Decreasing Proteomics Data Analysis Time by 62% to Scale Breakthroughs in Cancer Care
A leading biotechnology company based in Cambridge, Massachusetts, with a talented team of over 150 employees – is focused on advancing immunotherapies for a wide range of cancers. The company’s therapeutics pipeline includes monoclonal antibodies and small molecules, which are designed to activate macrophages and natural killer cells and suppress cell signaling molecules, including cytokines and metabolites. The holistic focus on the tumor microenvironment makes the research too complex for standard analytics technologies, yet with the introduction of advancing technologies and improved resource efficiency, the company is on the bridge to developing safer, more reliable, and accessible cancer therapies for patients around the world.
Increasing preclinical and clinical programs exposed the difficulty of organizing and optimizing omics data analysis processes in the lab through manual data handovers between biologists and bioinformaticians.
The lack of an interactive and accessible analysis system for experimental data made it difficult to identify patterns and discover the ideal therapeutic target, which hindered efficient decision-making.
Without a centralized system for experimental omics data, the team couldn’t easily get an overview of the obtained results. This made it difficult to understand the involved pathways.
decrease in time spent on omics data analytics.
Scientists agree Omics Playground increases the speed of collaboration.
Increase in quality of data insights and reproducibility.
Read about the perspective of a Bioinformatician:
“Building a robust Bioinformatics Infrastructure: Streamlining
RNA-seq data analysis to facilitate groundbreaking Gene & Cell R&D”
# of employees in 2023: 100-200
Field: Cancer Treatment Discovery
Headquarters: Boston, Massachusetts
Alissa P., Principal Scientist and Head of Translational R&D, knew about BigOmics from her previous company. She saw how using the Omics Playground as their omics analysis platform would provide both, the structure and the flexibility needed to unite scientific discovery and process development.
Early on, the company used individual scripts to manage all the sequence information it created and collected. Their bioinformaticians were responsible for processing the raw data with increasingly limited time resources. This was an annoyance for research and a danger in early development work to miss milestones due to bottlenecks in the data analysis process.
Furthermore, it was easy to overwhelm an unstructured system as the number of data sets grew with the increasing number of projects they were working on.
Alissa knew that in order to scale rapidly, her team would need a robust, cloud-based data analytics system to empower the scientists and levitate the pressure from their bioinformaticians. “We knew we needed to get a system in place early.
Would you rather implement that system after you scale, or would you rather have that system be the thing that helps you scale?” said Alissa.
Without such a system, scientists could spend up to 35% of their time waiting for static reports, organizing data to piece key information together, and unnecessarily repeating the time-consuming data analysis process due to misalignments between teams.
With strong executive support, scientists quickly adopted BigOmics. Clear leadership also helped rapidly build healthy processes around data and knowledge transfer between biologists and bioinformaticians and gaining insights from the system to make informed decisions. What emerged was a culture where Omics Playground is one of the most essential tools for sharing R&D data insights. For example, instead of using PowerPoint to communicate project progress and results in weekly meetings, Alissa uses Omics Playground. She can easily switch between different modules for a 360-degree view of project progress; and change parameters, resulting in instant updates across the entire data set.
With a single system to analyze all omics data, her team has become 57% more productive, and the company is now scaling rapidly and smoothly their sequence activities.
Proteomics data are analyzed through peer-reviewed algorithms, so scientists can quickly identify the most promising therapeutic targets without requiring any coding knowledge.
As more experiments are performed, the newly added data sets can be compared to previous results and more than 6,000 public data sets; providing the necessary context for scientific breakthroughs.
Since all omics data is in one place, scientists spend 62% less time on rerunning.
BigOmics helps managers and executives have an eye on day-to-day progress while also providing a cohesive overview of all different research projects.
Across the numerous projects, many decisions have to be made rapidly yet confidently. With the full history of their experiments at their fingertips, executives can make prompt, data-driven decisions.
Since BigOmics has improved the reproducibility of data, it’s much easier for leaders to forecast and set informed timelines.
Repetitive and time-consuming data analysis iterations can be avoided since biologists are enabled to gain insights from their data directly from the platform.
Everyone can easily access the same version of data and results shared in team meetings since scientists present directly from Omics Playground.
Bioinformatics resources are conserved, allowing them to process requests faster.
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