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Motivation The structure of chromatin impacts gene expression. Its alteration has been shown to coincide with the occurrence of cancer. A key challenge is in understanding the role of chromatin structure (CS) in cellular processes and its implications in diseases. Results We propose a comparative pipeline to analyze CSs and apply it to study chronic lymphocytic leukemia (CLL). We model the chromatin of the affected and control cells as networks and analyze the network topology by state-of-the-art methods. Our results show that CSs are a rich source of new biological and functional information about DNA elements and cells that can complement protein–protein and co-expression data. Importantly, we show the existence of structural markers of cancer-related DNA elements in the chromatin. Surprisingly, CLL driver genes are characterized by specific local wiring patterns not only in the CS network of CLL cells, but also of healthy cells. This allows us to successfully predict new CLL-related DNA elements. Importantly, this shows that we can identify cancer-related DNA elements in other cancer types by investigating the CS network of the healthy cell of origin, a key new insight paving the road to new therapeutic strategies. This gives us an opportunity to exploit chromosome conformation data in healthy cells to predict new drivers. Availability and implementation Our predicted CLL genes and RNAs are provided as a free resource to the community at https://life.bsc.es/iconbi/chromatin/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation Genetic alterations lead to tumor progression and cell survival but also uncover cancer-specific vulnerabilities on gene dependencies that can be therapeutically exploited. Results vulcanSpot is a novel computational approach implemented to expand the therapeutic options in cancer beyond known-driver genes unlocking alternative ways to target undruggable genes. The method integrates genome-wide information provided by massive screening experiments to detect genetic vulnerabilities associated to tumors. Then, vulcanSpot prioritizes drugs to target cancer-specific gene dependencies using a weighted scoring system based on well known drug-gene relationships and drug repositioning strategies. Availability and implementation http://www.vulcanspot.org. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation The fast growth of bioinformatics adds a significant difficulty to assess the contribution, geographical and thematic distribution of the research publications. Results To help researchers, grant agencies and general public to assess the progress in bioinformatics, we have developed BIOLITMAP, a web-based geolocation system that allows an easy and sensible exploration of the publications by institution, year and topic. Availability and implementation BIOLITMAP is available at http://socialanalytics.bsc.es/biolitmap and the sources have been deposited at https://github.com/inab/BIOLITMAP. Supplementary information Supplementary data are available at Bioinformatics online.

Summary: Transposable elements constitute a large fraction of vertebrate genomes and, during evolution, may be co-opted for new functions. Exonization of transposable elements inserted within or close to host genes is one possible way to generate new genes, and alternative splicing of the new exons may represent an intermediate step in this process. The genes TMPO and ZNF451 are present in all vertebrate lineages. Although they are not evolutionarily related, mammalian TMPO and ZNF451 do have something in common—they both code for splice isoforms that contain LAP2alpha domains. We found that these LAP2alpha domains have sequence similarity to repetitive sequences in non-mammalian genomes, which are in turn related to the first ORF from a DIRS1-like retrotransposon. This retrotransposon domestication happened separately and resulted in proteins that combine retrotransposon and host protein domains. The alternative splicing of the retrotransposed sequence allowed the production of both the new and the untouched original isoforms, which may have contributed to the success of the colonization process. The LAP2alpha-specific isoform of TMPO (LAP2α) has been co-opted for important roles in the cell, whereas the ZNF451 LAP2alpha isoform is evolving under strong purifying selection but remains uncharacterized. Contact: mtress@cnio.es or valencia@cnio.es Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: The evolution of proteins cannot be fully understood without taking into account the coevolutionary linkages entangling them. From a practical point of view, coevolution between protein families has been used as a way of detecting protein interactions and functional relationships from genomic information. The most common approach to inferring protein coevolution involves the quantification of phylogenetic tree similarity using a family of methodologies termed mirrortree. In spite of their success, a fundamental problem of these approaches is the lack of an adequate statistical framework to assess the significance of a given coevolutionary score (tree similarity). As a consequence, a number of ad hoc filters and arbitrary thresholds are required in an attempt to obtain a final set of confident coevolutionary signals. Results: In this work, we developed a method for associating confidence estimators (P values) to the tree-similarity scores, using a null model specifically designed for the tree comparison problem. We show how this approach largely improves the quality and coverage (number of pairs that can be evaluated) of the detected coevolution in all the stages of the mirrortree workflow, independently of the starting genomic information. This not only leads to a better understanding of protein coevolution and its biological implications, but also to obtain a highly reliable and comprehensive network of predicted interactions, as well as information on the substructure of macromolecular complexes using only genomic information. Availability and implementation: The software and datasets used in this work are freely available at: http://csbg.cnb.csic.es/pMT/. Contact: pazos@cnb.csic.es Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized. Results: To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins. Availability: EnrichNet is freely available at http://www.enrichnet.org. Contact: Natalio.Krasnogor@nottingham.ac.uk, reinhard.schneider@uni.lu or avalencia@cnio.es Supplementary Information: Supplementary data are available at Bioinformatics Online.

