CausalTAB: the PSI-MITAB 2.8 updated format for signalling data representation and dissemination.

MOTIVATION: Combining multiple layers of information underlying biological complexity into a structured framework represent a challenge in systems biology. A key task is the formalization of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signalling information, focus on capturing, organizing and displaying signalling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources. RESULTS: Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signalling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated PSI-MITAB 2.8. We define the new term 'causal interaction', and related child terms, which are children of the PSI-MI 'molecular interaction' term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signalling networks more precisely and with higher coverage than before. AVAILABILITY AND IMPLEMENTATION: PSI-MITAB 2.8 format and the new reference implementation of PSICQUIC are available online (https://psicquic.github.io/ and https://psicquic.github.io/MITAB28Format.html). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Encompassing new use cases - level 3.0 of the HUPO-PSI format for molecular interactions.

BACKGROUND: Systems biologists study interaction data to understand the behaviour of whole cell systems, and their environment, at a molecular level. In order to effectively achieve this goal, it is critical that researchers have high quality interaction datasets available to them, in a standard data format, and also a suite of tools with which to analyse such data and form experimentally testable hypotheses from them. The PSI-MI XML standard interchange format was initially published in 2004, and expanded in 2007 to enable the download and interchange of molecular interaction data. PSI-XML2.5 was designed to describe experimental data and to date has fulfilled this basic requirement. However, new use cases have arisen that the format cannot properly accommodate. These include data abstracted from more than one publication such as allosteric/cooperative interactions and protein complexes, dynamic interactions and the need to link kinetic and affinity data to specific mutational changes. RESULTS: The Molecular Interaction workgroup of the HUPO-PSI has extended the existing, well-used XML interchange format for molecular interaction data to meet new use cases and enable the capture of new data types, following extensive community consultation. PSI-MI XML3.0 expands the capabilities of the format beyond simple experimental data, with a concomitant update of the tool suite which serves this format. The format has been implemented by key data producers such as the International Molecular Exchange (IMEx) Consortium of protein interaction databases and the Complex Portal. CONCLUSIONS: PSI-MI XML3.0 has been developed by the data producers, data users, tool developers and database providers who constitute the PSI-MI workgroup. This group now actively supports PSI-MI XML2.5 as the main interchange format for experimental data, PSI-MI XML3.0 which additionally handles more complex data types, and the simpler, tab-delimited MITAB2.5, 2.6 and 2.7 for rapid parsing and download.

The IntAct molecular interaction database in 2010.

IntAct is an open-source, open data molecular interaction database and toolkit. Data is abstracted from the literature or from direct data depositions by expert curators following a deep annotation model providing a high level of detail. As of September 2009, IntAct contains over 200.000 curated binary interaction evidences. In response to the growing data volume and user requests, IntAct now provides a two-tiered view of the interaction data. The search interface allows the user to iteratively develop complex queries, exploiting the detailed annotation with hierarchical controlled vocabularies. Results are provided at any stage in a simplified, tabular view. Specialized views then allows 'zooming in' on the full annotation of interactions, interactors and their properties. IntAct source code and data are freely available at http://www.ebi.ac.uk/intact.

The IMEx coronavirus interactome: an evolving map of Coronaviridae-host molecular interactions.

The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by International Molecular Exchange (IMEx) Consortium curators. Currently, the dataset comprises over 4400 binarized interactions extracted from 151 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website (https://www.ebi.ac.uk/intact) and will be continuously updated as research on COVID-19 progresses.

The IMEx Coronavirus interactome: an evolving map of Coronaviridae-Host molecular interactions.

The current Coronavirus Disease 2019 (COVID-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions enables studying fine-grained resolution of the mechanisms behind the virus biology and the human organism response. Here we present a curated dataset of physical molecular interactions, manually extracted by IMEx Consortium curators focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family. Currently, the dataset comprises over 2,200 binarized interactions extracted from 86 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website ( www.ebi.ac.uk/intact ), and will be continuously updated as research on COVID-19 progresses.

IntAct--open source resource for molecular interaction data.

IntAct is an open source database and software suite for modeling, storing and analyzing molecular interaction data. The data available in the database originates entirely from published literature and is manually annotated by expert biologists to a high level of detail, including experimental methods, conditions and interacting domains. The database features over 126,000 binary interactions extracted from over 2100 scientific publications and makes extensive use of controlled vocabularies. The web site provides tools allowing users to search, visualize and download data from the repository. IntAct supports and encourages local installations as well as direct data submission and curation collaborations. IntAct source code and data are freely available from http://www.ebi.ac.uk/intact.

