MOTIVATION: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables. We compare a model encompassing history (H) with one not using this information (NH). RESULTS: The H-model demonstrates superior discriminative ability, with a higher ROC-AUC score (76.34%) than the NH-model (74.98%). Wilcoxon test results confirm significant improvement of predictive accuracy for treatment outcomes through incorporating historical information. The increased performance of the H-model might be attributed to its consideration of latent HIV reservoirs, probably obtained when leveraging historical information. The findings emphasize the importance of temporal dynamics in acquiring mutations. However, our result also shows that prediction accuracy remains relatively high even when no historical information is available. AVAILABILITY AND IMPLEMENTATION: This analysis was conducted using the Euresist Integrated DataBase (EIDB). For further validation, we encourage reproducing this study with the latest release of the EIDB, which can be accessed upon request through the Euresist Network.

MOTIVATION: While large amounts of epigenomic data are publicly available, their retrieval in a form suitable for downstream analysis is a bottleneck in current research. The DeepBlue Epigenomic Data Server provides a powerful interface and API for filtering, transforming, aggregating and downloading data from several epigenomic consortia. RESULTS: To make public epigenomic data conveniently available for analysis in R, we developed an R/Bioconductor package that connects to the DeepBlue Epigenomic Data Server, enabling users to quickly gather and transform epigenomic data from selected experiments for analysis in the Bioconductor ecosystem. AVAILABILITY AND IMPLEMENTATION: http://deepblue.mpi-inf.mpg.de/R . REQUIREMENTS: R 3.3, Bioconductor 3.4. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The recent editorial by Dr Longo and Dr Drazen in the New England Journal of Medicine (Longo and Drazen, 2016) has stirred up quite a bit of controversy. As Executive Officers of the International Society of Computational Biology, Inc. (ISCB), we express our deep concern about the restrictive and potentially damaging opinions voiced in this editorial, and while ISCB works to write a detailed response, we felt it necessary to promptly address the editorial with this reaction. Although some of the concerns voiced by the authors of the editorial are worth considering, large parts of the statement purport an obsolete view of hegemony over data that is neither in line with today’s spirit of open access nor furthering an atmosphere where the potential of data can be fully realized. ISCB acknowledges that the additional comment on the editorial (Drazen, 2016) eases some of the polemics unfortunately without addressing some of the core issues. We still feel, however, that we need to contrast the opinion voiced in the editorial with what we consider the axioms of our scientific society, statements that lead into a fruitful future of data-driven science: Data produced with public money should be public in benefit of the science and society Restrictions on the use of public data hamper science and slow progress Open data is the best way to combat fraud and misinterpretations Current large data collections proceed from many sources, are continually accumulated, and require a variety of analytical approaches. Data generation and data analysis overlap in time and are continually updated with new data sets produced by new techniques and new analysis methodologies. Furthermore, in many cases current science functions in consortia in which scientists collaborate toward common goals while preserving their own scientific objectives. Dividing scientists into data providers and data analysts is simplistic and gives a misleading impression of the actual state of biological and biomedical science. ISCB very much supports collaboration between disciplines, including experimental and clinical as well as bioinformatics, as the best way forward to address complex biological problems. But this collaboration cannot be based on imposed restrictions to data access and cannot be contained in professional silos. (The use of expressions such as ‘research parasites’ clearly does not help.) Many bio-communities have made significant progress by endorsing open data policies and, gratefully, public funding agencies have connected to the spirit that they are distributing taxpayers’ money to science and that, therefore, the data that are generated in the course belong to the public. It is, perhaps, natural that some areas of biomedical research are slow in adopting these policies. History and the confidential nature of the relevant data are surely among the reasons. However, in our opinion data hegemony is another, a reason that has to be overcome. The sooner these barriers to progress are removed the sooner the patients will benefit from the current flourishing of biomedical research. Conflict of Interest: none declared.

This message is a response from the ISCB in light of the recent the New England Journal of Medicine (NEJM) editorial around data sharing.

UNLABELLED: Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains and three-dimensional protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology 2016, Orlando, FL). CONTACT: [email protected] or [email protected].

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research.As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling.Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database.Taken together, such computational efforts could significantly accelerate traditional scientific approaches.In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus.ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology [ISMB] 2016, Orlando, Florida).

Like many other journals, the journal PLOS Computational Biology admits and in some cases requires presubmission inquiries to be submitted before the submission of a full paper. Presubmission inquiries serve the purpose of informing the journal’s Editorial Board of the essence of the intended submission. Based on the information in the inquiry, the editors can make a quick assessment of its contribution with respect to the criteria for publication in the journal. This assessment is then communicated to the authors. This enables fast turnaround to the authors about the basic suitability of a submission for processing by the journal and spares the editors and reviewers the effort of detailed inspection of submissions that clearly do not meet the criteria of the journal. In this Editorial, we give suggestions for preparing presubmission inquiries for journal submissions. We exemplify these suggestions with reference to presubmission inquiries for the Methods section of PLOS Computational Biology. However, our suggestions generalize to presubmission inquiries of other kinds and for other journals, and in places, we will make specific comments to that effect. Over two years ago, PLOS Computational Biology opened a special section dedicated to Methods papers. As the scope statement spells out, Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities. Since Methods papers are different from other research papers in PLOS Computational Biology and also differ from typical papers on bioinformatics methods published in other journals, a mandatory presubmission stage has been introduced for the submission of Methods papers to PLOS Computational Biology. (Note that a presubmission inquiry is not mandatory for general research papers in PLOS Computational Biology, though it is also mandatory for submission to the Software papers category.) Since the Methods section was launched in October 2012, we have received 334 presubmission inquiries. For roughly half of them (159), we encouraged submission and received full papers, of which 41 papers were published, so far, as Methods papers. We find that, while many presubmission inquiries are informative enough to make an educated decision on the submission, we also receive a number of presubmission inquiries that are not sufficiently informative, such that submission may be discouraged not on the basis of the quality or scope of the paper but on that of the presubmission inquiry. We generally do not allow revisions of presubmission inquiries. In order to minimize the number of papers that fail to get a chance to be published in PLOS Computational Biology merely because of the inadequacy of the presubmission inquiry, here we give a number of suggestions for preparing such an inquiry. The goal of a presubmission inquiry is to make the statement to the Editorial Board that the paper to be submitted reasonably satisfies the criteria detailed in the scope statement. The presubmission inquiry must be detailed enough to convincingly make that point. Most of the insufficiently informative inquiries that we get are either too terse, i.e., they do not give enough detail, or they are not specific enough. Therefore, we suggest a way of structuring a presubmission inquiry. These suggestions are the result of our experience with presubmission inquiries over the past couple of years. What is the problem? Please summarize the problem domain and statement and the relevance of the problem to the general readership of the journal—in the case of PLOS Computational Biology, the biological research community or a substantial subcommunity. What is that subcommunity? How relevant is the problem to them? What is the innovation? Here it is important that you give enough detail on your contribution to allow the Editorial Board to form an image of the substance and relevance of the advance over the state of the art in the field and over your previous work. If your paper presents material that rests on or is related to your own previous publications, this entails addressing dual publication issues. In order to argue your point, you have to summarize the state of the art on which you base your contribution and give the essential ingredients of your innovation. Depending on the journal, the innovation can take different shapes: a contribution to technology or experimental design, a biological finding, a methodical or theoretical piece of work, etc. For papers in the Methods section of PLOS Computational Biology, the computational method is expected to be at the center of the innovation. This section is not for papers whose methodical core has been published elsewhere and for which you present a—possibly extended or modified—application scenario. Also, studies presenting a comparative assessment of existing methods on an application domain are not within the scope of a Methods paper. We expect a concrete and specific relationship to underlying biological issues. This is why general methods on statistical learning that find their application in biology as well as in other fields of science are typically not considered in scope, unless the paper focuses on sufficiently deep issues of the configuration of the method that are specific to biology. Finally, the method must be the major innovation of the paper. However interesting it may be, a biological finding that has been obtained with methods that are prepublished or only minor modification of prepublished methods is not within the scope of the Methods papers category. (On the other hand, it may be a suitable General research paper for the journal.) How is the method validated? Validation can take manifold forms but is a key element in most scientific papers. For a theoretical paper, the validation often takes the form of a proof. In contrast, methods in computational biology have manifold forms of validation and, usually, a single form is not sufficient to make the point. For instance, for the Methods section of PLOS Computational Biology, we expect more than an anecdotal validation based on a couple of biological use cases. A validation purely on synthetic data is not sufficient either. Rather, the validation must make a convincing argument for the general applicability of the method in a substantial biological problem domain. Please note, however, that papers that center on the validation of a method that has been published elsewhere are not considered in scope either. The paper has to contain both the method and its application. How is the method being made available? Availability of research results becomes an increasingly desired and often required aspect of a publication. For papers that are based on experimental data, making the data and the protocols of the experimental design available is a prerequisite for making the research reproducible. For methods papers, reusability of the method also becomes an issue. For the Methods section of PLOS Computational Biology, we only accept papers on methods that are useful to and can be readily applied by other scientists. The best way of satisfying this criterion is to make the software implementation of the methods openly available. For methods that are not based on software, a workable protocol for how to use the methods must be provided. If you have the full submission ready at the time of the presubmission inquiry, we encourage you to attach it to the inquiry as an optional supplement and mention that you have done this in your cover letter. However, your presubmission inquiry should be worded such that the editors do not need to inspect the complete paper for making their assessment. We wish you much success with your future submission to the Methods section of PLOS Computational Biology.

