Recent advances in genomic technologies have led to the identification of many novel disease-associated genes, enabling more precise diagnoses. Along with the technologies enabling rapid DNA sequencing, multiple computational approaches have been developed to extract the genetic information from raw data, including The Broad Institute’s GATK, Seven Bridge’s GenomeGraph and Google’s DeepVariant. These workflows can lead to the identification of different genetic variants, raising the risk of missing disease-causing variants when using only one of these methods.

Unfortunately, many of the variants identified by these workflows are artifacts (absent in the biological sample), raising concerns that time and effort will be wasted on those artifacts instead of analyzing the causative genetic variant.

The goal of this project is to develop best practices to increase the chance of identifying causative genetic variants, while reducing the number of artifacts. We will use the raw data from whole-exome and whole-genome sequencing of patients with renal diseases.

The students will be expected to (1) Compare the output of 3 different workflows to call genetic variants from raw next-generation data (using R or Python) and (2) Identify workflow specific filters that can be used to differentiate between true variants and artifacts.

This is an UNPAID research project.

Faculty Advisor

  • Professor: Ali Gharavi
  • Department/School: Medicine
  • Location: CUIMC
  • The mission of the Center for Precision Medicine and Genomics (CPMG) is to improve human health through high quality research, education and clinical care.

Project Timeline

  • Earliest starting date: 3/1/2021
  • End date:
  • Number of hours per week of research expected during Spring 2021: ~10
  • Number of hours per week of research expected during Summer 2021: ~35

Candidate requirements

  • Skill sets: Fluent in at least one programing language (R, Python, Perl, Java), at least one course in statistics and knowledge in genetics
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes