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Paduano and Forbes

RESEARCH

Extended LineSets: A Visualization Technique forthe Interactive Inspection of Biological PathwaysFrancesco Paduano* and Angus Graeme Forbes

Abstract

Background: Biologists make use of pathwayvisualization tools for a range of tasks, includinginvestigating inter-pathway connectivity andretrieving details about biological entities andinteractions. Some of these tasks require anunderstanding of the hierarchical nature ofelements within the pathway or the ability to makecomparisons between multiple pathways. Weintroduce a technique inspired by LineSets thatenables biologists to fulfill these tasks moreeffectively.

Results: We introduce a novel technique,Extended LineSets, to facilitate new explorations ofbiological pathways. Our technique incorporatesintuitive graphical representations of different levelsof information and includes a well-designed set ofuser interactions for selecting, filtering, andorganizing biological pathway data gathered frommultiple databases.

Conclusions: Based on interviews with domainexperts and an analysis of two use cases, we showthat our technique provides functionality notcurrently enabled by current techniques, andmoreover that it helps biologists to betterunderstand both inter-pathway connectivity andthe hierarchical structure of biological elementswithin the pathways.

Keywords: Pathway Visualization; HierarchicalInspection; Eliminating Node Redundancy; LineSets; Interactive Visual Analysis

BackgroundMuch effort has been expended to organize the bodyof knowledge that is available regarding the structure

*Correspondence: [email protected]

Department of Computer Science, University of Illinois at Chicago

Full list of author information is available at the end of the article

and function of biological pathways. The ReactomeDatabase [1] and the KEGG Pathway Database [2] arejust two examples of publicly accessible resources ofbiological data. These databases, and the frameworkscreated to access, process, and query them, such asPathways Commons [3], allow biologists to investigatedifferent pathways that may share common elements,such as biochemical reactions or protein complexes.The flexibility of these search tools, and the scale of thedata that can be quickly retrieved, has motivated re-searchers to design new visualization tools to assist ina range of analysis tasks involving multiple pathways.A catalog of requirements for pathways visualizationtools are detailed by Saraiya et al. [4], who stress theneed of further research into interactive, dynamic so-lutions.

Pathways are typically represented as directed graphs,where nodes in the graph represent biological “partic-ipants,” such as proteins or protein complexes, andwhere the edges represent a biological functionality,such as a biochemical reaction. Often different shapesfor arrows and nodes are used to differentiate betweenthe different types of molecules or reactions. Thoughthis type of visual encoding is the most familiar, node-link diagrams are known to have a number of issues.A main issue is scalability; as the number of nodes oredges increases, it quickly becomes more difficult tomake sense of the data [5]. In the last decade, anal-yses that involve thousands of proteins or genes havebecome conventional. Numerous attempts have beenproposed to visualize and analyze large biological net-works, with particular attention to the topology of thenetwork and its hierarchical structure.

The importance of dynamic visualization has beendiscussed by Hu et al. [6] and Klukas and Schreiber [7].Static images can depict a carefully arranged, fine-tuned representation that improves readability, butthis advantage is exceeded by the navigation methodstypically supported by dynamic visualizations. Fur-thermore, it is important that the layout can be pro-grammatically changed by the user, and that addi-tional components can be added to the existing de-piction.

The structure of biological pathways can be formal-ized as a hypergraph, a graph that contains hyperedges

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which can connect to any number of nodes. Traditionalnode-link diagrams cannot provide sufficient informa-tion to represent all of the complexities in a biologicalpathway data: biochemical reactions can involve multi-participant relationships; pathways can contain mul-tiple subpathways; nodes within a pathway can rep-resent a nested structure containing many biologicalentities; and links between nodes can convey differ-ent biological meanings. Some techniques for the vi-sualization of pathways propose representations whichlimit the complexity of the data structure in favour ofa simpler design. This is the case of the SIF format,which is often used to encode data for generating vi-sualizations with tools such as Cytoscape [8]. This for-mat represents only binary relationships and excludesrich biological semantics. Other formats include a morecomplex description of biological interactions, and re-quire more sophisticated visual representations thatleverage user-driven interaction and innovative visualencodings.

This paper presents a novel technique, ExtendedLineSets, that more accurately represents the intri-cacy of interconnected pathways and subpathway con-nections, and moreover, that helps to reduce visualclutter that can interfere with visual analysis tasks.Additionally, we describe a prototype implementationthat makes use of this technique so that biologists canmore effectively search, filter, visualize, and comparepathways data.

Task AnalysisWe interviewed seven domain experts in order to un-derstand the type of tasks that could be usefully ac-celerated or augmented by visualization techniques.The experts are professors and researchers in differentdomains of cellular biology, molecular genetics, andinformatics. While each of the experts have differentresearch interests, we identified three high-level tasksthat were considered to be important to all of them.Through defining these tasks, we were able to identifythe primary requirements necessary for an effective vi-sualization technique. These tasks are not meant tobe comprehensive, but rather to provide insight intothe motivation for the development and design of Ex-tended LineSets.

