BACKGROUND
the integration and biological interpretation of large-scale datasets is currently one of the main challenges in bioinformatics research.
how can we extract meaningful information from a list of differentially regulated genes?
one possibility to understand, how regulated genes relate to each other is to view them in the context of their physical, genetic or regulatory interactions: network-based analysis using data from protein-protein or regulatory interactions can open new perspectives for further experimental studies.
quantitative values from a functional screen or a list of mutated genes identified in a cancer genomics study can be used to generate sub-networks from a large, biological interaction network.
these sub- or focus networks can be termed ‘disease’ or ‘state’ networks, as they describe the modules in the cell or the organism, which are affected by a certain experimental condition or by a particular disease.
this approach has for instance been employed by software like the database and web-tool string  <cit>  or the command-line based tool netbox  <cit> .
focus networks can also be created based on a specific biological question: how are two specific proteins - or two groups of proteins - connected with each other?
this approach allows an even more biologically focused view on the changes in the cellular network under different conditions.
focus networks allow us moreover to understand the cross-talk between two molecules or pathways, which in this context is defined by all paths between two proteins or two groups of proteins.
typically, some form of shortest-path algorithm like dijkstra’s algorithm  <cit>  is used to create sub-networks between two or more nodes.
the numeric values from functional genomics studies are used to score paths between two nodes.
methods like pathfinder  <cit>  or the reactome browser  <cit>  have implemented this functionality of connecting two molecules with each other within a biological network.
both tools use numeric values also to visualize regulatory changes that take place during state changes of the cell/organism under study.
focus networks can be further enriched using gene ontology  terms  <cit>  or pathways from different sources to provide additional functional information for data interpretation.
go biological processes can also be used to explore cross-connections between two or more pathways and find missing pathway components.
this provides a more integrative view of a biological network.
the drug family of the statins is currently widely used to lower cholesterol levels in the treatment of hypercholesterolemia.
statins, which act as hmg-coa  reductase  inhibitors, prevent the production of cholesterol by inhibiting the biosynthesis of isoprenoids and sterols in the mevalonate pathway  <cit> .
however, statins are known to have a variety of side effects, including muscle adverse effects, liver damage, cognitive impairment, cancer progression or diabetes mellitus .
functional genomics studies of statin-treated cell systems indicate extensive changes of expression levels upon drug treatment .
the detailed analysis of these transcriptional changes should therefore lead to a better understanding of the functions and pleiotropic effects of statins.
in this study, we re-analyzed the time-course expression data from atorvastatin-treated, primary human hepatocytes from six different patients published in a previous study  <cit> .
we focused our analysis on determining the regulation of downstream genes from statin drug targets as defined in stitch  <cit> .
we were especially interested in addressing the following issues: 1) how do statin targets and differentially regulated genes relate to each other?
2) which pathways are affected upon statin treatment?
3) how does the dynamics of the neighborhood of specific proteins change after statin treatment?
in order to answer those questions, we have developed two cytoscape plug-ins that work together: viper, the virtual pathway explorer, creates focus interaction networks by connecting two or more nodes with each other.
it applies user-provided expression data to score paths between two nodes and thus limits the network to functionally relevant paths.
the cytoscape plug-in peanut  upgrades interaction networks with pathway information and identifies enriched pathways in focus networks.
we have applied our toolbox to re-analyze the expression data from atorvastatin-treated, primary human hepatocytes and found that the transcription factors foxa <dig>   <dig> and  <dig> are important regulatory players in atorvastatin response.
implementation
viper
viper was written in java as a cytoscape plug-in.
the basis of all functions is a recursive method, which iterates through the members  of all paths emanating from a selected node.
the step depth is influenced by two parameters: 1) the maximum number of steps allowed .
2) the numerical values of the nodes.
we used the log2fold expression changes of atorvastatin treated primary hepatocytes described in  <cit>  as numerical values.
viper can be accessed under: http://sourceforge.net/projects/viperplugin/
viper has three main search options:‘a to b’: ‘a to b’ connects two selected nodes with each other.
we refer to the paths between nodes a and b as cross-talk.
mathematically, we define cross-talk as all paths between two nodes , where a single node in a path can only be passed once.
the result is a focus network, which is determined by the maximum number of steps allowed between the start and the target node.
the search is stopped when the target node is reached or the maximum number of steps is exceeded.
only if the target has been found, a path is stored, scored and displayed in the results tab.
the focus network is created based on all nodes that are present in all stored paths.
the connecting edges are taken from the original network.
therefore, all known interactions between the subset of nodes are included in the newly created focus network.
scoring of paths is done using the following equation: score=#ofdifferentlyregulatednodes∈pathpathlength <dig>
the p-values for discovered paths in focus networks are calculated based on the cumulative probability of the hypergeometric distribution to find k or more differentially expressed genes in a path of length n.
‘connecting in batch’: similarly to the ‘a to b’ search, two groups of nodes can be connected using the ‘connection in batch’ function.
for every node in the start list a, the recursive search is computed towards every node in the target list b. a results tab with scored paths is not created in this case.
‘environment search’: the third option is to explore the regulated proximity of a single node using the ‘environment search’.
just one starting node is selected in this case.
mathematically, we define the environment search as follows: a network is calculated from all outgoing paths of length l from x <dig>  where every node is allowed to be passed only once per path and all paths with at least two consecutive node scores below threshold t have been removed.
the iteration through emanating paths is carried out until the allowed maximum search depth is reached.
when exploring the neighborhood of a single node, the numerical data are used to select paths radiating from the selected node.
paths, in which at least two consecutive nodes are not differentially expressed, are removed from the resulting neighbor focus network.
thus, only paths that contain differentially regulated nodes are considered for the environment search, though single unregulated linker nodes are allowed.
the resulting network is referred to as a neighbor focus network.
using viper
starting from any existing network supplemented with expression data, the user has to select the attribute field containing the expression information.
a slider is automatically set to the respective range of expression values.
after adjusting the slider to the desired expression range, different options are available in the workflow .fig.
<dig> workflow for creating focus networks.
workflow of viper in creating focus networks between two nodes/two groups of nodes, or in exploring the neighborhood of a single node of interest.
the user must select two nodes or group of nodes for creating a focus network.
a single node is selected when exploring the neighborhood.
numerical data  must be added to the network for scoring paths of a focus network and for creating a neighbor focus network from a single node.
in both cases, the user selects the search depth.
after creating the focus network, the network can for instance be explored by using and visualizing go-terms.
peanut is used to find and visualize enriched pathways
‘a to b’
this function executes the path search algorithm between two selected nodes.
the result is a focus network of all identified paths of a certain length between two nodes.
the user selects the length  of the calculated paths.
all interconnecting edges are added to the focus network.
a result list, which includes every discovered path between the nodes, is located on the right side of the screen.
this list shows all paths, their respective members and the assigned score as described above.
the score can be used to further reduce the focus network or simply to visualize specific paths.
‘connecting in batch’
two groups of nodes can be connected in the ‘connecting in batch’ function of viper.
the same algorithm is used as in the ‘a to b’ search, except that all paths between all members of a start list and a target list are computed.
this algorithm can be applied to detect cross talk between two pathways, two protein complexes or two hit lists from different experiments.
three buttons have to be used for the ‘connecting in batch’ search: 1) a start protein list has to be defined by selecting all starting nodes and pressing the ‘select start protein list’; 2) the target protein list has to be selected accordingly and confirmed by pressing the ‘select target protein list’ button; 3) the button ‘start connection in batch’ executes the search.
