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The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project., Competing Interests: Conflicts of interest The authors declare no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The UniProt website receives about 800,000 unique visitors per month and is the primary means to access UniProt. It provides 10 searchable datasets and four main tools. The key UniProt datasets are the UniProt Knowledgebase (UniProtKB), the UniProt Reference Clusters (UniRef), the UniProt Archive (UniParc), and protein sets for completely sequenced genomes (Proteomes). Other supporting datasets include information about proteins that is present in UniProtKB protein entries, such as literature citations, taxonomy, and subcellular locations, among others. This article focuses on how to use UniProt datasets. The first basic protocol describes navigation and searching mechanisms for the UniProt datasets, and two additional protocols build on the first protocol to describe advanced search and query building. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Searching UniProt datasets Basic Protocol 2: Advanced search and query building Basis Protocol 3: Adding parameters using advanced search., (© 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.)

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data (UniProt Consortium, 2023). The UniProt website receives about 800,000 unique visitors per month and is the primary means to access UniProt. Along with various datasets that you can search, UniProt provides four main tools. These are the "BLAST" tool for sequence similarity searching, the "Align" tool for multiple sequence alignment, the "Peptide Search" tool for retrieving proteins containing a short peptide sequence, and the "Retrieve/ID Mapping" tool for using a list of identifiers to retrieve UniProt Knowledgebase (UniProtKB) proteins and to convert database identifiers from UniProt to external databases or vice versa. This article provides four basic protocols and seven alternate protocols for using UniProt tools. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Basic local alignment search tool (BLAST) in UniProt Alternate Protocol 1: BLAST through UniProt text search results pages Alternate Protocol 2: BLAST through UniProt basket Basic Protocol 2: Multiple sequence alignment in UniProt Alternate Protocol 3: Align tool through UniProt results pages and entry pages Alternate Protocol 4: Align tool through UniProt basket Basic Protocol 3: Peptide search in UniProt Basic Protocol 4: Batch retrieval and ID mapping in UniProt Alternate Protocol 5: Retrieve/ID Mapping tool through UniProt text search results pages and BLAST and Align results pages Alternate Protocol 6: Retrieve/ID Mapping tool through UniProt basket Alternate Protocol 7: Retrieve/ID Mapping tool through UniProt search box., (© 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.)

Interacting proteins tend to have similar functions, influencing the same organismal traits. Interaction networks can be used to expand the list of candidate trait-associated genes from genome-wide association studies. Here, we performed network-based expansion of trait-associated genes for 1,002 human traits showing that this recovers known disease genes or drug targets. The similarity of network expansion scores identifies groups of traits likely to share an underlying genetic and biological process. We identified 73 pleiotropic gene modules linked to multiple traits, enriched in genes involved in processes such as protein ubiquitination and RNA processing. In contrast to gene deletion studies, pleiotropy as defined here captures specifically multicellular-related processes. We show examples of modules linked to human diseases enriched in genes with known pathogenic variants that can be used to map targets of approved drugs for repurposing. Finally, we illustrate the use of network expansion scores to study genes at inflammatory bowel disease genome-wide association study loci, and implicate inflammatory bowel disease-relevant genes with strong functional and genetic support., (© 2023. The Author(s).)

