Example use of RIVET

For example datasets please refer to example_data folder in RIVET repo.

• microarray polysome profiling data: Silvera et al.,
• RNA sequencing polysome profiling data: Geter et al.,
• RNA sequencing ribosome profiling data: Hseih et al.,

Analyzing a dataset with multiple polysome fractions.

The following example will utilize the Geter_et_al.txt dataset. To begin the analysis, download any example dataset file.

Data Upload and Statistical Analysis

The following steps must be completed before navigating to transcription, translation, translational efficiency, or translational regulation tab.

1. To begin analysis navigate to normalization tab. In the sidebar panel on the left, use the browse button to upload the Geter et al. text file. Upon successful upload, an MDS plot will appear in the main panel under the plot sub tab. To download the MDS plot, click the download button beneath the plot. All plots are available for download as pdf files.

2. To initialize statistical analysis, navigate to samples tab. Add the appropriate samples to control and experiment under transcription and translation headers.

   • Click on the control or experiment box beneath the transcription header. A dropdown menu containing sample names should appear. To add samples click the appropriate label and repeat. All replicates should be added to either control or experiment box in transcription and translation category.
   • For this dataset, Samples should be input as follows:

3. Type in a label for control and experiment samples.

4. Input number of polysome fractions under "How many polysome fractions?". In this case, the input number should be 2.

5. The option for platform and statistical method on the sidebar panel can be adjusted. For this example the default, RNA seq and limma, are appropriate.

6. Upon completion of all input information, navigate to any tab or hit download to download the completed statistical analysis. The file will contain all genes as rows and fold-change, p-value, and adjusted p-value as columns.

Transcription, Translation, and Translational Efficiency

The layout of these 3 panels is comparable, so the instructions below can be used for either transcription, translation, and translational efficiency. Translational Efficiency has an additional option in the sidebar to choose between log 2 ratio and interaction method.

1. Navigate to transcription tab.
2. A volcano plot will be automatically generated in the main panel. The slider on the left panel will allow the user to select genes given a particular threshold. Genes that pass the criteria in the left panel will be displayed in blue on the volcano plot. For instance, if the user moves the slider to 2 in the log fold-change, the volcano plot will adjust to display genes in blue that have greater than 2 fold change with a p-value less than 0.05.
3. The download button in the sidebar allows for download of genes filtered to pass the threshold set by the user. The volcano plot can also be downloaded in the main panel.

Tanslational Regulation

1. Navigate to the translational regulation tab
2. Based on the number of polysomes input by the user, the fraction will automatically fill with choices to toggle between each input fraction. For this example, there will be "Poly1" and "Poly2" displayed so the user can toggle between them.
3. Under Regulation, there are a number of options to select from, each highlighting a different type of translational regulation:
   • None
   • Transcription alone
   • Translation alone
   • Opposite
   • Transcription and Translation
   • All Regulation
   • Translational Efficiency Each choice will highlight a different subset of genes on the scatter plot as well as give a quantification of the number of genes that fit that category in the bar plot. Thresholds for highlighted genes are determined by the user in transcription and translation tab. For instance, if we click on transcription alone in this case, the user defined transcription as greater than 2 fold-change, p-value less than 0.05, translation as default settings of p-value less than 0.05. Therefore, genes will have to pass both thresholds to be highlighted on the scatter.
4. The download button will download only genes for the highlighted polysome fraction and regulation type. In this instance, if we download genes, we will download genes pertaining to polysome fraction 1 that fit the category of transcription alone as we defined it.
5. Both scatter and bar plot can be downloaded utilizing the download buttons beneath each plot respectively.