MOTIVATION: Recurrent DNA breakpoints in cancer genomes indicate the presence of critical functional elements for tumor development. Identifying them can help determine new therapeutic targets. High-dimensional DNA microarray experiments like arrayCGH afford the identification of DNA copy number breakpoints with high precision, offering a solid basis for computational estimation of recurrent breakpoint locations. RESULTS: We introduce a method for identification of recurrent breakpoints (consensus breakpoints) from copy number aberration datasets. The method is based on weighted kernel counting of breakpoints around genomic locations. Counts larger than expected by chance are considered significant. We show that the consensus breakpoints facilitate consensus segmentation of the samples. We apply our method to three arrayCGH datasets and show that by using consensus segmentation we achieve significant dimension reduction, which is useful for the task of prediction of tumor phenotype based on copy number data. We use our approach for classification of neuroblastoma tumors from different age groups and confirm the recent recommendation for the choice of age cut-off for differential treatment of 18 months. We also investigate the (epi)genetic properties at consensus breakpoint locations for seven datasets and show enrichment in overlap with important functional genomic regions. AVAILABILITY: Implementation in R of our approach can be found at http://www.mpi-inf.mpg.de/ ∼laura/FeatureGrouping.html. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The relationship of HIV tropism with disease progression and the recent development of CCR5-blocking drugs underscore the importance of monitoring virus coreceptor usage. As an alternative to costly phenotypic assays, computational methods aim at predicting virus tropism based on the sequence and structure of the V3 loop of the virus gp120 protein. Here we present a numerical descriptor of the V3 loop encoding its physicochemical and structural properties. The descriptor allows for structure-based prediction of HIV tropism and identification of properties of the V3 loop that are crucial for coreceptor usage. Use of the proposed descriptor for prediction results in a statistically significant improvement over the prediction based solely on V3 sequence with 3 percentage points improvement in AUC and 7 percentage points in sensitivity at the specificity of the 11/25 rule (95%). We additionally assessed the predictive power of the new method on clinically derived 'bulk' sequence data and obtained a statistically significant improvement in AUC of 3 percentage points over sequence-based prediction. Furthermore, we demonstrated the capacity of our method to predict therapy outcome by applying it to 53 samples from patients undergoing Maraviroc therapy. The analysis of structural features of the loop informative of tropism indicates the importance of two loop regions and their physicochemical properties. The regions are located on opposite strands of the loop stem and the respective features are predominantly charge-, hydrophobicity- and structure-related. These regions are in close proximity in the bound conformation of the loop potentially forming a site determinant for the coreceptor binding. The method is available via server under http://structure.bioinf.mpi-inf.mpg.de/.

PLOS Computational Biology is the Public Library of Science journal that targets new biology that is facilitated by computational methods. Since the inception of the journal, biology has been at the center of the scope of PLOS Computational Biology. Thus, each submission is required to put advances in biology into its focus in a very concrete manner and not only focus on computation. This is in contrast to other, more technically oriented journals in the area of computational biology. The new Methods section of the journal acknowledges the fact that a major methodical advance can, in itself, be so relevant that it deserves transcending the technological domain and being presented to a broader readership including not only computational biologists, but also biologists, the targeted readership of this journal. For this reason, PLOS Computational Biology has installed a special type of submission, the Methods paper. As the scope statement of the journal spells out, “Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.” In order to render the processing of Methods papers as effective as possible and to limit the effort required on the part of authors and reviewers, a presubmission inquiry is mandatory for Methods papers. In such an inquiry, the authors are requested to present a concise abstract-like statement on what the manuscript they plan to submit entails and why the authors feel that it fits the Methods section of the journal. Presubmission inquiries are given top priority in the paper handling process; the median processing time should be about a week. Within that time, submission is either encouraged or discouraged. The Methods section of the journal is handled by Deputy Editor Thomas Lengauer.

In excess of 12% of human cancer incidents have a viral cofactor. Epidemiological studies of idiopathic human cancers indicate that additional tumor viruses remain to be discovered. Recent advances in sequencing technology have enabled systematic screenings of human tumor transcriptomes for viral transcripts. However, technical problems such as low abundances of viral transcripts in large volumes of sequencing data, viral sequence divergence, and homology between viral and human factors significantly confound identification of tumor viruses. We have developed a novel computational approach for detecting viral transcripts in human cancers that takes the aforementioned confounding factors into account and is applicable to a wide variety of viruses and tumors. We apply the approach to conducting the first systematic search for viruses in neuroblastoma, the most common cancer in infancy. The diverse clinical progression of this disease as well as related epidemiological and virological findings are highly suggestive of a pathogenic cofactor. However, a viral etiology of neuroblastoma is currently contested. We mapped 14 transcriptomes of neuroblastoma as well as positive and negative controls to the human and all known viral genomes in order to detect both known and unknown viruses. Analysis of controls, comparisons with related methods, and statistical estimates demonstrate the high sensitivity of our approach. Detailed investigation of putative viral transcripts within neuroblastoma samples did not provide evidence for the existence of any known human viruses. Likewise, de-novo assembly and analysis of chimeric transcripts did not result in expression signatures associated with novel human pathogens. While confounding factors such as sample dilution or viral clearance in progressed tumors may mask viral cofactors in the data, in principle, this is rendered less likely by the high sensitivity of our approach and the number of biological replicates analyzed. Therefore, our results suggest that frequent viral cofactors of metastatic neuroblastoma are unlikely.

MOTIVATION: Due to the high mutation rate of human immunodeficiency virus (HIV), drug-resistant-variants emerge frequently. Therefore, researchers are constantly searching for new ways to attack the virus. One new class of anti-HIV drugs is the class of coreceptor antagonists that block cell entry by occupying a coreceptor on CD4 cells. This type of drug just has an effect on the subset of HIVs that use the inhibited coreceptor. A good prediction of whether the viral population inside a patient is susceptible to the treatment is hence very important for therapy decisions and pre-requisite to administering the respective drug. The first prediction models were based on data from Sanger sequencing of the V3 loop of HIV. Recently, a method based on next-generation sequencing (NGS) data was introduced that predicts labels for each read separately and decides on the patient label through a percentage threshold for the resistant viral minority. RESULTS: We model the prediction problem on the patient level taking the information of all reads from NGS data jointly into account. This enables us to improve prediction performance for NGS data, but we can also use the trained model to improve predictions based on Sanger sequencing data. Therefore, also laboratories without NGS capabilities can benefit from the improvements. Furthermore, we show which amino acids at which position are important for prediction success, giving clues on how the interaction mechanism between the V3 loop and the particular coreceptors might be influenced. AVAILABILITY: A webserver is available at http://coreceptor.bioinf.mpi-inf.mpg.de. CONTACT: [email protected].

The International Society for Computational Biology, ISCB, organizes the largest event in the field of computational biology and bioinformatics, namely the annual international conference on Intelligent Systems for Molecular Biology, the ISMB. This year at ISMB 2012 in Long Beach, ISCB celebrated the 20th anniversary of its flagship meeting. ISCB is a young, lean and efficient society that aspires to make a significant impact with only limited resources. Many constraints make the choice of venues for ISMB a tough challenge. Here, we describe those challenges and invite the contribution of ideas for solutions.