Task 1: Examine the upstream and downstreamconnections between two entities within a path-way. Understanding how entities within pathways areconnected is of critical importance to all of the re-searchers we interviewed, and is essential to most re-search related to pathway data. When discussing di-rected paths between entities, one entity is said to beupstream or downstream of another. Understanding

upstream and downstream relationships is particularlyimportant to domains such as cancer drug research,where a drug may affect a small subset of genes orgene products, which in turn will affect various down-stream processes. In most cases, a directed relationshipis meant to represent a biochemical reaction, where oneentity is consumed as a reactant and another is pro-duced as a product. Thus, an upstream entity may beconnected to a downstream entity through a chain ofseveral directed links. In the most basic sense, the “en-tities” mentioned above are genes, gene products (suchas proteins or complexes), or other small moleculeswithin a cell. A researcher may be interested in under-standing the path of reactions (or other relationships)that connects two entities.

Task 2: Understand the hierarchical structureof protein complexes. Protein complexes are rep-resented as nested hierarchies of proteins. These hier-archies can be very intricate and potentially involvedozens of proteins and sub-complexes. Since the pro-teins and the complexes which compose these com-pound structures might be involved in other rele-vant complexes and biological processes, understand-ing their structure and organization is generally im-portant for pathway analysis tasks.

Task 3: Curate, edit, query and merge path-way data files, or construct user-defined path-ways. Several of the researchers mentioned the impor-tance of various tasks related to the curation, mainte-nance, and understanding of pathway data. They ex-pressed the need to create “personalized pathways”that only include a user-determined subset of enti-ties and relationships. They are especially interestedin mixing-and-matching information that is containedwithin multiple pathways, but then being able to filterthis information so that only information relevant fora particular task is displayed.

Related WorkIt can be challenging to represent multiple pathways si-multaneously in traditional node-link diagrams, due toissues of scalability and also due to difficulties in defin-ing a layout that accurately and efficiently displays thetopology of these pathways. In order to reduce the vi-sual clutter introduced by dense networks with manyedge crossings, many visualization tools rely on nodeduplication, which considerably reduces the overlap-ping of edges and enables a visually appealing arrange-ment of the nodes. However, as pointed out by Bourquiet al. [9], analysis tasks that rely on an understandingof the pathway topology might be hampered by theintroduction of duplicated nodes.


This section presents an overview of features imple-mented in popular pathway visualization tools. Onefeature missing from all of these tools is a specific visu-alization technique for presenting category data withinnode-link diagrams. Indeed, as indicated by Task 3,researchers may need to merge pathway componentsfrom multiple sources, while still retaining an indica-tion of the original source. For this reason, we alsoreview visualization techniques that are used for pre-senting category information.

Pathway Visualization ToolsBroadly speaking, current tools make use of one ofthree primary visual representations: node-link dia-grams, node-link diagrams with compound nodes, andadjacency matrices. Entourage [10], Reactome Path-way Browser [11], VisAnt [12], MetaViz [9] and Vi-taPad [13], each use a traditional node-link diagramas a primary visual representation. ChiBe [14] ex-tends the node-link representation, additionally dis-playing compound nodes that indicate the compositionof complexes. Rather than enabling an interactive ex-ploration of the hierarchical structure of protein com-plexes, it instead depicts all sub-elements in a singlenode. This approach might lead to visual clutter whendepicting complex nested structures. The tool has a“merge pathways” function that combines the visual-ization of multiple pathways in the same view. Thisapproach is similar to our technique, but we providea visual correspondence that emphasizes the origin ofelements from particular pathways.

The Reactome Pathway Browser is a tool for visu-alizing pathway diagrams included in the Reactomedatabase. It offers basic navigation and limited inter-activity, making use of side panels to enable the in-spection of complexes structure and analysis data. En-tourage is a tool for pathway visualization that enablesthe exploration of the “cross-talk” between pathwaysvia an “artificial partitioning” of the pathway struc-ture, helping to reduce the complexity of the visual-ization. Multiple pathways and sub-pathways are rep-resented in isolated views, which preserves the path-way structure but introduces node redundancy. Othertools represent multiple pathways in a single view, suchas MetaViz, which enables topological analyses acrossmultiple metabolic pathways simultaneously. Our workis inspired by MetaViz, but our technique tries to or-ganize the representation of all pathways accordingto a topological ordering and enables the interactiveinspection of hierarchical structures of certain nodes.BioFabric [15] also enables the exploration of large bio-logical networks composed by multiple pathways with-out replicating nodes. This tool is meant to visual-ize extremely large networks with thousands of nodes.

However, it was not specifically designed for visualiz-ing pathways and it does not enable pathway-specifictasks. VisAnt enables the user to search for the short-est path between two nodes [16] and to identify dense,highly-connected nodes. The positions of the nodes inthe graph are computed with a “relaxing layout” algo-rithm which models the network as a set of physical en-tities. This algorithm builds a graphical representationthat organizes the graph by density of the connectionsbetween cluster of nodes. A similar approach has beenadopted in this work. Furthermore, VisAnt permits adynamic exploration of a biological network composedby multiple pathways. However, if more than one path-way includes the same node, multiple instances of thenode will be present in the representation.

