Immunotherapy, a class of drugs that enable a patient’s immune system to fight cancer, has emerged as a promising area of cancer drug development in recent years. However, not all patients respond to these treatments, and many patients who do will have a recurrence of their cancer. The biological mechanisms behind these differences in response to immunotherapy are currently poorly understood. However, recent improvements in sequencing technology now allow scientists to examine the behavior of genes in individual cells and how those cells resemble or differ from other cells around them. With this new data also comes the need to create new computational methods to analyze it. On this project, the student will work at the intersection of algorithm development and cancer biology to interpret single-cell sequencing data of cancer samples with the goal of understanding how cancer cells interact with the nearby immune cell populations and how these interactions affect response to treatment. Students will work in a multidisciplinary environment, collaborating with biologists, software developers, and experts in the fields of immunology and oncology. Interested students should have prior experience with programming, preferably in Python or R. This project can be done for class credit or as an internship.

We seek an undergraduate student to write software to enhance our Molecular Signatures Database (MSigDB), a repository of gene sets utilized by a worldwide genomics community of over 180,000 users. These gene sets are used to identify and better understand activated pathways underlying human disease, e.g., cancer, and to interpret experimental results. The NIH recently retired the Cancer Genome Anatomy Project, which included the BioCarta collection of curated pathway diagrams. These images and associated metadata are only accessible via the Internet Archive “Wayback Machine”. The project involves retrieving this data from the archive to provide visualizations for entries in the MSigDB. The results of this work will have a great impact on biomedical investigations worldwide. Interested students should have some familiarity with either programming and/or web technologies such as HTML and a willingness to learn Python and how to use libraries such as urllib and BeautifulSoup to perform web scraping. Expertise in biology is not required. The project can be for class credit or as an internship which may lead to other research experience.

We have derived iPSC-CVPCs from 180 individuals and showed that their transcriptomes are more similar to fetal heart than to adult cardiac tissues. Our goal is to leverage these data in combination with WGS to perform eQTL analyses. We plan to assess whether fetal-specific eQTLs are associated with complex adult cardiac traits, by colocalizing eQTLs with summary statistics from GWAS (cardiac traits.) Our preliminary analyses show that eQTLs in iPSC-CVPCs identifies cardiac disease GWAS variants that are active in the fetal but not adult heart, indicating that they play a role in development. Our findings provide genetic evidence supporting the fetal origins of the cardiovascular disease hypothesis and highlight the importance of investigating genetic associations across stages of development (i.e. fetal and adult tissues) to fully understand the genetic underpinnings of complex traits and disease. We are looking for rotation students to conduct QTL analyses using large ATAC-seq and ChiP-seq for H3K27ac datasets generated from the iPSC-CVPCs.

Copy number variants (CNVs) represent significant risk factors for Autism Spectrum Disorders (ASD). One of the most frequent CNVs involved in ASD is a deletion or duplication of the 16p11.2 CNV locus, spanning 29 protein-coding genes. Despite the progress in linking 16p11.2 genetic changes with the phenotypic (macrocephaly and microcephaly) abnormalities in the patients and model organisms, the specific molecular pathways impacted by this CNV remain unknown. We generated bulk RNA-seq and TMT proteomic data from patient-derived cerebral organoids (3 deletion, 3 duplication and 3 control patients). The goal of the project is to analyze isoform-level RNA-seq data, as well as proteomics data to investigate functional impact of 16p11.2 CNV.

The project deals with constructing the isoform-level co-expression and protein interaction networks for predicting functional impact of mutations in high risk autism gene Cul3. We have collected RNA-seq and TMT-proteomics data from various brain regions of Cul3+/- transgenic mouse. We are aiming at integrating isoform-level RNA-seq data with quantitative proteomic (peptide-level) data from the same samples to understand the impact of Cul3 mutation.