transpath — a high quality database …downloads.hindawi.com/journals/ijg/2004/718908.pdf ·...

Comparative and Functional GenomicsComp Funct Genom 2004; 5: 163–168.Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.386

Conference Review

TRANSPATH — a high quality databasefocused on signal transduction

Claudia Choi1*, Mathias Krull1, Alexander Kel1, Olga Kel-Margoulis1, Susanne Pistor1, Anatolij Potapov1,Nico Voss1 and Edgar Wingender1,2

1BIOBASE GmbH, Halchtersche Strasse 33, 38304 Wolfenbuttel, Germany2Department of Bioinformatics, UKG, University of Gottingen, Goldschmidtstrasse 1, 37077 Gottingen, Germany

*Correspondence to:Claudia Choi, BIOBASE GmbH,Halchtersche Strasse 33, 38304Wolfenbuttel, Germany.E-mail: [email protected]

Received: 7 November 2003Revised: 15 December 2003Accepted: 23 December 2003

Abstract

TRANSPATH can either be used as an encyclopedia, for both specific and generalinformation on signal transduction, or can serve as a network analyser. Therefore,three modules have been created: the first one is the data, which have been manuallyextracted, mostly from the primary literature; the second is PathwayBuilder,which provides several different types of network visualization and hence faciliatesunderstanding; the third is ArrayAnalyzer, which is particularly suited to geneexpression array interpretation, and is able to identify key molecules within signallingnetworks (potential drug targets). These key molecules could be responsible forthe coordinated regulation of downstream events. Manual data extraction focuseson direct reactions between signalling molecules and the experimental evidence forthem, including species of genes/proteins used in individual experiments, experimentalsystems, materials and methods. This combination of materials and methods isused in TRANSPATH to assign a quality value to each experimentally provenreaction, which reflects the probability that this reaction would happen underphysiological conditions. Another important feature in TRANSPATH is the inclusionof transcription factor–gene relations, which are transferred from TRANSFAC,a database focused on transcription regulation and transcription factors. Sinceinteractions between molecules are mainly direct, this allows a complete andstepwise pathway reconstruction from ligands to regulated genes. More information isavailable at www.biobase.de/pages/products/databases.html. Copyright 2004 JohnWiley & Sons, Ltd.

Keywords: signal transduction; database; quality assessment; TRANSPATH; pro-tein interaction; experimental evidence; network analysis; gene expression array

Introduction

Uncovering cellular processes under normal condi-tions will improve our understanding of pathologi-cal situations. Key components of the cellular net-work are potential drug target candidates and betterknowledge about cellular processes will help us tolearn more about possible side-effects. However,cellular networks are highly connected and com-plex, thus understanding them is a challenging task.

Several distinct approaches are currently usedto identify molecular interactions within the cell.

The classical method used by most molecular biol-ogy laboratories in the past was the investiga-tion of single steps within the network. Gener-ally these data are of high quality, since con-trols have been conducted for individual interac-tion results. However, this approach is not suitedto the generation of holistic knowledge of cellularprocesses. More recently, high-throughput exper-iments have been developed that produce largemasses of data, the most widely used methodolo-gies being the yeast two-hybrid method [5,15], 2D-gel protein assays [4] and gene expression arrays

Copyright 2004 John Wiley & Sons, Ltd.

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[2]. Storage of these data in appropriate reposito-ries is an absolute prerequisite for making the bestuse of existing research results to achieve system-atic computational network analysis and to interpretthese data with regard to their biological meaning.One approach for creating such databases that hasbeen investigated is automatic text mining [12]. Sofar, this approach seems to be limited by severalproblems: e.g. protein identification is ambiguousbecause of the lack of a generally used terminol-ogy in the past; also, research language is hetero-geneous and negations are not always interpretedcorrectly. Moreover, the relevant information maybe extremely fragmented within individual, or evenbetween several, publications. However, automatictext mining may provide mass information, albeitwith lower quality and depth than data extractionby expert biologists. With the aim of achievingmore reliable data, several interaction databaseshave been initiated, such as DIP [18], BIND [1] andaMAZE [16], where interactions have been inferredmostly by high-throughput experiments and mainlyon yeast species. Databases concentrating more onhuman or mammalian proteins are CSNDB [14],TRANSPATH [6,8,13] and MINT [19]. Amongthese, TRANSPATH seems to contain the high-est number of mammalian data points, in relativeterms as well as in absolute numbers: over 72%of all so-called ‘basic’ molecules are mammalianproteins, of which about 2400 are human molecules(see Figure 1). In contrast to most of the databasesmentioned above, most reactions in TRANSPATH

are directed, and hence allow upstream and down-stream queries, as well as describing the func-tion of the reaction (activation vs. inhibition). Fur-thermore, direct interactions between moleculesare described, allowing the construction of step-wise and coherent pathways. TRANSPATH alsoincludes quality criteria, which are outlined below.

