Producing useful conclusions from biological data requires understanding the biology as well as the analysis. We leverage our experience with the R language to help you improve your bioinformatic analyses and extract relevant, actionable knowledge.
Data in biotechnology and healthcare are generated in an increasing pace and volume thanks to recent advances in high throughput technologies. R is one of the most popular languages for analysis of biological data covering most biological questions thanks to a large repository of packages and a highly active community of developers and (bio)statisticians. For a comprehensive, statistically correct and biologically meaningful analysis of biological data, these methods need to be combined and further customizedThe various methods used for the analysis of the biological data often need to be combined and further customized in order to produce comprehensive, statistically correct and biologically meaningful results.
Having developed an alternative interpreter for the R programming language (Renjin) and with many collaborations in the field, we offer a series of data analytics and bioinformatic services. Having developed an alternative interpreter for the R programming language (Renjin) and with many collaborations in the field, we offer a series of data analytics and management services.
Types of services
- Identification and /or implementation of relevant bioinformatic workflows
- Optimizations on code, processes and infrastructure to tackle computational limitations and challenges in bioinformatic workflows
- Data and knowledge integration to increase the power of your analysis with relevant sources from public or private databases
- Data and code versioning for reproducible processes, sandboxing for secure analytic workflows, and use of robust technologies
We have over a decade’s experience with the development of analytic tools and integration of (R-based) analytic workflows in existing enterprise infrastructures. We have in-house expertise on biological research questions and data. In recent years we have also collaborated with a number of top European universities (SOUND project) and biotech companies to address the computational issues they experience in their bioinformatic analysis.