SignaLink 2 – a signaling pathway resource with multi-layered regulatory networks
- Dávid Fazekas†1,
- Mihály Koltai†2, 3,
- Dénes Türei†1, 4,
- Dezső Módos1, 4, 5,
- Máté Pálfy1,
- Zoltán Dúl1, 4,
- Lilian Zsákai1, 4,
- Máté Szalay-Bekő4,
- Katalin Lenti1, 5,
- Illés J Farkas2,
- Tibor Vellai1,
- Péter Csermely4 and
- Tamás Korcsmáros1, 4Email author
© Fazekas et al; licensee BioMed Central Ltd. 2013
Received: 13 September 2012
Accepted: 16 January 2013
Published: 18 January 2013
Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor.
We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., BioPAX, Cytoscape, SBML). Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org.
With SignaLink 2 as a single resource, users can effectively analyze signaling pathways, scaffold proteins, modifier enzymes, transcription factors and miRNAs that are important in the regulation of signaling processes. This integrated resource allows the systems-level examination of how cross-talks and signaling flow are regulated, as well as provide data for cross-species comparisons and drug discovery analyses.
KeywordsSignal transduction Signaling network Regulatory network Scaffold miRNA Transcription factor Drug discovery Signaling database
Reliable analyses of signaling pathways need uniform pathway definitions and curation rules applied to all pathways. Accordingly, we previously created SignaLink, a resource containing major signaling pathways of the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and humans . Specific regulation of signaling flow is essential to ensure the appropriate response of the signaling system for a given input . Signaling flow is determined by the spatial and temporal properties of signaling proteins precisely regulated by cellular processes (e.g., endocytosis, transcription, miRNA regulation) . In addition, signaling components are modulated by proteins having no direct signaling functions, such as scaffold proteins and ubiquitin ligases [4, 5].
Despite the complexity of eukaryotic signaling networks, current signaling or regulation-related resources contain only specific parts of such a global signaling network. As a consequence, computational background and expertise in different bioinformatics resources are needed to answer questions about how a signaling pathway is regulated, or how the pathways influence each other through transcription and miRNA-mediated gene silencing. A few studies already combined regulatory and protein-protein interaction networks [6–9], while a new resource, TranscriptomeBrowser, integrates human regulatory networks with phosphorylation reactions . Recently, we developed a systems-level resource of the transcription factor NRF2, containing its transcriptional, post-transcriptional and post-translational modifiers, based on manual curation, in silico prediction and existing dataset imports .
Taking into consideration the need for a novel arrangement of signaling and regulatory data to examine signaling processes on a systems-level, we present SignaLink 2, a database with multi-layered (onion-like) structure. Our basic aim was to develop an integrated database that helps anyone to understand how cellular signaling pathways and their cross-talks are regulated. To accomplish this goal, SignaLink 2 contains for worms, flies and humans 1) pathway components and cross-talks; 2) interacting proteins that modify or facilitate signaling reactions; and 3) regulatory components (transcription factors and miRNAs) that affect the expression of pathway proteins and their interactors.
Construction and content
Compilation of the multi-layered network
The core of SignaLink 2 contains seven major pathways, which are biochemically and evolutionary defined, and encompass all major developmental signaling mechanisms : RTK (receptor tyrosine kinase), TGF-ß (transforming growth factor beta), WNT/Wingless, Hedgehog, JAK/STAT, Notch and NHR (Nuclear hormone receptor). We note that in the previous version of SignaLink, EGF/MAPK (epidermal growth factor /mitogen-activated protein kinase) and insulin/IGF (insulin growth factor) pathways were defined as separate pathways. In this upgraded and extended version, the RTK pathway contains both pathways and additional related receptors (e.g., VEGFR and FGFR). This grouping is more realistic and in line with evolutionary studies . While earlier in SignaLink the NHR pathway contained only the NHR proteins, it now includes their co-factors, too. The uniform manual curation protocol remained the same as developed and published earlier . Accordingly, we set the pathway boundaries based on expert-written reviews and manual search of the literature. We examined the signaling functions and interactions of the proteins mentioned in the reviews. For each signaling interaction, we listed the PubMed ID of the publication reporting the verifying experiment(s). In addition, we grouped all manually curated signaling pathway proteins to ‘core’ and ‘non-core’ proteins. A ‘core’ protein is essential for transmitting the signal of its pathway, while a ‘non-core’ protein modulates the pathway’s core proteins but not transmit the incoming signal. For a more detailed description on the curation protocol, please see the supplementary material of our earlier publication on SignaLink . The current curation update was closed in April, 2011.
We added two further extensions that can be optionally used. i) With manual curation, we collected scaffold proteins and endocytosis-related proteins and linked them to signaling pathway proteins, based on the scaffold protein list of the Ref.  and signaling-related endocytosis reviews, respectively. ii) We extended the number of transcription factors (TFs) in the database from 243 to 586 by connecting additional TFs to already curated TFs, based on protein-protein interaction (PPI) data from WI8, DroID, HPRD and BioGRID databases [14–17].
Next, using the ELM server , we searched for enzymes (i.e., phosphatases, ubiquitin-ligases, peptidases, etc.) that can directly modify signaling components involved in SignaLink 2. We then searched for other proteins previously not known to be as signaling-related ones, but having interaction with a component already included. For this, we used the same PPI resources as for the TF-network. Based on the algorithms of the Ref. , we predicted directions for the PPIs.