Although the majority of these pathway visual-ization tools use conventional graph representations,many of them decorate the node-link diagrams withexperimental data or other additional information. Forexample, VitaPad allows the user to incorporate mi-croarray data into pathways, Entourage enables theintegration of experimental data, and VisAnt permitsthe user to create and view annotations of nodes withinthe network.

Visualizing Categories on Node-Link DiagramsCombining multiple pathways in a single visualizationintroduces the need for displaying the relationships be-tween biological entities and the pathway or pathwaysthey are participants of. That is, biochemical reac-tions, proteins, or other biological compounds can beincluded in more than one pathway. This raises thefurther challenge of effectively displaying this mem-bership information on top of the traditional node-linkdiagram without introducing visual clutter.

Dinkla et al. [17] propose a solution which integratesnode-link diagrams with Euler Diagrams to displayset-based annotations on biological networks. How-ever, this may lead to visually confusing representa-tions if the data contains complex set intersections.For this reason a range of alternative visualizationtechniques has been developed to display elements be-longing to multiple sets [18]. Bubble Sets [19], Line-Sets [20], and KelpFusion [21] all address this issue.Each of these techniques are designed to be overlaidon top of existing visualizations, such as geographicalmaps. Some aspects of both Bubble Sets and KelpFu-sion prevent these techniques from being applicable topathway visualization. For example, Bubble Sets dis-plays set relations using isocontours, which can make itdifficult to distinguish elements belonging to multiplesets.

LineSets, developed by Alper et al. [20], is a tech-nique that represents sets as smooth curves and uses


Figure 1 The image shows the proposed technique to combinenode-link diagram with LineSets. Colors are used to indicateset membership of nodes and links; the nodes are arranged tofollow the topological ordering of the links; and the pointededges indicate directionality.

colors to indicate membership. This solution offersbetter readability than the Bubble Sets techniquewhen multiple sets overlap. KelpFusion uses continu-ous boundaries made by lines and hulls. The visual ap-pearance is generally comparable to LineSets, but thisstrongly depends on the spatial arrangements of ele-ments. Hulls are used to group elements that are bothspatially close to each other and that belong to thesame set. This technique can become confusing whenit is applied to biological networks, which have an ar-bitrary or dynamic spatial layout. LineSets, though ithas been applied to geographic datasets, is a more ex-tensible technique that is able to effectively representpathway data.

None of the above techniques scale well when thenumber of sets and number of intersections betweensets increases. UpSet [22] and OnSet [23] are two recentvisualization techniques designed to help users bet-ter understand complex relationships between a largenumber of sets. Although these sophisticated and scal-able tools enable several set visualization tasks, theydo not explicitly aim to integrate with node-link rep-resentations.

In this paper we introduce Extended LineSets, amodified version of the LineSets technique which intro-duces the inclusion of hierarchical structure and whichis presented as a more abstract node-link diagram,without the need for elements to have a spatial ref-erence. Our technique aims to enhance analysis tasksthat make use pathway visualization tools. It adds anextra information layer to the node-link diagram thatrepresents the intricate relationships within and be-tween pathways, and at the same time it provides an

Figure 2 The image compares the same dataset realized with(a) LineSets, where showing set information and networkinformation simultaneously leads to visual clutter, and (b)Extended LineSets, which mitigates visual clutter via a moreeffective depiction of category information.

Figure 3 On the left we depict a biochemical reactioninvolving two left components (A and B) and two rightcomponents (C and D). On the right we depict the “reactionrelationship” between these four biological participants in ourtechnique (both A and B link to C and D). That is, we encodethe biochemical reaction as links between input and outputelements.

approach that reduces clutter while avoiding node du-plication. In the following section we include a detaileddescription of the Extended LineSets technique. Afterpresenting a prototype implementation, we introducetwo use cases that demonstrate how our technique en-ables effective, real-world pathway exploration.

MethodsIn this section we describe the two primary represen-tations used in the Extended LineSets technique. Thefirst representation combines node-link diagrams withLineSets, allowing the user to concurrently identify setmemberships in a network and to visualize directionalrelationships of between elements. The second repre-sentation displays the hierarchical layout of elementswithin the network, allowing the user to interactivelyinspect their nested structure. These two primary rep-resentations are integrated to support the effective in-teractive investigation of biological pathways. Our pro-totype utilizes our technique and implements a varietyof user-driven interaction to enable pathway analysistasks.


Extending Node-Link Diagram with LineSetsOur first visual component presents a directed graphwhere nodes and edges belong to one or more sets. Weidentify every set with a color, and every element isdepicted as either a square or a circle. Edges linking el-ements are represented using colored lines with sharp-pointed tip. Fig. 1 shows an example of how nodes andlinks belonging to one or more sets are depicted usingour technique. If the same node is included in morethan one set, another colored border is added for eachadditional pathway it is associated with. Moreover, ifmore than one set includes an edge between the sameelements, then multiple sharp-pointed line are placedside by side. The line direction and colors indicate thecorresponding set and the direction of the edge. Thesegment connecting two components has a sharp tipon both extremities if two opposite edges in the sameset involve the same elements.

