Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.

SUMMARY: Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users. AVAILABILITY AND IMPLEMENTATION: https://github.com/debbiemarkslab/evcouplings.

PURPOSE: PaxtoolsR package enables access to pathway data represented in the BioPAX format and made available through the Pathway Commons webservice for users of the R language to aid in advanced pathway analyses. Features include the extraction, merging and validation of pathway data represented in the BioPAX format. This package also provides novel pathway datasets and advanced querying features for R users through the Pathway Commons webservice allowing users to query, extract and retrieve data and integrate these data with local BioPAX datasets. AVAILABILITY AND IMPLEMENTATION: The PaxtoolsR package is compatible with versions of R 3.1.1 (and higher) on Windows, Mac OS X and Linux using Bioconductor 3.0 and is available through the Bioconductor R package repository along with source code and a tutorial vignette describing common tasks, such as data visualization and gene set enrichment analysis. Source code and documentation are at http://www.bioconductor.org/packages/paxtoolsr This plugin is free, open-source and licensed under the LGPL-3. CONTACT: [email protected] or [email protected].

PURPOSE: The rcellminer R package provides a wide range of functionality to help R users access and explore molecular profiling and drug response data for the NCI-60. The package enables flexible programmatic access to CellMiner's unparalleled breadth of NCI-60 data, including gene and protein expression, copy number, whole exome mutations, as well as activity data for ∼21K compounds, with information on their structure, mechanism of action and repeat screens. Functions are available to easily visualize compound structures, activity patterns and molecular feature profiles. Additionally, embedded R Shiny applications allow interactive data exploration. AVAILABILITY AND IMPLEMENTATION: rcellminer is compatible with R 3.2 and above on Windows, Mac OS X and Linux. The package, documentation, tutorials and Shiny-based applications are available through Bioconductor (http://www.bioconductor.org/packages/rcellminer); ongoing updates will occur according to the Bioconductor release schedule with new CellMiner data. The package is free and open-source (LGPL 3). CONTACT: [email protected] or [email protected].

Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.

Tumorigenesis requires the re-organization of metabolism to support malignant proliferation. We examine how the altered metabolism of cancer cells is reflected in the rewiring of co-expression patterns among metabolic genes. Focusing on breast and clear-cell kidney tumors, we report the existence of key metabolic genes which act as hubs of differential co-expression, showing significantly different co-regulation patterns between normal and tumor states. We compare our findings to those from classical differential expression analysis, and counterintuitively observe that the extent of a gene's differential co-expression only weakly correlates with its differential expression, suggesting that the two measures probe different features of metabolism. Focusing on this discrepancy, we use changes in co-expression patterns to highlight the apparent loss of regulation by the transcription factor HNF4A in clear cell renal cell carcinoma, despite no differential expression of HNF4A. Finally, we aggregate the results of differential co-expression analysis into a Pan-Cancer analysis across seven distinct cancer types to identify pairs of metabolic genes which may be recurrently dysregulated. Among our results is a cluster of four genes, all components of the mitochondrial electron transport chain, which show significant loss of co-expression in tumor tissue, pointing to potential mitochondrial dysfunction in these tumor types.

Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.

MOTIVATION: Somatic homozygous deletions of chromosomal regions in cancer, while not necessarily oncogenic, may lead to therapeutic vulnerabilities specific to cancer cells compared with normal cells. A recently reported example is the loss of one of the two isoenzymes in glioblastoma cancer cells such that the use of a specific inhibitor selectively inhibited growth of the cancer cells, which had become fully dependent on the second isoenzyme. We have now made use of the unprecedented conjunction of large-scale cancer genomics profiling of tumor samples in The Cancer Genome Atlas (TCGA) and of tumor-derived cell lines in the Cancer Cell Line Encyclopedia, as well as the availability of integrated pathway information systems, such as Pathway Commons, to systematically search for a comprehensive set of such epistatic vulnerabilities. RESULTS: Based on homozygous deletions affecting metabolic enzymes in 16 TCGA cancer studies and 972 cancer cell lines, we identified 4104 candidate metabolic vulnerabilities present in 1019 tumor samples and 482 cell lines. Up to 44% of these vulnerabilities can be targeted with at least one Food and Drug Administration-approved drug. We suggest focused experiments to test these vulnerabilities and clinical trials based on personalized genomic profiles of those that pass preclinical filters. We conclude that genomic profiling will in the future provide a promising basis for network pharmacology of epistatic vulnerabilities as a promising therapeutic strategy. AVAILABILITY AND IMPLEMENTATION: A web-based tool for exploring all vulnerabilities and their details is available at http://cbio.mskcc.org/cancergenomics/statius/ along with supplemental data files.

