MOTIVATION: Knowledge of the specific cell types affected by genetic alterations in rare diseases is crucial for advancing diagnostics and treatments. Despite significant progress, the cell types involved in the majority of rare disease manifestations remain largely unknown. In this study, we integrated scRNA-seq data from non-diseased samples with known genetic disorder genes and phenotypic information to predict the specific cell types disrupted by pathogenic mutations for 482 disease phenotypes. RESULTS: We found significant phenotype-cell type associations focusing on differential expression and co-expression mechanisms. Our analysis revealed that 13% of the associations documented in the literature were captured through differential expression, while 42% were elucidated through co-expression analysis, also uncovering potential new associations. These findings underscore the critical role of cellular context in disease manifestation and highlight the potential of single-cell data for the development of cell-aware diagnostics and targeted therapies for rare diseases. AVAILABILITY AND IMPLEMENTATION: All code generated in this work is available at https://github.com/SergioAlias/sc-coex.

MOTIVATION: Predicting the residues controlling a protein's interaction specificity is important not only to better understand its interactions but also to design mutations aimed at fine-tuning or swapping them as well. RESULTS: In this work, we present a methodology that combines sequence information (in the form of multiple sequence alignments) with interactome information to detect that kind of residues in paralogous families of proteins. The interactome is used to define pairwise similarities of interaction contexts for the proteins in the alignment. The method looks for alignment positions with patterns of amino-acid changes reflecting the similarities/differences in the interaction neighborhoods of the corresponding proteins. We tested this new methodology in a large set of human paralogous families with structurally characterized interactions, and discuss in detail the results for the RasH family. We show that this approach is a better predictor of interfacial residues than both, sequence conservation and an equivalent 'unsupervised' method that does not use interactome information. AVAILABILITY AND IMPLEMENTATION: http://csbg.cnb.csic.es/pazos/Xdet/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Assignment of chemical compounds to biological pathways is a crucial step to understand the relationship between the chemical repertory of an organism and its biology. Protein sequence profiles are very successful in capturing the main structural and functional features of a protein family, and can be used to assign new members to it based on matching of their sequences against these profiles. In this work, we extend this idea to chemical compounds, constructing a profile-inspired model for a set of related metabolites (those in the same biological pathway), based on a fragment-based vectorial representation of their chemical structures. RESULTS: We use this representation to predict the biological pathway of a chemical compound with good overall accuracy (AUC 0.74-0.90 depending on the database tested), and analyzed some factors that affect performance. The approach, which is compared with equivalent methods, can in addition detect those molecular fragments characteristic of a pathway. CONCLUSIONS: The method is available as a graphical interactive web server http://csbg.cnb.csic.es/iFragMent .

Daily work in molecular biology presently depends on a large number of computational tools. An in-depth, large-scale study of that 'ecosystem' of Web tools, its characteristics, interconnectivity, patterns of usage/citation, temporal evolution and rate of decay is crucial for understanding the forces that shape it and for informing initiatives aimed at its funding, long-term maintenance and improvement. In particular, the long-term maintenance of these tools is compromised because of their specific development model. Hundreds of published studies become irreproducible de facto, as the software tools used to conduct them become unavailable. In this study, we present a large-scale survey of >5400 publications describing Web servers within the two main bibliographic resources for disseminating new software developments in molecular biology. For all these servers, we studied their citation patterns, the subjects they address, their citation networks and the temporal evolution of these factors. We also analysed how these factors affect the availability of these servers (whether they are alive). Our results show that this ecosystem of tools is highly interconnected and adapts to the 'trendy' subjects in every moment. The servers present characteristic temporal patterns of citation/usage, and there is a worrying rate of server 'death', which is influenced by factors such as the server popularity and the institutions that hosts it. These results can inform initiatives aimed at the long-term maintenance of these resources.

