Results: To isolate particular design principles, we have computationally evolved random regulatory networks with a preference to result either in hysteresis (switching threshold depending on current state), or in multistationarity (having multiple steady states), two commonly observed dynamical features of gene regulatory networks related to differentiation processes. We have analyzed the resulting evolved networks and compared their structures and characteristics with real gene regulatory networks reported from experiments.
Conclusion: We found that the artificially evolved networks have particular topologies and it was notable that these topologies share important features and similarities with the real gene regulatory networks, particularly in contrasting properties of positive and negative feedback loops. We conclude that the structures of real gene regulatory networks are consistent with selection to favor one or other of the dynamical features of multistationarity or hysteresis.
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Supplementary Material: Supplementary Material is available at Bioinformatics online
]]>Availability: The lumi Bioconductor package,
Contact: Pan Du (
Availability: The C++ source code for PatMaN is distributed under the GNU General Public License and has been tested on the GNU/Linux operating system. It is available from
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Results: In this article, we propose a novel approach to predict the HSE measures and infer residue contact numbers using the predicted HSE values, based on a well-prepared non-homologous protein structure dataset. In particular, we employ support vector regression to quantify the relationship between HSE measures and protein sequences and evaluate its prediction performance. We extensively explore five sequence encoding schemes to examine their effects on the prediction performance. Our method could achieve the correlation coefficients of 0.72 and 0.68 between the predicted and observed HSE-up and HSE-down measures, respectively. Moreover, contact number can be accurately predicted by the summation of the predicted HSE-up and HSE-down values, which has further enlarged the application of this method. The successful application of support vector regression approach in this study suggests that it should be more useful in quantifying the protein sequence-structure relationship and predicting the structural property profiles from protein sequences.
Availability: The prediction webserver and supplementary materials are accessible at
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Results: This work is inspired by the several studies suggesting that co-expression is mostly related to "static" stable binding relationships, like belonging to the same protein complex, rather than other types of interactions more of a "causal" and transient nature (e.g. transcription factor–binding site interactions). The aim of this work is to verify if direct or conditional network inference algorithms (e.g. Pearson correlation for the former, partial Pearson correlation for the latter) are indeed useful in discerning static from causal dependencies in artificial and real gene networks (derived from E.coli and S.cerevisiae).
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Results: We examined the mining capability of pairwise and multiple-toone association by using NETCAR to extract COGs relevant to six microbial phenotypes (aerobic, anaerobic, facultative, endospore, motility, and Gram negative) from 11,969 unique COG profiles across 155 prokaryotic organisms. With the same level of False Discovery Rate (FDR), multiple-to-one association can extract about 10 times more relevant COGs than one-to-one association. We also reveal various topologies of association networks among COGs (modules) from extracted multiple-to-one correlation rules relevant with the six phenotypes; including a well-connected network for motility, a startshaped network for aerobic, and intermediate topologies for the other phenotypes. NETCAR outperforms a standard Class Association Rule mining algorithm, CARAPRIORI, while requiring several orders of magnitude less computational time for extracting 3-COG sets.
Availability: Source code of the Java implementation is available as Supplementary material at the Bioinformatics online website, or upon request to the author.
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Results: We developed Annotation-Modules, which offers an improvement over the current tools which improves the current tools in two critical aspects. Firstly, the underlying annotation database implements features from many different fields like gene regulation and expression, sequence properties, evolution and conservation, genomic localization and functional categories - resulting in about 60 different annotation features. Secondly, it examines not only single annotations but also all the combinations, which is important to gain insight into the interplay of different mechanisms in the analyzed biological system.
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Availability: SYCAMORE is freely available for academic use at
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Results: We describe a methodology by which we have identified over 1,9800 instances of nonredundant nonalignments between terms from the GO biological-process (BP), cellular-component (CC), and molecular-function (MF) ontologies, ChEBI, and the Cell Type Ontology (CL). Many of the 39.838.1% of these nonalignments whose object terms are more atomic than the subject terms are not currently examined in other ontology-enrichment projects due to the fact that the necessary and sufficient conditions required for the inferences are not currently examined. Analysis of the ratios of nonalignments to assertions from which the nonalignments were identified suggests that BP-MF, BP-BP, BP-CL, and CC-CC, BP-BP, and BP-CL terms are relatively well-aligned, while BP-ChEBI-MF, BP-ChEBI, and CC-MF and ChEBI-MF terms are relatively not aligned well. We propose four ways to resolve an identified nonalignment and recommend an analogous implementation of our methodology in ontology-enrichment tools to identify types of nonalignments that are currently not detected.
