1 Anotation

1.1 Filter Non-Eukariote sequences

Table 2: summary table for only eukaryote transcritps as on BlastP
unique total
gene_id 29042 31169
transcript_id 30301 31169
sprot_Top_BLASTX_hit 14211 15022
prot_id 14449 14449
prot_coords 10773 14449
gene_ontology_blast 9866 14188
Kegg 9881 12276
sprot_Top_BLASTP_hit 11882 11937
eggnog 4905 11713
Pfam 10757 10814
gene_ontology_pfam 1413 6951
TmHMM 2320 2348
RNAMMER 0 0
SignalP 0 0
transcript 0 0
peptide 0 0

1.2 Extract each of the Trinotate report elements

1.3 Taxonomy plot based on results from nucleotides and ORFs (Bastx and Blastp)

1.4 Identity distribution of the annotated ORFs (BlastP)

1.5 GO distribution

2 Differential gene expression with DESeq2

2.1 MDS plot from the normalized data

TMM normalized data is used for this. This was also produced by the Trinity pipeline.

2.2 Number of differentially expressed transcripts among all contrasts

2.3 barplot for all contrast

2.4 barplot for all each treatment agaisnt control

2.5 Heatmap of differentially expressed transcripts

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3 Functional enrichment with TopGo

For this analysis, we used the extract_GO_assignments_from_Trinotate_xls.pl implementent in the Trinity pipeline to extract GO terms from the Trinotate report. Ancestral terms were included.

Thereafter, TopGO was used fur functional enrichment was performed using the complete GO terms as background and:

1.The up- and down-regulated DET subsets for each transcripts

  1. The complete set of Diferentially Expressed transcripts (DET) for each contrast

3.1 Display of functional enrichtment of the (a) up-regulated transcripts and (b) down-regulated transcripts

3.2 Display of Functional enrichment with GOplot

The GOplot package concentrates on the visualization of biological data. More precisely, the package will help combine and integrate expression data with the results of a functional analysis.

In the circular plot, the outer circle shows a scatter plot for each term of the logFC of the assigned genes. Red circles display up- regulation and blue ones down- regulation by default. The colours can be changed with the argument lfc.col. Therefore, it is easier to understand, why in some cases highly significant terms have a z-score close to zero. A z-score of zero does not mean that the term is not important. At least not as long as it is significantly enriched. It just shows that the z-score is a crude measure, because obviously the score does not take into account the functional level and activation dependencies of the single genes within a process. 1

4 Transcriptomic profile of Heat shock proteins