It is clear that we can benefit from integratingall of the major databases; their combined strengthmight consist in different depths of detail, simplymore data on protein interactions (better coverage)or other aspects. The integration of TRANSFAC

[10] and TRANSPATH is a good example foryielding benefits (see below). In the end morecomplete and broad cellular pathway knowledgewill be gained.

Currently a public demo version of TRANS-PATH is available at www.biobase.de/pages/products/transpath.html comprising the pathway

Figure 1. TRANSPATH concentrates on mammalian data.Release 4.3 (October 2003) contains about 2400 humanproteins, 34% of all basic proteins in the database; 70% ofthe basic proteins are either human, mouse or rat

of interleukin-1 (IL-1), a pro-inflammatory cyto-kine important in the innate immune response. Thisextract from TRANSPATH professional contains>600 interactions and >800 signalling molecules,mainly from mammalian organisms (which com-prise 4% and 6% of TRANSPATH Professional4.3, respectively).

Database structure

We segmented signal transduction into threemajor functional units: signalling componentscalled ‘molecules’, interactions connecting thesemolecules called ‘reactions’, and ‘genes’ todifferentiate transcriptional regulation of genes andprotein interactions [8]. They are connected bya bipartite directed graph, the nodes alternatelyrepresenting molecules and reactions, genes beingtreated as a subset of molecules. As of October2003, TRANSPATH Professional release 4.3contains about 13 800 molecules, >3800 genesand >17 000 reactions collected from about6500 references. Updates are released quarterly.Molecule and gene entries contain, among otherinformation, cross-references to numerous otherinformation resources, such as TRANSFAC,SwissProt, EMBL, LocusLink, Affymetrix chips,OMIM, InterPro and GO. For some of thereactions, links have been made to DIP and BIND(200 and 20, respectively). Data are extractedmainly from the primary literature by expert

Copyright 2004 John Wiley & Sons, Ltd. Comp Funct Genom 2004; 5: 163–168.

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biologists. During the curation process the databasecurator specializes in specific topics in orderto produce coherent networks in the database.Finalizing one specific topic will result in so-called‘clickable maps’, summarizing expert knowledgeinto one cartoon, from which molecule entries aredirectly accessible.

Because protein constructs used in experimentsare often from different species or are not indicatedin primary papers, the problem arises that completepathways cannot be constructed for a single speciesunless we combine the knowledge obtained frommaterial of different species. Therefore we intro-duced a hierarchical representation for moleculesand distinct types of reactions. For molecule entriesin TRANSPATH the lowest level corresponds tothe specific molecules of a particular species, whichare referred to as ‘basic’-type molecules. The sec-ond level, named ‘orthologue’-type molecules, isrepresented by generic entries, to which all of thecorresponding orthologous basic entries are linked.This hierarchical structure enables abstractions inorder to reflect interactions from several species toa more general signalling network.

Reaction entries also exist on two main levelsof detail: ‘mechanistic’ reactions, which representthe mechanism of reactions and usually connectbasic molecules, and ‘semantic’ ones, which depictdirection and function of the reaction occurringbetween ‘orthologue’ molecules and hence poolinformation from specific ‘mechanistic’ reactionsto show the signal flow in a more general manner.That way high connectivity is guaranteed withsemantic reactions and detailed information can beobtained from mechanistic reactions.

Quality assurance

Mass data in terms of protein interactions arealready available (see Introduction). The remainingchallenge is to structure these data to get biologi-cally relevant answers. In our approach we rely onhigh quality data.

We concentrate on manual data extraction ofprimary publication papers particularly includingdata from individual or ‘small-scale experiments’in contrast to high-throughput data or resultsfrom ‘large-scale experiments’, which often do notdepict physiological conditions [3]. Manual read-ing and extraction of the data also enables us to

pick up detailed information, in particular experi-mental conditions. In order to assess the relevanceof a reaction for the physiological situation and theimmediacy of the interaction between the reportedmolecules, we developed a quality scale system.Two aspects are integrated into our quality scalesystem: the material that has been used and themethod that has been applied in a reported exper-iment to prove a certain reaction. For each com-bination of material and method we have set a‘reliability value’ to assign a confidence level toeach reaction (see Figure 2a).