‘environment search’
in case only a single protein of interest exists, the algorithm can be used to observe the dynamics of expression in the environment of this protein using the ‘environment search’.
a single node is selected and the search is executed with the button ‘environment search’.
all regulated nodes within a certain step size of the selected protein give rise to the neighbor focus network.
peanut
peanut  is a cytoscape plug-in designed to annotate protein interaction networks with biological pathway information and to identify enriched pathways in focus networks.
the interactome of the organism denotes the background network.
next to visualizing enriched pathways in the focus networks, the results can be exported as a tab delimited file.
peanut can be accessed under: http://sourceforge.net/projects/peanut-cyto and was implemented in java.
the user can choose between the three databases consensuspathdb , pathway commons  and wikipathways  to annotate the network.
while consensuspathdb requires entrez gene ids as input, pathway commons and wikipathways require uniprot accession numbers.
annotation of nodes with these ids can be done within cytoscape using for instance the plug-in cythesaurus  <cit> .
consensuspathdb and pathway commons contain pathway data collected from publicly available pathway databases .
wikipathways is a database based on the ‘wiki principle’ and provides an open platform dedicated to collaborative registering, reviewing and curation of biological pathways.
while pathway commons and wikipathways work with a wide variety of organisms, consensuspathdb is specialized on human, mouse and yeast pathways.
when the user chooses to annotate his network of interest with consensuspathdb data, he can additionally import directed interactions from kegg to increase the amount of vertex degrees, enabling more complex path searches using viper.
information from pathway commons is accessed over their web service.
flat files from the consensuspathdb and wikipathways webpages are downloaded via the apache commons io library  and cytoscape internal downloader classes.
once downloaded, consensuspathdb and wikipathways can be used offline, while pathway commons requires internet access.
network annotation with pathway commons is slower, as it depends on the load and availability of the host server, as well as internet connection speed.
the probability value for the pathway enrichment in the focus network is determined using the apache commons math library  to calculate the cumulative probability of a hypergeometric distribution.
multiple testing correction is achieved by applying either bonferroni  <cit>  or benjamini-hochberg  <cit>  correction.
peanut has three sub-menus:‘find pathways’: the find pathways sub-menu annotates the networks in cytoscape with pathway data.
networks can be labeled using more than one pathway resource by re-using the sub-menu with different pathway selections.
‘show pathway statistics’: the ‘show pathway statistics’ sub-menu calculates enriched pathways in a selected focus network.
the user has to select the focus network of interest, the background network and choose a p-value cut-off.
enriched pathways can be selected for visualization and downloaded as a tab-delimited file.
‘download/update dependencies’: this sub-menu is used to download pathway information for network annotation.
it needs to be run before using peanut the first time and should be run regularly to update pathway information.
using peanut
after installing peanut in cytoscape by placing the plug-in in the cytoscape plug-in folder, the tool can be accessed via the plug-in menu.
the sub-menus are used as follows:‘find pathways’
this sub-menu allows the user to start the software and annotate the network of choice with pathway data.
in a simple dialog the user can select between three different databases: consensuspathdb, pathway commons or wikipathways.
the user can select different options for each database depending on preferences .
annotating the network with more than one database is possible by subsequently re-running this sub-menu with another selected database.
‘show pathway statistics’
when the user has finished annotating the network, clicking on the ‘show pathway statistics’ sub-menu will invoke a table, two combo boxes, two text fields and several buttons.
in one of the text fields the user can select the p-value cut-off for the enrichment calculations.
the combo boxes permit the user to select the focus and background network.
once the results have been calculated the user can export the results as a tab delimited file or search the table for pathways via the text box.
the user can select each pathway and highlight the members of the pathways in the focus network by clicking on ‘select’.
‘download/update dependencies’
in this sub-menu, dependencies can be used to download/update information from consensuspathdb and wikipathways.
‘download/update dependencies’ needs to be executed when first using the plug-in.
it is good practice to refresh the dependencies from time to time by clicking on the ‘download all’ button.
for more information on how to use peanut, see our wiki http://sourceforge.net/p/peanut-cyto/wiki/home/.
analysis of expression data
cluster analysis
we used preprocessed data from schröder et al.
<cit> , which have already been normalized and filtered and repeated the clustering step described in the original publication.
the patient data used here were part of a large-scale study originally published in  <cit>  and deposited at the gene expression ominbus  <cit>  resource .
tissues and corresponding blood samples of the original study were taken with written informed consent from donors.
the study was furthermore approved by the ethics committees of the medical faculties of the charite, humbold university and university of tübingen.
before clustering, the data were log <dig> transformed and filtered for probes, which were at least  <dig>  fold differentially expressed in at least  <dig> patients.
with these  <dig>  probes edisa clustering was performed, using the same parameters as in schröder et al.
<cit> , with tg of  <dig>  and tc of  <dig> .
creating the pp/tfg interaction data
the basis for biologically meaningful network analysis is a reliable interaction network with high confidence.
to accomplish this goal, we allowed only high confidence interaction data.
no predicted interactions or interactions based on co-expression were included.
as source for protein-protein interactions, we chose pathway commons  <cit>  due to its large number of curated interactions and the simple format, which can be directly loaded in cytoscape.
likewise, only experimentally verified transcription factor – gene interactions from transfac® were included in the network.
confidence scores from transfac® were used to identify reliable regulatory interactions.
go and pathway enrichment of gene lists and networks
to analyze go and pathway enrichment of focus networks, the official gene symbols  were submitted to david  <cit> .
additionally, the go annotation for all nodes was added to the network via cytoscapes’ built-in function.
clustering of patient data using cluster3
cluster <dig>  <cit>  was used to cluster patient data of the foxa <dig> neighbor focus network, as well as the  <dig> h time point of the probe id of all patients.
standard parameters and average linkage clustering were chosen.
the foxa <dig> network was analyzed for presence  or absence  of a node.
in case of up-regulation, the value  was assigned, while down-regulated nodes were assigned the value  .
RESULTS
expression analysis of hepatocyte time-course experiment
in a first analysis, we found a total of  <dig>  differentially expressed probes in at least two patients irrespective of time-points .
we processed these genes with edisa 3d clustering  <cit> , which resulted in  <dig> differentially regulated genes  that were grouped into  <dig> clusters .
further analysis was performed with the non-redundant version of this gene set.
go and pathway enrichment
we submitted the processed gene list to david  <cit>  for functional annotation and enrichment analysis .
david was chosen for its extended functionality beyond enrichment of gene ontology terms.
primarily, basic cellular and metabolic processes were enriched, such as amino acid or nucleic acid metabolism.
in addition, several pathways and processes related to cellular cholesterol and lipid -metabolism and -homeostasis were enriched.
based on these results we could however not make any conclusions on the regulatory or transcriptional network involved in atorvastatin response.
focus network analysis
next, we used focus network analysis to extract more information from the data.
we decided to use the interaction network provided by pathway commons  <cit>  as the basic protein-protein interaction network, as it contains information from more than one primary source .
given the type of data in protein interaction databases, we reasoned that we additionally needed information on gene regulatory relationships: which transcription factors are able to regulate which gene set?
we considered this information on transcription factor - gene interactions  as essential to identify the gene regulatory networks that control the cellular response to statins at a transcriptional level.
we retrieved tfgis from transfac® and combined them with the protein-protein interactions  from pathway commons to a single interaction network, which was used for all further analysis steps .
next, we extracted the top ten high confidence primary drug targets of atorvastatin from the stitch database  <cit>  , all of which were present in our interaction network.
we used the viper ‘connecting in batch’ function to connect the ten atorvastatin targets with the  <dig> differentially regulated, non-redundant genes we have identified with edisa 3d clustering, using a step-size of two.
this functionality of viper creates focus networks by searching for all paths between two proteins or two groups of proteins, up to a user-defined path length.
the nodes are supplemented with expression values.
these weights on the nodes are used to score all paths in the resulting focus networks.
the score of a path is dependent on its length and the number of differentially regulated nodes it contains.
the resulting atorvastatin focus network contained  <dig> nodes and  <dig> edges .
of those,  <dig> were pp interactions and  <dig> came from tfg interactions.
we next performed go enrichment of the atorvastatin focus network using david .