This visualization component has some substantialdifferences from the original LineSets implementation.First, every set is not represented by a single smoothcurve, but is identified only by the color of the linksand nodes. Second, the layout of the elements flowsfrom the top to the bottom, matching, when possi-ble, the direction of the relationships. A completelyconsistent representation cannot be achieved when thegraph contains cycles; in this case some directed edgeswill point upwards, but the general layout will stillmostly adhere to the flow from the top to the bot-tom of the viewport. Additionally, the layout of theelements is designed to minimize nodes overlappingand edges crossing. The resulting visualization com-bines the efficient identification of elements belongingto particular sets from the LineSets technique with atraditional node-link graph representation. Fig. 2 com-pares a small dataset realized using LineSets (Fig. 2a)with the same dataset realized using our technique(Fig. 2b).

Our technique avoids node duplications and effec-tively shows the relationships between different sets.Each individual pathway is considered a set and is as-signed a unique color. The elements in the set includeproteins, complexes, and links. The links between el-ements indicate reaction relationships. A reaction re-lationship exists between components A and B in thepathway P if P has at least one reaction in which theinputs include A and the outputs include B. Fig. 3 pro-vides an example of how a biochemical reaction thatinvolves two input participants and two output par-ticipants is converted to four distinct directed edges,in accordance with the previous definition. Further-more, each node is assigned either the shape of a circleor a square; circle-shaped nodes indicate proteins andsquare-shaped nodes indicate protein complexes.

Figure 4 The figure demonstrates hierarchical inspection usingthe symbolic overview and the the pruned tree. In (a), the userselects protein G from the expanded node within the node-linkdiagram; this protein is highlighted within the pruned treevisualization, which also provides more information about itssiblings (the proteins D, E, and F ) and its parent andgrandparent nodes (the complexes C, B, and A). In (b), theuser selects a complex B in the expanded node, which isdisplayed in a similar manner in the pruned tree.

Hierarchical InspectionIn order to support Task 2, a pathway visualizationtool must enable the inspection of the structure ofbiological complexes. The inspection of hierarchicalstructures is supported by our interaction visualiza-tion technique. Using the traditional notation for treestructures, we refer to the outermost component of thestructure as the root element, a parent is an elementthat contains children elements, and elements with nochildren are leaves. However, the conventional repre-sentations of hierarchical structures with traditionaltree diagrams are prone to visual clutter as the amountof leaves and the depth of the tree increases [24].

We mitigate this issue through using two coordi-nated views: a symbolic overview and a pruned treeschematic. The symbolic overview indicates the over-all structure of the protein complex using a rectan-gle packing layout, whereas the pruned tree schematic


Figure 5 This figure shows an example of a user inspectingthe structure of a protein complex by means of the twocoordinated views, the symbolic overview (left) and the prunedtree (right).

presents a more detailed view of the different levelsin the hierarchy. No labeling is used in the symbolicoverview in order to minimize visual clutter, but thesedetails can be seen in the pruned tree schematic.

When the user selects an element in the overview (byhovering over it with the mouse pointer), the prunedtree is updated to reflect the hierarchy of elements fromthe root to that currently selected element. The displayis “pruned” because it does not show the whole struc-ture, but instead only displays the direct path from theroot to the parent that contains the selected element.That is, the pruned tree enables the inspection of allthe components directly included in the parent.

Figure 4 illustrates how the symbolic overview andthe pruned tree are coordinated in order to enable theinspection of a hierarchical structure. In Figure 4(a),the user selects protein G, which brings up a prunedtree that indicates the hierarchy of parent complexesthat contain G, fist A, then B, and then the direct par-ent C. The right side of the pruned tree shows proteinG and its siblings D, E and F . Figure 4(b) illustratesa similar case for complex B and its correspondingpruned tree.

The symbolic overview provides a compact overviewof the structure of the complex, where the squares rep-resent complexes and the white dots are proteins con-tained within those complexes. Fig. 5 shows a childelement selected via the mouse pointer. This proteinchanges its color to gray, and a pruned tree pops up onthe display, providing details about this protein and itssiblings within the parent complex, as described in thepreceding paragraph. By inspecting the pruned treein Fig. 5, the reader can easily read the name of theparent complex of the selected protein, ORC:origin,and the related complex and five proteins. For clarity,the hovered component in the symbolic view is under-lined and emphasized in the pruned tree. This visualcomponent is integrated in the node-link component

Figure 6 This figure shows a user hovering over a node,causing the application to highlight only the nodes and linksthat are members of the same pathways as the selected node.In this case the node is a protein complex that belongs to asingle pathway (indicated by a light blue color). Each elementin this pathway (five nodes and four links) are highlighted,while all other elements in the network are grayed-out.

previously described. For this reason, the color paletteused to differentiate nested complexes is limited to onecolor hue and two different color intensities, as thisminimizes the potential conflict with other colors usedto visualize biological elements. Our technique aimsto find an optimal balance between reducing the com-plexity of the displayed structure while at the sameproviding as much information as necessary for a par-ticular task.