Publisher Correction: Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set.

In the original HTML version of this Article, the order of authors within the author list was incorrect. The IMEx Consortium contributing authors were incorrectly listed as the last author and should have been listed as the first author. This error has been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.

Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set.

The current wealth of genomic variation data identified at nucleotide level presents the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that sequence variation has on a molecule's interactions is a key step towards connecting mechanistic characterization of nonsynonymous variation to phenotype. We present an open access resource created over 14 years by IMEx database curators, featuring 28,000 annotations describing the effect of small sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website and is enhanced with every monthly release.

The Impact of Mathematical Modeling in Understanding the Mechanisms Underlying Neurodegeneration: Evolving Dimensions and Future Directions.

Neurodegenerative diseases are a heterogeneous group of disorders that are characterized by the progressive dysfunction and loss of neurons. Here, we distil and discuss the current state of modeling in the area of neurodegeneration, and objectively compare the gaps between existing clinical knowledge and the mechanistic understanding of the major pathological processes implicated in neurodegenerative disorders. We also discuss new directions in the field of neurodegeneration that hold potential for furthering therapeutic interventions and strategies.

The InterPro database, an integrated documentation resource for protein families, domains and functional sites.

Signature databases are vital tools for identifying distant relationships in novel sequences and hence for inferring protein function. InterPro is an integrated documentation resource for protein families, domains and functional sites, which amalgamates the efforts of the PROSITE, PRINTS, Pfam and ProDom database projects. Each InterPro entry includes a functional description, annotation, literature references and links back to the relevant member database(s). Release 2.0 of InterPro (October 2000) contains over 3000 entries, representing families, domains, repeats and sites of post-translational modification encoded by a total of 6804 different regular expressions, profiles, fingerprints and Hidden Markov Models. Each InterPro entry lists all the matches against SWISS-PROT and TrEMBL (more than 1,000,000 hits from 462,500 proteins in SWISS-PROT and TrEMBL). The database is accessible for text- and sequence-based searches at http://www.ebi.ac.uk/interpro/. Questions can be emailed to interhelp@ebi.ac.uk.

On the frequency of protein glycosylation, as deduced from analysis of the SWISS-PROT database.

The SWISS-PROT protein sequence data bank contains at present nearly 75,000 entries, almost two thirds of which include the potential N-glycosylation consensus sequence, or sequon, NXS/T (where X can be any amino acid but proline) and thus may be glycoproteins. The number of proteins filed as glycoproteins is however considerably smaller, 7942, of which 749 have been characterized with respect to the total number of their carbohydrate units and sites of attachment of the latter to the protein, as well as the nature of the carbohydrate-peptide linking group. Of these well characterized glycoproteins, about 90% carry either N-linked carbohydrate units alone or both N- and O-linked ones, attached at 1297 N-glycosylation sites (1.9 per glycoprotein molecule) and the rest are O-glycosylated only. Since the total number of sequons in the well characterized glycoproteins is 1968, their rate of occupancy is 2/3. Assuming that the same number of N-linked units and rate of sequon occupancy occur in all sequon containing proteins and that the proportion of solely O-glycosylated proteins (ca. 10%) will also be the same as among the well characterized ones, we conclude that the majority of sequon containing proteins will be found to be glycosylated and that more than half of all proteins are glycoproteins.

BioModels: Content, Features, Functionality, and Use.

BioModels is a reference repository hosting mathematical models that describe the dynamic interactions of biological components at various scales. The resource provides access to over 1,200 models described in literature and over 140,000 models automatically generated from pathway resources. Most model components are cross-linked with external resources to facilitate interoperability. A large proportion of models are manually curated to ensure reproducibility of simulation results. This tutorial presents BioModels' content, features, functionality, and usage.

Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development.

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

The role SWISS-PROT and TrEMBL play in the genome research environment.

SWISS-PROT, a curated protein sequence data bank, contains not only sequence data but also annotation relevant to a particular sequence. The annotation added to each entry is done by a team of biologists and comes, primarily, from articles in journals reporting the actual sequencing and sometimes characterisation. Review articles and collaboration with external experts also play a role along with the use of secondary databases like PROSITE and Pfam in addition to a variety of feature prediction methods. Annotation added by these methods is checked for relevance and likelihood to a particular sequence. The onset of genome sequencing has led to a dramatic increase in sequence data to be included in SWISS-PROT. This has led to the production of TrEMBL (Translation of the EMBL database). TrEMBL consists of entries in a SWISS-PROT format that are derived from the translation of all coding sequences in the EMBL nucleotide sequence database, that are not in SWISS-PROT. Unlike SWISS-PROT entries those in TrEMBL are awaiting manual annotation. However, rather than just representing basic sequence and source information, steps have been taken to add features and annotation automatically. In taking these steps it is hoped that TrEMBL entries are enhanced with some indication as to what a protein is, could or may be.

