What's new in BioXM version 3.0? · BioXM Knowledge Management Environment — what’s new? BioXM...

Biomax Informatics AG; Sitz: Martinsried bei München, Amtsgericht München, HRB 134442; Ust-IdNr. DE 191 603 142; Steuer-Nr.: 143 / 100 / 10945; Vorstand: Dr. Klaus Heumann (Vorsitzender), Dr. Hein Paul Osenberg; Vorsitzender des Aufsichtsrates: Prof. Dr. Hans-Werner Mewes

BioXM™ Knowledge Management Environment What’s new in BioXM version 3.0?

BioXM version 3.0 (199) August 2008

Biomax Informatics AG Lochhamer Str. 9

D-82152 Martinsried Germany

Tel: +49 89 895574-0 Fax: +49 89 895574-825 Email: [email protected]

BioXM Knowledge Management Environment — what’s new?

BioXM version 3.0 (199), August 2008

What is new in BioXM version 3.0?

Introduction ....................................................................................................................................................... 2 Network interpretation with graph information layers ........................................................................................ 3 Association Queries and virtual relations .......................................................................................................... 7 Data model viewer and graphical domain modeling ....................................................................................... 11

Introduction

Since the release of BioXM Knowledge Management Environment, version 2.0, in April 2006, Biomax has continuously added new functionality with minor releases of the platform. With an average of three releases per year, version 2.0 was transformed into a platform, broadening its application areas quite significantly. Important additions were, for example, conceptual extensions to support high-throughput, high-volume experimental data as well as extensive improvements to reporting and querying capabilities. With every addition to the platform, Biomax was dedicated to further improve ease of use and — although the BioXM platform provides unprecedented power for managing life science data, information and knowledge — to create a graphical user interface that can be used intuitively even by inexperienced users. Multiple APIs have been made available to developers, who very early expressed their eagerness to use the BioXM system as a platform for their own development and projects.



Figure 1. The BioXM Knowledge Management Environment, version 3.0. Here, a graphical representation of a dynamically generated connectivity network is shown. The selection in the opened "Information Layer Chooser" changes the default graph style definitions to dynamically visualize the strength of the available evidence supporting the relations found. In addition, the genes shown in the graph are automatically colored according to their overall connectivity with any cancer disease, based on evidence obtained by a literature analysis.

Today, Biomax introduces the next major version of the BioXM system: BioXM version 3.0. Though BioXM version 3.0 adds significant functionality in multiple areas, it focuses specifically on graphical network presentation and network interpretation. Other highlights include conceptual extensions such as "virtual relations" and graphical editing of the user-defined data model. With many new features and changes to existing functionality, improvements to the ease of use were of particular importance. Newly introduced application programming interfaces (APIs) include a new high-performance analysis API that seamlessly integrates external tools such as scripts developed using R, BioConductor or other statistical packages.

This document summarizes some of the highlights of the new BioXM version 3.0. For a complete overview of all changes and additions made since the release of BioXM version 2.0, refer to "Release Notes BioXM Knowledge Management Environment, Version 3.0" available from Biomax Informatics. In-depths information can be found in the release notes published with every public minor release of the BioXM system as well. Note that the release notes mention API-related features only briefly, if at all. Developers can find more information on the BioXM support web. Access to the support web, as well as any other documentation, can be requested with an email to [email protected].

Network interpretation with graph information layers

Graph information layers provide a very simple mechanism to answer numerous scientific questions graphically, with only few mouse clicks. The most basic feature of information layers is simple filtering of graph nodes or edges based on the definitions of the selected information layers. In addition, information layers also allow dynamic modification of graph nodes or edges visualization according to the rules defined for each information layer. The third functionality provided by information layers allows any additional information that is accessible through the reporting module and associated with either nodes or edges to be shown or hidden by selecting one or more information layers.



Information layers allow a set of any semantic object (elements, relations, contexts, etc.) as well as any "special" association, such as BioRS mapping, annotation assignments or ontology relationships, to be defined. The graph filtering functionality primarily uses this set definition to hide all graph objects not included in the definition upon selection of this information layer. Multiple information layers can be combined to "overlay" different sub-graphs.

For each item added to the information layer definition, item-specific properties can be defined to create rules for dynamically changing a graph with respect to object visualization or reporting additional information. Once defined, these dynamic visualization changes can be applied with single-click selection of the information layer in a floating dialog attached to the displayed graph.

In particular, the rules for dynamic visualization are tied to view items defined in the information layer definition. When view items reporting the result of embedded queries are used, dynamic changes can be based on, for example, numerical annotation as well as on the results of complex queries.

The following dynamic changes to graph object visualization can be configured with an information layer definition:

• Dynamic thickness of relation edges • Dynamic color of relation edges • Dynamic size of object nodes • Dynamic color of object nodes • Thickness and color of relation edges as well as size and color of object nodes can all be tied to

independent view items.

Any view item defined for a specific item in the information layer definition may be reported as "additional information". All view items selected will report their information upon selection of that information layer for a given graph.

Figure 2. Dynamic visualization: A graphical representation of the "catabolism of hexose derivatives". This particular graph presents only the main metabolites and enzymes of this biochemical pathway; co-factors are hidden using the information layer filtering options.



Figure 3. Dynamic visualization. In this graph, the same biochemical pathway as in Figure 2 is shown with the dynamic visualization of the information layer functionality used to overlay the results of an experiment series. The information about fold changes of the genes expressing the enzymes participating in this pathway are visualized by changing the color of the enzyme representation with measurements in the experiment series of interest.