MOTIVATION: The results of some experimental and computational techniques are given in terms of large sets of organisms, especially prokaryotic. While their distinctive features can provide useful data regarding specific phenomenon, there are no automated tools for extracting them. RESULTS: We present here the Bacterial Feature Finder web server, a tool to automatically interrogate sets of prokaryotic organisms provided by the user to evaluate their specific biological features. At the core of the system is a searchable database of qualitative and quantitative features compiled for more than 23 000 prokaryotic organisms. Both the input set of organisms and the background set used to calculate the enriched features can be directly provided by the user, or they can be obtained by searching the database. The results are presented via an interactive graphical interface, with links to external resources. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at http://csbg.cnb.csic.es/BaFF. It has been tested in the main web browsers and does not require any especial plug-ins or additional software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The exponential accumulation of new sequences in public databases is expected to improve the performance of all the approaches for predicting protein structural and functional features. Nevertheless, this was never assessed or quantified for some widely used methodologies, such as those aimed at detecting functional sites and functional subfamilies in protein multiple sequence alignments. Using raw protein sequences as only input, these approaches can detect fully conserved positions, as well as those with a family-dependent conservation pattern. Both types of residues are routinely used as predictors of functional sites and, consequently, understanding how the sequence content of the databases affects them is relevant and timely. RESULTS: In this work we evaluate how the growth and change with time in the content of sequence databases affect five sequence-based approaches for detecting functional sites and subfamilies. We do that by recreating historical versions of the multiple sequence alignments that would have been obtained in the past based on the database contents at different time points, covering a period of 20 years. Applying the methods to these historical alignments allows quantifying the temporal variation in their performance. Our results show that the number of families to which these methods can be applied sharply increases with time, while their ability to detect potentially functional residues remains almost constant. CONCLUSIONS: These results are informative for the methods' developers and final users, and may have implications in the design of new sequencing initiatives.

BACKGROUND: Clinical signs are a fundamental aspect of human pathologies. While disease diagnosis is problematic or impossible in many cases, signs are easier to perceive and categorize. Clinical signs are increasingly used, together with molecular networks, to prioritize detected variants in clinical genomics pipelines, even if the patient is still undiagnosed. Here we analyze the ability of these network-based methods to predict genes that underlie clinical signs from the human interactome. RESULTS: Our analysis reveals that these approaches can locate genes associated with clinical signs with variable performance that depends on the sign and associated disease. We analyzed several clinical and biological factors that explain these variable results, including number of genes involved (mono- vs. oligogenic diseases), mode of inheritance, type of clinical sign and gene product function. CONCLUSIONS: Our results indicate that the characteristics of the clinical signs and their related diseases should be considered for interpreting the results of network-prediction methods, such as those aimed at discovering disease-related genes and variants. These results are important due the increasing use of clinical signs as an alternative to diseases for studying the molecular basis of human pathologies.

Determining the residues that are important for the molecular activity of a protein is a topic of broad interest in biomedicine and biotechnology. This knowledge can help understanding the protein's molecular mechanism as well as to fine-tune its natural function eventually with biotechnological or therapeutic implications. Some of the protein residues are essential for the function common to all members of a family of proteins, while others explain the particular specificities of certain subfamilies (like binding on different substrates or cofactors and distinct binding affinities). Owing to the difficulty in experimentally determining them, a number of computational methods were developed to detect these functional residues, generally known as 'specificity-determining positions' (or SDPs), from a collection of homologous protein sequences. These methods are mature enough for being routinely used by molecular biologists in directing experiments aimed at getting insight into the functional specificity of a family of proteins and eventually modifying it. In this review, we summarize some of the recent discoveries achieved through SDP computational identification in a number of relevant protein families, as well as the main approaches and software tools available to perform this type of analysis.

MOTIVATION: Many diseases are related by shared associated molecules and pathways, exhibiting comorbidities and common phenotypes, an indication of the continuous nature of the human pathological landscape. Although it is continuous, this landscape is always partitioned into discrete diseases when studied at the molecular level. Clinical signs are also important phenotypic descriptors that can reveal the molecular mechanisms that underlie pathological states, but have seldom been the subject of systemic research. Here, we quantify the modular nature of the clinical signs associated with genetic diseases in the human interactome. RESULTS: We found that clinical signs are reflected as modules at the molecular network level, to at least to the same extent as diseases. They can thus serve as a valid complementary partition of the human pathological landscape, with implications for etiology research, diagnosis and treatment. CONTACT: [email protected] 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: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The so-called 'omics' approaches used in modern biology aim at massively characterizing the molecular repertories of living systems at different levels. Metabolomics is one of the last additions to the 'omics' family and it deals with the characterization of the set of metabolites in a given biological system. As metabolomic techniques become more massive and allow characterizing larger sets of metabolites, automatic methods for analyzing these sets in order to obtain meaningful biological information are required. Only recently the first tools specifically designed for this task in metabolomics appeared. They are based on approaches previously used in transcriptomics and other 'omics', such as annotation enrichment analysis. These, together with generic tools for metabolic analysis and visualization not specifically designed for metabolomics will for sure be in the toolbox of the researches doing metabolomic experiments in the near future.