Motivation: Chimeric RNA transcripts are generated by different mechanisms including pre-mRNA trans-splicing, chromosomal translocations and/or gene fusions. It was shown recently that at least some of chimeric transcripts can be translated into functional chimeric proteins. Results: To gain a better understanding of the design principles underlying chimeric proteins, we have analyzed 7,424 chimeric RNAs from humans. We focused on the specific domains present in these proteins, comparing their permutations with those of known human proteins. Our method uses genomic alignments of the chimeras, identification of the gene–gene junction sites and prediction of the protein domains. We found that chimeras contain complete protein domains significantly more often than in random data sets. Specifically, we show that eight different types of domains are over-represented among all chimeras as well as in those chimeras confirmed by RNA-seq experiments. Moreover, we discovered that some chimeras potentially encode proteins with novel and unique domain combinations. Given the observed prevalence of entire protein domains in chimeras, we predict that certain putative chimeras that lack activation domains may actively compete with their parental proteins, thereby exerting dominant negative effects. More generally, the production of chimeric transcripts enables a combinatorial increase in the number of protein products available, which may disturb the function of parental genes and influence their protein–protein interaction network. Availability: our scripts are available upon request. Contact: avalencia@cnio.es Supplementary information: Supplementary data are available at Bioinformatics online.

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ‘bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources. Contact: pengh@janelia.hhmi.org Supplementary information: Supplementary data are available at Bioinformatics online.

Solving relevant biological problems requires answering complex questions. Addressing such questions traditionally implied the design of time-consuming experimental procedures which most of the time are not accessible to average-sized laboratories. The current trend is to move towards a multidisciplinary approach integrating both theoretical knowledge and experimental work. This combination creates a powerful tool for shedding light on biological problems. To illustrate this concept, we present here a descriptive example of where computational methods were shown to be a key aspect in detecting crucial players in an important biological problem: the dimerization of chemokine receptors. Using evolutionary based sequence analysis in combination with structural predictions two CCR5 residues were selected as important for dimerization and further validated experimentally. The experimental validation of computational procedures demonstrated here provides a wealth of valuable information not obtainable by any of the individual approaches alone., Funding for the following projects: Temblor QLRT-2001-00015, Biosapiens BIO2004-00875, GeneFun LSHG-CT-2004-503567. D.I.O. is supported by CSIC and Pfizer.

Motivation: RUbioSeq has been developed to facilitate the primary and secondary analysis of re-sequencing projects by providing an integrated software suite of parallelized pipelines to detect exome variants (single-nucleotide variants and copy number variations) and to perform bisulfite-seq analyses automatically. RUbioSeq’s variant analysis results have been already validated and published. Availability: http://rubioseq.sourceforge.net/. Contact: mrubioc@cnio.es

Motivation: The exponential growth of scientific literature has resulted in a massive amount of unstructured natural language data that cannot be directly handled by means of bioinformatics tools. Such tools generally require structured data, often generated through a cumbersome process of manual literature curation. Herein, we present MyMiner, a free and user-friendly text annotation tool aimed to assist in carrying out the main biocuration tasks and to provide labelled data for the development of text mining systems. MyMiner allows easy classification and labelling of textual data according to user-specified classes as well as predefined biological entities. The usefulness and efficiency of this application have been tested for a range of real-life annotation scenarios of various research topics. Availability: http://myminer.armi.monash.edu.au. Contacts: david.salgado@monash.edu and christophe.marcelle@monash.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: The study of sequence space, and the deciphering of the structure of protein families and subfamilies, has up to now been required for work in comparative genomics and for the prediction of protein function. With the emergence of structural proteomics projects, it is becoming increasingly important to be able to select protein targets for structural studies that will appropriately cover the space of protein sequences, functions and genomic distribution. These problems are the motivation for the development of methods for clustering protein sequences and building families of potentially orthologous sequences, such as those proposed here. Results: First we developed a clustering strategy (Ncut algorithm) capable of forming groups of related sequences by assessing their pairwise relationships. The results presented for the ras super-family of proteins are similar to those produced by other clustering methods, but without the need for clustering the full sequence space. The Ncut clusters are then used as the input to a process of reconstruction of groups with equilibrated genomic composition formed by closely-related sequences. The results of applying this technique to the data set used in the construction of the COG database are very similar to those derived by the human experts responsible for this database. Availability: The analysis of different systems, including the COG equivalent 21 genomes are available at http://www.pdg.cnb.uam.es/GenoClustering.html Contact: valencia@cnb.uam.es * To whom correspondence should be addressed.

