Difference between revisions of "Discovering Significant Pathways of Gene Regulation"
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* Become familiar with the R Programming language | * Become familiar with the R Programming language | ||
| − | |Complete | + | | |
| + | * Complete | ||
|- | |- | ||
| − | ! | + | !Weeks 2 and 3 |
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| − | * Become familiar with general molecular biology concepts | + | * Become familiar with general molecular biology concepts |
* Conduct a literature search, reading through research papers on previous work in this area | * Conduct a literature search, reading through research papers on previous work in this area | ||
| − | | Complete | + | * Become familiar with the specific R libraries used most often in bioinformatics research |
| − | + | | | |
| + | * Complete | ||
| + | * Complete | ||
| + | * Complete | ||
|- | |- | ||
| − | !Week | + | !Week 4 |
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| − | * | + | * Download mRNA expression, miRNA expression, copy number alteration, and DNA methylation data from TCGA |
| − | | | + | * Reformat data sets so that they are easily readable by the applicable R functions and packages |
| + | * Present findings from literature search | ||
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| + | * Complete | ||
| + | * Complete | ||
| + | * Complete | ||
|- | |- | ||
| + | !Week 5 | ||
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| + | * Present on REU work completed so far | ||
| + | * Process TCGA data for differential expression analysis | ||
| + | * Download and preprocess putative miRNA - mRNA interaction data from miRTarBase and TargetScan databases | ||
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| + | * Complete | ||
| + | * Complete | ||
| + | * Complete | ||
| + | |- | ||
| + | !Week 6 | ||
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| + | * Run differential expression analysis | ||
| + | * Download and preprocess putative TF - mRNA interaction data several databases | ||
| + | * Filter putative interaction data using differential expression analysis results | ||
| + | * Create correlation matrices for mRNA and miRNA | ||
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| + | * Complete | ||
| + | * Complete | ||
| + | * Complete | ||
| + | * Complete | ||
| + | |- | ||
| + | !Week 7 | ||
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| + | * Filter regulator - target gene interactions by pvalue and correlation | ||
| + | * Identify hub genes in | ||
| + | * Prepare first draft of final research talk | ||
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| + | * Complete | ||
| + | * Complete | ||
| + | * Complete | ||
| + | |- | ||
| + | !Week 8 | ||
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| + | * Run clustering algorithm | ||
| + | * Create data structure to store information on target genes | ||
| + | * Write first draft of final paper | ||
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| + | * Complete | ||
| + | * Complete | ||
| + | * Complete | ||
| + | |- | ||
| + | !Week 9 | ||
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| + | * Perform enrichment analysis on clusters | ||
| + | * Analyze extracted miRNA - TF - target gene modules | ||
| + | * Make research poster | ||
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| + | * Complete | ||
| + | * In progress | ||
| + | * Complete | ||
| + | |- | ||
| + | !Week 10 | ||
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| + | * Perform survival analysis (if time allows) | ||
| + | * Present at poster session | ||
| + | * Prepare and present final research talk | ||
| + | * Finish final research paper | ||
| + | | | ||
| + | * In progress | ||
| + | * Complete | ||
| + | * Complete | ||
| + | * Complete | ||
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Latest revision as of 15:09, 30 August 2017
Researcher: Laura Poulton Mentor: Serdar Bozdag
Summary:
Recent advancements in biotechnology have made it possible to generate vast amounts of gene expression data for thousands of organisms. The collection of high-throughput gene expression data allows computational biologists to develop algorithms to reverse engineer the underlying gene regulatory network (GRN) of cells from their gene expression data. Among several software tools to reverse engineer GRNs, FastMEDUSA is a powerful tool. FastMEDUSA builds a model represented by an alternating decision tree (ADT) that predicts the potential regulators of genes.
We hypothesize that if there are significantly overrepresented branches in the ADT of FastMEDUSA, they could be biologically important pathways for gene regulation. In this project, we analyze the ADT built by FastMEDUSA to compute significantly overrepresented branches. We compute p-value for each branch based on results on randomly generated ADTs. For validation, we check public databases and literature to verify if genes in these branches have been reported as pathways of gene regulation.
Students will have the opportunity to work on graph theory, statistics, and biological databases to answer some high-impact biological questions.
Goals:
- Using an already existing computational model for predicting gene expression based on the presence of microRNA and other transcription factors, contribute to a list of known gene regulators and co-regulators.
- Create an R package from the current gene expression prediction model that can be downloaded as a library.
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