Genome wide CRISPR lethality screens show broad variability in cellular fitness phenotypes across cancer. We postulate that genes with overlapping functions should deliver similar responses enabling functional annotation of uncharacterized genes. Here we will build a network connecting genes based on the similarity of their knockout phenotypes, benchmark this network using protein interaction databases and functional transcriptomics, and leverage network analyses to identify mutational and transcriptional modulators of functional complexes.

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Recent advances in genomic technologies have led to the identification of many novel disease-gene associations, enabling more precise diagnoses. Along with the technologies enabling rapid DNA sequencing, multiple computational approaches have been developed to identify structural variants (i.e. relatively large deletions and duplications of genomic sequences). These workflows can lead to the identification of different structural variants, raising the risk of missing disease-causing variants when using only one of those methods.

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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.

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A highly collaborative project is available in Dr. Alison Taylor’s and Dr. Fatemeh Momen-Heravi’s lab. This project aims to identify molecular changes such as mutations and RNA signature of head and neck cancer in Black/African American and Hispanic minority populations with the goal of identifying novel therapies for cancer patients and reduce health disparities. The project entails analysis of DNA and RNA sequencing data. Basic coding skills are necessary and the student will be mentored by both principal investigators. The prospective candidate should be motivated, a fast learner, and be able to work in a highly collaborative team environment.

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Atherosclerosis, a chronic inflammatory disease of the artery wall, is the underlying cause of human coronary heart diseases. Single-cell genomics have catalyzed the revolution in understanding of cellular heterogeneity and dynamics in atherosclerotic vasculature. The goal of the project is to leverage published and our own single-cell genomic data and perform a meta-analysis. Meta-analysis allows integrated analysis of much larger cell numbers and helps resolve the full spectrum of cellular heterogeneity and dynamics in atherosclerotic vessels and facilitate therapeutic translation. The DSI scholar will: (1) use the latest bioinformatic pipeline to integrate the existing scRNA-seq, CITE-seq, and scATAC-seq datasets; (2) analyze the integrated datasets using R/Bioconductor packages (e.g. Seurat); (3) interpret the data using pathway and network analysis. Some relevant workflows are available through the “Resources” page of our lab website at https://hanruizhang.github.io/zhanglab/.

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We have a position open for a student(s) who is/are interested in working on systems biology projects in bladder and prostate cancer. Specifically, we are looking for students who are well versed in statistical analysis, basic understanding of standard statistical techniques (appied to biology is a plus) and knowledge of R is required. The position will entail supporting post-doctoral members of the lab with computational analyses of different types of biological data in a wide range of projects.

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This project will focus on the identification of genetic factors involved in various forms of hereditary diseases, including neurodevelopmental disorders, hearing loss, skeletal disorders and more. Some of these children endure years-long diagnostic odysseys of trial-and-error testing with inconclusive results and misdirected treatments. We are dedicated to track down their molecular causes by integrating various “-omics” technologies, including genomics, transcriptomics and epigenomics.

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