Finally, we identified underlying transcriptional and post-transcriptional regulatory networks that control the expression of signaling components and their interactors. TF–TF binding site interactions were used to list transcriptional connections between TFs and genes encoding signaling components or pathway interactors. We downloaded experimental and predicted data from the EdgeDB, REDFly, DroID, ABS, JASPAR, HTRIdb, OregAnno, ENCODE and PAZAR databases [15, 20–27]. We also included two types of miRNA network data: i) miRNA–mRNA interactions from miRBase, TarBase, Miranda, TargetScan and miRecords [28–32], and ii) TFs of these miRNAs from PutMir, TransMir and ENCODE [27, 33, 34]. For each regulatory interaction, binding scores were calculated based on position matrix values or inserted from the original sources.
Integrating the sources and quality control
Detailed statistics of SignaLink 2
Directed protein-protein interactors
Undirected protein-protein interactors
TFs of miRNAs
We also note that all experimental interactions in SignaLink either collected by manual curation or integration of other sources were coming from various cell types and experimental conditions. Therefore, interactions in SignaLink 2 are the sum of many possible interactions but not all of these interactions can happen at the same time or same place. Users can integrate cell localization and tissue expression data to the networks of SignaLink 2 to filter compartment- or tissue-specific interactions, respectively. We plan to include such data types in the next version of SignaLink.
User-friendly web interface
The webpage processes data with PHP on the server side, and jQuery on the client side, providing a great user experience in all standard compliant browsers. To display interactive networks, we use the Cytoscape Web plugin . The search method performs partial match on multiple types of names and database IDs, finding the proteins and miRNAs matching the text typed in by the user. The search field helps the user in autocompleting the text while typing.
The entire database is available as a MySQL dump file. Alternatively, we developed a BioMART-like customizable download page, where users can easily select and combine the species, pathways, layers and the file format of download. The customized subnetworks can be downloaded in various formats: CSV, BioPAX, SBML, PSI-MI tab or PSI-MI XML and in a Cytoscape CYS file. Data can be compressed by GNU zip or zip. After selecting the details of the download, for advanced users, we offer additional customization where within each layer the different source databases can be filtered by score values, or even excluded. A general switch is also available to exclude all predicted interactions.
All user-specified download options are automatically transformed to MySQL queries. For each download, we generate a URL, where users can access the data for 14 days. Optionally, users can provide their e-mail addresses to which files smaller than 10 MB will be e-mailed. We have developed a download module (written in Python), to manage the queries and to convert the result of the queries to user-specified file formats. In conclusion, SignaLink 2 serves as an integrated signaling resource where the origin, type and confidence level of each interaction are clearly listed, allowing the user to easily access and filter data.
Applications of SignaLink 2
Signaling cross-talks are important connections between different pathways and can generate novel input–output combinations as well as maintain the dynamic adaptation of the signaling system [39, 40]. We have previously shown the significance of multi-pathway proteins (i.e., proteins functioning in more than one pathway) in the intertwined network of signaling pathways . However, to ensure that an appropriate response is transduced, multi-pathway proteins need to be precisely regulated. SignaLink 2 contains multiple forms of protein regulation, including transcriptional, post-transcriptional and post-translational modifications. Thus, with SignaLink 2, regulation of each cross-talking protein can be analyzed.
Though cross-talk is generally defined as a physical interaction between pathway proteins, genetic studies often point out the importance of pathway cross-talks through transcription. In this case, cross-talk is mediated by a terminal transcription factor (TF) of a given pathway that regulates the expression of a component of another pathway [41, 42]. With SignaLink 2, transcription-mediated pathway connections can be mapped as it contains i) uniformly defined pathways, ii) TFs for each pathway, and iii) a TF-regulatory network. Transcription-mediated cross-talks can be identified between any two pathways or globally at the systems-level. Recently, miRNAs have also been shown as important regulators of signaling pathways and networks [43, 44]. As some miRNAs are known or predicted to be regulated by specific TFs [33, 34], pathway cross-talks can be formed by a terminal TF of a pathway that regulates the expression of a miRNA down-regulating a component of another pathway. As an integrated database, SignaLink 2 contains TF and miRNA regulation data as well as a uniformly curated pathway dataset. These properties allow researchers to analyze pathway cross-talks on the post-transcriptional level. Furthermore, systems-level comparison of transcriptional, post-transcriptional and post-translational (i.e., PPI mediated) cross-talks can be performed with SignaLink 2.
Modeling signaling networks is a key approach to understand their dynamic properties in adaptation and diseases [45, 46]. However, most PPI resources contain most interactions without direction (an information that is critical in signal transduction), pathway databases are often curated without uniform curation protocol and pathway definition, and generally lack important components having no direct signaling functions (i.e., scaffolds proteins, ubiquitin ligases and many phosphatases). As function of these components is the spatial and temporal regulation of the signaling flow, including them to a pathway resource could facilitate more precise modeling of signaling systems. As SignaLink 2 contains these components, it can enhance the development of models that can be successfully validated in wet lab experiments. Furthermore, data in SignaLink 2 is ready to use for modeling programs and scripts as users can download the files in well-known network and modeling formats (e.g., SMBL, BioPAX). User-specific selection of SignaLink 2 can be integrated with experimental data on enzyme activity or binding strength, thus, SignaLink 2 provides a general network topology for in-depth differential equitation modeling. Data from SignaLink 2 can be easily integrated to Boolean modeling frameworks, such as CellNetOptimizer . Combining SignaLink 2 dataset (i.e., signaling pathways and regulatory components) with network data of other cellular processes, such as autophagy or apoptosis, would allow Bayesian modeling on the regulation of these processes.
