Understanding the mechanisms behind the development of plants is important to agriculture because if we could understand how genes control development, we could more precisely engineer crops and, in turn, increase crop yield. I am working with developmental genes GRASSY TILLERS1 (GT1), which suppresses growth in the flowers and branches of Zea mays (Whipple et al., 2011), RAMOSA3 (RA3), which controls ear branching in Z. mays (Satoh-Nagasawa et al., 2006), and STERILE TASSEL SILKY EAR1 (STS1), which controls floral development in the tassels of Z. mays (Bartlett et al., 2015). Mutants in RA3 and STS1 alter the expression of GT1. My goal is to determine what happens to the expression of the other genes in the genome when GT1 does not function properly. I am performing comparative RNA-seq analysis to determine how the regulation of transcription factor GT1 is altered in the mutant backgrounds of double mutant gt1 ra3 and single mutant sts1. Through this approach, I will determine how GT1 acts to repress growth in plants.
RNA-seq is a technique used to determine how much RNA is transcribed from specific genes in a genome. With this information, I can determine how genes are expressed in different mutations, which genes are active, and if there are any patterns of expression present (Wang et al., 2009). Using the terminal emulator, PuTTY, I used the University of Massachusetts Green High-Performance Computing Cluster (GHPCC) to analyze datasets looking for specific patterns of regulation between gt1 ra3 and sts1 mutants. This summer, I began the process of performing RNA-seq analysis by taking short fragments of cDNA (reads) and aligning them to a reference maize genome to determine where the genes are expressed.
I first used the software package Trimmomatic, to trim any reads with low quality scores (Williams et al., 2016). Then, I used the software package Bowtie2, to make an index of the reference genome and align the trimmed reads to the Zea mays genome (Portwood et al., 2018). Lastly, I ran the software package featureCount which uses an algorithm to count reads per gene (Liao et al., 2014). With the results from featureCount, I will be able to plot and analyze the data to determine how the GT1 transcription factor regulation changes in each of the mutants and finally, conclude how GT1 represses growth.
This project helped me gain new skills in bioinformatics, which is something I did not have knowledge in before. My goal after college is to continue working in a research lab, so the coding skills I learned with this project will help my professional career, especially in a time where bioinformatics is a growing industry. Not only am I advancing my professional goals, but I am also understanding my future goals. Before this project, I did not know what bioinformatics encompassed. I assumed I would not enjoy it because coding was involved, but through this project I learned that I do enjoy the data analysis behind RNA-seq and I would want to continue working in the field of bioinformatics.
I would like to thank CAFE for sponsoring this project and the Bartlett lab, especially Madelaine Bartlett and Joseph Gallagher, for helping me with this project.