MOTIVATION: BioPAX is a standard language for representing complex cellular processes, including metabolic networks, signal transduction and gene regulation. Owing to the inherent complexity of a BioPAX model, searching for a specific type of subnetwork can be non-trivial and difficult. RESULTS: We developed an open source and extensible framework for defining and searching graph patterns in BioPAX models. We demonstrate its use with a sample pattern that captures directed signaling relations between proteins. We provide search results for the pattern obtained from the Pathway Commons database and compare these results with the current data in signaling databases SPIKE and SignaLink. Results show that a pattern search in public pathway data can identify a substantial amount of signaling relations that do not exist in signaling databases. AVAILABILITY: BioPAX-pattern software was developed in Java. Source code and documentation is freely available at http://code.google.com/p/biopax-pattern under Lesser GNU Public License.

MOTIVATION: Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used. RESULTS: In a small benchmark of 15 proteins, we show that the TM-scores of top-ranked models are improved by on average 33% using PconsFold compared with the original version of EVfold. In a larger benchmark, we find that the quality is improved with 15-30% when using PconsC in comparison with earlier contact prediction methods. Further, using Rosetta instead of CNS does not significantly improve global model accuracy, but the chemistry of models generated with Rosetta is improved. AVAILABILITY: PconsFold is a fully automated pipeline for ab initio protein structure prediction based on evolutionary information. PconsFold is based on PconsC contact prediction and uses the Rosetta folding protocol. Due to its modularity, the contact prediction tool can be easily exchanged. The source code of PconsFold is available on GitHub at https://www.github.com/ElofssonLab/pcons-fold under the MIT license. PconsC is available from http://c.pcons.net/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The interaction between drugs and their targets, often proteins, and between antibodies and their targets, is important for planning and analyzing investigational and therapeutic interventions in many biological systems. Although drug-target and antibody-target datasets are available in separate databases, they are not publicly available in an integrated bioinformatics resource. As medical therapeutics, especially in cancer, increasingly uses targeted drugs and measures their effects on biomolecular profiles, there is an unmet need for a user-friendly toolset that allows researchers to comprehensively and conveniently access and query information about drugs, antibodies and their targets. SUMMARY: The PiHelper framework integrates human drug-target and antibody-target associations from publicly available resources to help meet the needs of researchers in systems pharmacology, perturbation biology and proteomics. PiHelper has utilities to (i) import drug- and antibody-target information; (ii) search the associations either programmatically or through a web user interface (UI); (iii) visualize the data interactively in a network; and (iv) export relationships for use in publications or other analysis tools. AVAILABILITY: PiHelper is a free software under the GNU Lesser General Public License (LGPL) v3.0. Source code and documentation are at http://bit.ly/pihelper. We plan to coordinate contributions from the community by managing future releases.

SUMMARY: BioPAX is a community-developed standard language for biological pathway data. A key functionality required for efficient BioPAX data exchange is validation-detecting errors and inconsistencies in BioPAX documents. The BioPAX Validator is a command-line tool, Java library and online web service for BioPAX that performs >100 classes of consistency checks. AVAILABILITY AND IMPLEMENTATION: The validator recognizes common syntactic errors and semantic inconsistencies and reports them in a customizable human readable format. It can also automatically fix some errors and normalize BioPAX data. Since its release, the validator has become a critical tool for the pathway informatics community, detecting thousands of errors and helping substantially increase the conformity and uniformity of BioPAX-formatted data. The BioPAX Validator is open source and released under LGPL v3 license. All sources, binaries and documentation can be found at sf.net/p/biopax, and the latest stable version of the web application is available at biopax.org/validator.

A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools.

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.

The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

SUMMARY: Representing models of cellular processes or pathways in a graphically rich form facilitates interpretation of biological observations and generation of new hypotheses. Solving biological problems using large pathway datasets requires software that can combine data mapping, querying and visualization as well as providing access to diverse data resources on the Internet. ChiBE is an open source software application that features user-friendly multi-view display, navigation and manipulation of pathway models in BioPAX format. Pathway views are rendered in a feature-rich format, and may be laid out and edited with state-of-the-art visualization methods, including compound or nested structures for visualizing cellular compartments and molecular complexes. Users can easily query and visualize pathways through an integrated Pathway Commons query tool and analyze molecular profiles in pathway context. AVAILABILITY: http://www.bilkent.edu.tr/%7Ebcbi/chibe.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data. RESULTS: We implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature. CONCLUSION: Our approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks.