we observed that with the proteins from the focus network, we found more terms relating to regulatory functions, such as signaling or transcriptional control than with the  <dig> differentially expressed genes alone.
we searched for enriched pathways in the atorvastatin focus network.
to this end, we employed our pathway enrichment tool peanut on the focus network, taking the entire network of ppis and tfgis as a background.
using consensuspathdb  <cit>  as pathway resource, peanut identified a total of  <dig> pathways, many of which were redundant.
notably, again mainly signaling-, as well as transcriptional regulation pathways were identified .
we compared results of peanut with pathway enrichment results from david .
though different pathway resources are available in david, a similar set of pathways was enriched.
those included growth receptor signaling pathways such as the egf-receptor-, vegf-, insulin- and ras-signaling pathways, interleukin signaling pathways, as well as cancer pathways.
due to the availability of the pathway interaction database  of the consensuspathdb resource, however, more transcription factor pathways were identified with peanut.
involvement of the foxa transcription factors in atorvastatin response
in the list of enriched pathways, our attention was caught by the forkhead box a transcription factor pathways.
in adults, the forkhead box a transcription factors foxa <dig>  foxa <dig> and foxa <dig> are expressed in liver, pancreas and adipose tissue, where they regulate gene expression of metabolic genes  <cit> .
two direct targets of atorvastatin are also targets of the foxa transcription factors: foxa <dig> and foxa <dig> regulate apob , while cyp3a <dig> is a target of foxa <dig>  <cit> .
given our experimental set-up of atorvastatin-treatment of primary human hepatocytes and the fact that atorvastatin is primarily acting on cholesterol synthesis in the liver, we further focused our analysis on these two transcriptional pathways.
we created a viper focus network between the primary atorvastatin target hmgcr and the three transcription factors foxa <dig>  a <dig> and a <dig>  starting from the atorvastatin focus network with a maximal step size of two, without considering a fold-change.fig.
<dig> focus networks between the primary atorvastatin target hmgcr and the transcription factors foxa <dig>  a <dig> and a <dig> of patient  <dig>  time course of patient  <dig> is shown at  <dig> h ,  <dig> h ,  <dig> h ,  <dig> h  and  <dig> h .
the focus networks were created using viper, with a maximal path length of  <dig> without considering the log2fold change: this allows direct comparison of time-course data.
up regulated nodes are shown as upward triangles, colored in red.
down regulated nodes are displayed as downward arrows, colored green.
the fox transcription factors are colored orange, as is the hmgcr.
protein-protein interactions have grey edges; edges of transcription factor gene interactions are colored red
we visualized expression changes over time and patients in the hmgcr-foxa1/2/ <dig> focus network.
we first analyzed the expression changes over time from patient  <dig> in the focus network .
hmgcr itself is up-regulated at the first three time points, returning to normal expression values at the later two time points.
likewise, cyp3a <dig> is up regulated at time points  <dig>   <dig> and  <dig>  however becomes down regulated at  <dig> h.
next, we were interested, whether the expression response to atorvastatin was similar in the six different patients and plotted hmgcr-foxa1/2/ <dig> focus networks for each patient at  <dig> h after treatment .
this representation of the data was especially informative as it illustrated the obvious differences in response to atorvastatin in the primary hepatocytes from the six patients.
we observed that only donors  <dig>   <dig> and  <dig> showed somewhat overlapping regulatory responses at  <dig> h after treatment.
the expression pattern of patient  <dig> was in many cases opposite to the first group , while the hepatocytes from patients  <dig> and  <dig> showed milder responses to the drug.
interpreting the hmgcr-foxa1/2/ <dig> focus network
the focus network of hmgcr and the foxa transcription factors can be divided in two parts .
a small, tightly connected sub-cluster contains small molecules, as well as metabolic genes, while the larger sub-cluster contains signaling molecules, as well as transcription factors.
hmgcr is directly connected to two proteins from this cluster: the camp dependent protein kinase alpha, prkaca and rho gtpase activating protein  <dig>  arhgap <dig> fig.
<dig> enriched pathways identified by peanut on hmgcr/foxa focus networks.
three pathways were chosen for display: the integrated breast cancer pathway , the prostate cancer pathway  and the metabolism of lipids and lipoproteins pathway .
members of the respective pathways are highlighted in yellow.
up regulated nodes are shown as upward triangles, colored in red.
down regulated nodes are displayed as downward arrows, colored green.
the fox transcription factors are shown in orange, as is the hmgcr.
protein-protein interactions have grey edges; edges of transcription factor gene interactions are colored red
especially foxa <dig> is tightly integrated with other transcription factors from the large sub-cluster, including the transcription factors sp <dig>  tp <dig>  fos, jun or brca <dig>  it also binds several transcriptional co-activators.
note that hmgcr expression is not directly regulated by any of the transcription factors in the network.
the connections between the foxa proteins and hmgcr are mostly due to transcriptional targets of the foxa’s interacting with prkaca and arhgap <dig>
interestingly, statins have been implicated to have a preventive effect on different cancer types and to be beneficial for the treatment of several cancer types, among which are breast and prostate cancer .
bcl <dig>  a direct transcriptional target of foxa <dig>  <cit> , is also a direct interactor of the hmgcr interacting kinase prkaca.
previous studies have shown that bcl <dig> is down-regulated in response to statin-induced apoptosis ; prkaca has on the other hand been implicated in statin-resistance of tumors by phosphorylating the pro-apoptotic protein bad , thus allowing anti-apoptotic signaling  <cit> .
in conclusion, cross-talk via prkaca and arhgap <dig> is therefore one possible link between the mevalonate pathway and the foxa transcriptional network, which could in part explain the effect of statins on apoptosis of cancer cells.
pathway enrichment analysis on focus networks using the cytoscape plug-in peanut
we performed pathway enrichment analysis using peanut on the hmgcr-foxa1/2/ <dig> focus network .
again, many signaling, as well as metabolic pathways were enriched.
in accordance with previous reports on statin-sensitivity of cancer cells  <cit> , several cancer pathways are enriched in our focus network.
in fig.
<dig>  we illustrate the usefulness of peanut in visualizing components of enriched pathways in focus networks created with viper.
we chose to highlight the two cancer pathways ‘integrated breast cancer’  and ‘prostate cancer’ , as well as the pathway metabolism of lipids and lipoproteins  in the hmgcr-foxa1/2/ <dig> focus network.
similar pathways were identified using david .
exploring the molecular environment of a node using viper
in a next analysis step, we used viper’s functionality ‘environment search’ to explore the neighborhood of a single protein of interest.
as a rule, only one linker node without differential expression is allowed in the ‘environment search’.
the user sets again the search depth by defining the step size.
all paths that lack differentially expressed nodes are removed from the resulting neighbor focus network.
we chose one of the fox transcription factors, foxa <dig>  as the start node and used a search depth of two.
we assumed that an environment search with foxa <dig> might shed light on the pathways and genes regulated by this transcription factor during atorvastatin treatment.
we chose the  <dig> h time point for further analysis.
earlier time points showed only few differentially expressed genes for most of the patient samples.
in later time points, a considerable amount of genes was changed, possibly due to secondary effects.
data from the six patients at time point  <dig> h are shown in fig.
<dig> fig.
