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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.

Many human genes are transcribed from both strands and produce sense-antisense gene pairs. Sense-antisense (SAS) chimeric transcripts are produced upon the coalescing of exons/introns from both sense and antisense transcripts of the same gene. SAS chimera was first reported in prostate cancer cells. Subsequently, numerous SAS chimeras have been reported in the ChiTaRS-2.1 database. However, the landscape of their expression in human cells and functional aspects are still unknown. We found that longer palindromic sequences are a unique feature of SAS chimeras. Structural analysis indicates that a long hairpin-like structure formed by many consecutive Watson-Crick base pairs appears because of these long palindromic sequences, which possibly play a similar role as double-stranded RNA (dsRNA), interfering with gene expression. RNA–RNA interaction analysis suggested that SAS chimeras could significantly interact with their parental mRNAs, indicating their potential regulatory features. Here, 267 SAS chimeras were mapped in RNA-seq data from 16 healthy human tissues, revealing their expression in normal cells. Evolutionary analysis suggested the positive selection favoring sense-antisense fusions that significantly impacted the evolution of their function and structure. Overall, our study provides detailed insight into the expression landscape of SAS chimeras in human cells and identifies potential regulatory features. Israeli Council for Higher Education [PBC Fellowship for Outstanding Post-Doctoral Fellows, 2019-2021 to S.M.]; Israel Innovation Authority [66824, 2019–2021 to M.F-M.]; RSF [18–14-00240 to Y.A.M. (in part)].

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.

Hematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify 42 human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspects of human hematopoiesis. The analysis of the orthogonal dimension of the chromatin space identify 32,662 chromatin determinant regions (CDRs), genomic regions with different epigenetic characteristics between the cell types. Functional analysis revealed that these regions are linked with cell identities. The inclusion of leukemia epigenomes in the healthy hematological chromatin sample space gives us insights on the healthy cell types that are more epigenetically similar to the disease samples. Further analysis of tumoral epigenetic alterations in hematopoietic CDRs points to sets of genes that are tightly regulated in leukemic transformations and commonly mutated in other tumors. Our method provides an analytical approach to study the relationship between epigenomic changes and cell lineage differentiation. Method availability: https://github.com/david-juan/ChromDet. European Union’s Seventh Framework Programme [FP7/2007–2013, 282510 (BLUEPRINT)]; Spanish Ministry of Economy, Industry and Competitiveness and European Regional Development Fund [Project Retos BFU2015–71241-R]. Funding for open access charge: Project Retos BFU2015–71241-R (to A.V.).

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein-protein interactions (ChiPPI) that uses the domain-domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer. This work is funded by the Project Retos BFU2015- 71241-R of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC), co-funded by European Re-gional Development Fund (ERDF) and by the ProjectPT13/0001/0030, Instituto de Salud Carlos III (ISCIII),Strategic Action in Health, co-funded by European Re-gional Development Fund (ERDF). The work of MFM issupported by the Israel Cancer Association (ICA) fund, the work of ST is supported by the VaTaT Postdoctoral Fel-lowship for excellent students [22351, 20027, 26912]. AV issupported by the Joint BSC-CRG-IRB Programme in Com- Downloaded from https://academic.oup.com/nar/article-abstract/45/12/7094/3854948 by Spanish National Cancer Research Center user on 04 February 2019 putational Biology. Funding for open access charge: ICA[e-cancer-diagnosis].Conflict of interest statement.None declared. Sí

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.

This paper introduces the APPRIS WebServer (http: //appris.bioinfo.cnio.es) and WebServices (http:// apprisws.bioinfo.cnio.es). Both the web servers and the web services are based around the APPRIS Database, a database that presently houses annotations of splice isoforms for five different vertebrate genomes. The APPRIS WebServer and WebServices provide access to the computational methods implemented in the APPRIS Database, while the APPRIS WebServices also allows retrieval of the annotations. The APPRIS WebServer and WebServices annotate splice isoforms with protein structural and functional features, and with data from cross-species alignments. In addition they can use the annotations of structure, function and conservation to select a single reference isoform for each protein-coding gene (the principal protein isoform). APPRIS principal isoforms have been shown to agree overwhelmingly with the main protein isoform detected in proteomics experiments. The APPRIS WebServer allows for the annotation of splice isoforms for individual genes, and provides a range of visual representations and tools to allow researchers to identify the likely effect of splicing events. The APPRIS WebServices permit users to generate annotations automatically in high throughput mode and to interrogate the annotations in the APPRIS Database. The APPRIS WebServices have been implemented using REST architecture to be flexible, modular and automatic.