Motivation: We describe a new approach to the analysis of gene expression data coming from DNA array experiments, using an unsupervised neural network. DNA array technologies allow monitoring thousands of genes rapidly and efficiently. One of the interests of these studies is the search for correlated gene expression patterns, and this is usually achieved by clustering them. The Self-Organising Tree Algorithm, (SOTA) (Dopazo,J. and Carazo,J.M. (1997) J. Mol. Evol. , 44, 226–233), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network. Results: SOTA clustering confers several advantages over classical hierarchical clustering methods. SOTA is a divisive method: the clustering process is performed from top to bottom, i.e. the highest hierarchical levels are resolved before going to the details of the lowest levels. The growing can be stopped at the desired hierarchical level. Moreover, a criterion to stop the growing of the tree, based on the approximate distribution of probability obtained by randomisation of the original data set, is provided. By means of this criterion, a statistical support for the definition of clusters is proposed. In addition, obtaining average gene expression patterns is a built-in feature of the algorithm. Different neurons defining the different hierarchical levels represent the averages of the gene expression patterns contained in the clusters. Since SOTA runtimes are approximately linear with the number of items to be classified, it is especially suitable for dealing with huge amounts of data. The method proposed is very general and applies to any data providing that they can be coded as a series of numbers and that a computable measure of similarity between data items can be used. Availability: A server running the program can be found at: http://bioinfo.cnio.es/sotarray Contact: jdopazo@cnio.es * To whom correspondence should be addressed.

MOTIVATION: The explosive growth of the biological sequences databases stimulated by genome projects has modified the framework of several applications in the biological sequence analysis area. In most cases, this new scenario is characterized by studies on large sets of sequences, suggesting the need for effective and automatic methods for their clustering. A more effective clustering of the database could be followed by the application of common family analysis schemes to the groups so formed. RESULTS: In this work, we present a new strategy to reduce the computational cost associated with the clustering of large sets of sequences which are expected to contain several families. The strategy is based on the grouping of the sequences into families by using a dynamic threshold on a pairwise sequence similarity criterion. Routine clustering of large data sets can now be done very efficiently. The method developed here achieves a computational space reduction of about an order of magnitude over more traditional ones of all-versus-all comparisons. The outcome of this approach produces family groupings that reproduce closely already accepted biological results. Our work includes a parallel implementation for distributed memory multiprocessors with a dynamic scheduling strategy for performance optimization. AVAILABILITY: By anonymous ftp at ftp.ac.uma.es (/pub/ots/pCluster directory), or from our Web site http://www.cnb. uam.es/www/software/software_index.html CONTACT: ots@ac.uma.es

Summary: Downloading the information stored in relational databases into XML and other flat formats is a common task in bioinformatics. This periodical dumping of information requires considerable CPU time, disk and memory resources. YAdumper has been developed as a purpose-specific tool to deal with the integral structured information download of relational databases. YAdumper is a Java application that organizes database extraction following an XML template based on an external Document Type Declaration. Compared with other non-native alternatives, YAdumper substantially reduces memory requirements and considerably improves writing performance. Availability: YAdumper is freely available.

The ISMB Special Interest Group on Linking Literature, Information and Knowledge for Biology (BioLINK) organized a one-day workshop at ISMB/ECCB 2013 in Berlin, Germany. The theme of the workshop was ‘Roles for text mining in biomedical knowledge discovery and translational medicine’. This summary reviews the outcomes of the workshop. Meeting themes included concept annotation methods and applications, extraction of biological relationships and the use of text-mined data for biological data analysis. Availability and implementation: All articles are available at http://biolinksig.org/proceedings-online/ . Contact: karin.verspoor@unimelb.edu.au