<dig> neighbor focus networks of foxa <dig> in all six patients at  <dig> h. we chose to explore the environment of foxa <dig> with respect to differential expression.
foxa <dig> neighbor focus networks were created for patients  <dig> ,  <dig> ,  <dig> ,  <dig> ,  <dig>  and  <dig> .
up regulated nodes are shown as upward triangles, colored in red.
down regulated nodes are displayed as downward arrows, colored green.
the fox transcription factors are shown in orange, targets of atorvastatin are highlighted in cyan.
protein-protein interactions have grey edges; edges of transcription factor gene interactions are colored red
the striking difference of patient  <dig> is again visible: the environment search of foxa <dig> under the chosen conditions  produced the largest network of all patients.
most proteins in patient 67’s network are up regulated.
notably, the central transcription factor sp <dig> is up regulated, which is potentially the cause of the strong effect we observed in gene expression.
many of the transcription factors that were present in the hmgcr-foxa1/2/ <dig> focus network are also present in the neighbor focus networks of the six patients.
in all networks, sp <dig> seems to be a central hub.
the atorvastatin targets ccl <dig> and cyp3a <dig> are present in all but one network , while hmgcr is not found in any network.
note also that in patient  <dig>  most of the genes are down-regulated, including the transcriptional regulators tp <dig> and fos.
finally, we noticed that most nodes that are shared between all patients are linker nodes and thus not differentially expressed.
exceptions thereof are sp <dig>  fos and tp <dig>  all of which show differential expression in at least one sample.
we decided to cluster the patient data based on the differential regulation and presence or absence of nodes in the foxa <dig> neighbor focus network using cluster <dig>  <cit> .
the differential behavior of patient  <dig> is reflected in the resulting hierarchical tree .
furthermore, we observe a close clustering of patients  <dig> and  <dig>  as well as a sub-group formed by patients  <dig>   <dig> and  <dig>  we were interested, if the same groups would cluster at the  <dig> h time point in overall gene expression as well.
we therefore used the expression values of the probe ids at 24h  for cluster <dig> analysis .
indeed, the clustering of all expression values is highly comparable; however, at the level of all analyzed genes, patient  <dig> displays more similarity to patient  <dig> than  <dig> in this small sub-group.
comparison to previous analysis
we started with the same primary data, as well as clustering technique as schröder, et al.
<cit> , resulting in identical clusters of co-regulated genes.
yet, our approach has channeled the analysis of the data in a different direction.
in the previous study, enrichment analysis, followed by cis-regulatory module detection in combination with network analysis was done.
this yielded a set of transcription factors with a hypothetical function in atorvastatin-induced gene regulation.
factors such as krüppel-like factors klf <dig> and klf <dig>  hypoxia-inducible factor  <dig> , hnf <dig>  the nuclear receptor rxr in combination with other nuclear receptors, nuclear receptors ppara, nr1h <dig> and nr2c <dig>  sp <dig> and sp <dig>  as well as tgif <dig>  smad <dig> or elf <dig> were found in  <cit> .
in our primary atorvastatin focus network, we also found the transcription factors rxrb, hnf4a, ppara, nr2c <dig>  sp <dig>  sp <dig> and klf <dig>  the corresponding pathways of these transcription factors were likewise enriched .
in the second step, however, we did not pursue any of the above factors or pathways.
we rather focused on the foxa transcription factors and their potential role in atorvastatin response.
interestingly, we also found sp <dig> as a major transcriptional regulator in our focus networks.
sp <dig> is known to coordinate expression together with both, rxr/rar , as well as the foxa transcription factors .
both, sp <dig>  as well as the foxa transcription factors are also known to regulate some direct atorvastatin drug targets  <cit> .
viper was in this study used to point towards less well known players in atorvastatin response.
in fact, we see the advantage of a plug-in such as viper in exploring paths that are difficult to find otherwise.
if used with numerical values from experimental studies such as an expression screen, it will open up new avenues for experimental research.
CONCLUSIONS
here we present the cytoscape plug-ins viper  and peanut .
viper provides the possibility to navigate large interaction networks by linking two nodes or two groups of nodes with each other.
it can be used to identify potential links between processes or pathways.
viper furthermore enables users to explore the neighborhood of a single node with respect to the  quality of radiating paths.
the cytoscape plug-in peanut identifies enriched pathways in a focus network compared to a background network.
we used viper to create focus networks, as well as neighbor focus networks using time-series data on atorvastatin-treated, primary hepatocytes from six donors.
the focus network was analyzed using peanut.
we identified the foxa1/a2/a <dig> transcription factors to be involved in atorvastatin response.
furthermore, peanut revealed that the foxa networks were enriched in cancer, as well as metabolic pathways.
we found interesting differences in patient samples in the focus and neighbor focus networks, possibly explaining the often-observed, individual responses to drug treatment.
while we used viper with numerical values from a differential expression study, the plug-in can be used with numbers inferred from any experimental set-up and high-throughput assay.
functions ‘a to b’ and ‘connecting in batch’ work also without numerical values to create focus networks.
in this case, no scoring of paths is done.
the ‘environment search’, however, requires numerical values for the creation of a neighbor focus network.
availability and requirements
viper and peanut are all available via sourceforge.org.
both plug-ins have been tested for the versions  <dig> .
<dig> and  <dig> .
<dig> of cytoscape.
versions of viper and peanut are also available for cytoscape  <dig>  and higher .
please note that the current version of peanut v <dig> does not work with cytoscape versions below  <dig>  or  <dig> .
viper and peanut are fully integrated in both cytoscape versions and compatible with other cytoscape plug-ins.
viper and peanut will be maintained to work with future releases of cytoscape, as well as updates of public pathway database.
the required amount of memory depends mainly on the background network.
in general, we advise to have at least  <dig> gb of ram for the mouse or human interactome.
we furthermore advise to work with turned-off view of large background networks .
cytoscape itself is written in java and runs on linux, macos and windows systems.
we advise to use oracle java, as we have not extensively tested and validated our software for open jdk.
additional files
additional file 1: table s <dig>  log <dig> fold change of six selected patients at time points  <dig> h,  <dig> h,  <dig> h,  <dig> h and  <dig> h. data were taken from  <cit> , affymetrix probe ids are shown.
table s <dig>  time-course and patient- dependent clustering of probeids that showed differential expression in at least one time point or patient.
<dig> clusters were created using edisa 3d clustering, containing  <dig> non-redundant, and differentially expressed genes.
table s <dig>  annotation of the edisa 3d-selected, differentially expressed set of affymetrix probe ids.
table s <dig>  go enrichment of edisa 3d-selected, differentially expressed set of affymetrix probe ids.
table s <dig>  pathway enrichment of edisa 3d-selected, differentially expressed set of affymetrix probe ids.
table s <dig>  targets of atorvastatin as identified by stitch.
table s <dig>  go enrichment of the atorvastatin focus network.
table s <dig>  pathway enrichment of atorvastatin focus network using peanut.
table s <dig>  pathway enrichment of the atorvastatin focus network using david.
table s <dig>  pathway enrichment of the foxa1/a2/a <dig> – hmgcr neighbor focus network using peanut.
table s <dig>  pathway enrichment of the foxa1/a2/a <dig> – hmgcr neighbor focus network using david.
table s <dig>  node scores for foxa <dig> neighbor focus networks of the six patients used for clustering.
additional file 2: figure s <dig>  focus network of all atorvastatin targets and differentially expressed genes after edisa 3d clustering .
up regulated nodes are shown as upward triangles, colored in red.
down regulated nodes are displayed as downward arrows, colored green.
protein-protein interactions have grey edges; edges of transcription factor gene interactions are colored red.
additional file 3: figure s <dig>  focus networks of hmgcr and foxa1/a2/a <dig> of the six patients.
differential expression of nodes at  <dig> h is highlighted.
data are shown for patients  <dig> ,  <dig> ,  <dig> ,  <dig> ,  <dig>  and  <dig> .
up regulated nodes are shown as upward triangles, colored in red.
down regulated nodes are displayed as downward arrows, colored green.
the fox transcription factors are shown in orange, as is the hmgcr.
protein-protein interactions have grey edges; edges of transcription factor gene interactions are colored red.
additional file 4: figure  <dig>   clustering of patients based on the foxa <dig> neighbor focus network shown in fig.