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.

Gene duplication is regarded as the main source of adaptive functional novelty in eukaryotes. Processes such as neo- and subfunctionalization impact the evolution of paralogous proteins where functional divergence is frequently key to retain the gene copies. Here, we examined antisilencing function 1 (ASF1), a conserved eukaryotic H3-H4 histone chaperone, involved in histone dynamics during replication, transcription, and DNA repair. Although yeast feature a single ASF1 protein, two paralogs exist in most vertebrates, termed ASF1a and ASF1b, with distinct cellular roles in mammals. To explain this division of tasks, we integrated evolutionary and comparative genomic analyses with biochemical and structural approaches. First, we show that a duplication event at the ancestor of jawed vertebrates, followed by ASF1a relocation into an intron of the minichromosome maintenance complex component 9 (MCM9) gene at the ancestor of tetrapods, provided a different genomic environment for each paralog with marked differences of GC content and DNA replication timing. Second, we found signatures of positive selection in the N- and C-terminal regions of ASF1a and ASF1b. Third, we demonstrate that regions outside the primary interaction surface are key for the preferential interactions of the human paralogs with distinct H3-H4 chaperones. On the basis of these data, we propose that ASF1 experienced subfunctionalization shaped by the adaptation of the genes to their respective genomic context, reflecting a case of genomic context-driven escape from adaptive conflict.

Here, we present APPRIS (http://appris.bioinfo.cnio.es), a database that houses annotations of human splice isoforms. APPRIS has been designed to provide value to manual annotations of the human genome by adding reliable protein structural and functional data and information from cross-species conservation. The visual representation of the annotations provided by APPRIS for each gene allows annotators and researchers alike to easily identify functional changes brought about by splicing events. In addition to collecting, integrating and analyzing reliable predictions of the effect of splicing events, APPRIS also selects a single reference sequence for each gene, here termed the principal isoform, based on the annotations of structure, function and conservation for each transcript. APPRIS identifies a principal isoform for 85% of the protein-coding genes in the GENCODE 7 release for ENSEMBL. Analysis of the APPRIS data shows that at least 70% of the alternative (non-principal) variants would lose important functional or structural information relative to the principal isoform.

Advances in high-throughput mass spectrometry are making proteomics an increasingly important tool in genome annotation projects. Peptides detected in mass spectrometry experiments can be used to validate gene models and verify the translation of putative coding sequences (CDSs). Here, we have identified peptides that cover 35% of the genes annotated by the GENCODE consortium for the human genome as part of a comprehensive analysis of experimental spectra from two large publicly available mass spectrometry databases. We detected the translation to protein of “novel” and “putative” protein-coding transcripts as well as transcripts annotated as pseudogenes and nonsense-mediated decay targets. We provide a detailed overview of the population of alternatively spliced protein isoforms that are detectable by peptide identification methods. We found that 150 genes expressed multiple alternative protein isoforms. This constitutes the largest set of reliably confirmed alternatively spliced proteins yet discovered. Three groups of genes were highly overrepresented. We detected alternative isoforms for 10 of the 25 possible heterogeneous nuclear ribonucleoproteins, proteins with a key role in the splicing process. Alternative isoforms generated from interchangeable homologous exons and from short indels were also significantly enriched, both in human experiments and in parallel analyses of mouse and Drosophila proteomics experiments. Our results show that a surprisingly high proportion (almost 25%) of the detected alternative isoforms are only subtly different from their constitutive counterparts. Many of the alternative splicing events that give rise to these alternative isoforms are conserved in mouse. It was striking that very few of these conserved splicing events broke Pfam functional domains or would damage globular protein structures. This evidence of a strong bias toward subtle differences in CDS and likely conserved cellular function and structure is remarkable and strongly suggests that the translation of alternative transcripts may be subject to selective constraints.

firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php.

Bionemo (http://bionemo.bioinfo.cnio.es) stores manually curated information about proteins and genes directly implicated in the Biodegradation metabolism. When possible, the database includes information on sequence, domains and structures for proteins; and sequence, regulatory elements and transcription units for genes. Thus, Bionemo is a unique resource that complements other biodegradation databases such as the University of Minessota Biocatalysis/Biodegradation Database, or Metarouter, which focus more on the biochemical aspects of biodegradation than in the nature of the biomolecules carrying out the reactions. Bionemo has been built by manually associating sequences database entries to biodegradation reactions, using the information extracted from published articles. Information on transcription units and their regulation was also extracted from the literature for biodegradation genes, and linked to the underlying biochemical network. In its current version, Bionemo contains sequence information for 324 reactions and transcription regulation information for more than 100 promoters and 100 transcription factors. The information in the Bionemo database is available via a web server and the full database is also downloadable as a PostgresSQL dump. To facilitate the programmatic use of the information contained in the database, an object-oriented Perl API is also provided.

