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industry comment Best of both Configuration and customisation. Mike

Sanders, from GenoLogics Life Sciences Software, explains why next-generation genomics labs need software that offers both


In cutting-edge, next-generation genomics labs, change is the operative word. Methods used one day are practically obsolete the next. Or they may not exist at all – analytics and methods created to handle unique science are also key outputs of next-generation genomics labs. Matching the pace of change in this environment can be especially challenging for programmers who must find time to develop truly game-changing software, while simultaneously maintaining software that handles routine lab tasks, such as sample management and tracking, and instrument quality control. The question inevitably turns to whether

to build systems in-house to meet specific lab requirements or buy commercial systems. Answering this question is more difficult in the context of next-gen genomics, however. This is because the key requirement for this type of software is adaptability – something that is nearly impossible to buy (Has anyone ever seen a commercial system that encompasses all of the business logic and interactions users want?) and just as impossible to build (Anyone who has completed a custom project knows that these projects are expensive, hard to scale, and can quickly become unmanageable under changing requirements). So what is the answer to the build/buy

question? Neither. Or both. Look for and expect vendors to deliver a system that is as close as possible to the science you are trying to do, then build the parts that you are suited to build – and no more. Achieving the latter objective requires software that can be configured by scientists and customised by programmers and bioinformaticians using modern, familiar software development tools and application programming interfaces (APIs).

Step 1: Get the science right Even in a cutting-edge discipline like next- generation sequencing (NGS), certain tasks and workflows are universal: sample

management; experimental monitoring; and data collection, analysis, and reporting. Laboratory information management systems (LIMS) are a mature class of software introduced in the 1980s to handle these types of tasks, and today many vendors are introducing LIMS targeted to NGS. The best of these systems have been built from the ground up to deliver preconfigured, out-of-the-box workflows that codify and support NGS best practices. Developers considering an NGS LIMS should expect their vendor to deliver out-of-the-box functionality that meshes well with their lab’s preferred instrumentation and scientific techniques and methods. For instance, each NGS instrumentation vendor provides its own, specific kits and protocols to optimise instrument use and performance. The best LIMS for NGS should recognise this and provide integration options that make it far less onerous for labs to connect the software to their preferred instruments and begin producing results.

Step 2: Configure and customise For software to be truly adaptable, it must be both configurable and customisable. Software marketing often conflates these terms, but programmers understand they are distinct – and the distinction is an essential aspect of implementing software to streamline NGS research. Configuration refers to changes in existing software that can be made via the user interface by any user – no programming experience required. In an NGS LIMS, for instance, scientists might configure the software to add new lab methods, collect records off a new instrument, or specify a particular sample preparation procedure. These types of changes are best made by scientists who understand the requirements and how the system needs to work. Empowering scientists through

configuration frees developers to focus on more high-value projects, which typically

involve customisation. Scientific software customers equate this with (expensive) consulting services that software vendors may sell in addition to out-of-the-box software. But scientific programmers and bioinformaticians thrive when they have the power and control to change the code and make software do something new and different without involving the vendor.

An API for genomics Many APIs that claim to enable adaptation in fact lock down code in proprietary formats that can be difficult to modify without experience or training. True adaptability utilises tools already familiar to scientific programmers, such as modern architectural styles and familiar open-source and commercial scripting languages such as Groovy, PERL, or Python. The representational state transfer (REST) architectural style is particularly well suited to NGS. Unlike simple object access protocol (SOAP)-based services, RESTful services offer a limited set of methods (verbs) that can accommodate a wide range of resources (nouns). While either SOAP or REST services can be used to create interfaces to genomics data, REST interfaces emphasise the nouns (samples, projects, institutions, etc.) that define genomics research. Further, REST uses a simple, hierarchical

hyperlink structure to organise resources, which not only matches the data produced by next-gen genomics labs, but is human-friendly – the code both describes and provides access to the data. This makes REST scripts easy to decipher and modify. Scientists can use the API to develop scripts for a variety of tasks, such as automating sample tracking or quality-control procedures, initiating computational processing, or interfacing with instrumentation or custom analytics. Handy plug-ins can trigger scripts to run on demand in a scientist’s user interface, providing control and flexibility without disrupting the scientific workflow. In a fast-paced and ever-changing next-gen

genomics lab, scientists succeed by pushing the boundaries of innovation – and they cannot afford to be constrained by the software they implement to manage data and laboratory workflows. A next-gen genomics LIMS, built on standard tools and architectural styles, enables rapid implementation of today’s technologies and provides adaptability so that labs can quickly embrace the technologies favoured for doing tomorrow’s genomics research.

Mike Sanders is product manager, LIMS platform, for the GenoLogics LIMS

JUNE/JULY 2011 11

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