In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms. Differentially expressed genes are then be functionally annotated to understand the genetic response organisms have to experimental conditions. These de novo transcriptomes are further used in pairwise differentially gene expression analysis to investigate genes differing between two experimental conditions. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user.

This workflow is also designed to be manual rather than automated. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This protocol is designed to analyze only two experimental treatments or conditions. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. All command line code and further explanations of each command or step can be found on the wiki ( ). executing bash commands, visualization and management of large data sets.

It also serves as a primer for biologists to develop data scientist computational skills, e.g. AbstractThis workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics.

Cyberduck 3.4.1