[转载]Cytoscape插件列表
1、cytoHubba
Predicts and explores important nodes and subnetworks in a
given network by several topological algorithms.
Explore important nodes/hubs and fragile motifs in
an interactome network by several topological algorithms including
Degree, Edge Percolated Component (EPC), Maximum Neighborhood
Component (MNC), Density of Maximum Neighborhood Component (DMNC),
Maximal Clique Centrality (MCC) and centralities based on shortest
paths, such as Bottleneck (BN), EcCentricity, Closeness, Radiality,
Betweenness, and Stress.
2、CytoKavosh
Allows network motifs discovery
CytoKavosh is a Cytoscape plug-in which allows
discovering network motifs with less memory and CPU time in
comparison with other existing tools. CytoKavosh plug-in uses
Kavosh algorithm for finding network motifs and is based on
counting all k-size sub-graphs of a given network graph (directed
or undirected).
Clusters a network based on network topology using a
modified agglomerative clustering algorithm very similar to
MINE is an agglomerative clustering method that is
able to identify highly modular sets of gene products within dense
molecular interaction networks. It was created in response to the
challenge of discovering high quality modules of gene products from
dense biological networks. The algorithm allows a high degree of
flexibility and user-customisation of results with few adjustable
parameters.
Network Dynamics and Structure Analysis
NetDS is a novel Cytoscape plugin that
conveniently simulates dynamics related to robustness, and examines
structural properties with respect to feedforward/feedback loops.
It can evaluate how robustly a network sustains a stable state
against mutations by employing a Boolean network model. In
addition, the plugin can examine all feedforward/feedback loops
appearing in a network and determine whether or not a pair of loops
is coupled. Random networks can also be generated to evaluate
whether or not an interesting finding in real biological networks
is significantly random.
5、NetMatch
Finds user defined network motifs in a given network.
network motif can be created and edited using original
NetMatch is a Cytoscape plugin that finds user
defined network motifs in any Cytoscape network. Node and edge
attributes of any type and paths of unknown length can be specified
in the search.
6、Randomnetworks
Generates random network or randomizes already loaded
The plugin is used to generate random networks,
randomize existing networks and compare existing networks to random
Clusters a given network based on topology to find densely
connected regions.
MCODE finds clusters (highly interconnected
regions) in a network. Clusters mean different things in different
types of networks. For instance, clusters in a protein-protein
interaction network are often protein complexes and parts of
pathways, while clusters in a protein similarity network represent
protein families.
8、CentiScaPe
Find the most important nodes in a network, calculating
centrality parameters for each node.
CentiScaPe
Centiscape computes specific centrality parameters
describing the network topology. The centrality parameters aid the
users to find the most significant nodes in a complex network. The
plugin computation generates both numerical and graphical output
making easy to find the key nodes also in large networks. Network
topological quantification can be combined with other numerical
node attributes to provide biologically meaningful node
identification and functional classification. CentiScaPe
Network parameters:
Average Distance
Node parameters and their min, max and average
Eccentricity
Betweenness
9、JEPETTO
Performs biological gene sets enrichment analysis based on
interaction network and topological properties
PETTO stable version** is now released! ###
JEPETTO Jepetto performs integrated **gene set analysis** using
information from interaction, pathways and processes databases. It
uses the [TopoGSA](http://www.topogsa.org/) server to identify
**topological analogies** between the user selected gene set and
the known pathways and processes. TopoGSA finds the most similar
biological mechanism using the topological features of the
**interaction network** of a user selected gene set. It is also
able to suggest genes related to the query
10、APID2NET
Downloads PPI data from APID and performs various analyses
and visualizations.
Downloads PPI data from&&and
performs various analyses including protein motif, GO, hub
identification, etc. through the original GUI. Equipped with
impressing "rainbow" coloring of nodes.
Plugin designed to visualize, explore and analyze the
proteins and interactions retrieved from theunified
interactome&platform APID
(which integrates BIND, BioGrid, DIP, HPRD, IncAct and MINT). The
retrieved data include the&annotations and
attributes associated&to the network: GO
terms, InterPro domains, experimental methods that validate each
interaction, PubMed IDs, UniProt IDs. The tool provides interactive
graphical representation of the&protein-protein
interaction&(PPI) networks
within Cytoscape, plus new automatic tools to find&hubs&andconcurrent
attributes&(functional and
structural) along the protein pairs of a given network.