The recent discovery of a new class of 30-nucleotide long RNAs in mammalian testes, called PIWI-interacting RNA (piRNA), with similarities to microRNAs and repeat-associated small interfering RNAs (rasiRNAs), has raised puzzling questions regarding their biogenesis and function. We report a comparative analysis of currently available piRNA sequence data from the pachytene stage of mouse spermatogenesis that sheds light on their sequence diversity and mechanism of biogenesis. We conclude that (i) there are at least four times as many piRNAs in mouse testes than currently known; (ii) piRNAs, which originate from long precursor transcripts, are generated by quasi-random enzymatic processing that is guided by a weak sequence signature at the piRNA 5'ends resulting in a large number of distinct sequences; and (iii) many of the piRNA clusters contain inverted repeats segments capable of forming double-strand RNA fold-back segments that may initiate piRNA processing analogous to transposon silencing.

ajor advances in genome science and molecular technologies provide new opportunities at the interface between basic biological research and medical practice.The unprecedented completeness, accuracy, and volume of genomic and molecular data necessitate a new kind of computational biology for translational research.Key challenges are standardization of data capture and communication, organization of easily accessible repositories, and algorithms for integrated analysis based on heterogeneous sources of information.Also required are new ways of using complementary clinical and biological data, such as computational methods for predicting disease phenotype from molecular and genetic profiling.New combined experimental and computational methods hold the promise of more accurate diagnosis and prognosis as well as more effective prevention and therapy.

BACKGROUND: Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required. RESULTS: We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use. CONCLUSION: cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling.

The TGF-beta pathway plays a central role in tissue homeostasis and morphogenesis. It transduces a variety of extracellular signals into intracellular transcriptional responses that control a plethora of cellular processes, including cell growth, apoptosis, and differentiation. We use computational modeling to show that coupling of signaling with receptor trafficking results in a highly versatile signal-processing unit, able to sense by itself absolute levels of ligand, temporal changes in ligand concentration, and ratios of multiple ligands. This coupling controls whether the response of the receptor module is transient or permanent and whether or not different signaling channels behave independently of each other. Our computational approach unifies seemingly disparate experimental observations and suggests specific changes in receptor trafficking patterns that can lead to phenotypes that favor tumor progression.

SUMMARY: FuncAssociate is a web-based tool to help researchers use Gene Ontology attributes to characterize large sets of genes derived from experiment. Distinguishing features of FuncAssociate include the ability to handle ranked input lists, and a Monte Carlo simulation approach that is more appropriate to determine significance than other methods, such as Bonferroni or idák p-value correction. FuncAssociate currently supports 10 organisms (Vibrio cholerae, Shewanella oneidensis, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, Caenorhaebditis elegans, Drosophila melanogaster, Mus musculus, Rattus norvegicus and Homo sapiens). AVAILABILITY: FuncAssociate is freely accessible at http://llama.med.harvard.edu/Software.html. Source code (in Perl and C) is freely available to academic users 'as is'.

Abstract Motivation: Genome-wide functional annotation either by manual or automatic means has raised considerable concerns regarding the accuracy of assignments and the reproducibility of methodologies. In addition, a performance evaluation of automated systems that attempt to tackle sequence analyses rapidly and reproducibly is generally missing. In order to quantify the accuracy and reproducibility of function assignments on a genome-wide scale, we have re-annotated the entire genome sequence of Chlamydia trachomatis (serovar D), in a collaborative manner. Results: We have encoded all annotations in a structured format to allow further comparison and data exchange and have used a scale that records the different levels of potential annotation errors according to their propensity to propagate in the database due to transitive function assignments. We conclude that genome annotation may entail a considerable amount of errors, ranging from simple typographical errors to complex sequence analysis problems. The most surprising result of this comparative study is that automatic systems might perform as well as the teams of experts annotating genome sequences. Availability and supplementary information: http://www.ebi.ac.uk/research/cgg/annotation/cteval/ Contact: [email protected] * To whom correspondence should be addressed. † INA-EKETA, GR-57001 Thessaloniki, Greece ‡ Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA § Aetion Technologies LLC, Worthington, OH 43085, USA ¶ Institut Curie, F-75248 Paris, France ∥ CNRS, UMR6543, F-06108 Nice, France ** Alma Bioinformatics, E-28760 Madrid, Spain †† Cap Gemini Ernst & Young, London SW1X 7LX, UK ‡‡ Univ. of Rome ‘La Sapienza’, I-00185 Rome, Italy §§ MWG-Biotech AG, Ebersberg, D-85560 Berlin, Germany ¶¶ Wellcome Trust Biocentre, Univ. of Dundee, Dundee DD1 5HN, UK