The Prototype

We created a prototype application that implementsour Extended LineSets technique. It enables the userto narrow down a potentially large set of proteins andcomplexes contained within multiple pathways to anarbitrary small group of relevant components. Thatis, a biologist or bioinformatician can interactively ex-pand or reduce the visible pathways and componentsthat is relevant for his or her research. The prototypeuses a force-directed layout [25] to help organize thenodes on the screen. It is not necessarily meant toreplace the standard techniques for the visualizationof biological pathways, but to offer a novel way forexploring and understanding the relationships withinand among pathways. The prototype is accessible on-line (along with the open source code) at https://

github.com/CreativeCodingLab/pathways. By de-fault, for demonstration purposes, the prototype loadsin six pathways related to the Cell Cycle and 80 sub-pathways. The pathways are referred to as the field of


Figure 7 This figure depicts the interface of our prototypeapplication. The left side shows, from the top: the search field,the list of searched keywords, and the list of pathways in thefield of interest. The right side shows, from the top: aexpandable settings menu, and an application guide. In themiddle we see the elements of the interconnected pathwaysthat the user is currently investigating.

interest. The field of interest is comprised of the path-ways that the biologist or bioinformatician considersrelevant for his or her research. The field of interest ini-tially needs to contain all of the pathways that mightbe required for the analysis task.

We designed a set of user interactions for explor-ing, extending and inspecting the pathways at differentlevel of detail. The interaction tasks, discussed below,include the following:

• Pathway highlighting and labelling;• Pathway hiding and unhiding;• Keyword filtering;• Upstream and downstream expansion;• Layout rearranging, zooming, and panning;• Complex structure inspection;• Finding intermediate steps between two nodes in

a pathway.

Pathway Highlighting and Labelling

Effective labeling of biological components is requiredfor Task 1 and Task 3. By default, labels for all com-plexes and proteins are hidden. This reduces the po-tential clutter that might be introduced by having toomany overlapping labels shown in the graphical repre-sentation. Instead, when the user hovers over a com-ponent with the mouse pointer, only the labels of thecomponents which are included in the same pathwayas the selected component are displayed. The pathwayswhich include this component also stand out becausecomponents and reactions belonging to any other path-way are temporarily desaturated (see Fig. 6).

Figure 8 On the left, the user is in the process of dragging themouse from one complex to another to indicate to theapplication that he or she wants to see all the intermediatecomponents that connect these two nodes. On the right, wesee the visualization after it has been updated to include theaddition of these intermediate nodes and links.

Pathway Hiding and UnhidingIn order to selectively merge pathway information fromdifferent sources (Task 3 ), the user can hide or revealany pathway included in the field of interest by clickingon its name from the list on the left of the screen (seeFig. 7). A given pathway is visible if belongs to thefield of interest and is not hidden. When a pathway ishidden, its name is colored gray and the visualizationis updated to show only the components which areincluded in at least one visible pathway. Furthermore,the user can delve into the hierarchical structure ofsub-pathways by expanding and collapsing the namesin the list of pathways.

Keywords FilteringTask 3 requires the ability to create “personalizedpathways” on demand. The user can easily add or re-move keywords that the system uses to match biolog-ical elements. The visualization will display only theproteins (or the complexes containing proteins) whosename matches at least one of the searched keywords.

Upstream and Downstream ExpansionBy clicking on a protein or complexes the tool willexpand the visualization with one upstream step andone downstream step, starting from the selected com-ponent. A downstream step of one protein or complexis composed of all the reaction relationships that startfrom the given component. Similarly, an upstream stepinvolves all the reaction relationships that end at thatcomponent. To facilitate this operation, we place atriangle-shaped maker pointing upwards on the node ifit has an hidden upstream step, and a triangle-shapedmaker pointing downwards on the node if it has a hid-den downstream step. This interaction technique en-ables the following workflow to support both Task 1


Figure 9 This figure shows a sequence of user interactionsfrom the first use case, Exploring the role of ORC1-6 inassembly of the pre-replication complex. In (a), the userselects two pathways that he or she knows to beinterconnected; filtering the pathways via a set of sixkeywords. In (b), the user interactively expands the relevantnodes, and examines the interconnections between a node inone pathway and another node in another pathway. In (c), theuser inspects the hierarchical structure of different complexes.

and Task 3 : first the user searches for a set of proteinsand complexes, then he or she progressively exploresthe network of interconnections by revealing the up-stream and downstream nodes.

Layout rearranging, zooming and panningConsidering that the number of complexes and pro-teins involved might lead the visualization to exceedthe boundary of the screen, the user can interactivelyzoom in, zoom out, and pan across the viewport. Sincethe representation might occasionally lead to unavoid-able edge intersections, the user can also drag anddrop any component on a preferred location to im-prove viewability.

