Functional analysis of Prokaryotes using Gene Set Enrichment Analysis on Transcriptome (RNA-Seq) or Proteome data.
Gene Set Enrichment Analysis (GSEA) is a method to identify classes of genes or proteins that are over-represented in a set of genes or proteins, respectively. Two data sets are needed to perform a GSEA; 1) A list/set of genes or proteins derived from a transcriptomics or proteomics analysis, 2) A database of functional classes.
For all complete bacterial genomes of RefSeq and Genbank (>20.000) we (re-)constructed nine functional Classes; GO, InterPro, KEGG, COG, PFAM, SMART, Superfamily, KEYWORDS and OPERONS. GSEA-Pro support 4 types of input: 1) a single list of locus-tags, 2) a single list with values, 3) multiple experiments and 4) clusters. Use this powerpoint TUTORIAL to understand the four input options and how to interpretate the results. Some examples can be selected below.
Both RefSeq and Genbank annotations are supported. Optionally use Genome2D for conversion between Genbank (old_locus-tags) and RefSeq locus-tags
For annotating and Classifying your own genomes you can use the FACoP webserver (or stand-alone)
[anne.de.jong .. rug.nl]
Select Genome: Start typing parts of the genome name until the selection list is less than 15 genomes.
If one genome is in the list, the genome will be selected automatically.
e.g. to find Bacillus subtilis 168, just type 2 keywords such as 168 subt and select your genome from the list
RefSeq: Genbank: FACoP sessionID:
RefSeq: Genbank: FACoP sessionID:
Paste Tab delimited Data Table (such as Excel) below
Analyse data as:
Load Example Data:
User defined cutoff:
< values >
Auto detect cutoff values
User defined cutoff:
< values >
GSEA-Pro Overview table The nine classes and the number of genes found in overrepresented classes of each experiment/cluster.
Main Table
Over-represented classIDs and its significance in each experiment/clusterThe values given per experiment; 1) Score [0-9], 2) Hits / Class Size, 3) p-value.
Light to dark blue coloring represents low to high score [based one (Hits/ClassSize) * -log2(p-value)], respectivily
Columns LC and HM contain links to a graphical representation of the original data of the genes/proteins. Note; only shown in multiple experiment analyses
LC (Line Chart) shows the orginal data as scattered line
and
HM Heat Map of the original data 
Show;
Score
Hits/Class Size
p-value
Gene set
Hold the SHIFT-key to sort on multiple columns
Select the Class for Interactive Bar Graph
Description of all COG categories
| CELLULAR PROCESSES AND SIGNALING | INFORMATION STORAGE AND PROCESSING | METABOLISM | POORLY CHARACTERIZED | ||||
|---|---|---|---|---|---|---|---|
| D | Cell cycle control, cell division, chromosome partitioning | A | RNA processing and modification | C | Energy production and conversion | R | General function prediction only |
| M | Cell wall/membrane/envelope biogenesis | B | Chromatin structure and dynamics | E | Amino acid transport and metabolism | S | Function unknown |
| N | Cell motility | J | Translation, ribosomal structure and biogenesis | F | Nucleotide transport and metabolism | ||
| O | Post-translational modification, protein turnover, and chaperones | K | Transcription | G | Carbohydrate transport and metabolism | ||
| T | Signal transduction mechanisms | L | Replication, recombination and repair | H | Coenzyme transport and metabolism | ||
| U | Intracellular trafficking, secretion, and vesicular transport | I | Lipid transport and metabolism | ||||
| V | Defense mechanisms | P | Inorganic ion transport and metabolism | ||||
| W | Extracellular structures | Q | Secondary metabolites biosynthesis, transport, and catabolism | ||||
| Y | Nuclear structure | ||||||
| Z | Cytoskeleton | ||||||
2019 March. Department of Molecular Genetics, University of Groningen, Nijenborgh 7, 9747 AG Groningen, the Netherlands
