Inferring Cancer-Specific MicroRNA-gene Networks

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Title: Inferring cancer-specific microRNA-gene networks

Description: In living organisms, the biological processes are governed by proteins, which are end products of genes. Gene activity is controlled by various types of factors that are called regulators. To infer regulators of key genes during disease conditions is a challenging and crucial task as these regulators would be usual suspects of the disease initiation and potential target for new drugs. One type of gene regulators is microRNAs, which are short RNAs that interact with their target genes to inhibit their translation to proteins. MicroRNA-gene interaction networks have been studied using computational and experimental methods. However, disease-specific driver microRNA-gene networks have not been studied extensively. Identifying the cancer-specific driver microRNAs and their target genes would be a significant achievement towards targeted therapies for cancer patients. We recently developed a tool called ProcessDriver to infer cancer driver genes by leveraging structural aberrations in tumor DNAs.

The objective of this project is to extend ProcessDriver to infer cancer-specific driver microRNA-gene networks. The expected outcomes of this research will be computationally-predicted microRNA-gene networks in cancer. The developed tool will be tested on publicly available high dimensional cancer datasets. The results will be validated by comparing them to known cancer driver genes and by reproducing the results using other data resources for the same cancer type.

Students will work with a team of PhD students and the faculty mentor and contribute to various parts of this project.

Students are expected to be proficient in programming. Experience in molecular biology, basic Linux commands and high performance computing is preferred, but not required.

Student learning objectives: After this project, students will

  • Have a basic understanding of molecular biology and transcriptional/post-transcriptional regulatory factors.
  • Be familiar with R or Python programming language and some bioinformatics libraries in those languages.
  • Learn gather biological data from public repositories
  • Build a computational pipeline that pre-processes and integrates high-dimensional biological datasets
  • Be familiar with data visualization tools to analyze and visualize gene networks
  • Learn methods to evaluate predictive models by computing true positive rate, false positive rate, precision, recall, ROC curves, etc.