Transcriptomics is the study of an organism’s global messenger RNA population. It is is an incredibly useful extension of genomics, which is the study of the structure, function, and mapping of organisms’ genes. Transcriptomes contain important information about the expression profile of an organism’s genes and how the expression profile changes in different environments. For example, a transcriptome study can tell us which genes are active at different points in an organism’s development, or how expression of certain genes changes under different cellular conditions. In this study, I analyzed transcriptome data derived from a fungus used by a local company (Ecovative Design LLC) to produce environmentally friendly packing materials, foams, and composite materials.
Analyzing transcriptome data is a multi-step process involving more than six different intermediate file formats, with files that can be millions of lines long. Managing the workflow for these files can be quite an intensive process, and must be done manually for each file. Tools like PIVOT allow detailed analysis of expression data in an easy to use graphical interface, but require significant data processing steps in order to use. Here we present an automated workflow for processing raw RNA-Seq data into a format for easy analysis using tools like PIVOT.