In 2002, the journal Bioinformatics continued to grow with a significant increase in submissions to over 700 manuscripts for the year, from more than 1000 active authors around the globe.We anticipated this a year ago (Sander, 2002), but the 80% growth in one year is extraordinary and reflects the increased importance of the field of computational biology, both as an area of active research and of widespread application, in academia and industry.How did the journal cope?The editorial team, with Barbara Cox as Head of the Editorial Office at the European Bioinformatics Institute, and the Associate Editors, with Martin Bishop as most active contributor, as well as an active community of volunteer reviewers, many of them members of the International Society for Computational Biology (ISCB), have worked very hard and added tremendous information value during the selection process for publication.They deserve our gratitude and admiration.The result is an increased quality of information available to many thousands of researchers.Of the 2002 submissions, about 56% were articles and 41% Application Notes.The current acceptance rate is about 44%.Most papers come from North America and Europe (about 40% each), and there is a growing number of submissions from Asia, particularly China.Asian bioinformatics researchers, by the way, have had an active organization (APBioNet.org,the Asia Pacific Bioinformatics Network) since 1998, not unlike EMBnet in Europe, which I was glad to support in its early stages.The people of the Science Citation Index had the journal Bioinformatics ranked highly in the field of computer science and called it one of the 'most improved journals' for two bi-monthly periods in 2002.Just look at those impressive growth curves on www.in-cites.com/journals/bioinformatics.html!The impact factor remains high, at 3.4, not bad at all, but I think it could and should go higher -as a consequence of increased quality of papers, not as a goal in itself.Accordingly, Oxford University Press is increasing the frequency of the printed issues of Bioinformatics to 18 issues in 2003, with a corresponding ∼50% increase in the number of pages published.The editorial team is being expanded (see below) and we will need more reviewers in the field to donate their precious time.Do send email to [email protected].

How can we improve Bioinformatics, the journal, so that it becomes a premier communication medium for computational research in biology, the 'Nature' or 'Cell' of bioinformatics?At the transition '01/'02, we are taking a number of steps toward this end and others will follow during the year.

Bioinformatics -solving biological problemsWhen the department of Biocomputing was formed in 1986 at the EMBL Heidelberg, my colleague Arthur Lesk insisted on the spelling BIOinformatics.If we could not answer biological questions, we would fail as computational biologists.The same sort of thinking and the desire to focus on modern biology had an effect on the journal CABIOS (Computer Applications in the Biosciences), this journal's direct predecessor, renamed at the end of 1997.Five years ago, even Bioinformatics moved to reporting biologically driven research rather than simply descriptions of new software.Reports such as 'PRED: yet another implementation of the Chou-Fasman method on Windows 2.6' were no longer accepted and, more generally, software reports shortened to at most 2 pages as 'Application Notes'.Structured abstracts now encourage putting a paper into biological context.'Discovery Notes' report biologically interesting discoveries using computational techniques, with topics ranging from sequence motif detection, structural similarities, and gene structure prediction to comparative genomics and metabolic pathways.Review papers are actively solicited from all areas of computational biology, not just algorithmics.Let's look beyond the journal.With the entry of new researchers into the field of bioinformatics, many trained in computer science, we continually need to widen the roads that lead from algorithm and computation to biologically meaningful results.And as genomic technologies invade most wet labs, we have the opportunity, even the obligation, to lead fields of biological and medical research in the design of experiments and of tools used to generate the data on which computation is based.We assert: computational planning and analysis is an integral part of the biological discovery process.