11、dynamicXpr
Dynamically change colors of nodes like a movie according
to their expression level across many conditions.
Introduction
This plug-in loads an expression data file (consult the
Cytoscape manual to learn about the format of this type of file)
and then allows the user to color the nodes in a network according
to their expression values. The GUI works like a VCR, with play,
pause, and stop buttons. If the user presses the play button, the
plug-in will iterate over all the conditions in the expression file
and color the nodes according to their corresponding expression
Using the plug-in
The plug-in starts up with default minimum and maximum
expression values: -1 and 1. It colors nodes with a color ranging
from blue to red, depending on the value of their expression_r(blue
hue if close to -1, red hue if close to 1).
To change the colors or the minimum and maximum expression
values, press the icon that looks like a multi-colored pie on the
Cytoscape tool-bar (this is the Visual Styles dialog). Then edit
the current visual style. Look for the "dynamicXpr" Node Color
mapping, and you should be able to reset colors or add/remove
points in the continuous range of values. After making changes, you
can run the plug-in, and you will see the effects of your new
Additionally, the plug-in assigns to each node at each
condition a "significance" attribute (if present in the loaded
expression file). So, if you wish to map this attribute to a visual
node attribute, you can do so by creating a node visual calculator
(again, using the Visual Styles dialog) and then running the
plug-in. For example, some people like to map the significance to
the node border thickness.
Gene Prioritization and biomedical Evidence
Collection
Finding genes associated with a disease is an
important issue in the biomedical area and many gene prioritization
methods have been proposed for this goal. Among these,
network-based approaches are recently proposed and outperformed
functional annotation-based ones. Here, we introduce a novel
Cytoscape plug-in, GPEC, to help identify putative genes likely to
be associated with specific diseases or pathways. In the plug-in,
gene prioritization is performed through a random walk with restart
algorithm, a state-of-the art network-based method, along with a
gene/protein relationship network. The plug-in also allows users
efficiently collect biomedical evidence for highly ranked candidate
genes. A set of known genes, candidate genes and a gene/protein
relationship network can be provided in a flexible
Calculates overrepresented GO terms in the network and
display them as a network of significant GO terms.
BiNGO is a tool to determine which Gene
Ontology (GO) categories are statistically overrepresented in a set
of genes or a subgraph of a biological network. BiNGO maps the
predominant functional themes of a given gene set on the GO
hierarchy, and outputs this mapping as a Cytoscape graph. Gene sets
can either be selected or computed from a Cytoscape network (as
subgraphs) or compiled from sources other than Cytoscape (e.g. a
list of genes that are significantly upregulated in a microarray
experiment). The main advantage of BiNGO over other GO tools is the
fact that it can be used directly and interactively on molecular
interaction graphs. Another plus is that BiNGO takes full advantage
of Cytoscape's versatile visualization environment. This allows you
to produce customized high-quality figures. Features include
Assessing over-representation or under-representation of GO
categories
Graph or gene list input
batch mode: analyze several clusters simultaneously using
same settings
Different GO and GOSlim ontologies
Wide range of organisms
Evidence code filtering
Hypergeometric or binomial test for
over-representation
Multiple testing correction using Bonferroni (FWER) or
Benjamini&Hochberg (FDR) correction
Interactive visualization of results mapped on the GO
Extensive results in tab-delimited text file
Make and use custom annotations, ontologies and reference
Open source
If you use BiNGO in your research,
please cite:
Maere S, Heymans K, Kuiper M (2005)
BiNGO: a Cytoscape plugin to assess overrepresentation of Gene
Ontology categories in biological
networks.&Bioinformatics&21,
14、HOMECAT
Integrate data from multiple species, mapping multiple
A tool for interspecific comparative analysis, to add to an
existing network ortholog identifiers and custom data.
30 species are currently supported. The desired specificity
and sensitivity in orthology search can be obtained through
multiple homology assessment sources (currently OMA, Compara,
OrthoMCL and Homologene are available). Input identifiers are
automatically converted to query and combine orthology sources
results. All the identified orthologs will be converted to the
desired output format, to allow custom data mapping.
Orthologs can be used to create a new network of metanodes,
each representing an input and its orthologs identifiers. A
specific function allows to map data to metanodes. Metanodes are
depicted as pie-carts, reflecting user data, easing the comparison
of input and orthologs species values.
A particular emphasis is given to functional genomic data.
More than 70 microarrays formats are supported and data can be also
downloaded directly from arrayexpress ATLAS service to the
metanodes.
