Biomedical Informatics

ESI offers customized bioinformatics solutions to give you deep insight into your data. We help you see the underlying molecular and cellular systems and processes at work, as well as their impact on diseases such as cancer.

ESI Services

ESI can help you unlock the full potential of your life sciences data—whether you need a
next generation sequencing analysis done quickly or require a complex multimodal investigation.

 

Our staff is well-versed in today’s bioinformatic tools, programming languages, data types, and computing platforms, including high-performance and cloud environments. We also develop new tools and customized analyses specifically tailored to your needs.

Most importantly, because we’re scientists as well as computer specialists, we’re able to bridge the gap between biology and technology. We understand the scientific questions to ask, and we know how to use technology to get you answers to those questions.

 

To help with your genomic research, we offer a wide array of Next-Generation Sequencing (NGS) data analysis services, including:

 

  • whole-genome and whole-exome sequencing;
  • differential gene expression using bulk and single-cell RNAseq;
  • variant calling and filtering;
  • gene network analysis; and
  • ChIPseq, ATACseq, and methylation data analysis.

 

To aid in your proteomic research, we can help investigate:

 

  • Kinase Substrate Enrichment Analysis (KSEA) from quantitative
    phosphoproteomics,
  • Reverse-Phase Protein Array (RPPA) data to examine protein expression and
    post-translational modifications, and
  • various types of proteomics data to examine protein expression in the blood.

 

To help you make the most of machine learning and precision medicine, we can assist with:

 

  • logistic regression, Cox hazard modeling, Kaplan-Meier analysis, random forest
    searches, classification using XGBoost, and more; and
  • application of deep learning models to extract features from whole slide images
    and predict cancer subtypes.

 

To support your work in managing and sharing clinical data, we provide:

 

  • expertise in various public data sets, such as The Cancer Genome Atlas (TCGA),
    Cancer Research Data Commons, NIH database of Genotypes and Phenotypes
    (dbGaP), Therapeutically Applicable Research to Generate Effective Treatments
    (TARGET), etc.; and
  • pipeline development, testing, and deploy

ESI Solutions In Action

We support important data infrastructure projects for NCI’s Center for Biomedical Informatics and Information Technology (CBIIT), such as the Cancer Research Data Commons, the Childhood Cancer Data Initiative, and NCI’s Enterprise Vocabulary Services.

 

We also developed a tool called 3DVizSNP to view data in a new way. With only a few clicks of a mouse, you can visualize mutations in a 3D context, making it easier to assess whether a mutation is likely to contribute to disease. The tool is being deployed on the Cancer Genomics Cloud platform, where it can be easily integrated into existing workflows.

 

For NCI’s Division of Cancer Control and Population Sciences, we’re helping to make ground-breaking SEER (Surveillance, Epidemiology, and End Results) data more accessible to the research community. We’ve helped refine existing tools to allow you to track longitudinal data for patients and survivors. Some of these products include JPSurv, a web tool that lets you analyze trends, and RecurRisk, a software package for tracking disease-specific survival data

ESI Helps Decipher Proteins Linked to Glioblastoma Progression

In a recent study of glioblastoma (one of the most aggressive and deadly types of brain cancer), ESI staff worked alongside our federal partners at CBIIT’s Computational Genomics & Bioinformatics Branch and NCI’s Center for Cancer Research to better understand the genetic factors underlying this disease. ESI conducted proteomic analysis to search for patterns that could be used to predict patients who were most likely to respond to treatment. Using blood serum samples taken before and after combined chemotherapy and radiation treatment, our team found distinct protein signature alterations linked to cancer progression. Molecular changes such as these could give oncologists a new way of predicting outcomes and monitoring brain cancer using a less-invasive blood test. This study is the first to link proteomic changes before and after treatment to glioblastoma patient response. Now, we’re working to extend this study. We’re testing the approach in a larger patient group, incorporating metabolomic data, and adding radiological measurements to track changes in tumor volume.
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