Complex Structure InspectionIn order to support the user’s understanding of thehierarchical structure of protein complexes (Task 2 ),our prototype enables the hierarchical inspection ofcomplex structures, as described earlier in this paper.A complex can be enlarged by double-clicking on it.This enlarged complex then reveals its inner structure;all the other components are pushed away to avoidunwanted overlapping. The pruned tree is updated onthe right side of the screen when the user moves themouse over different parts of the hierarchy structure.A single click on an enlarged complex will hide thehierarchical structure, causing it to shrink the complexto the default size. More than one complex can beenlarged at the same time, enabling the user to easily

jump from the inspection of one complex structure toanother, and to find the same sub-complex in multiplecomplexes. Indeed, when the user moves the mouse ona component inside a complex, the same componentwill be highlighted in all the enlarged complexes thatcontain it.

Finding intermediate steps between two nodes in apathwayIn addition to the upstream and downstream expan-sion capabilities previously described, the user can alsoexpand multiple visible components and reaction re-lationships more quickly. Dragging the mouse whileholding right button from one component to anotherwill update the visual representation with all of thereaction relationships that start from the first compo-nent and end with the last component (as shown inFig. 8). The reaction relationships that are revealedbelong only to currently visible pathways. This typeof interaction has been designed to help the researcherto create “personalized pathways” that contains onlya subset of biological components of interest, which ishelpful for Task 3.

Results and DiscussionWe worked closely with domain experts to verify thatour prototype tool provides functionality to enable thetasks we identified in the Task Analysis subsection ofSection 2. Below we present two use cases to illustratepossible workflows enabled by our application, allow-ing a user to inspect and interact with a subset ofentities and reactions within a biological pathway. Wealso report expert feedback from two biologists whogave us detailed comments regarding our prototype.

Exploring the role of ORC1-6 in assembly of thepre-replication complexIn this use case we explore some of the interactions in-volved in the assembly of the pre-replication complex(preRC) [26]. We pre-load the prototype applicationwith six pathways involved in the cell cycle. The userexpands the menu on the left to view the subpathwaysof the M/G1 Transition pathway and activates the As-sembly of the pre-replicative complex (As-preRC) andActivation of the pre-replicative complex (Ac-preRC)subpathways. The user then searches for ORC1-6 andpreRC in the text field. The visualization is updatedwith the six ORC proteins and the preRC complexthat match the keywords entered by the user. The colorcoding shows that the ORC proteins are involved inthe As-preRC pathway, whereas preRC in involved inboth As-preRC and Ac-preRC.

The user then interacts with the prototype ap-plication to reveal the intermediate steps from the


Figure 10 This figure shows a sequence of user interactions from the second use case, Exploring pathway interconnections in the cellcycle. In (a), the user selects two pathways that he or she knows to be interconnected; filtering the pathways via a set of sixkeywords. In (b), the user interactively expands the relevant nodes, and examines the interconnections between a node in onepathway and another node in another pathway. In (c), the user decides to further investigate subpathways related to Miotic G1-G1/Sphases (G1S). In (d), the user inspects the hierarchical structure of different complexes.

ORC proteins to the pre-replication complex. Fur-thermore, the user expands the network to view theimmediate downstream elements of preRC, revealingreactions involved in the Ac-preRC pathway, suchas Mcm10:preRC, which is expanded again to showthe upstream MCM10 protein and the downstreamMcm10:active preRC protein.

The user then expands the upstream of ORC:origin,revealing the MCM8 protein, and expands the down-stream complex (CDC6:ORC:origin), revealing theupstream protein CDC6. Then the user chooses to in-spect the hierarchical structure of preRC, ORC:origin,along with other two complexes. By hovering over asub-complex of preRC, the user can see that these samesub-complexes appear in all the visible complex struc-tures (since our application automatically highlightsthem). This enables the user user to better under-stand the steps involved in the preRC assembly. Thisuse case is depicted via the three screenshots shown inFigs. 9a–c.

Exploring Pathway Interconnections in the Cell CycleIn this use case, the user selects three pathways fromthe larger set of pre-loaded pathways: Regulation ofDNA replication (RDR), M-G1 Transition (MG1) andMiotic G1-G1/S phases (G1S). The user types into thetextbox to search for the preRC complex and for pro-teins that match the ORC3 and Ubiquitin keywords.By selecting two nodes and dragging between themthe user is able to explore the missing steps between,first, ORC3 and preRC and, second, between Ubiqui-tin and preRC. The user can then easily detect whichproteins and complexes are involved in all three path-ways, such as the DNA replication factor (Cdt1) andthe Mini chromosome maintenance complex (MCM2-7) complex [27].

Using the integrated visualization of the hierarchyof elements within protein complexes, the user canfurther inspect the structure of preRC and the othercomplexes, showing, for example, the location withinthe hierarchical structure of MCM2-7 and its six re-lated polypeptides in all the expanded complexes. Thisuse case is explained via the four screenshots shown inFigs. 10a–d.