HOMECAT is easily extensible. The currently supported
homology sources are accessed through "components", that are freely
downloadable from our website, and easy to install. This allows any
user to modify homology data access and add the support to other
data sources, by developing only a component.
For the user manual, to download components allowing
multiple orthology databases support, and to learn how to easily
develop new components, see project website:
Metanodes and graphical representation require MetaNodes
and NodeCharts plugins.
15、iRegulon
Detects master regulators and cis-regulatory
interactions
A regulon consists of a transcription
factor (TF) and its direct transcriptional targets, which contain
common TF binding sites in their cis-regulatory control elements.
The iRegulon plugin allows you to identify regulons using motif
discovery in an existing network or in a set of co-regulated
·&&&&&&&&
Motif discovery is performed in proximal and distal
sequences, across ten vertebrate genomes, using more than six
thousand candidate motifs (position weight matrices or
·&&&&&&&&
The output of iRegulon is a list of enriched motifs,
alongside with candidate transcription factors. The Motif2TF
associations are based on PWM annotation, the use of TF homology,
and of motif similarity.
·&&&&&&&&
New networks can be automatically generated based on the
predicted TF-target interactions.
·&&&&&&&&
The iRegulon plugin can also be used to query
high-confidence target genes predicted from the systematic analysis
of thousands of cancer gene signatures.
A full version of the plugin including
TRANSFAC Professional motifs is provided from the website&(To
download the TRANSFAC PRO version, the user will need to have a
valid subscription to TRANSFAC Professional).
16、EnrichmentMap
Visualizes enrichment of specific functions (GO terms) by
interactions between functions.
The Enrichment Map Cytoscape Plugin allows you to visualize
the results of gene-set enrichment as a network. It will operate on
any generic enrichment results as well as specifically on Gene Set
Enrichment Analysis (GSEA) results. Nodes represent gene-sets and
edges repr in this way, highly redundant
gene-sets are grouped together as clusters, dramatically improving
the capability to navigate and interpret enrichment
Gene-set enrichment is a data analysis technique taking as
* a (ranked) gene list, from a genomic experiment&
* gene-sets, grouping genes on the basis of a-priori knowledge
(e.g. Gene Ontology) or experimental data (e.g. co-expression
and generating as output the list of enriched gene-sets,
i.e. best sets that summarizing the gene-list. It is common to
refer to gene-set enrichment as functional enrichment because
functional categories (e.g. Gene Ontology) are commonly used as
gene-sets.
17、clusterMaker
Clusters densely connected nodes and node attributes in a
given network.
clusterMaker is a Cytoscape plugin that unifies different
clustering techniques and displays into a single interface. Current
clustering algorithms include hierarchical, k-medoid, AutoSOME, and
k-means for clustering expres and MCL,
transitivity clustering, affinity propagation, MCODE, community
clustering (GLAY), SCPS, and AutoSOME for partitioning networks
based on similarity or distance values. Hierarchical, k-medoid,
AutoSOME, and k-means clusters may be displayed as hierarchical
groups of nodes or as heat maps. All of the network partitioning
cluster algorithms create collapsible "meta nodes" to allow
interactive exploration of the putative family associations within
the Cytoscape network, and results may also be shown as a separate
network containing only the intra-cluster edges, or with
inter-cluster edges added back.
New version will be released with Cytoscape
18、DisGeNET
Queries and analyzes networks of diseases and their
associated genes.
19、nodeCharts
Paints bar, line or pie graphs onto nodes.
This plugin allows you to paint nodes with a
variety of chart types representing multiple columns of data
simultaneously using CyCommands.
20、CyTargetLinker
Extends biological networks with regulatory
interactions.
Now released for Cytoscape 3!
The CyTargetLinker app extends biological networks with
regulatory interactions, for example miRNA-target, TF-target or
drug-target interactions. Network nodes need to have an attribute
containing a biological identifier, eg. Ensembl or NCBI Gene, to
link to regulatory information.
Regulatory data gets extracted from so called&Regulatory
Interaction Networks&(RINs).
You can download these network files from the CyTargetLinker
website or create your own.
Check out our tutorials on the website to learn how to use
CyTargetLinker.
