Transcriptomic Analysis of COVID-19 Patients by Maya Khan (V) This past summer, I participated in a virtual Transcriptomic Analysis + COVID-19 course run by Milrd, an educational organization in New York City, supported by the Mason Lab at Cornell Medical Center. I was new to transcriptomic analysis, the study of RNA transcripts, but the course gave me a strong foundation and incredible mentors. Through this program, I learned how to analyze a broad collection of data and use specific programs to compare reference genomes. This course would be perfect for anyone interested in computational biology and large-scale sequencing surveillance. The class focused on a two-step analysis of COVID-19 transcriptomic data. While the genome is a larger collection of all nucleic or mitochondrial DNA, the initial product of genome expression is the collection of mRNA copied from the genes during transcription. The transcriptome measures this complete set of RNA transcripts in the intermediary stage between genes and proteins. For the analysis, cDNA is synthesized from the single-stranded RNA. Using a series of programs to clean and organize the raw sequencing data, we transformed it into an understandable format. Then, using the Integrated Genomics Viewer (IGV), we generated visualizations of the genomic data and identified point mutations. Over the past decade, more reliable sequencing methods to track and map genes have been
Figure 1: Graphic showing the transcriptome’s variability due to alternative splicing.
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