MOTIVATION: Sensitive detection and masking of low-complexity regions in protein sequences. Filtered sequences can be used in sequence comparison without the risk of matching compositionally biased regions. The main advantage of the method over similar approaches is the selective masking of single residue types without affecting other, possibly important, regions. RESULTS: A novel algorithm for low-complexity region detection and selective masking. The algorithm is based on multiple-pass Smith-Waterman comparison of the query sequence against twenty homopolymers with infinite gap penalties. The output of the algorithm is both the masked query sequence for further analysis, e.g. database searches, as well as the regions of low complexity. The detection of low-complexity regions is highly specific for single residue types. It is shown that this approach is sufficient for masking database query sequences without generating false positives. The algorithm is benchmarked against widely available algorithms using the 210 genes of Plasmodium falciparum chromosome 2, a dataset known to contain a large number of low-complexity regions. AVAILABILITY: CAST (version 1.0) executable binaries are available to academic users free of charge under license. Web site entry point, server and additional material: http://www.ebi.ac.uk/research/cgg/services/cast/

MOTIVATION: Large-scale genome projects generate a rapidly increasing number of sequences, most of them biochemically uncharacterized. Research in bioinformatics contributes to the development of methods for the computational characterization of these sequences. However, the installation and application of these methods require experience and are time consuming. RESULTS: We present here an automatic system for preliminary functional annotation of protein sequences that has been applied to the analysis of sets of sequences from complete genomes, both to refine overall performance and to make new discoveries comparable to those made by human experts. The GeneQuiz system includes a Web-based browser that allows examination of the evidence leading to an automatic annotation and offers additional information, views of the results, and links to biological databases that complement the automatic analysis. System structure and operating principles concerning the use of multiple sequence databases, underlying sequence analysis tools, lexical analyses of database annotations and decision criteria for functional assignments are detailed. The system makes automatic quality assessments of results based on prior experience with the underlying sequence analysis tools; overall error rates in functional assignment are estimated at 2.5-5% for cases annotated with highest reliability ('clear' cases). Sources of over-interpretation of results are discussed with proposals for improvement. A conservative definition for reporting 'new findings' that takes account of database maturity is presented along with examples of possible kinds of discoveries (new function, family and superfamily) made by the system. System performance in relation to sequence database coverage, database dynamics and database search methods is analysed, demonstrating the inherent advantages of an integrated automatic approach using multiple databases and search methods applied in an objective and repeatable manner. AVAILABILITY: The GeneQuiz system is publicly available for analysis of protein sequences through a Web server at http://www.sander.ebi.ac. uk/gqsrv/submit

UNLABELLED: MView is a tool for converting the results of a sequence database search into the form of a coloured multiple alignment of hits stacked against the query. Alternatively, an existing multiple alignment can be processed. In either case, the output is simply HTML, so the result is platform independent and does not require a separate application or applet to be loaded. AVAILABILITY: Free from http://www.sander.ebi.ac.uk/mview/ subject to copyright restrictions. CONTACT: [email protected]

MOTIVATION: The underlying error rate for genomic sequencing sometimes results in the introduction of artificial frameshifts and in-frame stop codons into putative protein encoding genes. Severe errors are then introduced into the inferred transcripts through mis-translation or premature termination. RESULTS: We describe a system for screening segments of DNA for frameshift and in-frame stop errors in coding regions. The method is based on homology matching using blastx to compare all six reading frames of the query nucleotide sequence against selected protein sequence databases. Fragments of protein matching neighbouring regions of the query DNA are united and extended laterally to define candidate open reading frames, within which, frameshifts and stops are identified. Suitable targets include prokaryotic or other intron-free genomic sequence and complementary DNAs. As an example of its use, we report here two frameshifted ORFs that deviate from the original TIGR sequence annotations for the recently released Helicobacter pylori genome. AVAILABILITY: The tool is accessible via the URL http://www.sander.ebi.ac.uk/frame/. CONTACT: [email protected].

MOTIVATION: To maximize the chances of biological discovery, homology searching must use an up-to-date collection of sequences. However, the available sequence databases are growing rapidly and are partially redundant in content. This leads to increasing strain on CPU resources and decreasing density of first-hand annotation. RESULTS: These problems are addressed by clustering closely similar sequences to yield a covering of sequence space by a representative subset of sequences. No pair of sequences in the representative set has >90% mutual sequence identity. The representative set is derived by an exhaustive search for close similarities in the sequence database in which the need for explicit sequence alignment is significantly reduced by applying deca- and pentapeptide composition filters. The algorithm was applied to the union of the Swissprot, Swissnew, Trembl, Tremblnew, Genbank, PIR, Wormpep and PDB databases. The all-against-all comparison required to generate a representative set at 90% sequence identity was accomplished in 2 days CPU time, and the removal of fragments and close similarities yielded a size reduction of 46%, from 260 000 unique sequences to 140 000 representative sequences. The practical implications are (i) faster homology searches using, for example, Fasta or Blast, and (ii) unified annotation for all sequences clustered around a representative. As tens of thousands of sequence searches are performed daily world-wide, appropriate use of the non-redundant database can lead to major savings in computer resources, without loss of efficacy. AVAILABILITY: A regularly updated non-redundant protein sequence database (nrdb90), a server for homology searches against nrdb90, and a Perl script (nrdb90.pl) implementing the algorithm are available for academic use from http://www.embl-ebi.ac. uk/holm/nrdb90. CONTACT: [email protected]

