Internship/Co-Op, Computational Biology

Position Details

Date Posted:

September 29, 2020

Location:

Cambridge, MA

Description:

Rheos Medicines is seeking a creative and motivated individual to join the Computational Biology team. The intern/co-op will help to design, develop, and deploy bioinformatic pipelines and applications for visualization and analysis of multi-omics datasets. They will get to work closely with bioinformaticians, biologists and other scientists to understand how high-throughput omics data are generated, processed and analyzed to provide insights into the exciting field of immunometabolism. 

Requirements:

Below are some potential projects and can be adapted based on applicant’s expertise. The most important criteria we are looking for is passion in using omics data to understand biology towards drug discovery and development.

Potential projects and their required skills:

Multi-omics analytics and visualization

  • Design, development and optimization of Shiny apps for multi-omics data integration and visualization
  • Opportunity to work with different omics data to design applications that would help scientists find, retrieve, visualize and analyze data
  • Get to work with big omics datasets and learn how they are used to drive drug discovery

Required skills:

  • Basic R
  • Basic Shiny app development
  • Basic Postgres database

Preferred skills:

  • Basic JavaScript
  • Basic UX design
  • Basic biology 
  • Basic understanding of basic bioinformatics pipelines

Network-based omics method evaluation

  • Implement and evaluate state-of-the-art methods for network-based omics analyses to identify molecular signatures in omics datasets
  • Review set of network-based methods for transcriptomics and/or metabolomics data for characterization of molecular signatures

Required skills:

  • R or Python
  • Working in Linux environment

Preferred:

  • Basic database scripting and querying
  • Basic network theory 
  • Basic graph algorithms
  • Basic biology or biochemistry

Review and implementation single-cell RNAseq workflows

  • Single-cell RNAseq (scRNA-seq) analyses can enable us to identify novel disease driving cell relative to traditional profiling methods that assess bulk populations
  • scRNA-seq is a relatively new technology and hence there are many new pipelines being developed to process and analyze such datasets
  • A comparison of the leading methods and their performance would help select the pipeline to deploy within Rheos

Required skills:

  • R or Python
  • Working in Linux environment

Preferred:

  • Basic biology or biochemistry
  • Understand how to implement cloud-based high-performance computing pipelines