The last two decades have witnessed great advances in biological tissue labeling and automated microscopic imaging that, in turn, have revolutionized how biologists visualize molecular, sub-cellular, cellular, and super-cellular structures and study their respective functions. Tremendous volumes of multi-dimensional bioimaging data are now being generated in almost every branch of biology. How to interpret such image datasets in a quantitative, objective, automatic and efficient way has become a major challenge in current computational biology. Bioimage informatics methods have begun to turn image data into useful biological knowledge (Peng, 2008; Swedlow, et al., 2009; Shamir, et al., 2010; Danuser, 2011). The essential methods of bioimage informatics involve large-scale bioimage generation, visualization, analysis and management. Bioimage informatics also encompasses both hypothesis- and data-driven exploratory approaches, with an emphasis on how to generate biological knowledge and/or gain new insights that would otherwise be hard to achieve. Early work in bioimage informatics began in the late 1990s. Increasingly, computer vision, image analysis, data mining, machine learning and pattern recognition methods have been applied to microscopic images to extract biological information and to generate ontology databases. The growing amount of bioimage data are quickly imposing additional demands on how to store, manage and retrieve such image datasets as well as the associated secondary meta-data. Data analysis, fusion and reconstruction techniques have also been developed to facilitate better image acquisition and formation. Joint analysis of image data in combination with other biological datasets, such as genomes and gene expression profiles, is also becoming more and more commonplace. To meet the need of this growing field, the first international workshop on Bioimage Informatics was organized at Stanford University in 2005. It grew to be an annual event in this field. Other meetings on similar topics and related applications have also emerged since then. In 2010, the annual conference on Intelligent Systems for Molecular Biology (ISMB) established a paper-submission track on bioimaging data analysis and visualization. While there is a noticeable need to publish high quality papers on bioimage informatics, so far no high-impact journal explicitly accepts this category of papers. We believe it is an appropriate time to create this new category in Bioinformatics. As of February 2012, Bioinformatics now includes a new paper submission category in the scope described by the journal at its website as follows: ‘Informatics methods for the acquisition, analysis, mining and visualization of images produced by modern microscopy, with an emphasis on the application of novel computing techniques to solve challenging and significant biological and medical problems at the molecular, sub-cellular, cellular, and super-cellular (organ, organism, and population) levels. This category also encourages large-scale image informatics methods/applications/software, various enabling techniques (e.g. cyber-infrastructures, quantitative validation experiments, pattern recognition, etc.) for such large-scale studies, and joint analysis of multiple heterogeneous datasets that include images as a component. Bioimage related ontology and databases studies, image-oriented large-scale machine learning, data mining, and other analytics techniques are also encouraged. We will not consider image analysis and pattern recognition methods that are solely based on tuning parameters or swapping computational sub-steps, without an in-depth description or demonstration of why such changes are significantly superior for one or more biological problems.’ We would like to thank a number of colleagues and practitioners who have contributed to the creation of this new category. Especially, we thank Eugene Myers, B.S.Manjunath, Badri Roysam, Manfred Auer, Michael Hawrylycz, Jean-Christophe Olivo-Marin, Anne Carpenter and Vebjorn Ljosa in helping to define this new category of paper submissions. We hope the journal Bioinformatics becomes a valuable venue for bioimage informatics researchers to publish their most important work. Contact: gro.imhh.ailenaj@hgnep

With the growth of genomics, research institutes are increasingly confronted with the task of providing computational support to biologists and the provision of computational facilities, both for laboratory-based bioinformaticians and bioinformatics research groups. The optimal organization of bioinformatics support units to meet these needs is a common topic of discussion and consideration. During the recent evaluation of a bioinformatics core facility, we ended up discussing—in general terms—what we consider basic guidelines for the organization of bioinformatics core units in large research institutes, based on the experience and developments in our own institutes. We thought that part of our discussion could be useful for others, keeping in mind that every organization has specific needs and requirements, and that bioinformatics support is a fast moving area that requires continuous adaptation. In the following, we summarized what we consider some key recommendations: (1) Bioinformatics departments have to clearly separate their service units from their research laboratories. This contributes to the transparency of the organization, especially regarding the allocation of funds. (2) The organization of bioinformatics service unit should be clear with defined missions for each component. It is particularly important to separate tasks by topic. For example: support of database development and maintenance, statistical analysis of high-throughput data, automatic image acquisition and analysis of e.g. light microscopy data and analysis of next-generation sequencing data. (3) The unit should install a ‘users committee’ that includes users and specialists of the unit. This committee would be responsible for establishing clear priorities on what technical platforms are supported or aborted, and defining the rules applied to prioritize projects for bioinformatics support.

Summary: Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. Availability: http://cubic.bioc.columbia.edu/eva. Contact: eva@cubic.bioc.columbia.edu * To whom correspondence should be addressed.

SUMMARY: 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: valencia@cnb.uam.es 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