MOTIVATION: Classification and feature selection of genomics or transcriptomics data is often hampered by the large number of features as compared with the small number of samples available. Moreover, features represented by probes that either have similar molecular functions (gene expression analysis) or genomic locations (DNA copy number analysis) are highly correlated. Classical model selection methods such as penalized logistic regression or random forest become unstable in the presence of high feature correlations. Sophisticated penalties such as group Lasso or fused Lasso can force the models to assign similar weights to correlated features and thus improve model stability and interpretability. In this article, we show that the measures of feature relevance corresponding to the above-mentioned methods are biased such that the weights of the features belonging to groups of correlated features decrease as the sizes of the groups increase, which leads to incorrect model interpretation and misleading feature ranking. RESULTS: With simulation experiments, we demonstrate that Lasso logistic regression, fused support vector machine, group Lasso and random forest models suffer from correlation bias. Using simulations, we show that two related methods for group selection based on feature clustering can be used for correcting the correlation bias. These techniques also improve the stability and the accuracy of the baseline models. We apply all methods investigated to a breast cancer and a bladder cancer arrayCGH dataset and in order to identify copy number aberrations predictive of tumor phenotype. AVAILABILITY: R code can be found at: http://www.mpi-inf.mpg.de/~laura/Clustering.r.

MOTIVATION: In life sciences, interpretability of machine learning models is as important as their prediction accuracy. Linear models are probably the most frequently used methods for assessing feature relevance, despite their relative inflexibility. However, in the past years effective estimators of feature relevance have been derived for highly complex or non-parametric models such as support vector machines and RandomForest (RF) models. Recently, it has been observed that RF models are biased in such a way that categorical variables with a large number of categories are preferred. RESULTS: In this work, we introduce a heuristic for normalizing feature importance measures that can correct the feature importance bias. The method is based on repeated permutations of the outcome vector for estimating the distribution of measured importance for each variable in a non-informative setting. The P-value of the observed importance provides a corrected measure of feature importance. We apply our method to simulated data and demonstrate that (i) non-informative predictors do not receive significant P-values, (ii) informative variables can successfully be recovered among non-informative variables and (iii) P-values computed with permutation importance (PIMP) are very helpful for deciding the significance of variables, and therefore improve model interpretability. Furthermore, PIMP was used to correct RF-based importance measures for two real-world case studies. We propose an improved RF model that uses the significant variables with respect to the PIMP measure and show that its prediction accuracy is superior to that of other existing models. AVAILABILITY: R code for the method presented in this article is available at http://www.mpi-inf.mpg.de/ approximately altmann/download/PIMP.R CONTACT: [email protected], [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: As there exists no cure or vaccine for the infection with human immunodeficiency virus (HIV), the standard approach to treating HIV patients is to repeatedly administer different combinations of several antiretroviral drugs. Because of the large number of possible drug combinations, manually finding a successful regimen becomes practically impossible. This presents a major challenge for HIV treatment. The application of machine learning methods for predicting virological responses to potential therapies is a possible approach to solving this problem. However, due to evolving trends in treating HIV patients the available clinical datasets have a highly unbalanced representation, which might negatively affect the usefulness of derived statistical models. RESULTS: This article presents an approach that tackles the problem of predicting virological response to combination therapies by learning a separate logistic regression model for each therapy. The models are fitted by using not only the data from the target therapy but also the information from similar therapies. For this purpose, we introduce and evaluate two different measures of therapy similarity. The models are also able to incorporate phenotypic knowledge on the therapy outcomes through a Gaussian prior. With our approach we balance the uneven therapy representation in the datasets and produce higher quality models for therapies with very few training samples. According to the results from the computational experiments our therapy similarity model performs significantly better than training separate models for each therapy by using solely their examples. Furthermore, the model's performance is as good as an approach that encodes therapy information in the input feature space with the advantage of delivering better results for therapies with very few training samples. AVAILABILITY: Code of the efficient logistic regression is available from http://www.mpi-inf.mpg.de/%7Ejasmina/fastLogistic.zip.

MOTIVATION: Many hereditary human diseases are polygenic, resulting from sequence alterations in multiple genes. Genomic linkage and association studies are commonly performed for identifying disease-related genes. Such studies often yield lists of up to several hundred candidate genes, which have to be prioritized and validated further. Recent studies discovered that genes involved in phenotypically similar diseases are often functionally related on the molecular level. RESULTS: Here, we introduce MedSim, a novel approach for ranking candidate genes for a particular disease based on functional comparisons involving the Gene Ontology. MedSim uses functional annotations of known disease genes for assessing the similarity of diseases as well as the disease relevance of candidate genes. We benchmarked our approach with genes known to be involved in 99 diseases taken from the OMIM database. Using artificial quantitative trait loci, MedSim achieved excellent performance with an area under the ROC curve of up to 0.90 and a sensitivity of over 70% at 90% specificity when classifying gene products according to their disease relatedness. This performance is comparable or even superior to related methods in the field, albeit using less and thus more easily accessible information. AVAILABILITY: MedSim is offered as part of our FunSimMat web service (http://www.funsimmat.de).

BACKGROUND: Experimental screening of large sets of peptides with respect to their MHC binding capabilities is still very demanding due to the large number of possible peptide sequences and the extensive polymorphism of the MHC proteins. Therefore, there is significant interest in the development of computational methods for predicting the binding capability of peptides to MHC molecules, as a first step towards selecting peptides for actual screening. RESULTS: We have examined the performance of four diverse MHC Class I prediction methods on comparatively large HLA-A and HLA-B allele peptide binding datasets extracted from the Immune Epitope Database and Analysis resource (IEDB). The chosen methods span a representative cross-section of available methodology for MHC binding predictions. Until the development of IEDB, such an analysis was not possible, as the available peptide sequence datasets were small and spread out over many separate efforts. We tested three datasets which differ in the IC50 cutoff criteria used to select the binders and non-binders. The best performance was achieved when predictions were performed on the dataset consisting only of strong binders (IC50 less than 10 nM) and clear non-binders (IC50 greater than 10,000 nM). In addition, robustness of the predictions was only achieved for alleles that were represented with a sufficiently large (greater than 200), balanced set of binders and non-binders. CONCLUSIONS: All four methods show good to excellent performance on the comprehensive datasets, with the artificial neural networks based method outperforming the other methods. However, all methods show pronounced difficulties in correctly categorizing intermediate binders.

MOTIVATION: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet. RESULTS: We introduce the DASMI system for the dynamic exchange, annotation and assessment of molecular interaction data. DASMI is based on the widely used Distributed Annotation System (DAS) and consists of a data exchange specification, web servers for providing the interaction data and clients for data integration and visualization. The decentralized architecture of DASMI affords the online retrieval of the most recent data from distributed sources and databases. DASMI can also be extended easily by adding new data sources and clients. We describe all DASMI components and demonstrate their use for protein and domain interactions. AVAILABILITY: The DASMI tools are available at http://www.dasmi.de/ and http://ipfam.sanger.ac.uk/graph. The DAS registry and the DAS 1.53E specification is found at http://www.dasregistry.org/.

We address the problem of learning classifiers for a large number of tasks. We derive a solution that produces resampling weights which match the pool of all examples to the target distribution of any given task. Our work is motivated by the problem of predicting the outcome of a therapy attempt for a patient who carries an HIV virus with a set of observed genetic properties. Such predictions need to be made for hundreds of possible combinations of drugs, some of which use similar biochemical mechanisms. Multi-task learning enables us to make predictions even for drug combinations with few or no training examples and substantially improves the overall prediction accuracy.

MOTIVATION: Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy. RESULTS: Three different machine learning techniques were used: generative-discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome. AVAILABILITY: The combined prediction engine will be available from July 2008, see http://engine.euresist.org.

UNLABELLED: Rapidly increasing amounts of molecular interaction data are being produced by various experimental techniques and computational prediction methods. In order to gain insight into the organization and structure of the resultant large complex networks formed by the interacting molecules, we have developed the versatile Cytoscape plugin NetworkAnalyzer. It computes and displays a comprehensive set of topological parameters, which includes the number of nodes, edges, and connected components, the network diameter, radius, density, centralization, heterogeneity, and clustering coefficient, the characteristic path length, and the distributions of node degrees, neighborhood connectivities, average clustering coefficients, and shortest path lengths. NetworkAnalyzer can be applied to both directed and undirected networks and also contains extra functionality to construct the intersection or union of two networks. It is an interactive and highly customizable application that requires no expert knowledge in graph theory from the user. AVAILABILITY: NetworkAnalyzer can be downloaded via the Cytoscape web site: http://www.cytoscape.org

Epigenetic research aims to understand heritable gene regulation that is not directly encoded in the DNA sequence. Epigenetic mechanisms such as DNA methylation and histone modifications modulate the packaging of the DNA in the nucleus and thereby influence gene expression. Patterns of epigenetic information are faithfully propagated over multiple cell divisions, which makes epigenetic regulation a key mechanism for cellular differentiation and cell fate decisions. In addition, incomplete erasure of epigenetic information can lead to complex patterns of non-Mendelian inheritance. Stochastic and environment-induced epigenetic defects are known to play a major role in cancer and ageing, and they may also contribute to mental disorders and autoimmune diseases. Recent technical advances such as ChIP-on-chip and ChIP-seq have started to convert epigenetic research into a high-throughput endeavor, to which bioinformatics is expected to make significant contributions. Here, we review pioneering computational studies that have contributed to epigenetic research. In addition, we give a brief introduction into epigenetics-targeted at bioinformaticians who are new to the field-and we outline future challenges in computational epigenetics.