Genome programs and high-throughput screenings have greatly contributed to the construction of signaling networks in various model organisms, ranging from invertebrates to mammals. Reliable network resources enable the prediction of novel components and functions by analyzing cross-species data with the toolbox of functional genomics [48, 49]. Accordingly, for C. elegans, D. melanogaster and H. sapiens, we have predicted 271 novel signaling components (i.e., signalogs) based on ortholog information of the previous version of SignaLink . SignaLink 2 contains updated and extended dataset allowing the identification of further signalogs. In addition, SignaLink 2 enables the prediction of regulogs (i.e., predicted regulatory connections) as it contains TFs and regulatory connections for three metazoan species in a unified data structure .
Studying cross-talks, pathway regulator TFs and miRNAs have high pathological relevance as their malfunction often lead to diseases such as cancer . Earlier, we found a significant change in the expression level of multi-pathway proteins in hepatocellular carcinoma , indicating that integration of signaling networks with expression datasets could reveal novel diagnostic and prognostic markers. The multilevel regulatory networks of SignaLink 2 have higher coverage and could serve as a more precise resource to compare normal and disease states of signaling networks.
Pharmacological targeting of key signaling components, including multi-pathway proteins and miRNAs is a promising strategy [53–55]. But unfortunately, numerous failures are known where the drug target had undiscovered or underestimated cross-talk as well as regulatory effects [56, 57]. With SignaLink 2 different layers of signaling pathway regulation can be examined within a single resource. Performing an in silico perturbation analysis on the multi-layered signaling network of SignaLink 2 may facilitate the development of pharmacological interventions [54, 58, 59]. A perturbation analysis with SignaLink 2 can uncover key proteins or interactions important in the robustness of the signaling network. We have recently reviewed several such network perturbation approaches . SignaLink 2 allows drug developers to measure the regulatory influence of a drug target candidate as well as to predict the signaling effect of its targeting. For example, drug targeting of a TF or its upstream interactor may influence the expression of many target genes, including signaling-related feedback mechanisms or metabolic enzymes important in drug metabolism. Applying the multi-layered network of SignaLink 2 could help developers to identify and avoid such circuits. SignaLink 2 can also support the identification of multiple targets in a multi-target pharmacological approach : selecting the primarily suggested drug targets in the network of SignaLink 2 would allow the short listing of a minimal set of key targets with maximal impact on the network. Similarly, in anti-cancer strategy often not a single biochemical species is targeted but a complete pathway . The stimulatory and inhibitory cross-talks and regulatory circuits of SignaLink 2 allow the listing of key regulators of a pathway, whose modulation can have pathway-level effects.
First, we illustrate the usage of the http://SignaLink.org website with a protein, AXIN1. Next, the advantages of the download options of SignaLink 2 are illustrated with an integrated map of Notch and TGF-β pathways.
We selected AXIN1 to demonstrate how regulatory interactions can be examined for a given protein with the protein datasheet of the SignaLink 2 website. With the previous version of SignaLink, we found that AXIN1 is a multi-pathway protein having connection to more than one pathway . Accordingly, AXIN1 was also described as a master scaffold for multiple signaling pathways . Besides its distinct roles in the WNT, TGF-β and MAPK pathways, AXIN also has cross-talk functions . Thus, one can think that AXIN1 should be precisely regulated. Here, we intended to explore how AXIN1 can be regulated to act as a multi-pathway protein and a master scaffold for members of different pathway. If we search for ‘AXIN1’ on the SignaLink 2 website, we will get its protein datasheet. On the top of the datasheet basic information about AXIN1 are presented (Figure 5a): 1) hyperlinks to AXIN1's page in other databases (Ensembl and UniProt); 2) AXIN1 is listed in SignaLink 2 as a mediator and a scaffold protein, and had been assigned to three pathways: RTK, WNT/Wingless and TGF. Below this box, the list of AXIN1 interactions can be found, grouped by layers (Figure 5b). In some cases the list is rather long, so each layer is expanded only if the user clicks on the layer’s title, and an optional sliding box on the left side of the page helps in navigation. In this view the names of interacting protein pairs, the type of interactions (coded with arrows) and pathway memberships are also visible. We can see that SignaLink 2 contains: 1) 15 interactions between AXIN and different pathway member proteins; 2) 8 interactions where AXIN as a scaffold regulates pathway proteins; 3) 136 predicted post-translational modifications (i.e., enzymes that may modify AXIN1); 4) 9 known PPIs predicted to be directed; 5) 17 predicted or known transcription factors that regulate AXIN1; 6) 28 predicted or known miRNAs that could down-regulate AXIN1; 7) Finally, 30 PPIs without any direction. Note that interactions can overlap between the layers, but these overlaps are mentioned for each relevant interaction. In certain cases, some overlaps can point out important feedbacks, showing a protein that interacts with and regulates AXIN1. Users can search or browse for each interactor/regulator of AXIN at the top of the list of layers. If we expand the first layer (Figure 5b), we can see that the first two interactions are direct stimulatory (normal arrow), the third one is indirect and