Availability: The nonalignments discussed in this article may be viewed at
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Results: We present a Support Vector Machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity, and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of ~0.8 with a standard deviation of less than 0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage.
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Availability: DAnTE may be downloaded free of charge at
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Supplementary information: An example dataset with instructions on how to perform a series of analysis steps is available at
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Results: We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes.
Availability: The C++ code to implement the proposed method is available upon request for academic use.
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Availability: The mirEval web server is available at
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Availability: Executables for several platforms, user's manual, and source code are freely available under the GNU General Public License at
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Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a data set of SDPs. The resulting large data set, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolutionary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large data set of enzyme SDPs.
Availability: Data sets and GroupSim code are available online at
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Availability: The software is implemented in the R language and can be downloaded from the Bioconductor project website (
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Availability: jSquid is available for direct usage and download at
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Supplementary Information: available at Bioinformatics online
]]>Availability: The nuScore interface is freely available for use at
Supplementary information: The site contains user manual, description of the methodology, and examples.
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Results: I present a novel method to address this issue, and demonstrate that divergence in bacteria-killing activity among animal antimicrobial peptides is positively correlated with the log of the dN/dS ratio. The primary cause of this pattern appears to be that positively selected substitutions change protein function more than neutral substitutions do. Thus, the dN/dS ratio is an accurate estimator of adaptive functional divergence.
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Supplementary information: Supplementary data, including GenBank Accession numbers, are available at Bioinformatics Online
]]>Results: Our predictor, PEPITO, uses a combination of amino acid propensity scores and half sphere exposure values at multiple distances to achieve state-of-the-art performance. PEPITO achieves an Area Under the Curve (AUC) of 75.4 on the Discotope dataset. Additionally, we benchmark PEPITO as well as the Discotope predictor on the more recent Epitome datasaset, achieving AUCs of 68.3 and 66.0 respectively.
Availability: PEPITO is available as part of the SCRATCH suite of protein structure predictors via
Availability: The program is freely available from the Comprehensive R Archive Network (
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Availability:
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Supplementary information: Supplementary data are available on Bioinformatics online.
]]>One can define the architecture and initial parameters of the model in a text file and then use MAMOT for parameter optimization on example data, decoding (like predicting motif occurrence in sequences) and the production of stochastic sequences generated according to the probabilistic model. Two examples for which models are provided are coiled-coil domains in protein sequences and protein binding sites in DNA. A wealth of useful features include the use of pseudocounts, state tying and fixing of selected parameters in learning, and the inclusion of prior probabilities in decoding.
Availability: MAMOT is implemented in C++, and is distributed under the GNU General Public Licence (GPL). The software, documentation, and example model files can be found at
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Availability: The TOPDOM database and the constrained HMMTOP prediction server are available on the page
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Results: We have developed a computational approach to detect crosstalk among pathways based on protein interactions between the pathway components. We built a global mammalian pathway crosstalk network that includes 580 pathways (covering 4,753 genes) with 1,815 edges between pathways. This crosstalk network follows a power-law distribution: P(k) ~ k-, = 1.45, where P(k) is the number of pathways with k neighbors, thus pathway interactions may exhibit the same scale-free phenomenon that has been documented for protein interaction networks. We further used this network to understand colorectal cancer progression to metastasis based on transcriptomic data.
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Results: We present a new program, RNAplex, especially designed to quickly find possible hybridization sites for a query RNA in large RNA databases. RNAplex uses a slightly different energy model which reduces the computational time by a factor 10-27 compared to RNAhybrid. In addition a length penalty allows to focus the target search on short highly stable interactions.
