Gene and Pathways Interactions Graph User's Guide
Contents
Introduction
The Pathways and Gene Interactions graph accompanies the "Gene Interactions"
track and displays a detailed gene interaction and pathway graph based on data
collected from two sources: curated pathway/protein-interaction databases and
interactions found through text mining of PubMed abstracts.
The curated data were imported from 23 pathway or protein-interaction databases
(see the Methods section below). Curators at these databases typically read
research articles, collect protein interactions from them and store them in a
web-accessible database. Pathway databases such as Reactome or WikiPathways
describe a whole set of interactions, e.g. the WNT pathway, and the type of
effect, and sometimes annotate indirect or inferred effects as an interaction.
They often work from review articles. In contrast, protein
interaction databases focus more on the original literature that describes the
results of the biochemical assay and focus less on
the effect or direction of the interaction.
The text mining data was generated in collaboration with the
Microsoft Research Project Hanover Team using
Literome machine-reading.
Literome is a natural-language processing (NLP) system that analyzes sentences and tries to
extract names of proteins and the type of interaction. A simple example is a
sentence such as, "PTEN negatively regulates AKT3", which gets transformed to
"PTEN-AKT3" and "regulation: negative". The text mining system was run on all
20 million PubMed
abstracts at the end of 2014 and can also be queried through the website
Literome.
Configuring the Pathway and Gene Interaction Display
Clicking on an item in the track display takes you to a page that includes a gene
interaction graph with detailed information on the directionality and support
for the various interactions displayed. The graph is initially centered on the
gene clicked in the track display, with this gene highlighted in
yellow. For example, you can see that the primary gene "SOD1" is
highlighted in yellow in this image:
By default, only the top 25 best supported interactions
are displayed, but this number can be increased, decreased or filtered using
the controls above the image. The interaction display can be filtered using
the drop-down menu to display subsets by their support:
- All interactions, regardless of support type
- Curated interactions only
- Interactions present in a database, manually curated or not
Genes in the interaction graph are connected by a number different types of
lines, with each type of line and the line properties themselves indicating
different levels of support from text mining and databases.
- Solid grey lines - only text-mining support for this interaction, with the thickness of the
line indicating the number of articles supporting it.
- Dashed blue lines - at least one curated database supports this interaction.
- dark blue - the information is derived from a paper describing fewer than
10 interactions
- light blue - the information is derived from a high-throughput paper, describing more
than 10 interactions, e.g. a complex or a mass-spec study
- Solid blue lines - both databases and text mining support this interaction
Here you can see nearly all of the different types of lines in a single
gene interaction graph centered around the ROBO3 gene:
Lines may include arrows showing the directionality of this interaction. In
these cases, the directionality is determined by majority support. For example,
imagine an interaction between protein A and protein B; two articles support
that A acts on B while a single article supports the opposite, B acting on A.
In this case, because there are more articles supporting A acting on B, then the
arrow will be drawn such that it starts at A and points to B.
From the "Annotate Genes" drop-down, you can annotate genes based on GNF2
average expression, drugability from DrugBank
entries, cancer type in the COSMIC Cancer Gene Census, and the number of non-silent
mutations identified by the PanCancer analysis
project. For the
GNF2 expression and PanCancer Mutation coloring, genes will be colored on a
sliding scale from light grey to black, with those items with the highest
expression or the largest number of non-silent mutations being colored the
darkest and those with lower expression or fewer mutations being colored grey.
Genes will be colored dark blue if there is no information in the database.
In this image, you can see a set of 14 genes that interact with TP53
colored by their PanCancer Mutation number:
You can mouse-over items in the display to show more details about the gene
such as their product. If you've chosen to annotate genes with
one of the various databases, then it will display that information as well.
For example, hovering over the BAX gene in this exaple displays a description
of the gene product as wells as the number of Pan-Cancer mutations since that
option is selected:
You can mouse-over the connecting lines between genes to see more details about
the evidence that supports this connection. In this image,
you can see the details that pop-up when you mouse over such a line; information
displayed includes database support and text-mining support.
If you click on the line connecting two proteins, you can see a
SumBasic-selected
snippet of text from a Pubmed abstract and, if it is a curated interaction, the
supporting information from the pathway or interaction databases. This
example shows the text-mined support for an interaction between
CASP5 and HUNK:
Below the graph of gene interactions and pathways, there is table of less
supported interactions. These are interactions which were mentioned only a few
times each in the literature.