inhibitory (dashed line with blunted arrow). To get more information about an interaction (for example, about the interaction between AXIN1 and PPP2CA), a simple click on the list is enough. Then, in the same box detailed information is shown (expanded in Figure 5c): the interaction between AXIN1 and PPP2CA was manually curated, two references to articles hyperlinked to PubMed can also be seen. In the list of sources, beside SignaLink, two integrated databases, BioGrid and HPRD, are also listed as having data about this interaction. At the bottom of this box, a GO semantic similarity score with a value of 0.53 is shown as a predicted level of confidence. The details of this score can be examined with a hyperlink to its original article. On the right side of the protein datasheet page, an interactive image of the network of first neighbors of AXIN1 takes place (Figure 5d). By default, in this network we can see that AXIN1 has 17 first neighbors among pathway members and scaffold-partners. Many of these neighbors are connected also to each other, e.g. APC2 and GSK3B, which form a feed-forward loop with AXIN1. Layers are color-coded, while interaction types are signed by arrow shapes. Different layers can be shown or hidden, and the network image can be switched to full screen mode easily with a control panel that also serves as a figure legend. To facilitate further exploration of the AXIN1 network, any click on the nodes of the network image will direct the user to the datasheet any protein or miRNA. In conclusion, the integrated regulatory data shown for AXIN1 in the SignaLink 2 website lists and points out molecular components, which are capable to regulate the expression or the function of AXIN1. As malfunction of AXIN1 is implicated in many diseases, including for example colon cancer , identification of AXIN1 regulators could serve as novel therapeutic targets. A short list of suggested – alternative – targets that could modulate AXIN1 could be important as currently there is no drug against AXIN1 (according to DrugBank and PharmGKB [62, 63]). This strategy is in agreement with the recently proposed allo-network drug concept, whose effects can propagate across several proteins, to enhance or inhibit specific interactions along a pathway . However, further experimental tests and global screens should clarify the tissue- and context-specific roles of these AXIN1 regulators, as well as their possible pharmacological applicability. We believe that SignaLink 2 can serve as an initial resource to identify such promising components.
Comparison with other resources
Comparison of resources with SignaLink 2
Model species #
Containsmanual curation §
Contains integrated data
Reference for each interaction
Cross-talks and multi-pathway proteins *
Undirected protein-protein interactions
Directed protein-protein interactions †
Transcription factor regulation
Transcription factors that regulate miRNAs
Confidence score for the interactions
Customizable download options
Freely downloadable for academic users
We previously compared the number of proteins and interactions between SignaLink 1, KEGG, Reactome and NetPath . Since this comparison only the datasets of SignaLink and Reactome has improved significantly, thus, here we present an updated comparison between SignaLink 2 and Reactome. We compared the proteins and interactions of five pathways (JAK/STAT, Notch, RTK, TGF-β and WNT) present in both SignaLink 2 and Reactome. We found that 331 proteins and 848 interactions were present in both resources. 677 proteins and 5,962 interactions were SignaLink-specific, while 916 proteins and 5,365 interactions were Reactome-specific. Previously we also compared the curation strategies of SignaLink and Reactome and suggested the high number of protein complex based interactions in Reactome and the significant bias towards specific enzyme functions (e.g., proteolysis) as a possible explanation of the high number resource-specific proteins and interactions . Overall, we think that SignaLink 2 both complements the datasets found in other databases, such as Reactome and supports a more detailed analysis of these pathways by including transcriptional, post-transcriptional and possible post-translational regulators of a pathway.
Knowing that the list of components and interactions in each layer is not complete, we will include further experimentally validated datasets yearly, which will complement the manual curation update we perform every two years. We also intend to include cellular compartment and tissue-specific localization information to future versions of SignaLink. In addition, we will increase the number of integrated hyperlinks in the protein datasheet page and develop connections to medical and drug-related resources.
We presented the upgraded and extended version of the SignaLink resource that allows users to explore signaling pathway interactions and to identify pathway regulators, as well as transcriptional and post-transcriptional regulatory components. With SignaLink 2 users can examine in a single resource how scaffolds, enzymes, TFs or miRNAs regulate cross-talks and signaling flow. We hope that SignaLink 2 will be an efficient resource for modeling signaling systems as well as for signaling-related network medicine and pharmacology.
Availability and requirements
Non-profit users can access SignaLink 2 free of charge at http://SignaLink.org.
We thank the anonymous reviewers for their suggestions and the discussions to members of the Vellai lab, the NetBiol group and the LINK-Group. The authors are grateful for the technical assistance of Holger Dinkel (EMBL) and Dong Li (Beijing Proteome Research Center). This work was supported by the European Union and the European Social Fund [TAMOP-4.2.2/B-10/1-2010-0013 and TAMOP-4.2.1/B-09/1/KMR-2010-0003], the Hungarian Scientific Research Fund [OTKA K83314, K75334, NK78012], and János Bolyai Scholarships to TK and TV.