SUMMARY: Only recently the first resources devoted to the functional annotation of proteins at the domain level started to appear. The next step is to develop specific methodologies for predicting function at the domain level based on these resources, and to implement them in web servers to be used by the community. In this work, we present COPRED, a web server for the concomitant prediction of fold, molecular function and functional sites at the domain level, based on a methodology for domain molecular function prediction and a resource of domain functional annotations previously developed and benchmarked. AVAILABILITY AND IMPLEMENTATION: COPRED can be freely accessed at http://csbg.cnb.csic.es/copred. The interface works in all standard web browsers. WebGL (natively supported by most browsers) is required for the in-line preview and manipulation of protein 3D structures. The website includes a detailed help section and usage examples. CONTACT: [email protected].

Predicting the function of newly sequenced proteins is crucial due to the pace at which these raw sequences are being obtained. Almost all resources for predicting protein function assign functional terms to whole chains, and do not distinguish which particular domain is responsible for the allocated function. This is not a limitation of the methodologies themselves but it is due to the fact that in the databases of functional annotations these methods use for transferring functional terms to new proteins, these annotations are done on a whole-chain basis. Nevertheless, domains are the basic evolutionary and often functional units of proteins. In many cases, the domains of a protein chain have distinct molecular functions, independent from each other. For that reason resources with functional annotations at the domain level, as well as methodologies for predicting function for individual domains adapted to these resources are required.We present a methodology for predicting the molecular function of individual domains, based on a previously developed database of functional annotations at the domain level. The approach, which we show outperforms a standard method based on sequence searches in assigning function, concomitantly predicts the structural fold of the domains and can give hints on the functionally important residues associated to the predicted function.

UNLABELLED: We have implemented in a single package all the features required for extracting, visualizing and manipulating fully conserved positions as well as those with a family-dependent conservation pattern in multiple sequence alignments. The program allows, among other things, to run different methods for extracting these positions, combine the results and visualize them in protein 3D structures and sequence spaces. AVAILABILITY AND IMPLEMENTATION: JDet is a multiplatform application written in Java. It is freely available, including the source code, at http://csbg.cnb.csic.es/JDet. The package includes two of our recently developed programs for detecting functional positions in protein alignments (Xdet and S3Det), and support for other methods can be added as plug-ins. A help file and a guided tutorial for JDet are also available.

UNLABELLED: While many tools exist for performing enrichment analysis of transcriptomic and proteomic data in order to interpret them in biological terms, almost no equivalent tools exist for metabolomic data. We present Metabolite Biological Role (MBRole), a web server for carrying out over-representation analysis of biological and chemical annotations in arbitrary sets of metabolites (small chemical compounds) coming from metabolomic data of any organism or sample. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at http://csbg.cnb.csic.es/mbrole. It was tested in the main web browsers.

BACKGROUND: The prediction and study of protein interactions and functional relationships based on similarity of phylogenetic trees, exemplified by the mirrortree and related methodologies, is being widely used. Although dependence between the performance of these methods and the set of organisms used to build the trees was suspected, so far nobody assessed it in an exhaustive way, and, in general, previous works used as many organisms as possible. In this work we asses the effect of using different sets of organism (chosen according with various phylogenetic criteria) on the performance of this methodology in detecting protein interactions of different nature. RESULTS: We show that the performance of three mirrortree-related methodologies depends on the set of organisms used for building the trees, and it is not always directly related to the number of organisms in a simple way. Certain subsets of organisms seem to be more suitable for the predictions of certain types of interactions. This relationship between type of interaction and optimal set of organism for detecting them makes sense in the light of the phylogenetic distribution of the organisms and the nature of the interactions. CONCLUSIONS: In order to obtain an optimal performance when predicting protein interactions, it is recommended to use different sets of organisms depending on the available computational resources and data, as well as the type of interactions of interest.