The numbers shown on mouse-over for
each interaction represents the number of articles and number of databases that
support this interaction.
You can export the currently displayed gene interaction graph in a variety of formats
including PDF, SVG, Cytoscape, and JSON.
The gene interaction graph can be recentered around a new gene in a
few different ways: (1) clicking a gene in the existing interaction graph, (2)
clicking the triangle next to a gene in the table of minor interactions below
the graph, (3) searching for a gene name in the search box above the graph.
Data Sources and Methods
Human protein interactions from the following databases were imported:
- Protein interactions
- iRefIndex 13 which includes
BIND,
BioGRID,
CORUM,
DIP,
HPRD,
InnateDB,
IntAct,
MatrixDB,
MINT,
MPact,
MPIDB and
MPPI
- Androgen Responsive
Gene Database. This database is not available anymore on the internet, but we kept
a copy.
- String 9.1
- Negatome 2.0
- Corum Protein Complexes
- Gene Ontology Protein Complexes
- Pathways
The quantitative contribution of each database in terms of number of gene-pairs is available
here.
For text mining, PubMed abstracts were downloaded from the National Library of Medicine (NLM)
website. The abstracts were then
tokenized and
parsed syntactically using the SPLAT toolkit. Protein
and Gene names were identified and normalized after which potential
interactions were extracted using the Microsoft Research NLP "Protein and Pathway
Extractors". The results were then mapped to the genome using their HGNC gene symbols.
Text-mining results supporting by only a single abstract are in the database tables but are
not shown in the user interface.
Data Access
The raw data for these graphs can be accessed in multiple ways. They can be explored interactively
using the Table Browser, by selecting "group" -
"All Tables"
and "database" - "hgFixed". Under "table", select
"hgFixed.ggLink". You can then start to explore the
relationships between the database tables using the "describe table schema" button or
download tables with "get output". All database tables related to this viewer start with
the prefix "gg".
The database tables can also be accessed programmatically through our
public MySQL server or downloaded from our
downloads server for local
processing. The database tables are:
- ggLink - one row per gene/gene interaction. The field "minResCount" is
the minimum number of interactions obtained from the same supporting article.
E.g. if it is 10, then out of all supporting articles, there is one with 10 interactions
curated from it and maybe others with more interactions. A cutoff of 50 should remove
high-throughput data from the table. Note that while most databases are in the format source
-> target, in this table, the target comes first and the source second. Gene names are
separated by the "|"-symbol.
- ggLinkEvent - connections between a ggLink and one of the ggEvent tables.
The prefix of the eventId indicates the table: ppi/pwy links to ggEventDb, msr links
to ggEventText.
- ggEventDb - information about gene/gene interactions imported from protein
interaction or pathway databases. The structure is modeled after the NCI PID interactions
data schema and distinguishes genes, complexes and compounds on each side of the reaction,
the type of the relation and contains the curated display names for the genes. The compounds
are part of the table but not shown in our user interface.
- ggEventText - information about gene/gene interactions obtained from
text mining.
- ggDocEvent - connections between documents and events.
- ggDoc - information about documents referenced from ggEventText and ggDocEvent.
- ggGeneClass - the HPRD/Panther class, one for each gene symbol.
- ggGeneName - the HGNC name, one for each gene symbol.
For more details about the tables and their fields, use the Table Browser's
"describe schema" button.
The annotations (GNF2 average expression, DrugBank, etc.) for genes are accessed as text files
for performance reasons and can be downloaded from our
downloads server.
Credits
- The text-mined data for the gene interactions and pathways were generated by Chris Quirk and
Hoifung Poon as part of Microsoft Research Project Hanover.
- Pathway data was provided by the databases listed under methods.
- Thanks to Ian Donaldson for IRefIndex, the biggest and free collection of protein interaction databases.
- Arjun Rao (UCSC) provided the ArgDB converter.
- Thanks to Dexter Pratt for help with OpenBEL and to Charles Tepley Hoyt for the
pybel converter.
- Thanks to Alexander Pico for help with the WikiPathways data format GPML
- The short gene descriptions are a merge of the HPRD and PantherDB
gene/molecule classifications. Thanks to Arun Patil from HPRD for making them available
as a download.
- The track display and gene interaction graph were developed at the UCSC Genome Browser
by Max Haeussler.
References
Poon H, Quirk C, DeZiel C, Heckerman D.
Literome: PubMed-scale genomic knowledge base in the cloud
Bioinformatics. 2014 Oct;30(19):2840-2.
PMID: 24939151