<dig>  as well as the probe id data from all patients at  <dig> h , data were taken from additional file 1: table s <dig>
abbreviations
ppiprotein-protein interaction
tfgitranscription factor-gene interaction
hmgcrhmg-coa  reductase
competing interests
the authors declare that no competing interests exist.
authors’ contributions
mg conceived and implemented the software viper, constructed the interaction networks, analyzed the data and helped in writing this manuscript.
fh conceived and implemented peanut and helped in writing this manuscript.
vs ported peanut to cytoscape version  <dig>  tested peanut for cytoscape versions  <dig> and  <dig>  and helped with coding the cytoscape  <dig> version of viper.
mt and bak participated in data evaluation and helped with strategy discussion; bhh conceived and supervised this study, assisted in data analysis and wrote this manuscript.
all authors read and approved the final manuscript.

 the integration and biological interpretation of large-scale datasets is currently one of the main challenges in bioinformatics research. how can we extract meaningful information from a list of differentially regulated genes? one possibility to understand, how regulated genes relate to each other is to view them in the context of their physical, genetic or regulatory interactions: network-based analysis using data from protein-protein or regulatory interactions can open new perspectives for further experimental studies. quantitative values from a functional screen or a list of mutated genes identified in a cancer genomics study can be used to generate sub-networks from a large, biological interaction network. these sub- or focus networks can be termed ‘disease’ or ‘state’ networks, as they describe the modules in the cell or the organism, which are affected by a certain experimental condition or by a particular disease. this approach has for instance been employed by software like the database and web-tool string or the command-line based tool netbox. focus networks can also be created based on a specific biological question: how are two specific proteins - or two groups of proteins - connected with each other? this approach allows an even more biologically focused view on the changes in the cellular network under different conditions. focus networks allow us moreover to understand the cross-talk between two molecules or pathways, which in this context is defined by all paths between two proteins or two groups of proteins. typically, some form of shortest-path algorithm like dijkstra’s algorithm is used to create sub-networks between two or more nodes. the numeric values from functional genomics studies are used to score paths between two nodes. methods like pathfinder or the reactome browser have implemented this functionality of connecting two molecules with each other within a biological network. both tools use numeric values also to visualize regulatory changes that take place during state changes of the cell/organism under study. focus networks can be further enriched using gene ontology terms or pathways from different sources to provide additional functional information for data interpretation. go biological processes can also be used to explore cross-connections between two or more pathways and find missing pathway components. this provides a more integrative view of a biological network. the drug family of the statins is currently widely used to lower cholesterol levels in the treatment of hypercholesterolemia. statins, which act as hmg-coa reductase inhibitors, prevent the production of cholesterol by inhibiting the biosynthesis of isoprenoids and sterols in the mevalonate pathway. however, statins are known to have a variety of side effects, including muscle adverse effects, liver damage, cognitive impairment, cancer progression or diabetes mellitus. functional genomics studies of statin-treated cell systems indicate extensive changes of expression levels upon drug treatment. the detailed analysis of these transcriptional changes should therefore lead to a better understanding of the functions and pleiotropic effects of statins. in this study, we re-analyzed the time-course expression data from atorvastatin-treated, primary human hepatocytes from six different patients published in a previous study. we focused our analysis on determining the regulation of downstream genes from statin drug targets as defined in stitch. we were especially interested in addressing the following issues: 1) how do statin targets and differentially regulated genes relate to each other? 2) which pathways are affected upon statin treatment? 3) how does the dynamics of the neighborhood of specific proteins change after statin treatment? in order to answer those questions, we have developed two cytoscape plug-ins that work together: viper, the virtual pathway explorer, creates focus interaction networks by connecting two or more nodes with each other. it applies user-provided expression data to score paths between two nodes and thus limits the network to functionally relevant paths. the cytoscape plug-in peanut upgrades interaction networks with pathway information and identifies enriched pathways in focus networks. we have applied our toolbox to re-analyze the expression data from atorvastatin-treated, primary human hepatocytes and found that the transcription factors foxa and are important regulatory players in atorvastatin response. implementation viper viper was written in java as a cytoscape plug-in. the basis of all functions is a recursive method, which iterates through the members of all paths emanating from a selected node. the step depth is influenced by two parameters: 1) the maximum number of steps allowed. 2) the numerical values of the nodes. we used the log2fold expression changes of atorvastatin treated primary hepatocytes described in as numerical values. viper can be accessed under: http://sourceforge.net/projects/viperplugin/ viper has three main search options:‘a to b’: ‘a to b’ connects two selected nodes with each other. we refer to the paths between nodes a and b as cross-talk. mathematically, we define cross-talk as all paths between two nodes, where a single node in a path can only be passed once. the result is a focus network, which is determined by the maximum number of steps allowed between the start and the target node. the search is stopped when the target node is reached or the maximum number of steps is exceeded. only if the target has been found, a path is stored, scored and displayed in the results tab. the focus network is created based on all nodes that are present in all stored paths. the connecting edges are taken from the original network. therefore, all known interactions between the subset of nodes are included in the newly created focus network. scoring of paths is done using the following equation: score=#ofdifferentlyregulatednodes∈pathpathlength the p-values for discovered paths in focus networks are calculated based on the cumulative probability of the hypergeometric distribution to find k or more differentially expressed genes in a path of length n. ‘connecting in batch’: similarly to the ‘a to b’ search, two groups of nodes can be connected using the ‘connection in batch’ function. for every node in the start list a, the recursive search is computed towards every node in the target list b. a results tab with scored paths is not created in this case. ‘environment search’: the third option is to explore the regulated proximity of a single node using the ‘environment search’. just one starting node is selected in this case. mathematically, we define the environment search as follows: a network is calculated from all outgoing paths of length l from x where every node is allowed to be passed only once per path and all paths with at least two consecutive node scores below threshold t have been removed. the iteration through emanating paths is carried out until the allowed maximum search depth is reached. when exploring the neighborhood of a single node, the numerical data are used to select paths radiating from the selected node. paths, in which at least two consecutive nodes are not differentially expressed, are removed from the resulting neighbor focus network. thus, only paths that contain differentially regulated nodes are considered for the environment search, though single unregulated linker nodes are allowed. the resulting network is referred to as a neighbor focus network. using viper starting from any existing network supplemented with expression data, the user has to select the attribute field containing the expression information. a slider is automatically set to the respective range of expression values. after adjusting the slider to the desired expression range, different options are available in the workflow.fig. workflow for creating focus networks. workflow of viper in creating focus networks between two nodes/two groups of nodes, or in exploring the neighborhood of a single node of interest. the user must select two nodes or group of nodes for creating a focus network. a single node is selected when exploring the neighborhood. numerical data must be added to the network for scoring paths of a focus network and for creating a neighbor focus network from a single node. in both cases, the user selects the search depth. after creating the focus network, the network can for instance be explored by using and visualizing go-terms. peanut is used to find and visualize enriched pathways ‘a to b’ this function executes the path search algorithm between two selected nodes. the result is a focus network of all identified paths of a certain length between two nodes. the user selects the length of the calculated paths. all interconnecting edges are added to the focus network. a result list, which includes every discovered path between the nodes, is located on the right side of the screen. this list shows all paths, their respective members and the assigned score as described above. the score can be used to further reduce the focus network or simply to visualize specific paths. ‘connecting in batch’ two groups of nodes can be connected in the ‘connecting in batch’ function of viper. the same algorithm is used as in the ‘a to b’ search, except that all paths between all members of a start list and a target list are computed. this algorithm can be applied to detect cross talk between two pathways, two protein complexes or two hit lists from different experiments. three buttons have to be used for the ‘connecting in batch’ search: 1) a start protein list has to be defined