Abstract Summary: In genetics, many evolutionary pathways can be modeled by the ordered accumulation of permanent changes. Mixture models of mutagenetic trees have been used to describe disease progression in cancer and in HIV. In cancer, progression is modeled by the accumulation of chromosomal gains and losses in tumor cells; in HIV, the accumulation of drug resistance-associated mutations in the viral genome is known to be associated with disease progression. From such evolutionary models, genetic progression scores can be derived that assign measures for the disease state to single patients. Rtreemix is an R package for estimating mixture models of evolutionary pathways from observed cross-sectional data and for estimating associated genetic progression scores. The package also provides extended functionality for estimating confidence intervals for estimated model parameters and for evaluating the stability of the estimated evolutionary mixture models. Availability: Rtreemix is an R package that is freely available from the Bioconductor project at http://www.bioconductor.org and runs on Linux and Windows. Contact: [email protected]

UNLABELLED: Recent studies have revealed that alternative splicing plays an important role in the observed protein and interaction diversity. Special microarrays allow for measuring gene expression at the exon level and thus for studying alternative transcripts and their corresponding protein domain architecture. We have developed the Cytoscape plugin DomainGraph that enables the visualization and detailed study of domain-domain interactions forming protein interaction networks. In addition, the integration of exon expression data supports the analysis of alternative splicing events and the characterization of their effects on the protein and domain interaction network. Different expression patterns between human tissues or cells can be identified by comparing the generated domain graphs. AVAILABILITY: The plugin DomainGraph and the online documentation are available at http://domaingraph.bioinf.mpi-inf.mpg.de.

BACKGROUND: Mixture models of mutagenetic trees are evolutionary models that capture several pathways of ordered accumulation of genetic events observed in different subsets of patients. They were used to model HIV progression by accumulation of resistance mutations in the viral genome under drug pressure and cancer progression by accumulation of chromosomal aberrations in tumor cells. From the mixture models a genetic progression score (GPS) can be derived that estimates the genetic status of single patients according to the corresponding progression along the tree models. GPS values were shown to have predictive power for estimating drug resistance in HIV or the survival time in cancer. Still, the reliability of the exact values of such complex markers derived from graphical models can be questioned. RESULTS: In a simulation study, we analyzed various aspects of the stability of estimated mutagenetic trees mixture models. It turned out that the induced probabilistic distributions and the tree topologies are recovered with high precision by an EM-like learning algorithm. However, only for models with just one major model component, also GPS values of single patients can be reliably estimated. CONCLUSION: It is encouraging that the estimation process of mutagenetic trees mixture models can be performed with high confidence regarding induced probability distributions and the general shape of the tree topologies. For a model with only one major disease progression process, even genetic progression scores for single patients can be reliably estimated. However, for models with more than one relevant component, alternative measures should be introduced for estimating the stage of disease progression.

We assess the variability of protein function in protein sequence and structure space. Various regions in this space exhibit considerable difference in the local conservation of molecular function. We analyze and capture local function conservation by means of logistic curves. Based on this analysis, we propose a method for predicting molecular function of a query protein with known structure but unknown function. The prediction method is rigorously assessed and compared with a previously published function predictor. Furthermore, we apply the method to 500 functionally unannotated PDB structures and discuss selected examples. The proposed approach provides a simple yet consistent statistical model for the complex relations between protein sequence, structure, and function. The GOdot method is available online (http://godot.bioinf.mpi-inf.mpg.de).

MOTIVATION: Alternative structural models determined experimentally are available for an increasing number of proteins. Structural and functional studies of these proteins need to take these models into consideration as they can present considerable structural differences. The characterization of the structural differences and similarities between these models is a fundamental task in structural biology requiring appropriate methods. RESULTS: We propose a method for characterizing sets of alternative structural models. Three types of analysis are performed: grouping according to structural similarity, visualization and detection of structural variation and comparison of subsets for identifying and locating distinct conformational states. The alpha carbon atoms are used in order to analyse the backbone conformations. Alternatively, side-chain atoms are used for detailed conformational analysis of specific sites. The method takes into account estimates of atom coordinate uncertainty. The invariant regions are used to generate optimal superpositions of these models. We present the results obtained for three proteins showing different degrees of conformational variability: relative motion of two structurally conserved subdomains, a disordered subdomain and flexibility in the functional site associated with ligand binding. The method has been applied in the analysis of the alternative models available in SCOP. Considerable structural variability can be observed for most proteins. AVAILABILITY: The results of the analysis of the SCOP alternative models, the estimates of coordinate uncertainty as well as the source code of the implementation are available in the STRuster web site: http://struster.bioinf.mpi-inf.mpg.de.

MOTIVATION: Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology (GO) to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple GO terms. RESULTS: Using our similarity measure, we compare predicted domain-domain and human protein-protein interactions with experimentally derived interactions. The results show that our similarity measure is of significant benefit in quality assessment and confidence ranking of domain and protein networks. We also derive useful confidence score thresholds for dividing domain interaction predictions into subsets of low and high confidence. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: An approach for identifying similarities of protein-protein binding sites is presented. The geometric shape of a binding site is described by computing a feature vector based on moment invariants. In order to search for similarities, feature vectors of binding sites are compared. Similar feature vectors indicate binding sites with similar shapes. RESULTS: The approach is validated on a representative set of protein-protein binding sites, extracted from the SCOPPI database. When querying binding sites from a representative set, we search for known similarities among 2819 binding sites. A median area under the ROC curve of 0.98 is observed. For half of the queries, a similar binding site is identified among the first two of 2819 when sorting all binding sites according the proposed similarity measure. Typical examples identified by this method are analyzed and discussed. The nitrogenase iron protein-like SCOP family is clustered hierarchically according to the proposed similarity measure as a case study. AVAILABILITY: Python code is available on request from the authors.

CpG islands were originally identified by epigenetic and functional properties, namely, absence of DNA methylation and frequent promoter association. However, this concept was quickly replaced by simple DNA sequence criteria, which allowed for genome-wide annotation of CpG islands in the absence of large-scale epigenetic datasets. Although widely used, the current CpG island criteria incur significant disadvantages: (1) reliance on arbitrary threshold parameters that bear little biological justification, (2) failure to account for widespread heterogeneity among CpG islands, and (3) apparent lack of specificity when applied to the human genome. This study is driven by the idea that a quantitative score of "CpG island strength" that incorporates epigenetic and functional aspects can help resolve these issues. We construct an epigenome prediction pipeline that links the DNA sequence of CpG islands to their epigenetic states, including DNA methylation, histone modifications, and chromatin accessibility. By training support vector machines on epigenetic data for CpG islands on human Chromosomes 21 and 22, we identify informative DNA attributes that correlate with open versus compact chromatin structures. These DNA attributes are used to predict the epigenetic states of all CpG islands genome-wide. Combining predictions for multiple epigenetic features, we estimate the inherent CpG island strength for each CpG island in the human genome, i.e., its inherent tendency to exhibit an open and transcriptionally competent chromatin structure. We extensively validate our results on independent datasets, showing that the CpG island strength predictions are applicable and informative across different tissues and cell types, and we derive improved maps of predicted "bona fide" CpG islands. The mapping of CpG islands by epigenome prediction is conceptually superior to identifying CpG islands by widely used sequence criteria since it links CpG island detection to their characteristic epigenetic and functional states. And it is superior to purely experimental epigenome mapping for CpG island detection since it abstracts from specific properties that are limited to a single cell type or tissue. In addition, using computational epigenetics methods we could identify high correlation between the epigenome and characteristics of the DNA sequence, a finding which emphasizes the need for a better understanding of the mechanistic links between genome and epigenome.

The International Society for Computational Biology (ISCB) presents ISMB/ECCB 2007, the Fifteenth International Conference on Intelligent Systems for Molecular Biology (ISMB 2007), held jointly with the Sixth European Conference on Computational Biology (ECCB 2007) in Vienna, Austria, July 21–25, 2007 (http://www.iscb.org/ismbeccb2007). Now in the final phases of selecting papers, presentations, demonstrations, and posters, the organizers are preparing what will likely be recognized as the premier conference on computational biology in 2007. ISMB/ECCB 2007 has expanded in ways to specifically encourage increased participation from previously underrepresented disciplines of computational biology. This conference will feature the best of the computer and life sciences through a variety of new and core sessions running in multiple parallel tracks, along with an increase in keynote presentations, posters on display throughout the duration of the conference, and an extensive industry exhibition. Special interest group meetings, a satellite meeting, and tutorials all will precede the main conference dates.

