Agilent’s single quadrupole has moved from a Binary format to INI files (Chemstation & MassHunter), then XML formats (OpenLab 2), and now XML. The short format doesn’t clearly state which number represents SIM and which is dwell time. The unit of dwell-time is also not stated.
The ADF should be read and written in a commercially accessible environment for the Allotrope Foundation supporters.
OpenLAB ChemStation Edition has a prototype that can be used to demonstrate how this works. Two components make up the prototype. The first part, the ChemStation2ADF conversion, converts raw data into ADF with instrument traces as well as metadata. The Scheduler automatically uploads ADF files to OpenLAB Enterprise Content Management (ECM). The ADF Filter, which is the second component, reads out the Data Description of the ADF. This information is then placed into a database that can be immediately accessed by users using the ECM’s search and retrieval mechanism.
Upcoming Events
Support for other mass spectrometer types
Include qualitative results
Contribute the ADF MS standard
Other vendors’ ADFs can be read
Allotrope is a framework that allows organizations to achieve the maximum benefits from their Allotrope investment.
Learn what ADF and ADM stands for, as well as how they can be used.
Current state/format (including:
Allotrope Framework – a framework that can be incorporated into any project
Design
With the help of subject matter experts, determine what shape data you want (data description, format, raw data).
Allotrope allows you to explore the mapping between ontologies and datasets.
Create a project plan that will take you from your current state to where you want to be.
Plan downstream data uses, including
Compliance with regulatory requirements
Archiving data
Define which tools will be utilized to convert existing data into Allotrope files.
Carry out
Train and support internal resources
The system should be maintained and updated as the needs of the organization change.
Develop
Compliances and industry demands are ever-increasing.
Persistent Systems as a member Allotrope Partner Network can help you develop an Allotrope Framework strategy and architecture. Our organization is centered around digital transformation and informatics. Our professionals, armed with their extensive network of partners, can provide you with a wide range of services across the product’s lifecycle.
IT Systems for laboratories will become more service-oriented, plug-and play, and independent of vendor formats and software. Data standards will increase interoperability among software tools. This will create the reality of a digitally connected and intelligent laboratory. In addition, data synchronization will be improved, and downstream processes will be supported better to allow for rinse-and-repeat follow-on process. The Foundation’s goal is to create a laboratory environment with automated data analysis, reports that can be generated in one click, and scientific innovation.
Life Science Industry does not face unique data management problems. The digital potential that companies have today is being explored by many organizations. Budget constraints are one of the most important goals for businesses. Other objectives include sustainability, product and service development faster, as well as budget constraints. Standardization and interoperability are important to organizations in order to maximize innovation and maintain competitive advantage.