by selecting all starting nodes and pressing the ‘select start protein list’; 2) the target protein list has to be selected accordingly and confirmed by pressing the ‘select target protein list’ button; 3) the button ‘start connection in batch’ executes the search. ‘environment search’ in case only a single protein of interest exists, the algorithm can be used to observe the dynamics of expression in the environment of this protein using the ‘environment search’. a single node is selected and the search is executed with the button ‘environment search’. all regulated nodes within a certain step size of the selected protein give rise to the neighbor focus network. peanut peanut is a cytoscape plug-in designed to annotate protein interaction networks with biological pathway information and to identify enriched pathways in focus networks. the interactome of the organism denotes the background network. next to visualizing enriched pathways in the focus networks, the results can be exported as a tab delimited file. peanut can be accessed under: http://sourceforge.net/projects/peanut-cyto and was implemented in java. the user can choose between the three databases consensuspathdb, pathway commons and wikipathways to annotate the network. while consensuspathdb requires entrez gene ids as input, pathway commons and wikipathways require uniprot accession numbers. annotation of nodes with these ids can be done within cytoscape using for instance the plug-in cythesaurus. consensuspathdb and pathway commons contain pathway data collected from publicly available pathway databases. wikipathways is a database based on the ‘wiki principle’ and provides an open platform dedicated to collaborative registering, reviewing and curation of biological pathways. while pathway commons and wikipathways work with a wide variety of organisms, consensuspathdb is specialized on human, mouse and yeast pathways. when the user chooses to annotate his network of interest with consensuspathdb data, he can additionally import directed interactions from kegg to increase the amount of vertex degrees, enabling more complex path searches using viper. information from pathway commons is accessed over their web service. flat files from the consensuspathdb and wikipathways webpages are downloaded via the apache commons io library and cytoscape internal downloader classes. once downloaded, consensuspathdb and wikipathways can be used offline, while pathway commons requires internet access. network annotation with pathway commons is slower, as it depends on the load and availability of the host server, as well as internet connection speed. the probability value for the pathway enrichment in the focus network is determined using the apache commons math library to calculate the cumulative probability of a hypergeometric distribution. multiple testing correction is achieved by applying either bonferroni or benjamini-hochberg correction. peanut has three sub-menus:‘find pathways’: the find pathways sub-menu annotates the networks in cytoscape with pathway data. networks can be labeled using more than one pathway resource by re-using the sub-menu with different pathway selections. ‘show pathway statistics’: the ‘show pathway statistics’ sub-menu calculates enriched pathways in a selected focus network. the user has to select the focus network of interest, the background network and choose a p-value cut-off. enriched pathways can be selected for visualization and downloaded as a tab-delimited file. ‘download/update dependencies’: this sub-menu is used to download pathway information for network annotation. it needs to be run before using peanut the first time and should be run regularly to update pathway information. using peanut after installing peanut in cytoscape by placing the plug-in in the cytoscape plug-in folder, the tool can be accessed via the plug-in menu. the sub-menus are used as follows:‘find pathways’ this sub-menu allows the user to start the software and annotate the network of choice with pathway data. in a simple dialog the user can select between three different databases: consensuspathdb, pathway commons or wikipathways. the user can select different options for each database depending on preferences. annotating the network with more than one database is possible by subsequently re-running this sub-menu with another selected database. ‘show pathway statistics’ when the user has finished annotating the network, clicking on the ‘show pathway statistics’ sub-menu will invoke a table, two combo boxes, two text fields and several buttons. in one of the text fields the user can select the p-value cut-off for the enrichment calculations. the combo boxes permit the user to select the focus and background network. once the results have been calculated the user can export the results as a tab delimited file or search the table for pathways via the text box. the user can select each pathway and highlight the members of the pathways in the focus network by clicking on ‘select’. ‘download/update dependencies’ in this sub-menu, dependencies can be used to download/update information from consensuspathdb and wikipathways. ‘download/update dependencies’ needs to be executed when first using the plug-in. it is good practice to refresh the dependencies from time to time by clicking on the ‘download all’ button. for more information on how to use peanut, see our wiki http://sourceforge.net/p/peanut-cyto/wiki/home/. analysis of expression data cluster analysis we used preprocessed data from schröder et al., which have already been normalized and filtered and repeated the clustering step described in the original publication. the patient data used here were part of a large-scale study originally published in and deposited at the gene expression ominbus resource. tissues and corresponding blood samples of the original study were taken with written informed consent from donors. the study was furthermore approved by the ethics committees of the medical faculties of the charite, humbold university and university of tübingen. before clustering, the data were log transformed and filtered for probes, which were at least fold differentially expressed in at least patients. with these probes edisa clustering was performed, using the same parameters as in schröder et al., with tg of and tc of. creating the pp/tfg interaction data the basis for biologically meaningful network analysis is a reliable interaction network with high confidence. to accomplish this goal, we allowed only high confidence interaction data. no predicted interactions or interactions based on co-expression were included. as source for protein-protein interactions, we chose pathway commons due to its large number of curated interactions and the simple format, which can be directly loaded in cytoscape. likewise, only experimentally verified transcription factor – gene interactions from transfac® were included in the network. confidence scores from transfac® were used to identify reliable regulatory interactions. go and pathway enrichment of gene lists and networks to analyze go and pathway enrichment of focus networks, the official gene symbols were submitted to david. additionally, the go annotation for all nodes was added to the network via cytoscapes’ built-in function. clustering of patient data using cluster3 cluster was used to cluster patient data of the foxa neighbor focus network, as well as the h time point of the probe id of all patients. standard parameters and average linkage clustering were chosen. the foxa network was analyzed for presence or absence of a node. in case of up-regulation, the value was assigned, while down-regulated nodes were assigned the value.  expression analysis of hepatocyte time-course experiment in a first analysis, we found a total of differentially expressed probes in at least two patients irrespective of time-points. we processed these genes with edisa 3d clustering, which resulted in differentially regulated genes that were grouped into clusters. further analysis was performed with the non-redundant version of this gene set. go and pathway enrichment we submitted the processed gene list to david for functional annotation and enrichment analysis. david was chosen for its extended functionality beyond enrichment of gene ontology terms. primarily, basic cellular and metabolic processes were enriched, such as amino acid or nucleic acid metabolism. in addition, several pathways and processes related to cellular cholesterol and lipid -metabolism and -homeostasis were enriched. based on these results we could however not make any conclusions on the regulatory or transcriptional network involved in atorvastatin response. focus network analysis next, we used focus network analysis to extract more information from the data. we decided to use the interaction network provided by pathway commons as the basic protein-protein interaction network, as it contains information from more than one primary source. given the type of data in protein interaction databases, we reasoned that we additionally needed information on gene regulatory relationships: which transcription factors are able to regulate which gene set? we considered this information on transcription factor - gene interactions as essential to identify the gene regulatory networks that control the cellular response to statins at a transcriptional level. we retrieved tfgis from transfac® and combined them with the protein-protein interactions from pathway commons to a single interaction network, which was used for all further analysis steps. next, we extracted the top ten high confidence primary drug targets of atorvastatin from the stitch database, all of which were present in our interaction network. we used the viper ‘connecting in batch’ function to connect the ten atorvastatin targets with the differentially regulated, non-redundant genes we have identified with edisa 3d clustering, using a step-size of two. this functionality of viper creates focus networks by searching for all paths between two proteins or two groups of proteins, up to a user-defined path length. the nodes are supplemented with expression values. these weights on the nodes are used to score all paths in the resulting focus networks. the score of a path is dependent on its length and the number of differentially regulated nodes it contains. the resulting atorvastatin focus network contained nodes and edges. of those, were pp interactions and came from tfg interactions. we next performed go enrichment of the atorvastatin focus network using david. we observed