Listen to our newest podcast with Ruth Marinshaw, CTO of Research Computing at Stanford University.

Close this search box.

Data Science / Bioinformatics

Data science requires the integration of data, computing capabilities and software.

Data science, the analytical side of biomedical research, requires a holistic view of the scientific data ecosystem in order to be truly successful. Data science activities require that data compute, and software be brought together to create an efficient, user-friendly analytics and visualization system. Artificial Intelligence and machine learning (AI/ML), which are at the tip of the data science pyramid, will require even more capabilities and integration.

Icon 04

Systems for state-of-the-art bioinformatics

Data science requires a foundation of solid data management practices, processes and procedures to develop and track code and models, software to support data scientists and infrastructure to run the necessary algorithms. BioTeam works with clients to create these systems across a wide variety of scales, from the individual lab all the way through to international scientific cyberinfrastructure.

Support data-intensive bioinformatics on premises or on the cloud

Bioinformatics analyses common in genomics and imaging require access to relevant data combined with appropriate software running in a computational environment that is suitable for the job at hand. These analysis workflows are increasingly making use of specific hardware such as GPUs. BioTeam has a long history of designing systems to support bioinformatics on premises at an organization via dedicated compute solutions, including high-performance compute (HPC) systems. Similarly, we have helped many organizations take their analysis workflows into the cloud, or utilize a hybrid approach of on-premises plus cloud infrastructure.

Script and automate complex genomics and genetics workflows

BioTeam’s extensive bioinformatics experience includes scripting and automating complex genomics and genetics workflows involving gene, protein and genome annotation, RNA-Seq, NGS and variant calling. Our languages include Python, Perl, and R, using a variety of integrated development environments. We can also implement workflows using many different standards and tools, such as CWL, WDL, Airflow and Snakemake. Our collective background also includes deep practical knowledge of a very large number of biomedical databases, their APIs and the biomedical ontologies used to annotate these databases. BioTeam also brings a history of support for and direct contribution to the open-source world surrounding biomedical research.

Partner With Us

Partner with BioTeam to build a solid data-centric organization.

BioTeam has helped build solid data-centric organizations and put in place the capabilities required to support the latest AI/ML analytics and visualization techniques. We understand your data science needs and integrate them seamlessly with the entire scientific data ecosystem to avoid unnecessary infrastructure limitations.

Learn more about our other practice areas:

Data Science / Bioinformatics

Data Science / Bioinformatics