MOTIVATION: Gene Ontology (GO), the de facto standard for representing protein functional aspects, is being used beyond the primary goal for which it is designed: protein functional annotation. It is increasingly used to evaluate large sets of relationships between proteins, e.g. protein-protein interactions or mRNA co-expression, under the assumption that related proteins tend to have the same or similar GO terms. Nevertheless, this assumption only holds for terms representing functional groups with biological significance ('classes'), and not for the ones representing human-imposed aggregations or conceptualizations lacking a biological rationale ('categories'). RESULTS: Using a data-driven approach based on a set of high-quality functional associations, we quantify the functional coherence of GO biological process (GO:BP) terms as well as their explicit and implicit relationships, trying to distinguish classes and categories. We show that the quantification used is in agreement with the distinction one would intuitively make between these two concepts. As not all GO:BP terms and relationships are equally supported by current functional associations, any detailed validation of new experimental data using GO:BP, beyond whole-system statistics, should take such unbalance into account. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The Mirrortree server allows to graphically and interactively study the co-evolution of two protein families, and investigate their possible interactions and functional relationships in a taxonomic context. The server includes the possibility of starting from single sequences and hence it can be used by non-expert users. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at http://csbg.cnb.csic.es/mtserver. It was tested in the main web browsers. Adobe Flash Player is required at the client side to perform the interactive assessment of co-evolution. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: It has repeatedly been shown that interacting protein families tend to have similar phylogenetic trees. These similarities can be used to predicting the mapping between two families of interacting proteins (i.e. which proteins from one family interact with which members of the other). The correct mapping will be that which maximizes the similarity between the trees. The two families may eventually comprise orthologs and paralogs, if members of the two families are present in more than one organism. This fact can be exploited to restrict the possible mappings, simply by impeding links between proteins of different organisms. We present here an algorithm to predict the mapping between families of interacting proteins which is able to incorporate information regarding orthologues, or any other assignment of proteins to "classes" that may restrict possible mappings. RESULTS: For the first time in methods for predicting mappings, we have tested this new approach on a large number of interacting protein domains in order to statistically assess its performance. The method accurately predicts around 80% in the most favourable cases. We also analysed in detail the results of the method for a well defined case of interacting families, the sensor and kinase components of the Ntr-type two-component system, for which up to 98% of the pairings predicted by the method were correct. CONCLUSION: Based on the well established relationship between tree similarity and interactions we developed a method for predicting the mapping between two interacting families using genomic information alone. The program is available through a web interface.

BACKGROUND: A fundamental problem when trying to define the functional relationships between proteins is the difficulty in quantifying functional similarities, even when well-structured ontologies exist regarding the activity of proteins (i.e. 'gene ontology' -GO-). However, functional metrics can overcome the problems in the comparing and evaluating functional assignments and predictions. As a reference of proximity, previous approaches to compare GO terms considered linkage in terms of ontology weighted by a probability distribution that balances the non-uniform 'richness' of different parts of the Direct Acyclic Graph. Here, we have followed a different approach to quantify functional similarities between GO terms. RESULTS: We propose a new method to derive 'functional distances' between GO terms that is based on the simultaneous occurrence of terms in the same set of Interpro entries, instead of relying on the structure of the GO. The coincidence of GO terms reveals natural biological links between the GO functions and defines a distance model Df which fulfils the properties of a Metric Space. The distances obtained in this way can be represented as a hierarchical 'Functional Tree'. CONCLUSION: The method proposed provides a new definition of distance that enables the similarity between GO terms to be quantified. Additionally, the 'Functional Tree' defines groups with biological meaning enhancing its utility for protein function comparison and prediction. Finally, this approach could be for function-based protein searches in databases, and for analysing the gene clusters produced by DNA array experiments.

MOTIVATION: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. RESULTS: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions.

UNLABELLED: 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: [email protected]

Abstract Summary: We have developed a package for the interactive visualization of results from different threading programs. Additionally, we have integrated relevant information about protein sequence, function, evolution, and structure into the interface. Availability: A detailed documentation of THREADLIZE, and the binaries for IRIX, SunOS and Linux are available at http://www.cnb.uam.es/~pazos/threadlize. The package is free for academic users. Contact: [email protected] Supplementary information: http://www.cnb.uam.es/~pazos/threadlize