Expert FeedbackWe received detailed feedback from two domain ex-perts who confirmed that the design choices imple-mented in our prototype were effective for pathwayanalysis tasks. Both of the experts are professors ina biology department at a large public research uni-versity. Each of them told us that the ability to seeelements shared across multiple pathways (via the dif-ferent colors) is a very important feature of our pro-totype. Both experts found the integration of the hi-erarchical views within the network to be a novel andpotentially useful feature. They also appreciated thetwo coordinated views (the overview within the ex-panded node and the pruned tree) for inspecting thehierarchical structure of biological complexes.

Additionally, we received positive feedback regard-ing the search-by-keyword functionality, and especiallythe step-by-step exploration of the network intercon-nections. One of the experts agreed that in some casesa more natural way of exploring the network is to firstsearch for a set of known entities and then to freelyexplore the interconnections to their neighbours. Hewas especially excited by the fact that these neighborscould be located in different BioPAX files or differentdatabases. He also said that the ability to quickly re-veal all of the steps between two nodes could be usefulfor his research into interconnected pathways.


One expert argued that some direct labeling of thenodes would be necessary, at least as an option, as analternative to the dynamic labelling described earlier.Furthermore, he asked for the possibility to interac-tively dim all the other pathways to focus on a singlepathway. The other domain expert commented that itwould be helpful to be able to display the name ortype of the reaction between two nodes, perhaps whena user mouses over the link. Moreover, he suggestedthat it would be useful to include an “undo” opera-tion for when a particular exploration of the networkturned out not to be relevant; rather than startingover, he wanted to rewind his exploration to a pre-vious state. For instance, after a node is expanded andits interconnections are revealed, the user might wantto revert this operation and hide all of its neighbours.We plan to incorporate these suggestions in a futureimplementation.

Future Directions

Extended LineSets was developed through an iterativeprocess whereby design choices were informally evalu-ated through discussions with expert users. While weare for the most part happy with the visual encodingsand interaction methods that are currently used, wewill continue to experiment with alternative encodingsand interactions in future iterations. We plan to con-duct a thorough empirical evaluation in order to testthe effectiveness of our design choices. Although ourtechnique eliminates node redundancy, multiple edgesoften originate from the same biological reaction. Newinteractions and visual paradigms should be designedto better represent this information.

Our prototype will require further work in order tobecome a full-fledged application for enabling pathwayvisualization tasks. Currently our tool assumes that allrelevant pathways are pre-loaded; but the selection ofpathways could instead be dynamically loaded from apublic database of pathways. Our prototype alreadyreads the BioPax 3 format, and a future improvementwill be to integrate our tool with the Pathway Com-mons Web API [3]. When our tool is used to find theintermediate steps between two nodes in a pathway,it reveals all the possible sequences of reaction rela-tionships. This could potentially lead, in certain cases,to a jarring increase in the number of elements dis-played in the network, especially when dealing withlarger pathways with dense interconnections. Futureinvestigations will include the possibility of choosingonly the paths that match specified metrics, or by dis-playing only the shortest path between any two givennodes.

ConclusionsExtended LineSets is a novel technique that enablesthe interactive visualization of a network of multiplepathways. It introduces a novel graph representationand an effective interactive interface. The visualizationaims to provide a better understanding of the biolog-ical components and reactions shared among differentpathways. Colors are assigned to pathways for a clearvisual encoding. The user can dynamically focus on alimited set of pathways to reduce the amount of infor-mation and the number of different colors displayed atthe same time. User-driven interactions enable a proce-dural exploration of the network, with the aim of lim-iting the visual clutter of the graphical representationand minimizing excessive edge crossings. Furthermore,the dynamic labeling of components allows the user tofocus on the pathway topology to further reduce thecomplexity of the visualization. Finally, the inspectionof the hierarchical structure of complexes permits theuser to change the level of detail of the representation,enabling the simultaneous display of different abstrac-tion layers. Based on real-world use cases and expertfeedback, our initial prototype of Extended LineSetshas already proven to be an effective representationfor a range of tasks common to domain experts in bi-ology and bioinformatics.

AcknowledgmentsWe thank the domain experts who shared their exper-tise by participating in surveys, interviews, and focusgroups during the development of this work. We es-pecially thank Guang Yao and Ryan Gutenkunst ofthe University of Arizona for their helpful feedbackand continued encouragement. Paul Murray and TuanDang of the University of Illinois at Chicago also pro-vided invaluable support. This publication is based onwork supported by the DARPA Big Mechanism Pro-gram under ARO contract WF911NF-14-1-0395.

References1. Joshi-Tope, G., Gillespie, M., Vastrik, I., D’Eustachio, P., Schmidt, E.,

de Bono, B., Jassal, B., Gopinath, G., Wu, G., Matthews, L., et al.:

Reactome: A knowledgebase of biological pathways. Nucleic acids

research 33(suppl 1), 428–432 (2005)

2. Qiu, Y.-Q.: KEGG pathway database. Encyclopedia of Systems

Biology, 1068–1069 (2013)

3. Cerami, E.G., Gross, B.E., Demir, E., Rodchenkov, I., Babur, O.,

Anwar, N., Schultz, N., Bader, G.D., Sander, C.: Pathway Commons, a

web resource for biological pathway data. Nucleic acids research

39(suppl 1), 685–690 (2011)

4. Saraiya, P., North, C., Duca, K.: Visualizing biological pathways:

Requirements analysis, systems evaluation and research agenda.

