Biomedical Data Science and Bioinformatics
At Emmes, we realize that technology has changed the possibilities of global clinical research. Advances in high-throughput laboratory testing and digital technologies enable our clients to conduct sophisticated systems biology, biomarker, and other clinical studies with large scale endpoints, deepening knowledge of clinical outcomes.
Emmes supports your clinical research by providing a collaborative and reproducible approach to large-scale biomedical data science & bioinformatics analysis. We help you integrate and analyze data across various high-throughput technologies, including next-generation sequencing (NGS), microarrays, mass spectrometry and others that measure “-omics” outcomes. Our capabilities empower you to simultaneously analyze genetic alterations in combination with changes of thousands of molecules (mRNAs, proteins, small molecules) in plasma, cell populations or single cells. This gives you an unprecedented view of human responses to vaccines, therapeutics or medical devices enabling biomarker discovery and paving the way to precision medicine.
We’ve learned that collaboration between scientists, clinicians, biostatisticians and IT experts produces the highest quality results. That kind of thinking guides us.
To detect key patterns in your “-omics” or other large-scale data, we employ state-of-the-art statistical, data science, and computational methodologies including machine learning, advanced data visualization, pathways analysis, scalable cloud computing, clinical data management and reproducible data analysis methods.
To ensure timely delivery of high quality data for your study, we utilize cloud computing and automation to scale resources on demand with your study’s data processing and analysis needs. We ensure that all analytical steps are fully automated, beginning with raw data and ending with report generation. Armed with such research, you gain more robust and compressive assessments, e.g., better understanding of treatment response on the molecular level including subject-level response heatmaps, pathway maps color-coded by treatment effect, as well as time trends of co-regulated functional modules.
Our best practice biomedical data science & bioinformatics solutions include
- Genomics (WGS, WES, targeted panels)
- Transcriptomics (RNA-Seq, ribosomal profiling, DNA microarrays)
- Proteomics (LC-MS/MS, iTRAQ/TMT, 2D-DIGE, and protein/peptide microarrays)
- Metabolomics/lipidomics (LC-MS)
- Metagenomics (WGS, 16S)
We customize analytical modules to your project needs, such as
- Data normalization, missing value imputation and batch effect correction
- Data reduction methods, heatmaps, and pathway maps for visualizing key trends in highdimensional data
- Application of multivariate data analysis, machine learning and AI
- Identification of functional modules and multi-omics data integration
- Pathway enrichment analysis
The goal of this study was to assess how antibody responses against Plasmodium falciparumantigens in children change over the malaria season, and how these changes compare to those seen in adults. Serum that contained antibodies was extracted from blood and antibody responses against more than 100,000 unique peptide sequences were measured using a peptide array. Results from this study provided an unprecedented view of antibody responses against P.falciparum antigens on the protein and protein epitope level. We identified antibody markers that were indicative of the adult response as well as markers in children that were increased during peak malaria season.
For the National Myelodysplastic Syndrome (MDS) study, we support targeted exon sequencing data management and analysis to identify and characterize somatic variants related to rare bone marrow diseases. We applied machine learning to differentiate between true and artifact somatic variants and assist with diagnosis of MDS. We also developed interactive web applications that allow dynamic sub-setting of the data with real-time updated graphical and tabular summaries.
To better understand how the AS03 vaccine adjuvant enhances immune responses to flu vaccine on the molecular level, changes in thousands of white blood cell molecules were measured simultaneously. We used scalable cloud resources for analyzing two terabytes of raw data generated by transcriptomics and proteomics assays. These analyses provided evidence that AS03 administered with flu vaccine stimulated subsets of white blood cells to improve uptake and processing of antigens.
Our experience across Phase I-IV studies includes:
- Influenza vaccine and adjuvant
- Tularemia vaccine
- HSV2 vaccine
- Smallpox vaccine
- Malaria natural infection, vaccine and challenge
- Plague vaccine
- CMV vaccine
- Pertussis vaccine
- Yellow fever vaccine
- Tuberculosis vaccine
- Tularemia vaccine
- Filovirus vaccine and adjuvant