Integr8: enhanced inter-operability of European molecular biology databases.

OBJECTIVES: The increasing production of molecular biology data in the post-genomic era, and the proliferation of databases that store it, require the development of an integrative layer in database services to facilitate the synthesis of related information. The solution of this problem is made more difficult by the absence of universal identifiers for biological entities, and the breadth and variety of available data. METHODS: Integr8 was modelled using UML (Universal Modelling Language). Integr8 is being implemented as an n-tier system using a modern object-oriented programming language (Java). An object-relational mapping tool, OJB, is being used to specify the interface between the upper layers and an underlying relational database. RESULTS: The European Bioinformatics Institute is launching the Integr8 project. Integr8 will be an automatically populated database in which we will maintain stable identifiers for biological entities, describe their relationships with each other (in accordance with the central dogma of biology), and store equivalences between identified entities in the source databases. Only core data will be stored in Integr8, with web links to the source databases providing further information. CONCLUSIONS: Integr8 will provide the integrative layer of the next generation of bioinformatics services from the EBI. Web-based interfaces will be developed to offer gene-centric views of the integrated data, presenting (where known) the links between genome, proteome and phenotype.

Disrupted in Schizophrenia 1 Interactome: evidence for the close connectivity of risk genes and a potential synaptic basis for schizophrenia.

Disrupted in Schizophrenia 1 (DISC1) is a schizophrenia risk gene associated with cognitive deficits in both schizophrenics and the normal ageing population. In this study, we have generated a network of protein-protein interactions (PPIs) around DISC1. This has been achieved by utilising iterative yeast-two hybrid (Y2H) screens, combined with detailed pathway and functional analysis. This so-called 'DISC1 interactome' contains many novel PPIs and provides a molecular framework to explore the function of DISC1. The network implicates DISC1 in processes of cytoskeletal stability and organisation, intracellular transport and cell-cycle/division. In particular, DISC1 looks to have a PPI profile consistent with that of an essential synaptic protein, which fits well with the underlying molecular pathology observed at the synaptic level and the cognitive deficits seen behaviourally in schizophrenics. Utilising a similar approach with dysbindin (DTNBP1), a second schizophrenia risk gene, we show that dysbindin and DISC1 share common PPIs suggesting they may affect common biological processes and that the function of schizophrenia risk genes may converge.

VARSPLIC: alternatively-spliced protein sequences derived from SWISS-PROT and TrEMBL.

UNLABELLED: The program varsplic.pl uses information present in the SWISS-PROT and TrEMBL databases to create new records for alternatively spliced isoforms. These new records can be used in similarity searches. AVAILABILITY: The program is available at ftp://ftp.ebi.ac.uk/pub/software/swissprot/, together with regularly updated output files. CONTACT: pkersey@ebi.ac.uk

RIFLE: rapid identification of microorganisms by fragment length evaluation.

Biological macromolecules represent a valuable source of information for the identification and phylogenetic classification of microorganisms. One of the most commonly used macromolecules for this task is the 16S rDNA. The WWW-based RIFLE system presented here supports large-scale identification tasks by comparing 16S rDNA restriction patterns to a database of restriction patterns derived from sequence databases. Computing efficiency and robustness against experimental errors are gained by employing a new distance measure for restriction patterns, the fragment length distance. Results from the application of the system to the identification of uncultured microorganisms associated with the seagrass halophila stipulacea show the reliability of the method.

Swissknife - 'lazy parsing' of SWISS-PROT entries.

UNLABELLED: We present Swissknife, a set of Perl modules which provides a fast and reliable object-oriented interface to parsing and modifying files in SWISS-PROT format. AVAILABILITY: The Swissknife modules are available at ftp://ftp.ebi.ac. uk/pub/software/swissprot/. CONTACT: hhe@ebi.ac.uk

A comparison of signal sequence prediction methods using a test set of signal peptides.

We describe the creation of a test set containing secretory and non-secretory proteins. Five existing prediction programs for signal sequences and their cleavage sites are compared on the basis of this test set: SPScan, SigCleave, SignalP V1.1, SignalP V2.0. b2-HMM and SignalP V2.0.b2-NN.