Availability: RNAplex can be downloaded at
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The swissPIT (Swiss Protein Identification Toolbox) follows this approach but goes a step further by providing the user an expand-able multi-tool platform capable of executing workflows to analyze Tandem MS-based data. One of the major problems in proteomics is the absent of standardized workflows to analyze the produced data. This includes the pre-processing part as well as the final identifica-tion of peptides and proteins. The main idea of swissPIT is not only the usage of different identification tool in parallel but also the mean-ingful concatenation of different identification strategies at the same time. The swissPIT is open source software but we also provide a user-friendly web platform, which demonstrates the capabilities of our software and which is available at
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Results: Analyzing phosphoproteomic data derived from the human mitotic spindle apparatus, we found that 95.2 % of 1744 phosphorylation sites are conserved in at least one of six other vertebrate species. Using a new score, termed CZ-Score, we demonstrate that phosphorylation sites are significantly better conserved than other S/T/Y sites, a conclusion validated from several kinase consensus motifs. Most importantly, phosphorylation sites with experimentally verified biological functions were significantly better conserved than other phosphorylation sites, indicating that analysis utilizing evolutionary conservation may constitute a powerful basis for the development of improved phosphorylation site predictors.
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Results: We developed a new prediction method for disordered regions in proteins based on the meta approach and implemented a web-server for this prediction method named "metaPrDOS". The method predicts the disorder tendency of each residue using support vector machines from the prediction results of the seven independent predictors. Evaluation of the meta approach was performed using the CASP7 prediction targets to avoid an overestimation due to the inclusion of proteins used in the training set of some component predictors. As a result, the meta approach achieved higher prediction accuracy than all methods participating in CASP7.
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Results: We devise a new calculation method termed sweep calculation and reduce the time complexity of the current grid layout algorithms through its encoding and decoding processes. We conduct practical experiments by using 95 pathway models of various sizes from TRANSPATH and show that our new grid layout algorithm is much faster than existing grid layout algorithms. For the cost function, we introduce a new component that penalizes undesirable edge directions to avoid the lack of traceability in pathways due to the differences in direction between in-edges and out-edges of each vertex.
Availability: Java implementations of our layout algorithms are available in Cell Illustrator.
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Availability: simuPOP is freely available at
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Availability: IDMap executable and its user manual including color images are freely available to non-commercial users at
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Results: After the 2003 study, three follow-up studies were conducted in 2004, 2005 and 2007. Unfortunately, no significant change was found in the rate of URL decay among any of the studies. However, only 5% of URLs cited more than twice have decayed versus 20% of URLs cited once or twice. The most common types of lost content were computer programs (43%), followed by scholarly content (38%) and databases (19%). Compared to URLs still available, no lost content type was significantly over or under-represented. Searching for 30 of these websites using Google, 11 (37%) were found relocated to different URLs.
Conclusions: URL decay continues unabated, but URLs published by organizations tend to be more stable. Repeated citation of URLs suggests calculation of an electronic impact factor (eIF) would be an objective, quantitative way to measure the impact of Internet-based resources on scientific research.
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Availability: This new algorithm is implemented in the program Geneland. It is freely available under GNU public license as an R package on the Comprehensive R Archive Network. It now includes a fully clickable graphical interface. Informations on how to get the software are available on
Supplementary material: Details on the simulation study are available from
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The aim of this work is to integrate a gene network of the TH differentiation in an agent-based model of the hyper-sensitivity reaction. The implementation of such a system introduces a second level of description beyond the mesoscopic level of the inter-cellular interaction of the agent-based model.
The intra-cellular level consists in the cell internal dynamics of gene activation and transcription. The gene regulatory network includes genes-related molecules that have been found to be involved in the differentiation process in TH cells.
Results: The simulator reproduces the hallmarks of an IgEmediated hypersensitive reaction and provides an example of how to combine the mesoscopic level description of immune cells with the microscopic gene-level dynamics.
Availability: The basic version of the simulator of the immune response can be downloaded here:
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Availability: The algorithm has been implemented in the web-accessible servers NetMHC-3.0:
Results: The results show that the linear is the dominating expression pattern in the same pathway. The time-shifted pattern is another important relationship pattern. Many genes from the different pathway also present co-expression patterns. The nonlinear, non-monotonic and time-delayed relationship patterns reflect the remote interactions between the genes in cellular processes. Gene co-expression phenomena in the same pathways are diverse in different species. Genes in Saccharomyces cerevisiae and Caenorhabditis elegans present strong co-expression relationships, especially in Caenorhabditis elegans, co-expression is more universal and stronger due to its special array of genes. However in Human, gene co-expression is not apparent and the human genome involves more complicated functional relationships. In conclusion, different patterns corresponding to different coordinating behaviors coexist. The patterns trees of different species give us comprehensive insight and understanding of genes expression activity in the cellular society.