- Korcsmaros T, Farkas IJ, Szalay MS, Rovo P, Fazekas D, Spiro Z, Bode C, Lenti K, Vellai T, Csermely P: Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery. Bioinformatics. 2010, 26: 2042-2050. 10.1093/bioinformatics/btq310.PubMedView ArticleGoogle Scholar
- Haney S, Bardwell L, Nie Q: Ultrasensitive responses and specificity in cell signaling. BMC Syst Biol. 2010, 4: 119-10.1186/1752-0509-4-119.PubMedPubMed CentralView ArticleGoogle Scholar
- Kholodenko BN: Cell-signalling dynamics in time and space. Nat Rev Mol Cell Biol. 2006, 7: 165-176. 10.1038/nrm1838.PubMedPubMed CentralView ArticleGoogle Scholar
- Grabbe C, Husnjak K, Dikic I: The spatial and temporal organization of ubiquitin networks. Nat Rev Mol Cell Biol. 2011, 12: 295-307.PubMedPubMed CentralView ArticleGoogle Scholar
- Bhattacharyya RP, Remenyi A, Yeh BJ, Lim WA: Domains, motifs, and scaffolds: the role of modular interactions in the evolution and wiring of cell signaling circuits. Annu Rev Biochem. 2006, 75: 655-680. 10.1146/annurev.biochem.75.103004.142710.PubMedView ArticleGoogle Scholar
- Lin CC, Chen YJ, Chen CY, Oyang YJ, Juan HF, Huang HC: Crosstalk between transcription factors and microRNAs in human protein interaction network. BMC Syst Biol. 2012, 6: 18-10.1186/1752-0509-6-18.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang L, Hou L, Qian M, Deng M: Integrating phosphorylation network with transcriptional network reveals novel functional relationships. PLoS One. 2012, 7: e33160-10.1371/journal.pone.0033160.PubMedPubMed CentralView ArticleGoogle Scholar
- Hwang S, Kim S, Shin H, Lee D: Context-dependent transcriptional regulations between signal transduction pathways. BMC Bioinformatics. 2011, 12: 19-10.1186/1471-2105-12-19.PubMedPubMed CentralView ArticleGoogle Scholar
- Huang SS, Fraenkel E: Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Sci Signal. 2009, 2: ra40-10.1126/scisignal.2000350.PubMedPubMed CentralGoogle Scholar
- Lepoivre C, Bergon A, Lopez F, Perumal NB, Nguyen C, Imbert J, Puthier D: TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks. BMC Bioinformatics. 2012, 13: 19-10.1186/1471-2105-13-19.PubMedPubMed CentralView ArticleGoogle Scholar
- Papp D, Lenti K, Modos D, Fazekas D, Dul Z, Turei D, Foldvari-Nagy L, Nussinov R, Csermely P, Korcsmaros T: The NRF2-related interactome and regulome contain multifunctional proteins and fine-tuned autoregulatory loops. FEBS Lett. 2012, 586: 1795-1802. 10.1016/j.febslet.2012.05.016.PubMedView ArticleGoogle Scholar
- Pires-daSilva A, Sommer RJ: The evolution of signalling pathways in animal development. Nat Rev Genet. 2003, 4: 39-49. 10.1038/nrg977.PubMedView ArticleGoogle Scholar
- Ramirez F, Albrecht M: Finding scaffold proteins in interactomes. Trends Cell Biol. 2010, 20: 2-4. 10.1016/j.tcb.2009.11.003.PubMedView ArticleGoogle Scholar
- Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S, Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A: Human Protein Reference Database–2009 update. Nucleic Acids Res. 2009, 37: D767-D772. 10.1093/nar/gkn892.PubMedPubMed CentralView ArticleGoogle Scholar
- Yu J, Pacifico S, Liu G, Finley RL: DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions. BMC Genom. 2008, 9: 461-10.1186/1471-2164-9-461.View ArticleGoogle Scholar
- Simonis N, Rual JF, Carvunis AR, Tasan M, Lemmens I, Hirozane-Kishikawa T, Hao T, Sahalie JM, Venkatesan K, Gebreab F, Cevik S, Klitgord N, Fan C, Braun P, Li N, Ayivi-Guedehoussou N, Dann E, Bertin N, Szeto D, Dricot A, Yildirim MA, Lin C, de Smet AS, Kao HL, Simon C, Smolyar A, Ahn JS, Tewari M, Boxem M, Milstein S: Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network. Nat Meth. 2009, 6: 47-54. 10.1038/nmeth.1279. et alView ArticleGoogle Scholar
- Stark C, Breitkreutz BJ, Chatr-aryamontri A, Boucher L, Oughtred R, Livstone MS, Nixon J, Van Auken K, Wang X, Shi X, Reguly T, Rust JM, Winter A, Dolinski K, Tyers M: The BioGRID Interaction Database: 2011 update. Nucleic Acids Res. 2011, 39: D698-D704. 10.1093/nar/gkq1116.PubMedPubMed CentralView ArticleGoogle Scholar