that with the proteins from the focus network, we found more terms relating to regulatory functions, such as signaling or transcriptional control than with the differentially expressed genes alone. we searched for enriched pathways in the atorvastatin focus network. to this end, we employed our pathway enrichment tool peanut on the focus network, taking the entire network of ppis and tfgis as a background. using consensuspathdb as pathway resource, peanut identified a total of pathways, many of which were redundant. notably, again mainly signaling-, as well as transcriptional regulation pathways were identified. we compared results of peanut with pathway enrichment results from david. though different pathway resources are available in david, a similar set of pathways was enriched. those included growth receptor signaling pathways such as the egf-receptor-, vegf-, insulin- and ras-signaling pathways, interleukin signaling pathways, as well as cancer pathways. due to the availability of the pathway interaction database of the consensuspathdb resource, however, more transcription factor pathways were identified with peanut. involvement of the foxa transcription factors in atorvastatin response in the list of enriched pathways, our attention was caught by the forkhead box a transcription factor pathways. in adults, the forkhead box a transcription factors foxa foxa and foxa are expressed in liver, pancreas and adipose tissue, where they regulate gene expression of metabolic genes. two direct targets of atorvastatin are also targets of the foxa transcription factors: foxa and foxa regulate apob, while cyp3a is a target of foxa. given our experimental set-up of atorvastatin-treatment of primary human hepatocytes and the fact that atorvastatin is primarily acting on cholesterol synthesis in the liver, we further focused our analysis on these two transcriptional pathways. we created a viper focus network between the primary atorvastatin target hmgcr and the three transcription factors foxa a and a starting from the atorvastatin focus network with a maximal step size of two, without considering a fold-change.fig. focus networks between the primary atorvastatin target hmgcr and the transcription factors foxa a and a of patient time course of patient is shown at h, h, h, h and h. the focus networks were created using viper, with a maximal path length of without considering the log2fold change: this allows direct comparison of time-course data. up regulated nodes are shown as upward triangles, colored in red. down regulated nodes are displayed as downward arrows, colored green. the fox transcription factors are colored orange, as is the hmgcr. protein-protein interactions have grey edges; edges of transcription factor gene interactions are colored red we visualized expression changes over time and patients in the hmgcr-foxa1/2/ focus network. we first analyzed the expression changes over time from patient in the focus network. hmgcr itself is up-regulated at the first three time points, returning to normal expression values at the later two time points. likewise, cyp3a is up regulated at time points and however becomes down regulated at h. next, we were interested, whether the expression response to atorvastatin was similar in the six different patients and plotted hmgcr-foxa1/2/ focus networks for each patient at h after treatment. this representation of the data was especially informative as it illustrated the obvious differences in response to atorvastatin in the primary hepatocytes from the six patients. we observed that only donors and showed somewhat overlapping regulatory responses at h after treatment. the expression pattern of patient was in many cases opposite to the first group, while the hepatocytes from patients and showed milder responses to the drug. interpreting the hmgcr-foxa1/2/ focus network the focus network of hmgcr and the foxa transcription factors can be divided in two parts. a small, tightly connected sub-cluster contains small molecules, as well as metabolic genes, while the larger sub-cluster contains signaling molecules, as well as transcription factors. hmgcr is directly connected to two proteins from this cluster: the camp dependent protein kinase alpha, prkaca and rho gtpase activating protein arhgap fig. enriched pathways identified by peanut on hmgcr/foxa focus networks. three pathways were chosen for display: the integrated breast cancer pathway, the prostate cancer pathway and the metabolism of lipids and lipoproteins pathway. members of the respective pathways are highlighted in yellow. up regulated nodes are shown as upward triangles, colored in red. down regulated nodes are displayed as downward arrows, colored green. the fox transcription factors are shown in orange, as is the hmgcr. protein-protein interactions have grey edges; edges of transcription factor gene interactions are colored red especially foxa is tightly integrated with other transcription factors from the large sub-cluster, including the transcription factors sp tp fos, jun or brca it also binds several transcriptional co-activators. note that hmgcr expression is not directly regulated by any of the transcription factors in the network. the connections between the foxa proteins and hmgcr are mostly due to transcriptional targets of the foxa’s interacting with prkaca and arhgap interestingly, statins have been implicated to have a preventive effect on different cancer types and to be beneficial for the treatment of several cancer types, among which are breast and prostate cancer. bcl a direct transcriptional target of foxa, is also a direct interactor of the hmgcr interacting kinase prkaca. previous studies have shown that bcl is down-regulated in response to statin-induced apoptosis; prkaca has on the other hand been implicated in statin-resistance of tumors by phosphorylating the pro-apoptotic protein bad, thus allowing anti-apoptotic signaling. in conclusion, cross-talk via prkaca and arhgap is therefore one possible link between the mevalonate pathway and the foxa transcriptional network, which could in part explain the effect of statins on apoptosis of cancer cells. pathway enrichment analysis on focus networks using the cytoscape plug-in peanut we performed pathway enrichment analysis using peanut on the hmgcr-foxa1/2/ focus network. again, many signaling, as well as metabolic pathways were enriched. in accordance with previous reports on statin-sensitivity of cancer cells, several cancer pathways are enriched in our focus network. in fig.  we illustrate the usefulness of peanut in visualizing components of enriched pathways in focus networks created with viper. we chose to highlight the two cancer pathways ‘integrated breast cancer’ and ‘prostate cancer’, as well as the pathway metabolism of lipids and lipoproteins in the hmgcr-foxa1/2/ focus network. similar pathways were identified using david. exploring the molecular environment of a node using viper in a next analysis step, we used viper’s functionality ‘environment search’ to explore the neighborhood of a single protein of interest. as a rule, only one linker node without differential expression is allowed in the ‘environment search’. the user sets again the search depth by defining the step size. all paths that lack differentially expressed nodes are removed from the resulting neighbor focus network. we chose one of the fox transcription factors, foxa as the start node and used a search depth of two. we assumed that an environment search with foxa might shed light on the pathways and genes regulated by this transcription factor during atorvastatin treatment. we chose the h time point for further analysis. earlier time points showed only few differentially expressed genes for most of the patient samples. in later time points, a considerable amount of genes was changed, possibly due to secondary effects. data from the six patients at time point h are shown in fig.  fig. neighbor focus networks of foxa in all six patients at h. we chose to explore the environment of foxa with respect to differential expression. foxa neighbor focus networks were created for patients,,,, and. up regulated nodes are shown as upward triangles, colored in red. down regulated nodes are displayed as downward arrows, colored green. the fox transcription factors are shown in orange, targets of atorvastatin are highlighted in cyan. protein-protein interactions have grey edges; edges of transcription factor gene interactions are colored red the striking difference of patient is again visible: the environment search of foxa under the chosen conditions produced the largest network of all patients. most proteins in patient 67’s network are up regulated. notably, the central transcription factor sp is up regulated, which is potentially the cause of the strong effect we observed in gene expression. many of the transcription factors that were present in the hmgcr-foxa1/2/ focus network are also present in the neighbor focus networks of the six patients. in all networks, sp seems to be a central hub. the atorvastatin targets ccl and cyp3a are present in all but one network, while hmgcr is not found in any network. note also that in patient most of the genes are down-regulated, including the transcriptional regulators tp and fos. finally, we noticed that most nodes that are shared between all patients are linker nodes and thus not differentially expressed. exceptions thereof are sp fos and tp all of which show differential expression in at least one sample. we decided to cluster the patient data based on the differential regulation and presence or absence of nodes in the foxa neighbor focus network using cluster. the differential behavior of patient is reflected in the resulting hierarchical tree. furthermore, we observe a close clustering of patients and as well as a sub-group formed by patients and we were interested, if the same groups would cluster at the h time point in overall gene expression as well. we therefore used the expression values of the probe ids at 24h for cluster analysis. indeed, the clustering of all expression values is highly comparable; however, at the level of all analyzed genes, patient displays more similarity to patient than in this small sub-group. comparison to previous analysis we started with the same primary data, as well as clustering technique as schröder, et al., resulting in identical clusters of co-regulated genes. yet, our approach has channeled the analysis of the data in a different direction. in the previous study, enrichment analysis, followed by cis-regulatory module detection in combination with network analysis was done. this yielded a set of transcription factors with a hypothetical function in atorvastatin-induced gene regulation. factors such as krüppel-like factors klf and klf hypoxia-inducible factor, hnf the nuclear receptor rxr in combination with other nuclear receptors, nuclear receptors ppara, nr1h and nr2c sp and sp as well as tgif smad or elf were found in. in our primary atorvastatin focus network, we also found the transcription factors rxrb, hnf4a, ppara, nr2c sp sp and klf the corresponding pathways of these transcription factors were likewise enriched. in the second step, however, we did not pursue any of the above factors or pathways. we rather focused on the foxa transcription factors and their potential role in atorvastatin response. interestingly, we also found sp as a major transcriptional regulator in our focus networks. sp is known to coordinate expression together with both, rxr/rar, as well as the foxa transcription factors. both, sp as well as the foxa transcription factors are also known to regulate some direct atorvastatin drug targets. viper was in this study used to point towards less well known players in atorvastatin response. in fact, we see the advantage of a plug-in such as viper in exploring paths that are difficult to find otherwise. if used with numerical values from experimental studies such as an expression screen, it will open up new avenues for experimental research.  