Permanent link to this recordhttp://hdl.handle.net/10754/325454
MetadataShow full item record
AbstractProtein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k = 8 ?10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors' websites: e.g. http://www.cs.toronto.edu/?wkc/kmerHMM. 2013 The Author(s).
CitationWong K-C, Chan T-M, Peng C, Li Y, Zhang Z (2013) DNA motif elucidation using belief propagation. Nucleic Acids Research 41: e153-e153. doi:10.1093/nar/gkt574.
PublisherOxford University Press (OUP)
JournalNucleic Acids Research
PubMed Central IDPMC3763557
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- A Comparison Study for DNA Motif Modeling on Protein Binding Microarray.
- Authors: Wong KC, Li Y, Peng C, Wong HS
- Issue date: 2016 Mar-Apr
- RankMotif++: a motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors.
- Authors: Chen X, Hughes TR, Morris Q
- Issue date: 2007 Jul 1
- UniPROBE, update 2015: new tools and content for the online database of protein-binding microarray data on protein-DNA interactions.
- Authors: Hume MA, Barrera LA, Gisselbrecht SS, Bulyk ML
- Issue date: 2015 Jan
- PscanChIP: Finding over-represented transcription factor-binding site motifs and their correlations in sequences from ChIP-Seq experiments.
- Authors: Zambelli F, Pesole G, Pavesi G
- Issue date: 2013 Jul
- A general approach for discriminative de novo motif discovery from high-throughput data.
- Authors: Grau J, Posch S, Grosse I, Keilwagen J
- Issue date: 2013 Nov
Showing items related by title, author, creator and subject.
ProDis-ContSHC: Learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrievalWang, Jim Jing-Yan; Gao, Xin; Wang, Quanquan; Li, Yongping (BMC Bioinformatics, Springer Nature, 2012-05-08) [Article]Background: The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.Results: In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context N (i) and N (j) is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context N (i) and N (j). Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update the Protein Hierarchial Context Coherently in an iterative algorithm--ProDis-ContSHC.We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.Conclusions: Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature. 2012 Wang et al.; licensee BioMed Central Ltd.
The human interactome knowledge base (hint-kb): An integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid techniqueTheofilatos, Konstantinos A.; Dimitrakopoulos, Christos M.; Likothanassis, Spiridon D.; Kleftogiannis, Dimitrios A.; Moschopoulos, Charalampos N.; Alexakos, Christos; Papadimitriou, Stergios; Mavroudi, Seferina P. (Artificial Intelligence Review, Springer Nature, 2013-07-12) [Article]Proteins are the functional components of many cellular processes and the identification of their physical protein–protein interactions (PPIs) is an area of mature academic research. Various databases have been developed containing information about experimentally and computationally detected human PPIs as well as their corresponding annotation data. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://biotools.ceid.upatras.gr/hint-kb/), a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, cal-culatesasetoffeaturesofinterest and computesaconfidence score for every candidate protein interaction. This confidence score is essential for filtering the false positive interactions which are present in existing databases, predicting new protein interactions and measuring the frequency of each true protein interaction. For this reason, a novel machine learning hybrid methodology, called (Evolutionary Kalman Mathematical Modelling—EvoKalMaModel), was used to achieve an accurate and interpretable scoring methodology. The experimental results indicated that the proposed scoring scheme outperforms existing computational methods for the prediction of PPIs.
Auxin efflux by PIN-FORMED proteins is activated by two different protein kinases, D6 PROTEIN KINASE and PINOIDZourelidou, Melina; Absmanner, Birgit; Weller, Benjamin; Barbosa, Inês CR; Willige, Björn C; Fastner, Astrid; Streit, Verena; Port, Sarah A; Colcombet, Jean; de la Fuente van Bentem, Sergio; Hirt, Heribert; Kuster, Bernhard; Schulze, Waltraud X; Hammes, Ulrich Z; Schwechheimer, Claus (eLife, eLife Sciences Organisation, Ltd., 2014-06-19) [Article]The development and morphology of vascular plants is critically determined by synthesis and proper distribution of the phytohormone auxin. The directed cell-to-cell distribution of auxin is achieved through a system of auxin influx and efflux transporters. PIN-FORMED (PIN) proteins are proposed auxin efflux transporters, and auxin fluxes can seemingly be predicted based on the-in many cells-asymmetric plasma membrane distribution of PINs. Here, we show in a heterologous Xenopus oocyte system as well as in Arabidopsis thaliana inflorescence stems that PIN-mediated auxin transport is directly activated by D6 PROTEIN KINASE (D6PK) and PINOID (PID)/WAG kinases of the Arabidopsis AGCVIII kinase family. At the same time, we reveal that D6PKs and PID have differential phosphosite preferences. Our study suggests that PIN activation by protein kinases is a crucial component of auxin transport control that must be taken into account to understand auxin distribution within the plant.