The FireDB database is a databank for functional information relating to proteins with known structures. It contains the most comprehensive and detailed repository of known functionally important residues, bringing together both ligand binding and catalytic residues in one site. The platform integrates biologically relevant data filtered from the close atomic contacts in Protein Data Bank crystal structures and reliably annotated catalytic residues from the Catalytic Site Atlas. The interface allows users to make queries by protein, ligand or keyword. Relevant biologically important residues are displayed in a simple and easy to read manner that allows users to assess binding site similarity across homologous proteins. Binding site residue variations can also be viewed with molecular visualization tools. The database is available at http://firedb.bioinfo.cnio.es.

An entire family of methodologies for predicting protein interactions is based on the observed fact that families of interacting proteins tend to have similar phylogenetic trees due to co-evolution. One application of this concept is the prediction of the mapping between the members of two interacting protein families (which protein within one family interacts with which protein within the other). The idea is that the real mapping would be the one maximizing the similarity between the trees. Since the exhaustive exploration of all possible mappings is not feasible for large families, current approaches use heuristic techniques which do not ensure the best solution to be found. This is why it is important to check the results proposed by heuristic techniques and to manually explore other solutions. Here we present TSEMA, the server for efficient mapping assessment. This system calculates an initial mapping between two families of proteins based on a Monte Carlo approach and allows the user to interactively modify it based on performance figures and/or specific biological knowledge. All the explored mappings are graphically shown over a representation of the phylogenetic trees. The system is freely available at http://pdg.cnb.uam.es/TSEMA. Standalone versions of the software behind the interface are available upon request from the authors.

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.

Here we introduce EVAcon, an automated web service that evaluates the performance of contact prediction servers. Currently, EVAcon is monitoring nine servers, four of which are specialized in contact prediction and five are general structure prediction servers. Results are compared for all newly determined experimental structures deposited into PDB ( approximately 5-50 per week). EVAcon allows for a precise comparison of the results based on a system of common protein subsets and the commonly accepted evaluation criteria that are also used in the corresponding category of the CASP assessment. EVAcon is a new service added to the functionality of the EVA system for the continuous evaluation of protein structure prediction servers. The new service is accesible from any of the three EVA mirrors: PDG (CNB-CSIC, Madrid) (http://www.pdg.cnb.uam.es/eva/con/index.html); CUBIC (Columbia University, NYC) (http://cubic.bioc.columbia.edu/eva/con/index.html); and Sali Lab (UCSF, San Francisco) (http://eva.compbio.ucsf.edu/~eva/con/index.html).

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

IntAct provides an open source database and toolkit for the storage, presentation and analysis of protein interactions. The web interface provides both textual and graphical representations of protein interactions, and allows exploring interaction networks in the context of the GO annotations of the interacting proteins. A web service allows direct computational access to retrieve interaction networks in XML format. IntAct currently contains approximately 2200 binary and complex interactions imported from the literature and curated in collaboration with the Swiss-Prot team, making intensive use of controlled vocabularies to ensure data consistency. All IntAct software, data and controlled vocabularies are available at http://www.ebi.ac.uk/intact.

EVA (http://cubic.bioc.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods.

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.

Information extraction has become a very active field in bioinformatics recently and a number of interesting papers have been published. Most of the efforts have been concentrated on a few specific problems, such as the detection of protein-protein interactions and the analysis of DNA expression arrays, although it is obvious that there are many other interesting areas of potential application (document retrieval, protein functional description, and detection of disease-related genes to name a few). Paradoxically, these exciting developments have not yet crystallised into general agreement on a set of standard evaluation criteria, such as the ones developed in fields such as protein structure prediction, which makes it very difficult to compare performance across these different systems. In this review we introduce the general field of information extraction, we outline the status of the applications in molecular biology, and we then discuss some ideas about possible standards for evaluation that are needed for the future development of the field.