UNLABELLED: A tool is described for the automatic classification of sequences in functional classes using their database annotations. The Euclid system is based on a simple learning procedure from examples provided by human experts. AVAILABILITY: Euclid is freely available for academics at http://www.gredos.cnb.uam.es/EUCLID, with the corresponding dictionaries for the generation of three, eight and 14 functional classes. CONTACT: E-mail: [email protected] SUPPLEMENTARY INFORMATION: The results of the EUCLID classification of different genomes are available at http://www.sander.ebi.ac. uk/genequiz/. A detailed description of the different applications mentioned in the text is available at http://www.gredos.cnb.uam. es/EUCLID/Full_Paper

Miguel Andrade, Georg Casari, Antoine de Daruvar, Chris Sander, Reinhard Schneider, Javier Tamames, Alfonso Valencia, Christos Ouzounis; Sequence analysis

Statistical methods that compare observed and expected distributions of experimental observables provide powerful tools for the quality control of protein structures. The distribution of backbone dihedral angles (‘Ramachandran plot’) has often been used for such quality control, but without a firm statistical foundation. A new and simple method is presented for judging the quality of a protein structure based on the distribution of backbone dihedral angles. Inputs to the method are 60 torsion angle distributions extracted from protein structures solved at high resolution; one for each combination of residue type and tri-state secondary structure. Output for a protein is a Ramachandran Z-score, expressing the quality of the Ramachandran plot relative to current state-of-the-art structures. The Ramachandran test is available as part of the free WHAT_CHECK program. Information about this program can be obtained on the WWW from http://swift.embl-heidelberg.de/whatcheck/ E-mail: [email protected]

MOTIVATION: The Protein Data Bank currently contains more than 4700 protein coordinate sets. It is often desirable to make a selection from these files based on a criterion like R-factor, experimental method, length of the amino acid sequence, or the number of homologous sequences in SWISSPROT. Doing this using the distributed form of the Protein Data Bank can be a tedious task, because (1) this requires reading one file for every single entry, and (2) not all of the information is present in a consistent computer readable way in all of the entries. RESULTS: The PDBFINDER database provides an easy to interpret file containing summary information about all Protein Data Bank files. Summary information from the DSSP (Definition of Secondary Structure of Proteins) and HSSP (Homology derived Secondary Structure of Proteins) databases is also included. Furthermore, where essential data were missing from the Protein Data Bank file, this information has been retrieved from the original literature. AVAILABILITY: The latest version of the PDBFINDER database can be downloaded by anonymous ftp from swift.embl-heidelberg.de, directory:/pdbfinder. CONTACT: E-mail address [email protected].

By the middle of 1993, > 30,000 protein sequences has been listed. For 1000 of these, the three-dimensional (tertiary) structure has been experimentally solved. Another 7000 can be modelled by homology. For the remaining 21,000 sequences, secondary structure prediction provides a rough estimate of structural features. Predictions in three states range between 35% (random) and 88% (homology modelling) overall accuracy. Using information about evolutionary conservation as contained in multiple sequence alignments, the secondary structure of 4700 protein sequences was predicted by the automatic e-mail server PHD. For proteins with at least one known homologue, the method has an expected overall three-state accuracy of 71.4% for proteins with at least one known homologue (evaluated on 126 unique protein chains).

The quality of a multi-layered network predicting the secondary structure of proteins is improved substantially by: (i) using information about evolutionarily conserved amino acids (increase of overall accuracy by six percentage points), (ii) balancing the training dynamics (increase of accuracy for strand), and (iii) combining uncorrelated networks in a jury (increase two percentage points). In addition, appending a second level structure-to-structure network results in better reproduction of the length of secondary structure segments.