HIV-1 cell entry commonly uses, in addition to CD4, one of the chemokine receptors CCR5 or CXCR4 as coreceptor. Knowledge of coreceptor usage is critical for monitoring disease progression as well as for supporting therapy with the novel drug class of coreceptor antagonists. Predictive methods for inferring coreceptor usage based on the third hypervariable (V3) loop region of the viral gene coding for the envelope protein gp120 can provide us with these monitoring facilities while avoiding expensive phenotypic tests. All simple heuristics (such as the 11/25 rule) as well as statistical learning methods proposed to date predict coreceptor usage based on sequence features of the V3 loop exclusively. Here, we show, based on a recently resolved structure of gp120 with an untruncated V3 loop, that using structural information on the V3 loop in combination with sequence features of V3 variants improves prediction of coreceptor usage. In particular, we propose a distance-based descriptor of the spatial arrangement of physicochemical properties that increases discriminative performance. For a fixed specificity of 0.95, a sensitivity of 0.77 was achieved, improving further to 0.80 when combined with a sequence-based representation using amino acid indicators. This compares favorably with the sensitivities of 0.62 for the traditional 11/25 rule and 0.73 for a prediction based on sequence information as input to a support vector machine and constitutes a statistically significant improvement. A detailed analysis and interpretation of structural features important for classification shows the relevance of several specific hydrogen-bond donor sites and aliphatic side chains to coreceptor specificity towards CCR5 or CXCR4. Furthermore, an analysis of side chain orientation of the specificity-determining residues suggests a major role of one side of the V3 loop in the selection of the coreceptor. The proposed method constitutes the first approach to an improved prediction of coreceptor usage based on an original integration of structural bioinformatics methods with statistical learning.

MOTIVATION: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. RESULTS: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.

MOTIVATION: Potassium channels are mainly known for their role in regulating and maintaining the membrane potential. Since this is one of the key mechanisms of signal transduction, malfunction of these potassium channels leads to a wide variety of severe diseases. Thus potassium channels are priority targets of research for new drugs, despite the fact that this protein family is highly variable and closely related to other channels, which makes it very difficult to identify new types of potassium channel sequences. RESULTS: Here we present a new method for identifying potassium channel sequences (PSM, Property Signature Method), which-in contrast to the known methods for protein classification-is directly based on physicochemical properties of amino acids rather than on the amino acids themselves. A signature for the pore region including the selectivity filter has been created, representing the most common physicochemical properties of known potassium channels. This string enables genome-wide screening for sequences with similar features despite a very low degree of amino acid similarity within a protein family.

MOTIVATION: Recombination plays an important role in the evolution of many pathogens, such as HIV or malaria. Despite substantial prior work, there is still a pressing need for efficient and effective methods of detecting recombination and analyzing recombinant sequences. RESULTS: We introduce Recco, a novel fast method that, given a multiple sequence alignment, scores the cost of obtaining one of the sequences from the others by mutation and recombination. The algorithm comes with an illustrative visualization tool for locating recombination breakpoints. We analyze the sequence alignment with respect to all choices of the parameter alpha weighting recombination cost against mutation cost. The analysis of the resulting cost curve yields additional information as to which sequence might be recombinant. On random genealogies Recco is comparable in its power of detecting recombination with the algorithm Geneconv (Sawyer, 1989). For specific relevant recombination scenarios Recco significantly outperforms Geneconv.

From October 5–7, 2005, Hamburg hosted the German Conference on Bioinformatics (GCB 05) and this journal section contains a selection of the material which eventually became full-length papers. The conference (GCB) has been run every year since 1994, moving between host institutions and attracting contributions from around Europe. This gathering had 235 registrants and 126 posters. From 73 extended abstracts and a first round of review and revision, the Program Committee chose 19 oral presentations. The seven papers in this issue were accepted after going through another round of refereeing in the journal's normal review procedure. The selection of papers for publication was based purely on quality and no attempt was made to survey the interests of the meeting, which was more diverse. This selection certainly does not reflect the amount of activity in the analysis of microarray data or the curation of specialized nucleotide and protein datasets. It also does not convey the breadth of topics amongst the plenary speakers. These ranged from molecular dynamics simulations (Case, USA) to evolutionary landscapes (Schuster, Austria). The plenary lectures also included some very topical themes such as the use of sequence analysis in detecting bio-attacks (Slezak, USA) and the story of the SARS virus from the point of view of its genetics and epidemiology (Guo-Ping Zhao, China/USA) and structural biology (Hilgenfeld, Germany). The running of the meeting relied on a bevy of helpers who may be unnamed, but not unappreciated. Scientifically, the process relied on the expertise of the Program Committee and the additional reviewers they drafted. Finally, these papers also reflect the suggestions of a large number of anonymous referees. Thomas Dandekar Roland Eils Robert Giegerich Oliver Kohlbacher Stefan Kurtz Hans-Peter Lenhof Hans-Werner Mewes Matthias Rarey Knut Reinert Dietmar Schomburg Jeremy Smith Peter Stadler Andrew Torda Martin Vingron Ute Willhöft Ralf Zimmer Mohamed I. Abouelhoda Jannis Apostolakis Rolf Backofen Michael Beckstette Fabian Birzele Torsten Blum Nicoleta Bondar Bogdan Costescu Zoe Cournia Pierre Dönnes Qunfeng Dong Katrin Fundel Jan Erik Gewehr Niels Grabe Volker Heun Andreas Hildebrandt Matthias Hoechsmann Annette Höglund Petra Imhof Andreas Kerzmann Robert Küffner Jan Küntzer Vandana Kurkal Alessandro Macri Lars Meinhold Kei Moritsugu Dirk Neumann Frank Noe Heike Pospisil Jörg Rahnenführer Durba Sengupta Florian Sohler Marc Sturm Sebastian Will

BACKGROUND: In recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results. We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction methods. For each local sequence window of a protein sequence the method predicts probability estimates for the sequence to attain particular local structures from a set of predefined local structure candidates. The first step is to define a set of local structure representatives based on clustering recurrent local structures. In the second step a discriminative model is trained to predict the local structure representative given local sequence information. RESULTS: The step of clustering local structures yields an average RMSD quantization error of 1.19 A for 27 structural representatives (for a fragment length of 7 residues). In the prediction step the area under the ROC curve for detection of the 27 classes ranges from 0.68 to 0.88. CONCLUSION: The described method yields probability estimates for local protein structure candidates, giving signals for all kinds of local structure. These local structure predictions can be incorporated either into fold recognition algorithms to improve alignment quality and the overall prediction accuracy or into new fold prediction methods.

BACKGROUND: Gene Ontology (GO) is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role. RESULTS: We present a new method for comparing sets of GO terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; simRel and funSim. One measure (simRel) is applied in the comparison of the biological processes found in different groups of organisms. The other measure (funSim) is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families. CONCLUSION: The approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families.

BACKGROUND: In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish high quality protein structure models from inferior models. Here, we propose a new method to use an MQAP to improve the quality of models. With a given target sequence and template structure, we construct a number of different alignments and corresponding models for the sequence. The quality of these models is scored with an MQAP and used to choose the most promising model. An SVM-based selection scheme is suggested for combining MQAP partial potentials, in order to optimize for improved model selection. RESULTS: The approach has been tested on a representative set of proteins. The ability of the method to improve models was validated by comparing the MQAP-selected structures to the native structures with the model quality evaluation program TM-score. Using the SVM-based model selection, a significant increase in model quality is obtained (as shown with a Wilcoxon signed rank test yielding p-values below 10(-15)). The average increase in TMscore is 0.016, the maximum observed increase in TM-score is 0.29. CONCLUSION: In template-based protein structure prediction alignment is known to be a bottleneck limiting the overall model quality. Here we show that a combination of systematic alignment variation and modern model scoring functions can significantly improve the quality of alignment-based models.

BACKGROUND: Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available. RESULTS: Six interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure. CONCLUSION: NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at http://noxclass.bioinf.mpi-inf.mpg.de/.

