About the Client

A global pharmaceutical company’s R&D department discovers and develops innovative medicines that ease patients’ suffering and solve the most important unmet medical needs of our time.  Researchers in the Antibody Discovery department attempt to solve these problems by targeting specific genetic biomarkers.

The Challenge

Experiments are processed using high-throughput DNA sequencing technologies, also known as Next Generation Sequencing or NGS.  Results from NGS machines generate an enormous amount of genetic information and data; sifting through this output is a time-consuming and daunting task.


Researchers encountered several issues when analyzing NGS results:


  • Processing the results was extremely time-consuming, often taking four to six weeks to complete.
  • There was no existing process to follow, and each analysis contained numerous manual and semi-automated steps.
  • Every team used different methods to analyze the data, so it was difficult to reproduce steps taken by other researchers.
  • It was nearly impossible to compare results across multiple experiments, even within the same team.
  • Collaboration was extremely difficult because there was no single location to search through NGS results.
  • The entire month-long process was repeated on existing data-sets whenever a new potential target was discovered.
  • Any necessary reports or graphs based on NGS results were created by hand in Excel

Our Approach and Results

We worked with the team to design and develop a new web-based NGS application that dramatically improves the end-user experience.  We built the application using Oracle, PL/SQL, Perl, the Moose Framework, and JavaScript.  The improved NGS system provides several benefits:


  • Time savings: we reduced the four-to-six week processing time down to less than an hour for each analysis, an improvement of more than 200 times.
  • Simplified processing: the improved NGS analysis process simply requires a researcher to upload his or her data to a server and provide some high-level biomarker information.
  • Transparency: the NGS system analyzes data using a uniform, well-documented algorithm; this enables researchers to reproduce results from other data sets when necessary.
  • Searching: the web application includes an advanced searching algorithm enabling researchers to query targets across multiple experiments.
  • Accessibility: all NGS data and results are stored in a single web application.  When researchers have a new target of interest, this enables them to search for results across multiple departments.
  • Repeatability: researchers can query new genetic target regions on previously processed data-sets without re-running the entire analysis process.
  • Collaboration: multiple reports and graphs can be generated automatically.  This makes it easier to explain results to different business groups and departments within the company.