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GeneScape: Gene Function Visualization
GeneScape is a software tool for visualizing gene functions. Users enter a list of genes, the software then draws a subgraph of the Gene Ontology (GO) terms associated with the genes.
GeneScape is a Python-based Shiny application that be run both at the command line and also via a graphical user interface.
The public version of the software can be accessed at:
Local installation
Users can also run the program on their system by installing the software via pip
:
pip install genescape
After installation, the Shiny interface can be started via:
genescape web
Visit the url http://localhost:8000 URL in your browser to see the interface.
Once you are done running the web interface, press CTRL+C to stop the program from running.
Command line use
The program can also be used at the command line to generate images or annotations:
genescape tree
draws informative Gene Ontology (GO) subgraphsgenescape annotate
annotates a list of genes with GO functionsgenescape web
provides a web interface for thetree
command
What does GeneScape do?
GeneScape works the following way:
- It first reads genes from an Input List
- Then extracts the Annotations associated with the input genes
- Finally it builds and visualizes the functional subtree tree based on these Annotations.
Note: Even short lists of genes (under ten genes) can create large trees. Filter by minimum counts (how many genes share the function) or functional patterns (functions that match a pattern).
When not explicitly specified, GeneScape will try to find a reasonable coverage threshold for the input genes.
Node Labeling
The labels in the graph carry additional information on the number of genes in the input that carry that function as well as an indicator for the specificity of the function in the organism. For example, the label:
GO:0004866
endopeptidase
inhibitor activity [39]
(1/5)
Indicates that the function endopeptidase inhibitor activity
was seen as an annotation to 39
of all genes in the original association file (for the human there are over 19K gene symbols). Thus, the [39]
is a characteristic of annotation of the organism.
The (1/5)
means that 1
out of 5
genes in the input list carry this annotation. Thus the value is a characteristic of the input list. The mincount
filter is applied to the count value to filter out functions that are under a threshold.
It is possible to compute a p-value to determine whether an observed enrichment difference is statistically significant. We'll just note that assigning p-values to enrichment counts is fraught with several challenges. In our opinion, GO annotations are neither complete, nor independent, nor precise enough to satisfy mathematical requirements. In addition, appropriate selection of the background (aka the 19000
above) to correct p-values for multiple comparisons also presents many challenges. For these reasons, we do not compute p-values in our application.
Node Coloring
The colors in the tree carry additional meaning:
- Light green nodes represent functions that are in the input list.
- Dark green nodes are functions present in the input and are leaf nodes in the terminology, the most granular annotation possible
A dark green means that the ontology has no terms that would be even more specific than that specific annotation. A light green means there are more specific annotations, but none of the genes were annotated as such. In both cases, the green color indicates that the function was present in the input list.
Each subtree in a different GO category has a different color:
- Biological Process (BP)
- Molecular Function (MF)
- Cellular Component (CC)
The subtree coloring is meant to help you understand the level of detail and the specificity of the functional terms you visualize.
Numbers such as 1/4 mean how many genes in the input carry that function.
Reducing the tree size
The trees can get huge, even for a small number of genes.
Users can greatly reduce the size of the graph by removing functions that are not well represented in the input list or by focusing the graph to contain only functions that match a pattern.
Just by setting the mincount
to 2 or higher is often enough to simplify the graph to a manageable size.
The filtering conditions that users can apply are:
- a pattern that matches the Function columns
- a minimum Coverage that means the minimum number of genes that carry that function
- the GO subtree shown in the root column
Filters are applied during the annotation step and will filter the GO terms derived from the gene list.
In the Shiny interface use the coverage filter to remove functions that are not well represented in the input list. Recall that coverage
represents the number of genes in the input list that carry that function. You can see the counts for each annotation in the Function Annotations box as the first column.
Command line requirements
To generate images from command line the graphviz
software must be installed. You can install it via conda
conda install graphviz
or via apt
or brew
.
Those unable to install the graphviz
package can save the output as a .dot
file:
genescape tree --test -o output.dot
Then use an online tool like viz-js to visualize the graph.
genescape tree
We packaged test data with the software so you can test it like so:
genescape tree --test
Which will generate a tree visualization of the test data.
Reducing the graph size
We can pass the tree visualizer a list of genes or a list of GO ids, or even a mix of both.
We run the tree
command to visualize the relationships between the GO terms that includes all coverages:
genescape tree genes.txt --mincov 1
For many (most) gene lists resulting functional graph might be huge. If no coverage is specified, the software will try to find a reasonable coverage threshold for the input genes.
We can narrow down the visualization in multiple ways, for example, we can select only terms that match the word lipid
:
genescape tree -m repair genes.txt
When filtered as shown above, the output is much more manageable:
genetrack annotation
The annotator operates on gene names. Suppose you have a list of gene names in the format:
Cyp1a1
Sphk2
Sptlc2
Smpd3
The command:
genescape annotate genelist.txt
will produce the output:
Coverage,Function,GO,Genes
3,protein binding,GO:0005515,CYP1A1|SMPD3|SPHK2
2,cytoplasm,GO:0005737,SMPD3|SPHK2
2,mitochondrial inner membrane,GO:0005743,CYP1A1|SPHK2
2,endoplasmic reticulum membrane,GO:0005789,CYP1A1|SPTLC2
2,sphingolipid biosynthetic process,GO:0030148,SPHK2|SPTLC2
2,intracellular membrane-bounded organelle,GO:0043231,CYP1A1|SPHK2
2,sphingosine biosynthetic process,GO:0046512,SPHK2|SPTLC2
genescape build
The software is currently packaged with a human and a mouse genome derived data index.
To build an index for a different organism, download the GAF association file from the Gene Ontology website.
To build the new index use:
genescape build --gaf mydata.gaf.gz --obo go.basic.gz -i mydata.index.gz
To use the custom index, pass the -i
(--index
) option to any of the commands, web
, tree
and annotate
like so:
genescape web --index mydata.index.gz
See the --help
for more options.
Odds and ends
It is possible to mix gene and ontology terms. The following is a valid input:
GO:0005488
GO:0005515
Cyp1a1
Sphk2
Sptlc2
Testing
Tests are run via a Makefile
as:
make test
Additional customizations
The software can be customized by creating a copy of the config.toml
file and settings the GENESCAPE_CONFIG
environment variable to point to the new configuration file.
In this file the lines that have an index
type will be used to build the dropdown menu in the web interface.
Contributing
See CONTRIBUTING.md for information on how to contribute to the development of GeneScape.
License
genescape
is distributed under the terms of the MIT license.
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