SUMMARY: Mixture models of mutagenetic trees constitute a class of probabilistic models for describing evolutionary processes that are characterized by the accumulation of permanent genetic changes. They have been applied to model the accumulation of chromosomal gains and losses in tumor development and the development of drug resistance-associated mutations in the HIV genome.Mtreemix is a software package for estimating mutagenetic trees mixture models from observed cross-sectional data and for using these models for predictions. We provide programs for model fitting, model selection, simulation, likelihood computation and waiting time estimation. AVAILABILITY: Mtreemix, including source code, documentation, sample data files and precompiled Solaris and Linux binaries, is freely available for non-commercial users at http://mtreemix.bioinf.mpi-sb.mpg.de/

The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data.

SUMMARY: Manual processing of DNA methylation data from bisulfite sequencing is a tedious and error-prone task. Here we present an interactive software tool that provides start-to-end support for this process. In an easy-to-use manner, the tool helps the user to import the sequence files from the sequencer, to align them, to exclude or correct critical sequences, to document the experiment, to perform basic statistics and to produce publication-quality diagrams. Emphasis is put on quality control: The program automatically assesses data quality and provides warnings and suggestions for dealing with critical sequences. The BiQ Analyzer program is implemented in the Java programming language and runs on any platform for which a recent Java virtual machine is available. AVAILABILITY: The program is available without charge for non-commercial users and can be downloaded from http://biq-analyzer.bioinf.mpi-inf.mpg.de/

MOTIVATION: In cancer research, prediction of time to death or relapse is important for a meaningful tumor classification and selecting appropriate therapies. Survival prognosis is typically based on clinical and histological parameters. There is increasing interest in identifying genetic markers that better capture the status of a tumor in order to improve on existing predictions. The accumulation of genetic alterations during tumor progression can be used for the assessment of the genetic status of the tumor. For modeling dependences between the genetic events, evolutionary tree models have been applied. RESULTS: Mixture models of oncogenetic trees provide a probabilistic framework for the estimation of typical pathogenetic routes. From these models we derive a genetic progression score (GPS) that estimates the genetic status of a tumor. GPS is calculated for glioblastoma patients from loss of heterozygosity measurements and for prostate cancer patients from comparative genomic hybridization measurements. Cox proportional hazard models are then fitted to observed survival times of glioblastoma patients and to times until PSA relapse following radical prostatectomy of prostate cancer patients. It turns out that the genetically defined GPS is predictive even after adjustment for classical clinical markers and thus can be considered a medically relevant prognostic factor. AVAILABILITY: Mtreemix, a software package for estimating tree mixture models, is freely available for non-commercial users at http://mtreemix.bioinf.mpi-sb.mpg.de. The raw cancer datasets and R code for the analysis with Cox models are available upon request from the corresponding author.

UNLABELLED: ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R's powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage. AVAILABILITY: http://rocr.bioinf.mpi-sb.mpg.de. ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent. CONTACT: [email protected].

BACKGROUND: With microarray technology the expression of thousands of genes can be measured simultaneously. It is well known that the expression levels of genes of interacting proteins are correlated significantly more strongly in Saccharomyces cerevisiae than those of proteins that are not interacting. The objective of this work is to investigate whether this observation extends to the human genome. RESULTS: We investigated the quantitative relationship between expression levels of genes encoding interacting proteins and genes encoding random protein pairs. Therefore we studied 1369 interacting human protein pairs and human gene expression levels of 155 arrays. We were able to establish a statistically significantly higher correlation between the expression levels of genes whose proteins interact compared to random protein pairs. Additionally we were able to provide evidence that genes encoding proteins belonging to the same GO-class show correlated expression levels. CONCLUSION: This finding is concurrent with the naive hypothesis that the scales of production of interacting proteins are linked because an efficient interaction demands that involved proteins are available to some degree. The goal of further research in this field will be to understand the biological mechanisms behind this observation.

We introduce a mixture model of trees to describe evolutionary processes that are characterized by the accumulation of permanent genetic changes. The basic building block of the model is a directed weighted tree that generates a probability distribution on the set of all patterns of genetic events. We present an EM-like algorithm for learning a mixture model of K trees and show how to determine K with a maximum likelihood approach. As a case study we consider the accumulation of mutations in the HIV-1 reverse transcriptase that are associated with drug resistance. The fitted model is statistically validated as a density estimator and the stability of the model topology is analyzed. We obtain a generative probabilistic model for the development of drug resistance in HIV that agrees with biological knowledge. Further applications and extensions of the model are discussed.

SUMMARY: The Helmholtz Network for Bioinformatics (HNB) is a joint venture of eleven German bioinformatics research groups that offers convenient access to numerous bioinformatics resources through a single web portal. The 'Guided Solution Finder' which is available through the HNB portal helps users to locate the appropriate resources to answer their queries by employing a detailed, tree-like questionnaire. Furthermore, automated complex tool cascades ('tasks'), involving resources located on different servers, have been implemented, allowing users to perform comprehensive data analyses without the requirement of further manual intervention for data transfer and re-formatting. Currently, automated cascades for the analysis of regulatory DNA segments as well as for the prediction of protein functional properties are provided. AVAILABILITY: The HNB portal is available at http://www.hnbioinfo.de

MOTIVATION: Arby is a new server for protein structure prediction that combines several homology-based methods for predicting the three-dimensional structure of a protein, given its sequence. The methods used include a threading approach, which makes use of structural information, and a profile-profile alignment approach that incorporates secondary structure predictions. The combination of the different methods with the help of empirically derived confidence measures affords reliable template selection. RESULTS: According to the recent CAFASP3 experiment, the server is one of the most sensitive methods for predicting the structure of single domain proteins. The quality of template selection is assessed using a fold-recognition experiment. AVAILABILITY: The Arby server is available through the portal of the Helmholtz Network for Bioinformatics at http://www.hnbioinfo.de under the protein structure category.

MOTIVATION: We introduce a new approach to using the information contained in sequence-to-function prediction data in order to recognize protein template classes, a critical step in predicting protein structure. The data on which our method is based comprise probabilities of functional categories; for given query sequences these probabilities are obtained by a neural net that has previously been trained on a variety of functionally important features. On a training set of sequences we assess the relevance of individual functional categories for identifying a given structural family. Using a combination of the most relevant categories, the likelihood of a query sequence to belong to a specific family can be estimated. RESULTS: The performance of the method is evaluated using cross-validation. For a fixed structural family and for every sequence, a score is calculated that measures the evidence for family membership. Even for structural families of small size, family members receive significantly higher scores. For some examples, we show that the relevant functional features identified by this method are biologically meaningful. The proposed approach can be used to improve existing sequence-to-structure prediction methods. AVAILABILITY: Matlab code is available on request from the authors. The data are available at http://www.mpisb.mpg.de/~sommer/Fun2Struc/

Abstract Fungal pyranose oxidase is a flavoenzyme whose preferred substrate among several monosaccharides is d-glucose. After a comprehensive analysis of conserved features in a structure-based multiple sequence alignment of homologous proteins, we could classify this enzyme into the GMC oxidoreductase family. The identified homology also suggests a three-dimensional protein structure similar to the functionally related glucose oxidase. Contact: [email protected] * To whom correspondence should be addressed.

Sequence variations in the gene products PYPAF1/CIAS1 and NOD2/CARD15 have been associated with several autoinflammatory diseases that, although clinically different, share a similar inflammatory pathophysiology. A multiple sequence alignment of homologous proteins demonstrates that some of the missense variants are located in highly conserved regions of the NTPase domain and possibly impair NTP-hydrolysis. Intriguingly, one of the variations, which is found identically in PYPAF1 and NOD2, is located at the same alignment position. Our findings suggest that evolutionary gene duplication can give rise to disease families because variants affect conserved sequence in a similar fashion.

MOTIVATION: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. RESULTS: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.

We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. Current knowlegde seems to suffice for the construction of models that are realistic with respect to searches for individual differentially expressed genes. Such models allow to investigate the dependence of the required number of samples on the relevant parameters: the biological variability of the samples within each class; the fold changes in expression; the detection sensitivity of the microarrays; and the acceptable error rates of the results. We supply experimentalists with general conclusions as well as a freely accessible Java applet at http://cartan.gmd.de/~zien/classsize/ for fine tuning simulations to their particular actualities. Since the situation can be assumed to be very similar for large scale proteomics and metabolomics studies, our methods and results might also apply there.

