Signalogs: Orthology-Based Identification of Novel Signaling Pathway Components in Three Metazoans

Korcsmáros, Tamás; Szalay, Máté; Rovó, Petra; Palotai, Robin; Fazekas, Dávid; Lenti, Katalin; Farkas, Illés J.; Csermely, Péter; Vellai, Tibor

doi:10.1371/journal.pone.0019240

Cited by 23 publications

(24 citation statements)

References 65 publications

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“…In the absence of such appropriate direct interaction information, wefollowed another approach called Signalogs identification [Kor11] based on pathway annotation transfer. In short, this approach advocates transferring interaction information from a certain pair of interacting genes/proteins in some species to some other, homologous pair in another species.…”

Section: Database Explorationmentioning

confidence: 99%

Scientific report: Training workshop interdisciplinary life sciences

Akudibillah

Boas

Carrères

et al. 2014

Preprint

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This preprint is the outcome of the “Training Workshop Interdisciplinary Life Sciences”, held in October 2013 in the Lorentz Center, Leiden, The Netherlands. The motivation to organize this event stems from the following considerations: The enormous progress in laboratory techniques and facilities leads to the availability of huge amounts of data at all levels of complexity (molecules, cells, tissues, organs, organisms, populations, ecosystems). Especially data at the cellular level reveal details of life processes we were unconscious of until recently. However, it becomes clear that huge amounts of data alone do not automatically lead to understanding. The data explosion in Life Sciences teaches one lesson: life processes are of a highly intricate and integrative nature. To really understand the dynamic processes in living organisms one must integrate experimental data sets in quantitative and predictive models. Only then one may hope to grasp the functioning of these complex systems and be able to convert information in understanding. In the field of physics, for instance, this strong interaction between experiment and theory is already common practice since centuries, culminating in the 20th century being called the ’Century of Physics’. In contrast to physics, the complex nature of the Life Sciences forces us to work in an interdisciplinary fashion. The necessary expertise is available, but scattered over many scientific disciplines. Only the combined efforts of biologists, chemists, mathematicians, physicists, engineers, and informaticians will lead to progress in tackling the huge challenge of understanding the complexity of life. Researchers in the Life Sciences often focus their research on a rather narrow research field. However, the majority of the upcoming generation of researchers in the Life Sciences should be trained to expand their skills, becoming able to tackle complex, multi-dimensional systems. The knowledge they have to incorporate in their research will stem from a diverse range of disciplines, So, they should be trained to integrate a broad range of modelling approaches in order to deduce quantitative, predictive and often multi-scale models from highly diverse data sets. Present curricula in the Life Sciences hardly offer this kind of training yet. This workshop intends to start filling this gap. Three teams worked on the following open problems: 1) Modeling the influence of temperature on the Regulation of flowering time in Arabidopsis thaliana; 2) Validation of computational models of angiogenesis to experimental data; 3) Reconstructing the gene network that regulates branching in Tomato. This preprint bundles the reports of the three teams.

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Section: Database Explorationmentioning

confidence: 99%

Scientific report: Training workshop interdisciplinary life sciences

Akudibillah

Boas

Carrères

et al. 2014

Preprint

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“…Because in SignaLink a protein can belong to more than one pathway [12], a signalog can also be annotated to more than one pathway. Using this approach we were able to predict 88, 92, and 73 novel signaling proteins in worms, flies, and humans, respectively [10]. For the limitations of orthology-based pathway annotation transfer, see Note 3.…”

Section: Prediction Of Signalogsmentioning

confidence: 99%

“…Despite a great wealth of protein interaction data obtained from HTP experiments, such as yeast two-hybrid screens, the low abundance of extracellular, membrane-bound, and nuclear signaling components (e.g., ligands, receptors, and transcription factors) make these experimental techniques only partially efficient for identifying signaling interactions [10]. Accordingly, several signaling pathway databases have been generated manually by collecting relevant data from the literature [11].…”

mentioning

confidence: 99%

“…Accordingly, several signaling pathway databases have been generated manually by collecting relevant data from the literature [11]. However, so far most of them lack those key features (e.g., uniform pathway curation across more than one species) that would be necessary for transferring signaling pathway membership information between species [10]. Reliable and detailed signaling pathway databases are crucial for predicting novel signaling components because they are needed (1) as sources of known pathway information from which prediction can be performed (i.e., seed data) and (2) as reference data sets against which the novelty of predictions can be tested (i.e., those predicted signaling pathway member proteins that are already known pathway members should be removed from the list of predictions, while others can be regarded as predicted components).…”

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confidence: 99%

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Uniform Curation Protocol of Metazoan Signaling Pathways to Predict Novel Signaling Components

Pálfy

Farkas

Vellai

et al. 2013

Methods in Molecular Biology

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A relatively large number of signaling databases available today have strongly contributed to our understanding of signaling pathway properties. However, pathway comparisons both within and across databases are currently severely hampered by the large variety of data sources and the different levels of detail of their information content (on proteins and interactions). In this chapter, we present a protocol for a uniform curation method of signaling pathways, which intends to overcome this insufficiency. This uniformly curated database called SignaLink (http://signalink.org) allows us to systematically transfer pathway annotations between different species, based on orthology, and thereby to predict novel signaling pathway components. Thus, this method enables the compilation of a comprehensive signaling map of a given species and identification of new potential drug targets in humans.We strongly believe that the strict curation protocol we have established to compile a signaling pathway database can also be applied for the compilation of other (e.g., metabolic) databases. Similarly, the detailed guide to the orthology-based prediction of novel signaling components across species may also be utilized for predicting components of other biological processes.

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“…The signaling interactions in SignaFish were coming from (1) manual curation of zebrafish papers, where we performed review-based literature searches, which identified the primary zebrafish-related paper that described the interactions; or (2) we used the curated signaling interactions of C. elegans, D. melanogaster and H. sapiens from SignaLink2 [4], and based on the interolog and signalog concepts [6,7], we predicted potential zebrafish signaling interactions. Interologs are interactions detected in one species and predicted to be potentially exist in another species (in this case in zebrafish) as sequence homology shows the presence of the interacting protein pairs in zebrafish.…”

Section: Introductionmentioning

confidence: 99%