Document Type
Poster
Campus where you would like to present
Ellensburg
Event Website
http://digitalcommons.cwu.edu/source/
Start Date
18-5-2017
End Date
18-5-2017
Keywords
Biostatistics, Genetics, Mimulus guttatus, Yellow Monkey Flower, VCF, Genome sequencing, C#, Software development, programming, Biology, analysis
Abstract
The cost of genome sequencing has decreased rapidly, expanding availability for many biological applications (Muir 2016). For example, researchers can now obtain genome sequences from multiple populations under different types of selection. Comparison of these sequences allows for identification of chromosome regions and specific genes associated with adaptive evolution (Kelly 2013). As an increasing number of researchers engage in this type of inquiry, many have created in-house computer scripts to analyze the raw sequence data (e.g., Kelly 2013), creating a gap in both continuity and standardization.
Using a test dataset and preliminary results from an ongoing artificial selection experiment in Mimulus guttatus (Yellow Monkeyflower), I translated, verified, and expanded five software programs representing stages of a single analysis into one software package written in the C# programming language. This program is helping researchers to streamline their analysis and increase precision, while remaining dynamic enough that it can be expanded to any like-set of data, regardless of species.
Recommended Citation
Farr, David, "Software Development for Genome Sequence Analysis" (2017). Symposium Of University Research and Creative Expression (SOURCE). 316.
https://digitalcommons.cwu.edu/source/2017/Posters/316
Poster Number
2
Department/Program
CWU Department of Biological Sciences
Included in
Bioinformatics Commons, Evolution Commons, Genomics Commons, Software Engineering Commons
Software Development for Genome Sequence Analysis
Ellensburg
The cost of genome sequencing has decreased rapidly, expanding availability for many biological applications (Muir 2016). For example, researchers can now obtain genome sequences from multiple populations under different types of selection. Comparison of these sequences allows for identification of chromosome regions and specific genes associated with adaptive evolution (Kelly 2013). As an increasing number of researchers engage in this type of inquiry, many have created in-house computer scripts to analyze the raw sequence data (e.g., Kelly 2013), creating a gap in both continuity and standardization.
Using a test dataset and preliminary results from an ongoing artificial selection experiment in Mimulus guttatus (Yellow Monkeyflower), I translated, verified, and expanded five software programs representing stages of a single analysis into one software package written in the C# programming language. This program is helping researchers to streamline their analysis and increase precision, while remaining dynamic enough that it can be expanded to any like-set of data, regardless of species.
https://digitalcommons.cwu.edu/source/2017/Posters/316
Faculty Mentor(s)
Dr. Alison Scoville, CWU Dept. of Biological Sciences