21、CytoNCA
Cytoscape apps for network centrality analysis and
evaluation
CytoNCA is a&&app
for network centrality analysis,and has been tested on Cytoscape
1. Introduction
Based on the open-source platform Cytoscape, a convenient
app called CytoNCA for network centrality analysis has been
designed and achieved.This app support eight typical centralities
called Betweeness Centrality (BC) [1], Closeness Centrality (CC)
[2], Degree Centrality (DC) [3], Eigenvector Centrality (EC) [4],
Local Average Connectivity-based method (LAC) [5], Network
Centrality (NC) [6], Subgraph Centrality (SC) [7], Information
Centrality (IC) [8] for undirected network.
Moreover, CytoNCA provides evaluating performance of the
centrality measures in ten statistical measures, including True
Positives (TP), False Positives (FP), True Negatives (TN), False
Negatives (FN), Sensitivity (SN), Specificity (SP), Positive
Predictive Value (PPV), Negative Predictive Value (NPV), F-measure
(F) and Accuracy (ACC).
This app is developed by Yu Tang, supported and directed by
Dr. Jianxin Wang and Dr. Min Li, from Central South
University.
2. Quick Start
Following is a short quick start for the usage of CytoNCA,
detailed information of the algorithms implemented in this plug-in
can be found in related papers.
Download and install Cytoscape3.0().
Download the Jar version of CytoNCA(&).
Start Cytoscape, install the CytoNCA.jar through App
Manager(Apps → App Manager → Install from
Import or open a network.
Click&App →
CytoNCA → Start.
It will switch to CytoNCA tab on left panel, choose one or
multiple centralities as you want.
Click&upload
essential protein list&button to upload
the essential protein list of the current protein network.
(optional).
Click&Analysis&button.
After analysis is done, results will be shown on the right
panel. If you upload the essential protein list of current protein
network, evaluation panel will be shown in the button.
22、DynNetwork
Visualize dynamic networks in Cytoscape 3.0
DynNetwork is a OSGi bundle to provide support for
importing, visualizing, and analysing dynamical networks in
Cytoscape 3.0 beta. Dynamical networks are imported in form of
XGMML files, and the dynamics of nodes, edges, attributes can be
visualized at the desired time points. To improve visualization,
the nodes can be animated with a dynamic force layout
algorithm.
Example of dynamic XGMML files can be found here:
This plugin has been developed in the context of the Google
Summer of Code 2012 / Cytoscape 3.0 by Sabina Sara Pfister under
the supervision of John A Brown and Jason Montojo.
23、NetworkAnalyzer
Computes basic properties of whole network (degree
distribution, clustering coefficients, centrality,
etworkAnalyzer performs analysis of biological networks and
calculates network topology parameters including the diameter of a
network, the average number of neighbors, and the number of
connected pairs of nodes. It also computes the distributions of
more complex network parameters such as node degrees, average
clustering coefficients, topological coefficients, and shortest
path lengths. It displays the results in diagrams, which can be
saved as images or text files.
Note:&This plugin is now
included in the Cytoscape installation. It is pre-installed in both
2.x and 3.x versions of Cytoscape.
24、DomainGraph
Visualizes domain-domain interactions which connect pairs
of interacting proteins.
The main functionality of the DomainGraph plugin
is the visual analysis of the effects of alternative splicing on
genes, protein isoforms, molecular interactions, pathways and miRNA
binding sites. Statistical results of Exon Array data computed with
AltAnalyze can be imported into DomainGraph and affected genes,
pathways and miRNA binding sites are automatically annotated. From
these annotations, genes, gene products, and pathways can directly
be loaded and visualized via DomainGraph and occurrences of
alternative splicing are highlighted. Therefore, an in-depth
analysis of Exon Array data regarding alternative splicing events
and their biological impact is easily possible without the need of
prior knowledge. Furthermore, the detailed analysis of interaction
networks and pathways is possible. Given a gene interaction
network, DomainGraph visualizes the genes together with all known
encoded protein isoforms and their respective domain compositions.
Given a protein interaction network, DomainGraph decomposes the
proteins into their domains and visualizes the domain interactions
underlying the protein interactions. These networks can be
integrated with exon expression data produced by the Affymetrix
Exon Array. Genes, protein isoforms and domains are highlighted
according to potentially occurring alternative splicing events or
differential expression in different groups of
25、NetworkEvolution
Allows interactive comparative analysis of networks across
different species.
The plugin allows the user to map selected network
regions to interactomes of evolutionary predecessors and
descendants, making it possible to interactively inspect
evolutionary changes and observe both conservation and
diversification of specific network components.
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