- Dinkel H, Michael S, Weatheritt RJ, Davey NE, Van Roey K, Altenberg B, Toedt G, Uyar B, Seiler M, Budd A, Jodicke L, Dammert MA, Schroeter C, Hammer M, Schmidt T, Jehl P, McGuigan C, Dymecka M, Chica C, Luck K, Via A, Chatr-aryamontri A, Haslam N, Grebnev G, Edwards RJ, Steinmetz MO, Meiselbach H, Diella F, Gibson TJ: ELM–the database of eukaryotic linear motifs. Nucleic Acids Res. 2012, 40: D242-D251. 10.1093/nar/gkr1064.PubMedPubMed CentralView ArticleGoogle Scholar
- Liu W, Li D, Wang J, Xie H, Zhu Y, He F: Proteome-wide prediction of signal flow direction in protein interaction networks based on interacting domains. Mol Cell Proteomics. 2009, 8: 2063-2070. 10.1074/mcp.M800354-MCP200.PubMedPubMed CentralView ArticleGoogle Scholar
- Portales-Casamar E, Thongjuea S, Kwon AT, Arenillas D, Zhao X, Valen E, Yusuf D, Lenhard B, Wasserman WW, Sandelin A: JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles. Nucleic Acids Res. 2010, 38: D105-D110. 10.1093/nar/gkp950.PubMedPubMed CentralView ArticleGoogle Scholar
- Portales-Casamar E, Kirov S, Lim J, Lithwick S, Swanson MI, Ticoll A, Snoddy J, Wasserman WW: PAZAR: a framework for collection and dissemination of cis-regulatory sequence annotation. Genome Biol. 2007, 8: R207-10.1186/gb-2007-8-10-r207.PubMedPubMed CentralView ArticleGoogle Scholar
- Barrasa MI, Vaglio P, Cavasino F, Jacotot L, Walhout AJ: EDGEdb: a transcription factor-DNA interaction database for the analysis of C. elegans differential gene expression. BMC Genom. 2007, 8: 21-10.1186/1471-2164-8-21.View ArticleGoogle Scholar
- Gallo SM, Gerrard DT, Miner D, Simich M, Des SB, Bergman CM, Halfon MS: REDfly v3.0: toward a comprehensive database of transcriptional regulatory elements in Drosophila. Nucleic Acids Res. 2011, 39: D118-D123. 10.1093/nar/gkq999.PubMedPubMed CentralView ArticleGoogle Scholar
- Griffith OL, Montgomery SB, Bernier B, Chu B, Kasaian K, Aerts S, Mahony S, Sleumer MC, Bilenky M, Haeussler M, Griffith M, Gallo SM, Giardine B, Hooghe B, Van Loo P, Blanco E, Ticoll A, Lithwick S, Portales-Casamar E, Donaldson IJ, Robertson G, Wadelius C, De Bleser P, Vlieghe D, Halfon MS, Wasserman W, Hardison R, Bergman CM, Jones SJ: ORegAnno: an open-access community-driven resource for regulatory annotation. Nucleic Acids Res. 2008, 36: D107-D113. 10.1093/nar/gkn457.PubMedPubMed CentralView ArticleGoogle Scholar
- Bovolenta LA, Acencio ML, Lemke N: HTRIdb: an open-access database for experimentally verified human transcriptional regulation interactions. BMC Genom. 2012, 13: 405-10.1186/1471-2164-13-405.View ArticleGoogle Scholar
- Blanco E, Farre D, Alba MM, Messeguer X, Guigo R: ABS: a database of Annotated regulatory Binding Sites from orthologous promoters. Nucleic Acids Res. 2006, 34: D63-D67. 10.1093/nar/gkj116.PubMedPubMed CentralView ArticleGoogle Scholar
- Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana E, Rozowsky J, Alexander R, Min R, Alves P, Abyzov A, Addleman N, Bhardwaj N, Boyle AP, Cayting P, Charos A, Chen DZ, Cheng Y, Clarke D, Eastman C, Euskirchen G, Frietze S, Fu Y, Gertz J, Grubert F, Harmanci A, Jain P, Kasowski M: Architecture of the human regulatory network derived from ENCODE data. Nature. 2012, 489: 91-100. 10.1038/nature11245. et alPubMedPubMed CentralView ArticleGoogle Scholar
- Vergoulis T, Vlachos IS, Alexiou P, Georgakilas G, Maragkakis M, Reczko M, Gerangelos S, Koziris N, Dalamagas T, Hatzigeorgiou AG: TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res. 2012, 40: D222-D229. 10.1093/nar/gkr1161.PubMedPubMed CentralView ArticleGoogle Scholar
- Kozomara A, Griffiths-Jones S: miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 2011, 39: D152-D157. 10.1093/nar/gkq1027.PubMedPubMed CentralView ArticleGoogle Scholar
- Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T: miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009, 37: D105-D110. 10.1093/nar/gkn851.PubMedPubMed CentralView ArticleGoogle Scholar
- Betel D, Wilson M, Gabow A, Marks DS, Sander C: The microRNA.org resource: targets and expression. Nucleic Acids Res. 2008, 36: D149-D153.PubMedPubMed CentralView ArticleGoogle Scholar
- Lewis BP, Burge CB, Bartel DP: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell. 2005, 120: 15-20. 10.1016/j.cell.2004.12.035.PubMedView ArticleGoogle Scholar
