These are projects posted by the students of Dr. Gove Allen at Brigham Young University. These students have taken one semester-long course on VBA and generally have had no prior programming experience

Tuesday, April 12, 2016

Automation of Analysis for Quantitative Polymerase Chain Reaction

Executive Summary

The BYU Department of Nutrition enlists students as research assistants from a variety of majors. Research assistants are generally looking for lab experience necessary for graduate programs. This project was created as a solution to a question in Dr. Jason Kenealey’s lab. Dr. Kenealey’s lab focuses on understanding the biochemical mechanisms and effects of naturally occurring molecules on different cancers. Dr. Kenealey’s research assistants use many different machines to assist their research that eventually export an Excel file with results after the experiment is completed.
After finishing an experiment, students will take the readout exported by the machine, format a new workbook, copy and paste data from the readout to the new workbook, write in formulas to analyze the data, and finally create graphics that clearly convey the data. The process is cumbersome and repetitive, all while leaving plenty of room for errors that skew data.
I recently started running experiments using the Polymerase Chain-Reaction (PCR) ThermoCycler.  The objective of the experiment is to determine the quantity of transcriptions or copies of genes that are being expressed. Cells treated with different pharmacological agents are lysed open to examine mRNA content, which is an indication of genes being expressed. After a few steps the mRNA is converted to more stable cDNA and quantitative PCR (qPCR) is run against each of the different treated-cellular samples. qPCR uses different primers to target specific genes of interest and amplify the number of gene transcripts to readable levels. In order to control for differing levels of overall cDNA content between different treatments, scientists use an internal standard gene as a reference. The gene expression of this internal standard is consistent regardless of treatment options. The change in expression of the target genes is important to understand because expression of different genes indicates a change in biological function. For example, cancer cells often decrease the amount of important tumor-suppressing proteins (like p53) and the targets of those tumor-suppressing proteins (like TP53INP, PUMA, NOXA, etc.). The ability of pharmacological agents to upregulate (or in some cases downregulate) the target genes of interest can be an important mechanism of their efficacy in treating disease.
My system is designed to first prompt the user to choose the file path of the readouts to be analyzed (with the option to select an internal standard from a previous experiment on a separate workbook). Then, the procedure accepts input from the user regarding the control samples, the internal target gene, and the target gene of interest. After, it analyzes the different readouts from the PCR machine to determine the values of ΔCt(control), ΔCt(treated), fold change, and standard deviations for each sample, important values in determining the number of transcripts in each gene. Ultimately, this data is converted into an easy to read bar chart. The system is designed to expand or contract to fit different amounts of sample, target, and replicate numbers, and should accommodate any other students running qPCR from the same machine. The output of my system is a workbook with a copy of the original readout from the ThermoCycler along with a new summary sheet that contains a formatted table summarizing the above stated values and chart.


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