Multiomics is revolutionizing research and pushing the limits of scientific discovery. There are many ways to combine multiple “-omes” into one experiment, but all require a powerful and thorough data analysis approach to yield impactful results.
Easier Than Ever Data Analysis Workflows for Genomics and Transcriptomics
Data analysis has historically been a complex and labor-intensive process. Fortunately, technological advancements have improved and simplified this exercise, allowing researchers with less experience to collaborate with core facility experts to execute accurate and comprehensive data analysis. When handling genomic and transcriptomic sequencing data, there are four key steps that must occur to get from the original data file (FASTQ) to data visualization.
Figure 1: A simplified example of a multiomic data analysis workflow for genomic and transcriptomic data.
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