- Bandyopadhyay S, Bhattacharyya M: PuTmiR: a database for extracting neighboring transcription factors of human microRNAs. BMC Bioinformatics. 2010, 11: 190-10.1186/1471-2105-11-190.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang J, Lu M, Qiu C, Cui Q: TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Res. 2010, 38: D119-D122. 10.1093/nar/gkp803.PubMedPubMed CentralView ArticleGoogle Scholar
- Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A: UniProtKB/Swiss-Prot: The Manually Annotated Section of the UniProt KnowledgeBase. Meth Mol Biol. 2007, 406: 89-112.Google Scholar
- Jain S, Bader GD: An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology. BMC Bioinformatics. 2010, 11: 562-10.1186/1471-2105-11-562.PubMedPubMed CentralView ArticleGoogle Scholar
- Li D, Liu W, Liu Z, Wang J, Liu Q, Zhu Y, He F: PRINCESS, a protein interaction confidence evaluation system with multiple data sources. Mol Cell Proteomics. 2008, 7: 1043-1052. 10.1074/mcp.M700287-MCP200.PubMedView ArticleGoogle Scholar
- Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD: Cytoscape Web: an interactive web-based network browser. Bioinformatics. 2010, 26: 2347-2348. 10.1093/bioinformatics/btq430.PubMedPubMed CentralView ArticleGoogle Scholar
- Fraser ID, Germain RN: Navigating the network: signaling cross-talk in hematopoietic cells. Nat Immunol. 2009, 10: 327-331. 10.1038/ni.1711.PubMedPubMed CentralView ArticleGoogle Scholar
- Papin JA, Hunter T, Palsson BO, Subramaniam S: Reconstruction of cellular signalling networks and analysis of their properties. Nat Rev Mol Cell Biol. 2005, 6: 99-111. 10.1038/nrm1570.PubMedView ArticleGoogle Scholar
- Neumann CJ, Cohen SM: A hierarchy of cross-regulation involving Notch, wingless, vestigial and cut organizes the dorsal/ventral axis of the Drosophila wing. Development. 1996, 122: 3477-3485.PubMedGoogle Scholar
- Fisher J, Piterman N, Hubbard EJ, Stern MJ, Harel D: Computational insights into Caenorhabditis elegans vulval development. Proc Natl Acad Sci U S A. 2005, 102: 1951-1956. 10.1073/pnas.0409433102.PubMedPubMed CentralView ArticleGoogle Scholar
- Vlachos IS, Kostoulas N, Vergoulis T, Georgakilas G, Reczko M, Maragkakis M, Paraskevopoulou MD, Prionidis K, Dalamagas T, Hatzigeorgiou AG: DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res. 2012, 40: W498-W504. 10.1093/nar/gks494.PubMedPubMed CentralView ArticleGoogle Scholar
- Shirdel EA, Xie W, Mak TW, Jurisica I: NAViGaTing the micronome–using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. PLoS One. 2011, 6: e17429-10.1371/journal.pone.0017429.PubMedPubMed CentralView ArticleGoogle Scholar
- Kestler HA, Wawra C, Kracher B, Kuhl M: Network modeling of signal transduction: establishing the global view. Bioessays. 2008, 30: 1110-1125. 10.1002/bies.20834.PubMedView ArticleGoogle Scholar
- Kirouac DC, Saez-Rodriguez J, Swantek J, Burke JM, Lauffenburger DA, Sorger PK: Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks. BMC Syst Biol. 2012, 6: 29-10.1186/1752-0509-6-29.PubMedPubMed CentralView ArticleGoogle Scholar
- Terfve CD, Cokelaer T, Henriques D, Macnamara A, Goncalves E, Morris MK, Van IM, Lauffenburger DA, Saez-Rodriguez J: CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Syst Biol. 2012, 6: 133-10.1186/1752-0509-6-133.PubMedPubMed CentralView ArticleGoogle Scholar
- Gabaldon T, Huynen MA: Prediction of protein function and pathways in the genome era. Cell Mol Life Sci. 2004, 61: 930-944. 10.1007/s00018-003-3387-y.PubMedView ArticleGoogle Scholar
- Kuzniar A, van Ham RC, Pongor S, Leunissen JA: The quest for orthologs: finding the corresponding gene across genomes. Trends Genet. 2008, 24: 539-551. 10.1016/j.tig.2008.08.009.PubMedView ArticleGoogle Scholar
- Korcsmaros T, Szalay MS, Rovo P, Palotai R, Fazekas D, Lenti K, Farkas IJ, Csermely P, Vellai T: Signalogs: orthology-based identification of novel signaling pathway components in three metazoans. PLoS One. 2011, 6: e19240-10.1371/journal.pone.0019240.PubMedPubMed CentralView ArticleGoogle Scholar
- Yu H, Luscombe NM, Lu HX, Zhu X, Xia Y, Han JD, Bertin N, Chung S, Vidal M, Gerstein M: Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. Genome Res. 2004, 14: 1107-1118. 10.1101/gr.1774904.PubMedPubMed CentralView ArticleGoogle Scholar
- Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation. Cell. 2011, 144: 646-674. 10.1016/j.cell.2011.02.013.PubMedView ArticleGoogle Scholar
