Informatics
and validate experimental runs. It also speeds downstream analysis by ensuring that a sample’s history and origin is tied directly to the results obtained (Table 1).
The unprecedented throughput, experimental complexity and changeability associated with NGS create unique challenges for traditional LIMS. The rapid timescales associated with sequencing require LIMS that can be quickly implemented and easily configured by lab staff with no programming experience. Additionally, bioinformaticians and scientific programmers must have the power to make changes through the software’s application programming inter- faces (APIs) in order to accommodate the unique workflows that drive next-generation genomics research. Finally, NGS requires iterative, collab- orative work that is performed by many differ- ent types of scientists. User-specific interfaces can ensure that these workers have access to all and only the information they need to do their jobs effectively.
Selection criterion #1: Does the LIMS enable labs to get up and running quickly?
One of the primary selling points of LIMS since its inception has been its ability to integrate with laboratory instrumentation. As recently as 2009, LIMS users across all industries cited instrument integration as the most desired capability in a LIMS, with fully 70% of academic laboratories ranking it number one. In NGS, however, LIMS must do more than simply run and interface with instrumentation – it must provide a framework to appropriately capture data and streamline and automate mundane, routine tasks to eliminate the bottlenecks that can slow or even stall sequencing workflows and analysis. Data management and experimental tracking is even more difficult for labs using DNA indexing (also known as barcod- ing or tagging) to pool and multiplex samples from diverse, unrelated sources on a single flow cell lane. These techniques have in fact created a bottleneck at the library preparation step, where sheer throughput combined with the need to track which samples have been pooled in which runs, delays the rate at which labs can get samples on to sequencers.
While each type of NGS instrumentation comes with vendor-specified kits and protocols to opti- mise use and performance, the generalised task of integrating with sequencing instrumentation encompasses three primary phases, each of which should be supported out of the box by a LIMS.
Drug Discovery World Summer 2011 75
First, organisations must consider how they are collecting information about samples and associat- ing them with runs. Traditionally, scientists have spent many hours poring over Excel spreadsheets to check sample preparation and run assignments. Any LIMS should provide some level of integration with major NGS instrumentation. How the LIMS integrates with the instrumentation may differ: some LIMS may integrate more tightly with partic- ular instrumentation, and organisations should verify the connectivity between LIMS and their preferred instrumentation. For instance, some inte- grations may require scientists to specify the sam- ples they wish to run so that the LIMS can gener- ate the appropriate files for the lab’s sequencing equipment. Conversely, NGS instruments can be configured to hand off information on performed runs directly to the LIMS, reducing hands-on time for lab staff.
The second phase of instrumentation integration is configuring the LIMS to track the quality of sequencing data coming off instruments. Many sequencing instruments run for days on end, mak- ing it wasteful and inefficient for organisations to wait until runs are completed before evaluating the quality of the data obtained. In addition to moni- toring the status of runs in progress, LIMS can also collect metrics, such as the total bases yielded from a run or the percentage of base calls with a Phredquality score of more than Q30. Over time, these metrics can aid in assessing instrument per- formance. With data from sample runs archived and searchable in a centralised LIMS, labs can make better, more informed decisions about which samples to rework, whether to request more sam- ples for further experimentation, or how much time to spend on additional analysis.
Figure 2
Number of genomes entered into GenBank by year as of September 2009
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