MOTIVATION: We present an extensive evaluation of different methods and criteria to detect remote homologs of a given protein sequence. We investigate two associated problems: first, to develop a sensitive searching method to identify possible candidates and, second, to assign a confidence to the putative candidates in order to select the best one. For searching methods where the score distributions are known, p-values are used as confidence measure with great success. For the cases where such theoretical backing is absent, we propose empirical approximations to p-values for searching procedures. RESULTS: As a baseline, we review the performances of different methods for detecting remote protein folds (sequence alignment and threading, with and without sequence profiles, global and local). The analysis is performed on a large representative set of protein structures. For fold recognition, we find that methods using sequence profiles generally perform better than methods using plain sequences, and that threading methods perform better than sequence alignment methods. In order to assess the quality of the predictions made, we establish and compare several confidence measures, including raw scores, z-scores, raw score gaps, z-score gaps, and different methods of p-value estimation. We work our way from the theoretically well backed local scores towards more explorative global and threading scores. The methods for assessing the statistical significance of predictions are compared using specificity--sensitivity plots. For local alignment techniques we find that p-value methods work best, albeit computationally cheaper methods such as those based on score gaps achieve similar performance. For global methods where no theory is available methods based on score gaps work best. By using the score gap functions as the measure of confidence we improve the more powerful fold recognition methods for which p-values are unavailable. AVAILABILITY: The benchmark set is available upon request.

Various bioinformatics problems require optimizing several different properties simultaneously. For example, in the protein threading problem, a scoring function combines the values for different parameters of possible sequence-to-structure alignments into a single score to allow for unambiguous optimization. In this context, an essential question is how each property should be weighted. As the native structures are known for some sequences, a partial ordering on optimal alignments to other structures, e.g., derived from structural comparisons, may be used to adjust the weights. To resolve the arising interdependence of weights and computed solutions, we propose a heuristic approach: iterating the computation of solutions (here, threading alignments) given the weights and the estimation of optimal weights of the scoring function given these solutions via systematic calibration methods. For our application (i.e., threading), this iterative approach results in structurally meaningful weights that significantly improve performance on both the training and the test data sets. In addition, the optimized parameters show significant improvements on the recognition rate for a grossly enlarged comprehensive benchmark, a modified recognition protocol as well as modified alignment types (local instead of global and profiles instead of single sequences). These results show the general validity of the optimized weights for the given threading program and the associated scoring contributions.

Along the long path from genomic data to a new drug, the knowledge of three-dimensional protein structure can be of significant help in several places. This paper points out such places, discusses the virtues of protein structure knowledge and reviews bioinformatics methods for gaining such knowledge on the protein structure.

Abstract Motivation: A number of metabolic databases are available electronically, some with features for querying and visualizing metabolic pathways and regulatory networks. We present a unifying, systematic approach based on Petri nets for storing, displaying, comparing, searching and simulating such nets from a number of different sources. Results: Information from each data source is extracted and compiled into a Petri net. Such Petri nets then allow to investigate the (differential) content in metabolic databases, to map and integrate genomic information and functional annotations, to compare sequence and metabolic databases with respect to their functional annotations, and to define, generate and search paths and pathways in nets. We present an algorithm to systematically generate all pathways satisfying additional constraints in such Petri nets. Finally, based on the set of valid pathways, so-called differential metabolic displays (DMDs) are introduced to exhibit specific differences between biological systems, i.e. different developmental states, disease states, or different organisms, on the level of paths and pathways. DMDs will be useful for target finding and function prediction, especially in the context of the interpretation of expression data. Availability: Part of this work has been integrated into the software package ToPLign available for use over the WWW at http://cartan.gmd.de/ToPLign.html. Contact: [email protected]; [email protected]; [email protected] To whom correspondence should be addressed.

MOTIVATION: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). RESULTS: The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.

T Lengauer; The Seventh International Conference on Intelligent Systems for Molecular Biology (ISMB'99), Bioinformatics, Volume 15, Issue 10, 1 October 1999, Pa

MOTIVATION: Matching of chemical interacting groups is a common concept for docking and fragment placement algorithms in computer-aided drug design. These algorithms have been proven to be reliable and fast if at least a certain number of hydrogen bonds or salt bridges occur. However, the algorithms typically run into problems if hydrophobic fragments or ligands should be placed. In order to dock hydrophobic fragments without significant loss of computational efficiency, we have extended the interaction model and placement algorithms in our docking tool FlexX. The concept of multi-level interactions is introduced into the algorithms for automatic selection and placement of base fragments. RESULTS: With the multi-level interaction model and the corresponding algorithmic extensions, we were able to improve the overall performance of FlexX significantly. We tested the approach with a set of 200 protein-ligand complexes taken from the Brookhaven Protein Data Bank (PDB). The number of test cases which can be docked within 1.5 A RMSD from the crystal structure can be increased from 58 to 64%. The performance gain is paid for by an increase in computation time from 73 to 91 s on average per protein-ligand complex. AVAILABILITY: The FlexX molecular docking software is available for UNIX platforms IRIX, Solaris and Linux. See http://cartan.gmd.de/FlexX for additional information.

Abstract Motivation: Prediction of protein secondary structure provides information that is useful for other prediction methods like fold recognition and ab initio 3D prediction. A consensus prediction constructed from the output of several methods should yield more reliable results than each of the individual methods. Method: We present an approach that reveals subtle but systematic differences in the output of different secondary structure prediction methods allowing the derivation of coherent consensus predictions. The method uses a machine learning technique that builds decision trees from existing data. Results: The first results of our analysis show that consensus prediction of protein secondary structure may be improved both quantitatively and qualitatively. Availability: Our method for consensus secondary structure prediction CoDe (Consensus formation by Decision tree learning) is based on machine learning and will be integrated into the ToPLign system (Toolbox for Protein aLignment) which can be accessed at http://cartan.gmd.de/ToPLign.html. Contact: {mevissen,selbig}@gmd.de

Article Random generation of embedded graphs and an extension to Dobrushin uniqueness (extended abstract) Share on Authors: Marcus Peinado Institute for Algorithms and Scientific Computing, German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, Germany and Internationl Computer Science Institute, Berkeley, California Institute for Algorithms and Scientific Computing, German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, Germany and Internationl Computer Science Institute, Berkeley, CaliforniaView Profile , Thomas Lengauer Institute for Algorithms and Scientific Computing, German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, Germany Institute for Algorithms and Scientific Computing, German National Research Center for Information Technology (GMD), 53754 Sankt Augustin, GermanyView Profile Authors Info & Claims STOC '98: Proceedings of the thirtieth annual ACM symposium on Theory of computingMay 1998 Pages 176–185https://doi.org/10.1145/276698.276729Online:23 May 1998Publication History 1citation235DownloadsMetricsTotal Citations1Total Downloads235Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Parametric alignment is an approach towards dealing with the uncertainties that are inherent in the cost parameters involved in the alignment of biosequences. In parametric alignment, a selected set of cost parameters is kept variable. The resulting space of optimal alignments is then analyzed with respect to the dependencies on these cost parameters. The number n of variable parameters is called the dimension of the parametric aligment problem. If the cost function is linear in the variable parameters, then the alignment space has the structure of a subdivision of the n-dimensional euclidean space into a collection of convex polyhedra. Each polyhedron represents a region of parameter settings that lead to the same set of optimal alignments. Efficient algorithms have been put forth for computing the subdivisions arising from one- and two-dimensional parametric alignment. General algorithmic schemes have been developed for n> 2, but these algorithms are inefficient, in practice. To our knowledge, all algorithms devised so far are plagued by numerical instabilities. We propose an algorithm for two-dimensional parametric alignment which is both faster than existing algorithms and numerically stable. We report on experimental results with this algorithm on biological data and comment on the potential role of parametric alignment in the analysis of biosequences. 1

We present a new methodology for high-level synthesis, which incorporates placement in an early phase of the synthesis process. This placement, prior to interconnect and storage allocation, allows for early and accurate estimates on area and net length. These cost factors are critical for the quality of the chip. The system has been implemented and extensive tests verify the estimates to be accurate.

Due to the application of fast device technologies and the increasing complexity of printed-circuit boards, electromagnetic phenomena, e.g. reflections and crosstalk, gain more and more importance and may even disturb the function of a circuit. In the future, it will be indispensable to already consider phenomena of electromagnetic compatibility (EMC) during layout synthesis. In this paper, robust methods are presented that make it possible for the first time to incorporate complex EMC constraints and cost criteria into printed-circuit board routing. This includes both concepts for the development and specification of EMC design models and robust and efficient algorithms for EMC-driven routing that can handle these models.>

Efficient ways of analyzing families of graphs that are generated by a certain type of context-free graph grammars are considered. These graph grammars are called cellular graph grammars . They generate the same graph families as hyperedge replacement systems, but are defined in a way that supports complexity analysis. A characteristic called “finiteness” of graph properties are defined, and combinatorial algorithms are presented for deciding whether a graph language generated by a given cellular graph grammar contains a graph with a given finite graph property. Structural parameters are introduced that bound the complexity of the decision procedure and special cases for which the decision can be made in polynomial time are discussed. Extensions to graph grammars that are not context-free are also given. Our results provide explicit and efficient combinatorial algorithms where, so far, only the existence of algorithms has been shown, or the best known algorithms are highly inefficient.