- Korcsmaros T, Szalay MS, Bode C, Kovacs IA, Csermely P: How to design multi-target drugs: Target-search options in cellular networks. Exp Op Drug Discovery. 2007, 2: 799-808. 10.1517/174604126.96.36.1999.View ArticleGoogle Scholar
- Farkas IJ, Korcsmaros T, Kovacs IA, Mihalik A, Palotai R, Simko GI, Szalay KZ, Szalay-Beko M, Vellai T, Wang S, Csermely P: Network-based tools for the identification of novel drug targets. Sci Signal. 2011, 4: pt3-10.1126/scisignal.2001950.PubMedView ArticleGoogle Scholar
- Spiro Z, Kovacs IA, Csermely P: Drug-therapy networks and the prediction of novel drug targets. J Biol. 2008, 7: 20-10.1186/jbiol81.PubMedPubMed CentralView ArticleGoogle Scholar
- Sergina NV, Rausch M, Wang D, Blair J, Hann B, Shokat KM, Moasser MM: Escape from HER-family tyrosine kinase inhibitor therapy by the kinase-inactive HER3. Nature. 2007, 445: 437-441. 10.1038/nature05474.PubMedPubMed CentralView ArticleGoogle Scholar
- Jia J, Zhu F, Ma X, Cao Z, Li Y, Chen YZ: Mechanisms of drug combinations: interaction and network perspectives. Nat Rev Drug Discov. 2009, 8: 111-128. 10.1038/nrd2683.PubMedView ArticleGoogle Scholar
- Berger SI, Iyengar R: Network analyses in systems pharmacology. Bioinformatics. 2009, 25: 2466-2472. 10.1093/bioinformatics/btp465.PubMedPubMed CentralView ArticleGoogle Scholar
- Barabasi AL, Gulbahce N, Loscalzo J: Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011, 12: 56-68. 10.1038/nrg2918.PubMedPubMed CentralView ArticleGoogle Scholar
- Csermely P, Korcsmaros T, Kiss HJ, London G, Nussinov R: Structure and dynamics of molecular networks: A novel paradigm of drug discovery. 2012, Preprint: http://arxiv.org/abs/1210.0330Google Scholar
- Parveen N, Hussain MU, Pandith AA, Mudassar S: Diversity of axin in signaling pathways and its relation to colorectal cancer. Med Oncol. 2011, 28 (Suppl 1): S259-S267.PubMedView ArticleGoogle Scholar
- Wishart DS: DrugBank and its relevance to pharmacogenomics. Pharmacogenomics. 2008, 9: 1155-1162. 10.2217/146224188.8.131.525.PubMedView ArticleGoogle Scholar
- Medina MW, Sangkuhl K, Klein TE, Altman RB, Pharm GKB: very important pharmacogene - HMGCR. Pharmacogenet Genom. 2010, 21 (2): 98-101.View ArticleGoogle Scholar
- Nussinov R, Tsai CJ, Csermely P: Allo-network drugs: harnessing allostery in cellular networks. Trends Pharmacol Sci. 2011, 32: 686-693. 10.1016/j.tips.2011.08.004.PubMedView ArticleGoogle Scholar
- Hurlbut GD, Kankel MW, Lake RJ, Artavanis-Tsakonas S: Crossing paths with Notch in the hyper-network. Curr Opin Cell Biol. 2007, 19: 166-175. 10.1016/j.ceb.2007.02.012.PubMedView ArticleGoogle Scholar
- Lutz M, Knaus P: Integration of the TGF-beta pathway into the cellular signalling network. Cell Signal. 2002, 14: 977-988. 10.1016/S0898-6568(02)00058-X.PubMedView ArticleGoogle Scholar
- Kluppel M, Wrana JL: Turning it up a Notch: cross-talk between TGF beta and Notch signaling. Bioessays. 2005, 27: 115-118. 10.1002/bies.20187.PubMedView ArticleGoogle Scholar
- Guo X, Wang XF: Signaling cross-talk between TGF-beta/BMP and other pathways. Cell Res. 2009, 19: 71-88. 10.1038/cr.2008.302.PubMedPubMed CentralView ArticleGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13: 2498-2504. 10.1101/gr.1239303.PubMedPubMed CentralView ArticleGoogle Scholar
- Pang RW, Poon RT: Cancer Stem Cell as a Potential Therapeutic Target in Hepatocellular Carcinoma. Curr Canc Drug Targets. 2012, 12 (9): 1081-1094.Google Scholar
- Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999, 27: 29-34. 10.1093/nar/27.1.29.PubMedPubMed CentralView ArticleGoogle Scholar
- Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, Lewis S, Birney E, Stein L: Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 2005, 33: D428-D432.PubMedPubMed CentralView ArticleGoogle Scholar
- Paz A, Brownstein Z, Ber Y, Bialik S, David E, Sagir D, Ulitsky I, Elkon R, Kimchi A, Avraham KB, Shiloh Y, Shamir R: SPIKE: a database of highly curated human signaling pathways. Nucleic Acids Res. 2011, 39: D793-D799. 10.1093/nar/gkq1167.PubMedPubMed CentralView ArticleGoogle Scholar
- Kamburov A, Pentchev K, Galicka H, Wierling C, Lehrach H, Herwig R: ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res. 2011, 39: D712-D717. 10.1093/nar/gkq1156.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.