Overview

This Core provides access to current and emerging technologies at the interface of genetics, genomics, and computational research as well as to bioinformatic and statistical analysis. The Core pursues robust performance at the highest quality standards and is committed to drive development toward future needs, with an emphasis on advancing machine learning approaches across basic, translational, and clinical studies. Core services are broadly divided between training and education, access to tools for molecular biology and ‘omics data generation, and computational resources for data analysis.

In addition to building and supporting a vibrant community around a common interest, the leadership’s involvement in large-scale, international research projects also results in early access to emerging results. These and other Center data resources are accessible through the Digestive Disease Data Science Commons, which provides CSIBD members with access to a secure cloud-based platform for management and computational analysis of large scale datasets.

Core Personnel

Mark Daly, PhD
Co-Director

Caroline Uhler, PhD
Co-Director

Daniel Graham, PhD
Associate Director

Orr Ashenberg, PhD
Associate Director

Christine Stevens
Project Manager

Objectives

  1. Facilitate the application of advanced experimental platforms for genetics, genomics, and variant analysis to discovery efforts relevant to IBD and digestive disease research.
  2. Provide a centralized facility and personnel for performing state-of-the-art recombinant and PCR-based DNA procedures and provide cost-effective and high-quality molecular biology reagents and services.
  3. Provide a resource for disseminating varied molecular biology, genetic, and bioinformatics technologies.
  4. Deepen human genetics insights into disease progression, therapeutic response, and laboratory values in clinical contexts.
  5. Provide methods for multimodal data integration that work across all scales of disease, from molecule to patient.
  6. Train ML models to predict the impact of clinically-relevant genetic variants on cellular function.
  7. Develop algorithms to design meaningful perturbation experiments and identify and manipulate cell programs in disease.

Services

  • Consultation, training, and education. Central to all services are formal and informal end-to-end consultations on grant proposal development, experimental design, data generation, bioinformatics analysis and data interpretation. A variety of training opportunities and workshops are also offered to aid CSIBD investigators in study design and data analysis from genetics- and genomics-based projects. For experimental work, full access is granted to protocols and reagents for sophisticated molecular biology approaches.

  • Bioinformatics support. A team of bioinformaticists and software developers with in-depth expertise are available within the Core to provide collaborative capacity for data analysis. This includes handling RNA-Seq, high-throughput proteomics and metabolomics data, pathway reconstruction tools, and state-of-the-art pipelines for genome annotation, exome analysis, and interpretation. To accommodate the increasing quantity of large-scale data generated by human genetics and microbiome studies, the HGML and Microbiome Cores co-host the Digestive Disease Data Science Commons, which organizes and disseminates Center data and resources.

  • Tools for molecular biology:

    • Routine molecular biology tools including PCR, DNA procedures, oligonucleotide synthesis, real-time qPCR, and microsatellite fragment analysis
    • Resource-intensive methodologies including cDNA library prep and siRNA/shRNA and CRISPR screens
    • Genotyping and DNA fragment analysis
    • CRISPR amplicon sequencing
    • Complete plasmid and RNA sequencing using next-generation sequencing

    • Repository of plasmids, libraries, RNA samples and probes

    • Gene profiling services by deep sequencing

  • Machine learning cross-disciplinary collaborations. The Core strives to bring together different scientific communities and bridge important biological questions with computational methods. Efforts include machine learning fellowship programs, competitions, meetings, and events.