here we present the cytoscape plug-ins viper and peanut. viper provides the possibility to navigate large interaction networks by linking two nodes or two groups of nodes with each other. it can be used to identify potential links between processes or pathways. viper furthermore enables users to explore the neighborhood of a single node with respect to the quality of radiating paths. the cytoscape plug-in peanut identifies enriched pathways in a focus network compared to a background network. we used viper to create focus networks, as well as neighbor focus networks using time-series data on atorvastatin-treated, primary hepatocytes from six donors. the focus network was analyzed using peanut. we identified the foxa1/a2/a transcription factors to be involved in atorvastatin response. furthermore, peanut revealed that the foxa networks were enriched in cancer, as well as metabolic pathways. we found interesting differences in patient samples in the focus and neighbor focus networks, possibly explaining the often-observed, individual responses to drug treatment. while we used viper with numerical values from a differential expression study, the plug-in can be used with numbers inferred from any experimental set-up and high-throughput assay. functions ‘a to b’ and ‘connecting in batch’ work also without numerical values to create focus networks. in this case, no scoring of paths is done. the ‘environment search’, however, requires numerical values for the creation of a neighbor focus network. availability and requirements viper and peanut are all available via sourceforge.org. both plug-ins have been tested for the versions. and. of cytoscape. versions of viper and peanut are also available for cytoscape and higher. please note that the current version of peanut v does not work with cytoscape versions below or. viper and peanut are fully integrated in both cytoscape versions and compatible with other cytoscape plug-ins. viper and peanut will be maintained to work with future releases of cytoscape, as well as updates of public pathway database. the required amount of memory depends mainly on the background network. in general, we advise to have at least gb of ram for the mouse or human interactome. we furthermore advise to work with turned-off view of large background networks. cytoscape itself is written in java and runs on linux, macos and windows systems. we advise to use oracle java, as we have not extensively tested and validated our software for open jdk. additional files additional file 1: table s log fold change of six selected patients at time points h, h, h, h and h. data were taken from, affymetrix probe ids are shown. table s time-course and patient- dependent clustering of probeids that showed differential expression in at least one time point or patient. clusters were created using edisa 3d clustering, containing non-redundant, and differentially expressed genes. table s annotation of the edisa 3d-selected, differentially expressed set of affymetrix probe ids. table s go enrichment of edisa 3d-selected, differentially expressed set of affymetrix probe ids. table s pathway enrichment of edisa 3d-selected, differentially expressed set of affymetrix probe ids. table s targets of atorvastatin as identified by stitch. table s go enrichment of the atorvastatin focus network. table s pathway enrichment of atorvastatin focus network using peanut. table s pathway enrichment of the atorvastatin focus network using david. table s pathway enrichment of the foxa1/a2/a – hmgcr neighbor focus network using peanut. table s pathway enrichment of the foxa1/a2/a – hmgcr neighbor focus network using david. table s node scores for foxa neighbor focus networks of the six patients used for clustering. additional file 2: figure s focus network of all atorvastatin targets and differentially expressed genes after edisa 3d clustering. up regulated nodes are shown as upward triangles, colored in red. down regulated nodes are displayed as downward arrows, colored green. protein-protein interactions have grey edges; edges of transcription factor gene interactions are colored red. additional file 3: figure s focus networks of hmgcr and foxa1/a2/a of the six patients. differential expression of nodes at h is highlighted. data are shown for patients,,,, and. up regulated nodes are shown as upward triangles, colored in red. down regulated nodes are displayed as downward arrows, colored green. the fox transcription factors are shown in orange, as is the hmgcr. protein-protein interactions have grey edges; edges of transcription factor gene interactions are colored red. additional file 4: figure clustering of patients based on the foxa neighbor focus network shown in fig.  as well as the probe id data from all patients at h, data were taken from additional file 1: table s abbreviations ppiprotein-protein interaction tfgitranscription factor-gene interaction hmgcrhmg-coa reductase competing interests the authors declare that no competing interests exist. authors’ contributions mg conceived and implemented the software viper, constructed the interaction networks, analyzed the data and helped in writing this manuscript. fh conceived and implemented peanut and helped in writing this manuscript. vs ported peanut to cytoscape version tested peanut for cytoscape versions and and helped with coding the cytoscape version of viper. mt and bak participated in data evaluation and helped with strategy discussion; bhh conceived and supervised this study, assisted in data analysis and wrote this manuscript. all authors read and approved the final manuscript. 
we introduce two cytoscape plug-ins, which support the generation and interpretation of experiment-based interaction networks. numerical values from expression studies assigned to the nodes serve to score identified paths. based on our investigations using viper and peanut, we suggest a role of the foxa1/a2/a transcriptional network in the cellular response to atorvastatin treatment. it supports the interpretation and navigation of large-scale datasets by creating focus networks, facilitating mechanistic predictions from –omics studies. using time series expression data of atorvastatin treated primary hepatocytes from six patients, we demonstrate the handling and applicability of viper and peanut. moreover, we find an enrichment of metabolic and cancer pathways in the fox transcriptional network and demonstrate a patient-specific reaction to the drug. the virtual pathway explorer viper creates so-called focus networks by joining a list of experimentally determined genes with the interactome of a specific organism. peanut provides an up-front method to identify underlying biological principles by calculating enriched pathways in focus networks. the pathway enrichment analysis tool peanut annotates networks with pathway information from various sources and calculates enriched pathways between a focus and a background network.
 interpreting large-scale studies from microarrays or next-generation sequencing for further experimental testing remains one of the major challenges in quantitative biology. combining expression with physical or genetic interaction data has already been successfully applied to enhance knowledge from all types of high-throughput studies. yet, toolboxes for navigating and understanding even small gene or protein networks are poorly developed.  we introduce two cytoscape plug-ins, which support the generation and interpretation of experiment-based interaction networks. the virtual pathway explorer viper creates so-called focus networks by joining a list of experimentally determined genes with the interactome of a specific organism. viper calculates all paths between two or more user-selected nodes, or explores the neighborhood of a single selected node. numerical values from expression studies assigned to the nodes serve to score identified paths. the pathway enrichment analysis tool peanut annotates networks with pathway information from various sources and calculates enriched pathways between a focus and a background network. using time series expression data of atorvastatin treated primary hepatocytes from six patients, we demonstrate the handling and applicability of viper and peanut. based on our investigations using viper and peanut, we suggest a role of the foxa1/a2/a transcriptional network in the cellular response to atorvastatin treatment. moreover, we find an enrichment of metabolic and cancer pathways in the fox transcriptional network and demonstrate a patient-specific reaction to the drug.  the cytoscape plug-in viper integrates –omics data with interactome data. it supports the interpretation and navigation of large-scale datasets by creating focus networks, facilitating mechanistic predictions from –omics studies. peanut provides an up-front method to identify underlying biological principles by calculating enriched pathways in focus networks. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords focus networkdisease stateshortest path algorithmnode neighborhoodpathway enrichmentissue-copyright-statement© the author 2015 