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

Recurrent chromosome aberrations are an important resource when associating human pathologies to specific genes. However, for technical reasons a large number of chromosome breakpoints are defined only at the level of cytobands and many of the genes involved remain unidentified. We developed a web-based information system that mines the scientific literature and generates textual and comprehensive information on all human breakpoints. We show that the statistical analysis of this textual information and its combination with genomic data can identify genes directly involved in DNA rearrangements. The Human Chromosome Aberration Database (HCAD) is publicly accessible at http://www.pdg.cnb.uam.es/UniPub/HCAD/.

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.

We performed whole exome sequencing in individuals from a family with autosomal dominant gastropathy resembling Méné trier disease, a premalignant gastric disorder with epithelial hyperplasia and enhanced EGFR signalling. Méné trier disease is believed to be an acquired disorder, but its aetiology is unknown. In affected members, we found a missense p.V742G variant in MIB2, a gene regulating NOTCH signalling that has not been previously linked to human diseases. The variant segregated with the disease in the pedigree, affected a highly conserved amino acid residue, and was predicted to be deleterious although it was found with a low frequency in control individuals. The purified protein carrying the p.V742G variant showed reduced ubiquitination activity in vitro and white blood cells from affected individuals exhibited significant reductions of HES1 and NOTCH3 expression reflecting alteration of NOTCH signalling. Because mutations of MIB1, the homolog of MIB2, have been found in patients with left ventricle non-compaction (LVNC), we investigated members of our family with Méné trier-like disease for this cardiac abnormality. Asymptomatic left ventricular hypertrabeculation, the mildest end of the LVNC spectrum, was detected in two members carrying the MIB2 variant. Finally, we identified an additional MIB2 variant (p.V984L) affecting protein stability in an unrelated isolated case with LVNC. Expression of both MIB2 variants affected NOTCH signalling, proliferation and apoptosis in primary rat cardiomyocytes. In conclusion, we report the first example of left ventricular hypertrabeculation/LVNC with germline MIB2 variants resulting in altered NOTCH signalling that might be associated with a gastropathy clinically overlapping with Méné trier disease.

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

BioCreative: Critical Assessment of Information Extraction in Biology is an international community-wide effort for evaluating text mining and information extraction systems applied to the biological domain (http://www.biocreative.org/). The Challenge Evaluations and the accompanying BioCreative Workshops bring together the text mining and biology communities to drive the development of text mining systems that can be integrated into the biocuration workflow and the knowledge discovery process. To address the current barriers in using text mining in biology, BioCreative has further been conducting user requirement analysis, user-based evaluations and fostering standard development for text mining tool re-use and integration. This DATABASE virtual issue captures the major results from the BioCreative-2012 Workshop on Interactive Text Mining in the Biocuration Workflow and is the fifth special issue devoted to BioCreative.

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

FUNDING National Institutes of Health [U41 HG007234, 2U41 HG007234]; Spanish Ministry of Economics and Com- petitiveness [BIO2015-67580-P]; Spanish National Institute of Bioinformatics (www.inab.org) [INB-ISCIII, PRB2 toJ.M.R.]; ProteoRed [IPT13/0001-ISCIII-SGEFI/FEDERto J.V.]; Joint BSC-IRB-CRG Program in Computational Downloaded from https://academic.oup.com/nar/article-abstract/46/D1/D213/4561658 by Spanish National Cancer Research Center user on 30 October 2018 Nucleic Acids Research, 2018, Vol. 46, Database issue D217 Biology and Award Severo Ochoa [SEV 2015-0493 to A.V.]. Funding for open access charge: U.S. Depart- ment of Health and Human Services; National Institutes of Health; National Human Genome Research Institute [2U41 HG007234]. Conflict of interest statement.None declared. The APPRIS database (http://appris-tools.org) uses protein structural and functional features and information from cross-species conservation to annotate splice isoforms in protein-coding genes. APPRIS selects a single protein isoform, the 'principal' isoform, as the reference for each gene based on these annotations. A single main splice isoform reflects the biological reality for most protein coding genes and APPRIS principal isoforms are the best predictors of these main proteins isoforms. Here, we present the updates to the database, new developments that include the addition of three new species (chimpanzee, Drosophila melangaster and Caenorhabditis elegans), the expansion of APPRIS to cover the RefSeq gene set and the UniProtKB proteome for six species and refinements in the core methods that make up the annotation pipeline. In addition APPRIS now provides a measure of reliability for individual principal isoforms and updates with each release of the GENCODE/Ensembl and RefSeq reference sets. The individual GENCODE/Ensembl, RefSeq and UniProtKB reference gene sets for six organisms have been merged to produce common sets of splice variants. Sí