Figure 4. Dynamic visualization (example 2). A standard signaling pathway (here: type II diabetes mellitus pathway) with its components mapped to actual human genes. In this example, this standard pathway is used below to visualize the results of a series of gene expression experiments.



Figure 5. Dynamic visualization (example 2). This screenshot shows a data matrix that was generated as the result of a series of gene expression experiments. In this example, cluster analysis scripts using R and BioConductor are integrated into the BioXM system using its analysis API, providing on-the-fly reporting of cluster numbers and heat maps.

Figure 6. Dynamic visualization (example 2). Using information layers, the graph shown in Figure 4 is dynamically changed to visualize a genes participation in a specific cluster by changing the gene objects background color. In addition, the generated heat map is directly shown for the shown genes.

Association queries and virtual relations

BioXM version 3.0 introduces the concept of "association queries". Association queries allow the definition of "virtual" relationships through definition of any advanced query. Virtual relationships defined by an



association query may be visualized directly in the graph viewer. Alternatively, association queries can be used to trigger addition of the corresponding query result to a graph panel without the specific visualization of virtual relationships.

The visualization of a virtual relationship defined by an association query can be configured with respect to its line style, arrowhead, and label font style and line color.

Applying an association query to a selection of multiple graph objects can trigger either a single query with the selected objects as a combined input set or multiple queries with each selected object as input for a separate query.

An association query is project-owned. Any user without access to the project owning a particular association query will not be able to use the association query.

Figure 7- Association queries (example 1). This simple example shows how to use association queries for visualization of virtual relations in the BioXM graph viewer. The graph shows the drug albuterol as an instance of an entry from the NCI Thesaurus ontology. The "Display Associated Objects…" context-menu item allows the execution of the "Substance class members" association query for the selected "Anti-asthmatic Agent" ontology entry.



Figure 8. Association queries (example 1). This screenshot shows the definition of the association query used in the previous figure. This simple query searches for all compounds classified by the NCI Thesaurus, including all compounds that are inferred from sub-classes of the selected ontology entry. The query builder allows fast and flexible creation of queries answering even very complex scientific questions.

Figure 9. Association queries (example 1). This result view of the executed association query shows all inferred instances of the NCI Thesaurus "Anti-asthmatic Agent" entry as virtual relations. The



visualization style of virtual relations (a dashed line is used in this example) can easily be configured..

Figure 10- Association queries (example 2). This example shows the application of association queries without visualization of virtual relations. Here, three genes of interest are selected. Their "Display Associated Options" context-menu option allows the execution of an association query, user-configured specifically for objects of type "gene". This example, "Connecting Network" searches for an optimized network connecting the three genes of interest based on all known relationships of the global BioXM knowledge network using shortest path algorithms.



Figure 11. Association queries (example 2). The association query "Connecting Network" searches for an optimized network connecting the three genes of interest. The network is created based on all known relationships of the global BioXM knowledge network using shortest path algorithms. This screenshot shows the result of executing the association query, displaying, for example, associated disease phenotypes based on literature-derived relations and other relations, such as functional classification using Gene Ontology (GO).

Data model viewer and graphical domain modeling

In addition to the data modeling functionality BioXM offered since its initial release, BioXM version 3.0 adds a graphical data model viewer. The data model viewer allows both visualization and modification and extension of the user-configured data model. The graphical modeling functionality for example supports creation and deletion of object types, drag-and-drop creation of relation classes and annotation assignments and, among other tasks, allows ontologies to be imported and object scopes to be edited.



Figure 12. Data model viewer and graphical domain modeling: view of a user-configured data model. This simple example shows a data model specific for managing proteins and peptides and their relationships. The graph shows a selection of the “building blocks” a BioXM user can use to create and extend a scientific data model. Here, the semantic object types “Elements” and “Ontologies” are used. “MEDLINE” is integrated as external database using the BioRS Integration and Retrieval middleware.



Contact Biomax

For more information about the BioXM platform and the changes implemented in this version contact Biomax:

Biomax Informatics AG Lochhamer Str. 9 D-82152 Martinsried Germany

Tel: +49 89 895574-0 Fax: +49 89 895574-825

Email: [email protected]

Website: www.biomax.com/bioxm

Biomax, BioLT, BioRS and BioXM are registered trademarks of Biomax Informatics AG in Germany and other countries. Apple, Leopard, Macintosh, Mac OS, Safari and Tiger are trademarks or registered trademarks of Apple, Inc. Google is a trademark of Google Inc. Java, Solaris and Sun are trademarks of Sun Microsystems, Inc. Microsoft and Windows are registered trademarks of Microsoft Corporation. Linux is a registered trademark of Linus Torvalds. SUSE is a registered trademark of Novell, Inc. Intel is a registered trademark and Xeon is a trademark of Intel Corporation. AMD and AMD Opteron are trademarks of Advanced Micro Devices, Inc. UNIX is a registered trademark of The Open Group. Mozilla and Firefox are registered trademarks of the Mozilla Foundation. Red Hat is a registered trademark of Red Hat, Inc. Debian is a registered trademark of Software in the Public Interest, Inc. Registered names, trademarks, etc., used in this document, even when not specifically marked as such, are not to be considered unprotected by law. The BioXM software was developed in collaboration with RingRows GmbH, Martinsried.

What's new in BioXM version 3.0? · BioXM Knowledge Management Environment — what’s new? BioXM...

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