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Results: We present the Divisive Correlation Clustering Algorithm (DCCA) that is suitable for finding a group of genes having similar pattern of variation in their expression values. To detect clusters with high correlation and biological significance, we use the correlation clustering concept introduced by Bansal's et al. (Bansal et al. (2004)). Our proposed algorithm DCCA produces a clustering solution without taking number of clusters to be created as an input. DCCA uses the correlation matrix in such a way that all genes in a cluster have highest average correlation with genes in that cluster. To test the performance of the DCCA, we have applied DCCA and some well-known conventional methods to an artificial data set, and nine gene expression datasets, and compared the performance of the algorithms. The clustering results of the DCCA are found to be more significantly relevant to the biological annotations than those of the other methods. All these facts show the superiority of the DCCA over some others for the clustering of gene-expression data.
Availability of the software: The software has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from
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Supplementary Material: Supplementary Material has been uploaded as a separate file.
]]>Results: We have developed a simple and elegant approach to resolve the problem of model corruption in PSI-BLAST searches. We hypothesized that combining results from the first (least-corrupted) profile with results from later (most sensitive) iterations of PSI-BLAST provides a better discriminator for true and false hits. Accordingly, we have derived a formula that utilizes the E-values from these two PSI-BLAST iterations to obtain a figure of merit for rank-ordering the hits. Our verification results based on a "gold-standard" test set indicate that this figure of merit does indeed delineate true positives from false positives better than PSI-BLAST E-values. Perhaps what is most notable about this strategy is that it is simple and straightforward to implement.
]]>Results: Firstly FastNCA is compared to NCA and NCA-r using synthetic data. The reconstruction of FastNCA is more accurate than that of NCA-r and comparable to that of properly converged NCA. FastNCA is not sensitive to the correlation among the input signals, while its performance does degrade a little but not as dramatically as that of NCA. Like NCA, FastNCA is not very sensitive to small inaccuracies in a priori information on the network topology. FastNCA is about several tens times faster than NCA and several hundreds times faster than NCA-r. Then, the method is applied to real yeast cell cycle microarray data. The activities of the estimated cell cycle regulators by FastNCA and NCA-r are compared to the semiquantitative results obtained independently by Lee et al. (2002). It is shown here that there is a greater agreement between the results of FastNCA and Lee's, which is represented by the ratio 23/33, than that between the results of NCA-r and Lee's, which is 14/33.
Availability: Software and supplementary materials are available from
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Availability: Stable version is available from CRAN:
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Results: We investigate the patterns of dotplots between species of bacteria with the purpose of model selection in gene order problems. We find several categories of data which can be explained by carefully weighing the contributions of reversals, transpositions, symmetrical reversals, single gene transpositions, and single gene reversals. We also derive method of moments distance estimates for some previously uncomputed cases, such as symmetrical reversals, single gene reversals and their combinations, as well as the single gene transpositions edit distance.
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Supplementary information: Available at Bioinformatics online.
]]>Availability:
Contact: David J. States (
Results: We extend previous work by Markowetz et al., who proposed a statistical framework to score different network hypotheses. Our extensions go in several directions: We show how prior assumptions on the network structure can be incorporated into the scoring scheme by defining appropriate prior distributions on the network structure as well as on hyperparameters. An approach called module networks is introduced to scale up the original approach, which is limited to around 5 genes, to infer large scale networks of more than 30 genes. Instead of the data discretization step needed in the original framework, we propose the usage of a beta-uniform mixture distribution on the p-value profile, resulting from differential gene expression calculation, to quantify effects. Extensive simulations on artificial data and application of our module network approach to infer the signaling network between 13 genes in the ER- pathway in human MCF-7 breast cancer cells show that our approach gives sensible results. Using a bootstrapping and a jackknife approach this reconstruction is found to be statistically stable.
Availability: The proposed method is available within the Bioconductor R-package nem.
Contact: h.froehlich@dkfz.de
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