laboratory informatics


of valuable information will likely have to be passed along by email, or via antiquated SharePoint sites, or even just on paper, and so much of this data will lack any scientific context.’


Seamless communication It is relatively painless and cost-effective for the client and each of its collaborators or service providers to adopt the same cloud platform, Uzzo pointed out. Tis gives the opportunity to communicate seamlessly, and to capture raw data from the instruments at the CRO or collaborator’s sites. ‘Scientists can then gain maximum value from the use and analysis of that data, and avoid the need to repeat experiments either because they can’t actually find who has carried out the experiment before, or because the protocol, results or data analysis don’t carry any context. Tis avoidance of repetition saves money in absolute terms, but it also saves time, which can ultimately speed a project through to completion.’ Financial constraints and collaborative


With a cloud


infrastructure, you can go global in minutes


most small and medium enterprises (SMEs), in particular, this sort of investment is prohibitive.’ Herein lies a major drawback with


implementing on-premises solutions, Medina stressed. ‘Many traditional LIMS packages provide customisation or authoring tools to personalise the system to the company’s requirements. Te trouble with customisations is that you have to train dedicated staff to maintain the system, roll them out to additional departments and geographies, and customise for specific application requirements. If and when those staff members move on, the ‘know-how’ goes right out the door with them. Te company is then tasked with rehiring and retraining new individuals, or relying on the vendor to provide interim support services at an additional cost.’ Tere are further financial benefits of cloud


solutions that relate to time saved and quality and quantity of usable data collected – in addition to lessening the burden of purchasing, deployment, and maintenance – commented Anthony Uzzo, president and cofounder of Core Informatics. ‘Cloud solutions can effectively and efficiently capture and secure data that has been generated by CROs and collaborators who are outside of the client’s infrastructure. Tink about all the money that large pharmaceutical companies spend on outsourcing and collaborative research. If the partners are not all using the same platforms for data collection and sharing, a huge amount


www.scientific-computing.com l @scwmagazine


R&D models are also ticked off as major drivers for the uptake of cloud solutions by Jens Hoefens, director of research, strategic marketing at PerkinElmer, and Brian Gilman, strategic marketing, elements at PerkinElmer. ‘Tere has been an incredible appetite for cloud technology in the life science and healthcare


sectors, as companies transfer the costs of maintaining an informatics infrastructure to vendors like ourselves,’ Gilman notes. ‘And with business models for pharma R&D increasingly focused on outsourcing or collaboration, cloud technology makes it so


MOVING INTO THE CLOUD IS A


COMMERCIAL, RATHER THAN A TECHNICAL DECISION


much easier to support data distribution and sharing across a firewall,’ Hoefens added, mirroring Uzzo’s sentiments. ‘Cloud is also ideally suited to situations and


environments in which scale is otherwise an issue,’ suggested Nic Encina, VP, Innovation Lab, at PerkinElmer. ‘Scale may mean housing the wealth of data that is being generated by scientific instrumentation and workflows, or it may refer to computing capacity to enrich and analyse that data as quickly as possible.’


Capex vs opex Ease of adoption, flexibility and support were additional factors that steered GoInformatics to build its R&D platform in the cloud from inception, Medina explained. ‘Te cloud and


Distinct data types supported by PerkinElmer Signals


Raw data l


➤


Large blocks of information (files)


l Relatively few per experiment (1,000s) l Requires process to extract value l Similar to a document store


Measurements l


Small blocks of information (rows)


l Huge numbers per experiment (1,000,000s) l Many branching analysis paths l Structured data store


l Curation and ontology applied


Entities l


l


Relatively few but relational complexity l Fundamental to cross-link experiments


Different scientific domains require different entity models


DECEMBER 2015/JANUARY 2016 9


Goinformatics


PerkinElmer


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