During the validation process for these reliabilityvalues, a trade-off has been made between evidenceof a direct interaction and physiological conditions.Detection of direct interactions is necessary forunderstanding the pathway in a stepwise manner,whereas physiological conditions refer to the rel-evance of a proven direct interaction in vivo. Dif-ferences in the physiological relevance arise, e.g.by in vitro translated material vs. correctly folded,naturally occurring, endogenous proteins.

While extracting information from a scientificpaper, the origin of molecules and the methodsused for proving a reaction between moleculesare stored, from which the reliability value willbe automatically assigned in the ‘quality’ field.By this means network analysis can be viewedand filtered according to the reliability of theunderlying experimental evidences. The assignedreliability values have been revised by molecularbiologists. If the user is not in agreement withour reliability values, he/she can change the valuesin the menu ‘edit the quality reaction matrix’according to his own knowledge. These valueswill appear in the reaction table in addition to thepreset ones. Along with this, a quality assessmentof the experimental system, i.e. the cell lines ortissues used, is recorded. Furthermore, reactions inTRANSPATH are often proved independently byseveral papers, which also confirms the reliabilityof a reaction (see Figure 2b).

One other quality criterion of TRANSPATH isthat species information of the interacting proteinsis retained (see Figure 1), which is not the casein other databases, such as HPRD [11], whereinteractions reported for distinct species are allrepresented as part of the human network. Previousarticles often have to be consulted to retrieve thespecies information, or the authors have to becontacted directly. This distinction is necessary,


166 C. Choi et al.

(a)

(b)

Figure 2. Quality assessment in TRANSPATH. (A) Integration of data about the specific experimental conditions.Depending on the biological material and method applied in the experiment, the reaction is more or less likely to happenin the cell and organism. Therefore, we listed material sources and methods crucial for signal transduction and assignedreliability values (scale 1–5) for each combination of material and method (quality matrix). This value is provided in thereaction entry. (B) Frequency of evidences. About one-third of experimentally proven reactions are identified by morethan one experiment, usually conducted using different methods by different research groups, and hence these reactionsare more likely to occur in vivo


TRANSPATH 167

since it has been shown that orthologous proteinsmay exert different functions in different species[17]. Storing species details thus enables filtering innetwork visualization and viewing of discrepanciesin signalling between different species.

TRANSPATH is also integrated with TRANS-FAC [10], a database focused on transcriptionalregulation. This allows the acquisition of high-quality data for a complete stepwise signallingpathway from ligand to gene. Hence, signallingnetwork analysis can be directed towards transcrip-tional regulation or protein networks.

Network analysis

For drug target identification it is crucial to gainknowledge on signalling networks and their keysignalling components. With TRANSPATH, tar-get genes or key regulators can be identified. Start-ing with a set of genes (or their protein products)that may have been deduced from expression arraydata, a subnetwork can be reconstructed that com-prises a maximal number of affected molecules.This can be visualized with PathwayBuilder,in combination with ArrayAnalyzer, which isa first step towards efficient drug target pre-diction. ArrayAnalyzer is able to find promi-nent molecules which are highly connected to theregulated genes. Since genes and their respec-tive gene products (molecules) are separated inTRANSPATH, the user can distinguish betweenregulation of gene expression and protein signallingnetworks. An example is given in Krull et al. [8].

Conclusions and outlook

So far, high quality data cannot be extracted bytext mining, in particular the retrieval of speciesand of experimental details. On the other hand, itmay provide higher coverage of relevant publisheddata and, thus, may allow us to reconstruct net-works more comprehensively. Among the existingdatabases, only a few concentrate on mammalianspecies, and differentiate between gene regulationand protein networks, as in CSNDB [14], MINT[19] and TRANSPATH. TRANSPATH also con-tains tools for visualization and analysis, leading toreliable pathway and network construction.

Another point of view has been neglected so far:the spatio-temporal specificity of cellular processes.Reliable information about this is still sparse, butis expected to grow with new upcoming methods.The integration of this kind of data and their use fora more specific visualization and prediction is a keypoint for further development of TRANSPATH.

Nevertheless, common efforts are desirable andwill be necessary for creating a more comprehen-sive multi-dimensional integration, including datafrom all current databases in terms of protein inter-action as well as signalling and metabolic path-ways, from high-throughput assays, or data fromtext mining, and even from in-silico analyses,since different approaches and methodologies willstrengthen the knowledge of cellular processes andthus will help to further improve our chances ofcombating disease.

Acknowledgements

Many thanks to Volker Matys for proofreading. We aregrateful to the whole BIOBASE team for stimulatingdiscussions and for their help.

References

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3. Deane CM, Salwinski L, Xenarios I, Eisenberg D. 2002.Protein interactions: two methods for assessment of thereliability of high throughput observations. Mol Cell Proteom1: 349–356.

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