Bioinformatics Service Report
IMMAGINA Biotechnology S.r.l.
04/03/2024
Thank you for choosing IMMAGINA Biotechnology for your analysis needs!
This interactive report provides a comprehensive overview of your
results, with easy-to-read graphs and charts that bring your data to
life.
If you have any questions about the analyses, please don’t hesitate to
contact us at info@immaginabiotech.com
Workflow
1. Summary Table
Table 1:
Summary of sample names and groups used to Calculate Translation Efficency (TE)
2. Principal component analysis (PCA)
PCA Riboseq
This plot shows the Principal component analysis (PCA) of Ribo-Seq experiment. The PCA plot shows the data points projected onto the first two principal components, which are the two new axes that account for the most variance in the data.
PCA RNAseq
This plot shows the Principal component analysis (PCA) of RNAseq
experiment. The PCA plot shows the data points projected onto the first
two principal components, which are the two new axes that account for
the most variance in the
data.
3. Translation Efficency (TE) volcano plot and Directional plot
TE volcano plot
The Translation Efficency (TE) volcano plot shows the distribution of gene based on their log2FoldChange in TE and their p-value. TE = log2(Condition_1(RPF/mRNA) / Condition_2(RPF/mRNA))
Directional plot
The ‘Directional plot’ shows the log2 fold change in Ribo-seq (x-axis) versus the log2 fold change in RNA-seq (y-axis) for each gene. The four dashed lines indicate log2 fold change values of -1 or +1. ‘Homodirectional’ genes have increased or decreased reads in both Ribo-seq and RNA-seq relative to the control (log2 fold change). ‘Crossdirectional’ genes have increased reads in Ribo-seq or RNA-seq relative to the control, but decreased reads in the other assay.