Information Visualization 4(3), 191–205 (2005)

5. Herman, I., Melancon, G., Marshall, M.S.: Graph visualization and

navigation in information visualization: A survey. Visualization and

Computer Graphics, IEEE Transactions on 6(1), 24–43 (2000)

6. Hu, Z., Mellor, J., Wu, J., Kanehisa, M., Stuart, J.M., DeLisi, C.:

Towards zoomable multidimensional maps of the cell. Nature

biotechnology 25(5), 547–554 (2007)


7. Klukas, C., Schreiber, F.: Dynamic exploration and editing of KEGG

pathway diagrams. Bioinformatics 23(3), 344–350 (2007)

8. Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T.,

Ramage, D., Amin, N., Schwikowski, B., Ideker, T.: Cytoscape: A

software environment for integrated models of biomolecular interaction

networks. Genome research 13(11), 2498–2504 (2003)

9. Bourqui, R., Cottret, L., Lacroix, V., Auber, D., Mary, P., Sagot,

M.-F., Jourdan, F.: Metabolic network visualization eliminating node

redundance and preserving metabolic pathways. BMC systems biology

1(1), 29 (2007)

10. Lex, A., Partl, C., Kalkofen, D., Streit, M., Gratzl, S., Wassermann,

A.M., Schmalstieg, D., Pfister, H.: Entourage: Visualizing relationships

between biological pathways using contextual subsets. Visualization

and Computer Graphics, IEEE Transactions on 19(12), 2536–2545

(2013)

11. Croft, D., Mundo, A.F., Haw, R., Milacic, M., Weiser, J., Wu, G.,

Caudy, M., Garapati, P., Gillespie, M., Kamdar, M.R., et al.: The

Reactome pathway knowledgebase. Nucleic acids research 42(D1),

472–477 (2014)

12. Hu, Z., Mellor, J., Wu, J., DeLisi, C.: VisANT: An online visualization

and analysis tool for biological interaction data. BMC bioinformatics

5(1), 17 (2004)

13. Holford, M., Li, N., Nadkarni, P., Zhao, H.: VitaPad: Visualization

tools for the analysis of pathway data. Bioinformatics 21(8),

1596–1602 (2005)

14. Babur, O., Dogrusoz, U., Demir, E., Sander, C.: ChiBE: Interactive

visualization and manipulation of BioPAX pathway models.

Bioinformatics 26(3), 429–431 (2010)

15. Longabaugh, W.J.: Combing the hairball with BioFabric: A new

approach for visualization of large networks. BMC bioinformatics

13(1), 275 (2012)

16. Hu, Z., Chang, Y.-C., Wang, Y., Huang, C.-L., Liu, Y., Tian, F.,

Granger, B., DeLisi, C.: VisANT 4.0: Integrative network platform to

connect genes, drugs, diseases and therapies. Nucleic acids research

41(W1), 225–231 (2013)

17. Dinkla, K., El-Kebir, M., Bucur, C.-I., Siderius, M., Smit, M.J.,

Westenberg, M.A., Klau, G.W.: eXamine: Exploring annotated

modules in networks. BMC bioinformatics 15(1), 201 (2014)

18. Riche, N.H., Dwyer, T.: Untangling Euler diagrams. IEEE Transactions

on Visualization and Computer Graphics 16(6), 1090–1099 (2010)

19. Collins, C., Penn, G., Carpendale, S.: Bubble sets: Revealing set

relations with isocontours over existing visualizations. Visualization and


20. Alper, B., Riche, N.H., Ramos, G., Czerwinski, M.: Design study of

LineSets, a novel set visualization technique. Visualization and


21. Meulemans, W., Riche, N.H., Speckmann, B., Alper, B., Dwyer, T.:

KelpFusion: A hybrid set visualization technique. Visualization and


22. Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., Pfister, H.:

UpSet: Visualization of intersecting sets (2014)

23. Sadana, R., Major, T., Dove, A., Stasko, J.: OnSet: A visualization

technique for large-scale binary set data (2014)

24. Munzner, T.: Visualization Analysis and Design. CRC Press, Boca

Raton (2014)

25. Barnes, J., Hut, P.: A hierarchical O (N log N) force-calculation

algorithm. Nature 324, 446–449 (1986)

26. Yoshida, K.: Hierarchical order of initiator protein assembly in

pre-replication complex. Protein Conformation: New Research, 201

(2008)

27. Tachibana, K.E., Gonzalez, M.A., Coleman, N.: Cell-cycle-dependent

regulation of DNA replication and its relevance to cancer pathology.

The Journal of pathology 205(2), 123–129 (2005)

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