Project 467175

A novel computational approach for transcript isoform quantification and discovery in neuronal cell lines

467175

A novel computational approach for transcript isoform quantification and discovery in neuronal cell lines

$17,500
Project Information
Study Type: Unclear
Research Theme: N/A
Institution & Funding
Principal Investigator(s): Apostolides, Michael J
Institution: McGill University
CIHR Institute: N/A
Program: Master's Award: Canada Graduate Scholarships
Peer Review Committee: Special Cases - Awards Programs
Competition Year: 2021
Term: 1 yr 0 mth
Abstract Summary

Each cell in our body contains tens of thousands of genes, which are coded in DNA. In order for a cell to use these genes, it needs to create a temporary disposable copy. This copy is called messenger RNA (mRNA), and is used by the cell, then thrown away once the cell is finished with it. Different versions of mRNA can be made from a single gene by taking different combinations of the genes pieces and creating a unique combination. An analogy is that of editing film footage: we can create different movie versions by splicing together the footage in different combinations. There are many cell types in our body, and each type stitches these mRNA copies together differently. The cell type, or life story, of each cell is determined by the different ways mRNAs are stitched together. So, to understand how different cell types work, we need to identify and quantify the different mRNA versions that cells create. Identifying and quantifying different mRNAs is difficult because current technologies give only one of the following. Either:1. Short portions of the mRNAs: Using our analogy, this would be like only having short clips from each movie version, making it difficult to identify which version a clip comes from, or;2. Only identify some of the total number of mRNAs, like having only a few of the total existing full-length movie versions, causing us to miss some versions that exist.In this project, we are developing new computer algorithms to combine information from these two types of technologies for accurate measurement of the different mRNA copies that the cell creates. We will then apply our method to neuronal cells, so we can better understand these cells.

No special research characteristics identified

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Keywords
Algorithms Bioinformatics Cell Differentiation Data Integration Genomics Rna-Seq Transcriptomics