Translating scientists’ needs into next-gen IT infrastructures
Our scientists, data scientists and technologists have deep biotech, pharma, and government experience. With hands-on knowledge of laboratory technologies, bioinformatics, clinical informatics, genomics, structural biology and more, we are adept at understanding scientific needs and translating them into powerful, implementable IT plans and designs. We have the scientific expertise to increase research performance by building or improving all aspects of scientific data ecosystems at the corporate or departmental level.
BioTeam brings a broad understanding of omics technologies and real-world lab experience to engagements with our customers. Our professional experience includes expert support for organizations engaged in instrumented studies of gene expression, proteomics, metabolomics, and genetic variation.
We have provided assessments of infrastructure intending to support data-intensive technologies such as Cryo-EM and other data microscopy platforms. We have also designed custom platforms for labs looking for novel approaches, such as light-sheet microscopy.
Our bioinformatics experience includes extensive experience 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 workflow 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.
BioTeam works closely with scientific teams engaged in basic, pre-clinical, and clinical research. In engagements with clinical researchers we have evaluated requirements for databases, clinical data standards, and applications. We also have experience with and a strong interest in advancing the use of the emerging FHIR standard in clinical research.
Artificial Intelligence / Machine Learning
Our developers and scientists have created a new specialty within BioTeam for biomedical research, dedicated to ML and AI. We have been working with research laboratories and have created custom codebases for image registration, image quantification, image segmentation, and image classification. We have used open source libraries to create novel tools for biological sequence classification and annotation using Deep Learning, in order to study uncharacterized genes and proteins, where we also use established ML approaches such as Hidden Markov Models. We are also building workflows for analysis of signal processing data and biomedical analytics using convolutional and recurrent Neural Networks.