HTSense: Simplifying the analysis and design of high throughput screens

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High throughput screening (HTS) has found widespread use in the field of drug discovery, allowing users to conduct hundreds or thousands of biological experiments simultaneously. Despite the rapid advancement of HTS methods, there are very few tools capable of analysing the complex and varied data produced.

Researchers at the University of Oxford have developed HTScape, a tool that can analyse data from a range of HTS experiments. By normalising the HTS data, HTScape can offer a truly flexible method for visualising complex datasets and designing validation studies.

High throughput screening

The advent of automated high throughput screening (HTS) has revolutionised drug discovery process. Lead compound identification has been streamlined as hundreds or thousands of reactions and interactions can be evaluated simultaneously. Due to its complex nature, HTS generates unprecedented amounts of data. The analysis of which requires specialist tools.

Many experiments mean many tools

Scientists currently use a multitude of different tools for analysing the data produced from HTS experiments. Tools exist for specific experiments, such as siRNA pooling, which explores a single interaction. However, increasing the complexity of the system generally means that the existing tools lack sufficient flexibility to process the additional parameters that are required to make sense of such rich datasets.

HTSense: Embracing complexity

Researchers at the University of Oxford have developed HTSense; a tool capable of analysing the data arising from highly complex HTS experiments. HTSense is able to normalise the results from a range of experiments and conditions. This allows for the biological data to be inspected in a different context that can include plate artefacts, systematic bias, compound/gene information, cell line/cancer profile and many others.

HTSense offers a number of benefits over the current data analysis tools:

  • Accommodation of complex design
  • Ability to reuse the data and analysis steps
  • Allows for different normalisation and QC approaches
  • Gives contextual data with respect to the existing knowledge landscape

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Oxford University Innovation is seeking commercial and non-commercial licensees for the HTSense software.

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