Many popular floorplanning methods are based on circuit partitioning. Besides effectiveness, their main advantages are the easy handling of flexible cells and the ability to incorporate global wiring in a straightforward manner. Several proven approaches to partitioning-based floorplanning and hierarchical global wiring are unified and enhanced with new ideas. The result is a robust algorithmic framework for floorplanning with integrated global wiring that incorporates a new efficient sizing method for cut trees of arbitrary degree, an accurate procedure for wiring area estimation, and a method to select a suitable floorplan topology using a systematic optimization procedure that integrates floorplanning and hierarchical wiring. Experiments with a prototype implementation called the FRODO-system prove the effectiveness of this approach.>

The authors point out that, in order to perform the design of printed circuit boards as time- and cost-efficiently as possible, electromagnetic compatability (EMC) phenomena have to be taken into account during layout synthesis. The EMC router HERO offers a robust framework for incorporating EMC constraints and cost criteria into routing. Using HERO, it will not be possible to obtain a completely failsafe layout, in general. However, experimental results for typical boards prove that a great number of EMC problems can be avoided during layout synthesis and that the effects of EMC phenomena can be reduced substantially. Detailed reports of EMC design rule violations provide effective input to the succeeding EMC verification phase. Violations of EMC design rules are mainly caused by an inappropriate placement. Therefore, it seems to be of great promise to combine hierarchical placement methods with this approach for hierarchical routing.>

Hierarchical methods for global routing are addressed. These methods break down the integer program that formulates the routing problem into small integer programs. These programs are solved exactly by using integer programming methods. Then the exact solutions of the small integer programs are pieced together to form an approximate solution of the original routing problem. The structure of the small integer programs is investigated. A greedy preprocessing method is proposed that, in effect, preroutes easily routable nets and reduces the remaining integer program to a fraction of the size in some cases, and eliminates it altogether in other cases. This yields a substantial reduction of the time required to solve the small integer programs.>

A floorplanning system was implemented that is based on circuit partitioning and incorporates hierarchical global wiring. Besides unifying several existing ideas in floorplanning, the system introduces the following components: (1) a novel and more accurate estimate of wiring area during the floorplan sizing process and (2) a systematic optimization procedure during the selection of suitable floorplan patterns in the top-down phase that integrates floorplanning and hierarchical wiring. Experiments with the system prove the feasibility of the approach.>

Using hierarchical definitions, one can describe very large graphs in small space. The blow-up from the length of the hierarchical description to the size of the graph can be as large as exponential. If the efficiency of graph algorithms is measured in terms of the length of the hierarchical description rather than in terms of the graph size, algorithms that do not exploit the hierarchy become hopelessly inefficient. Whether the hierarchy can be exploited to speed up the solution of graph problems depends on the hierarchical graph model. In the literature, hierarchical graph models have been described that allow almost no exploitation of the hierarchy [ 16]. In this paper, a hierarchical graph model that permits taking advantage of the hierarchy is presented. For this model algorithms are given that test planarity of a hierarchically described graph in linear time in the length of the hierarchical description.

Using hierarchical definitions we can describe very large graphs in small spaces. In this paper we discuss how such succinct graph descriptions can be used to speed up the solution of graph problems. We present the bottom-up method that solves graph problems without expanding the hierarchical description. This allows solutions that are efficient in terms of the hierarchical graph description instead of the size of the expanded graph. We exemplify the method by giving efficient solutions to connectivity problems on hierarchical graphs. Our results have applications in computer-aided design for integrated circuit design and other engineering problems.

We present a theory of one-dimensional layout compaction that is based on the graph theoretic approach used in such compacters as reported in [5], [7], [11], and [26]. Compaction here consists of two steps. In the first stage, a directed graph is extracted from the layout. In the second stage, compaction is performed by solving a single-source shortest path problem on this graph. The paper presents new efficient algorithms and/or heuristics for the first stage of the compaction process. A compacter based on the framework presented here that incorporates the algorithms described is a powerful tool that can easily be tailored to specific applications. It allows a tradeoff between the space and runtime performance of the compacter and the quality of the compaction.

In many CAD systems for VLSI design the specification of a layout is internally represented by a set of geometric constraints that take the form of linear inequalities between pairs of layout components. Some of the constraints may be explicitly stated by the circuit designer. Others are internally generated by the CAD system, using the design rules of the fabrication process. Layout compaction is then equivalent to finding a minimum area layout satisfying all constraints. We discuss the complexity of the constraint resolution problem arising in this context. Hereby we allow circuits to be specified hierarchically. The complexity of the constraint resolution is then measured in terms of the length of the hierarchical specification. We show the following results: 1. It is decidable in polynomial (cubic) time whether a given hierarchical layout specification yields a consistent set of geometric constraints. The size of minimum area layouts satisfying the constraints can also be determined in cubic time. 2. For every layout specification that is consistent a hierarchical description L of a minimum area layout can be computed in polynomial time in the length of L. 3. There is a consistent layout specification with the following property: No layout satisfying the constraints is concise, i.e., every hierarchical layout description consistent with the specification has a length which grows exponentially in the length of the specification. 4. We define a subclass of so-called well-formed layout specifications. Each well-formed specification has a concise layout, which can be hierarchically described in linear space. Such a layout can be found in polynomial time. However, it is in general not a minimum area layout. Indeed, there is a consistent well-formed specification all of whose minimum area layouts are inconcise,.i.e., need exponential space to be described.

Asymptotically Ught tune-space trade-offs for pebblmg three d~fferent classes of directed aeychc graphs are derived Let N be the size of the graph, S the number of avadable pebbles, and T the time necessary for pebbling the graph A time-space trade-off of the form ST = O(N 2) ls proved for pebbhng (usmg only black pebbles) a specml class of permutaaon graphs that tmplement the bR-reversal permutation.If we are allowed to use black and whtte pebbles~ the time-space trade-off is shown to be of the form (:) r=o T¢ +0(~.A tune-space trade-off of the form /N\OIN/S~ T= SOI~ ) ~s proved for pebbling a class of graphs constructed by stacking superconcentrators m series.This tunespace trade-off holds whether we use only black or black and white pebbles A tune-space trade-off of the form T --$2 2°~N/s) Is proved for the class of all directed acychc graphs This trade-off also holds whether we use only black or black and white pebbles Categories and Subject Descriptors: F 1.3 [Computation by Abstract Devices]: Complexity Classes-relatwns among complexay measures, F.2 3 [Analysis of Algorithms and Problem Complexity] Trade-offs Among Complextty Measures; G.2.

In this paper we study a pebbling problem that models the storage requirements of various kinds of computation. Sethi has shown this problem to be $NP$-hard and Lingas has shown a generalization to be P-space complete. We prove the original problem P-space complete by using a modification of Lingas’s proof. The pebbling problem is an example of a P-space complete problem not exhibiting any obvious quantifier alternation.

We examine a pebbling problem which has been used to study the storage requirements of various models of computation. Sethi has shown this problem to be NP-hard and Lingas has shown a generalization to be P-space complete. We prove the original problem P-space complete by employing a modification of Lingas's proof. The pebbling problem is one of the few examples of a P-space complete problem not exhibiting any obvious quantifier alternation.

This paper derives asymptotically tight bounds on the time-space tradeoffs for pebbling three different classes of directed acyclic graphs. Let N be the size of the graph, S the number of available pebbles, and T the time necessary for pebbling the graph. (a) A time space tradeoff of the form ST = t(N2) is proved for a special class of permutation graphs which implement the bit reversal permutation. (b) A time-space tradeoff of the form T = S t(N/S)t(N/S) is proved for a class of graphs constructed by stacking superconcentrators in series. (c) A time-space tradeoff of the form T = S.22t(N/S)is proved for pebbling general directed acyclic graphs.

A fast algorithm for finding dominators in a flowgraph is presented. The algorithm uses depth-first search and an efficient method of computing functions defined on paths in trees. A simple implementation of the algorithm runs in O ( m log n ) time, where m is the number of edges and n is the number of vertices in the problem graph. A more sophisticated implementation runs in O ( m α( m , n )) time, where α( m , n ) is a functional inverse of Ackermann's function. Both versions of the algorithm were implemented in Algol W, a Stanford University version of Algol, and tested on an IBM 370/168. The programs were compared with an implementation by Purdom and Moore of a straightforward O ( mn )-time algorithm, and with a bit vector algorithm described by Aho and Ullman. The fast algorithm beat the straightforward algorithm and the bit vector algorithm on all but the smallest graphs tested.