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing the foundations for tools and research insights that transform fields across the life sciences., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Enzymes play essential roles in all life processes and are used extensively in the biomedical and biotechnological fields. However, enzyme-related information is spread across multiple resources making its retrieval time-consuming. In response to this challenge, the Enzyme Portal has been established to facilitate enzyme research, by providing a freely available hub where researchers can easily find and explore enzyme-related information. It integrates relevant enzyme data for a wide range of species from various resources such as UniProtKB, PDBe and ChEMBL. Here, we describe what type of enzyme-related data the Enzyme Portal provides, how the information is organized and, by show-casing two potential use cases, how to access and retrieve it., (© 2021 The Authors. The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Objectives: Polypharmacy and frailty are two common geriatric conditions. In community-dwelling healthy older adults, we examined whether polypharmacy is associated with frailty and affects disability-free survival (DFS), assessed as a composite of death, dementia, or persistent physical disability., Methods: We included 19,114 participants (median age 74.0 years, IQR: 6.1 years) from ASPirin in Reducing Events in the Elderly (ASPREE) clinical trial. Frailty was assessed by a modified Fried phenotype and a deficit accumulation Frailty Index (FI). Polypharmacy was defined as concomitant use of five or more prescription medications. Multinomial logistic regression was used to examine the cross-sectional association between polypharmacy and frailty at base line, and Cox regression to determine the effect of polypharmacy and frailty on DFS over five years., Results: Individuals with polypharmacy (vs. <5 medications) were 55% more likely to be pre-frail (Relative Risk Ratio or RRR: 1.55; 95%Confidence Interval or CI:1.44, 1.68) and three times more likely to be frail (RRR: 3.34; 95%CI:2.64, 4.22) according to Fried phenotype. Frailty alone was associated with double risk of the composite outcome (Hazard ratio or HR: 2.16; 95%CI: 1.56, 2.99), but frail individuals using polypharmacy had a four-fold risk (HR: 4.24; 95%CI: 3.28, 5.47). Effect sizes were larger when frailty was assessed using the FI., Conclusion: Polypharmacy was significantly associated with pre-frailty and frailty at baseline. Polypharmacy-exposed frailty increased the risk of reducing disability-free survival among older adults. Addressing polypharmacy in older people could ameliorate the impact of frailty on individuals' functional status, cognition and survival., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated, encyclopaedic database of macromolecular complexes with known function from a range of model organisms. It summarizes complex composition, topology and function along with links to a large range of domain-specific resources (i.e. wwPDB, EMDB and Reactome). Since the last update in 2019, we have produced a first draft complexome for Escherichia coli, maintained and updated that of Saccharomyces cerevisiae, added over 40 coronavirus complexes and increased the human complexome to over 1100 complexes that include approximately 200 complexes that act as targets for viral proteins or are part of the immune system. The display of protein features in ComplexViewer has been improved and the participant table is now colour-coordinated with the nodes in ComplexViewer. Community collaboration has expanded, for example by contributing to an analysis of putative transcription cofactors and providing data accessible to semantic web tools through Wikidata which is now populated with manually curated Complex Portal content through a new bot. Our data license is now CC0 to encourage data reuse. Users are encouraged to get in touch, provide us with feedback and send curation requests through the 'Support' link., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

As computational modeling becomes more essential to analyze and understand biological regulatory mechanisms, governance of the many databases and knowledge bases that support this domain is crucial to guarantee reliability and interoperability of resources. To address this, the COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC, CA15205, www.greekc.org) organized nine workshops in a four-year period, starting September 2016. The workshops brought together a wide range of experts from all over the world working on various steps in the knowledge management process that focuses on understanding gene regulatory mechanisms. The discussions between ontologists, curators, text miners, biologists, bioinformaticians, philosophers and computational scientists spawned a host of activities aimed to standardize and update existing knowledge management workflows and involve end-users in the process of designing the Gene Regulation Knowledge Commons (GRKC). Here the GREEKC consortium describes its main achievements in improving this GRKC., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Molecular interaction databases aim to systematically capture and organize the experimental interaction information described in the scientific literature. These data can then be used to perform network analysis, to assign putative roles to uncharacterized proteins and to investigate their involvement in cellular pathways.This chapter gives a brief overview of publicly available molecular interaction databases and focuses on the members of the IMEx Consortium, on their curation policies and standard data formats. All of the goals achieved by IMEx databases over the last 15 years, the data types provided and the many different ways in which such data can be utilized by the research community, are described in detail. The IMEx databases curate molecular interaction data to the highest caliber, following a detailed curation model and supplying rich metadata by employing common curation rules and harmonized standards. The IMEx Consortium provides comprehensively annotated molecular interaction data integrated into a single, non-redundant, open access dataset., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The role of the blood-brain barrier (BBB) in Alzheimer's and other neurodegenerative diseases is still the subject of many studies. However, those studies using high-throughput methods have been compromised by the lack of Gene Ontology (GO) annotations describing the role of proteins in the normal function of the BBB. The GO Consortium provides a gold-standard bioinformatics resource used for analysis and interpretation of large biomedical data sets. However, the GO is also used by other research communities and, therefore, must meet a variety of demands on the breadth and depth of information that is provided. To meet the needs of the Alzheimer's research community we have focused on the GO annotation of the BBB, with over 100 transport or junctional proteins prioritized for annotation. This project has led to a substantial increase in the number of human proteins associated with BBB-relevant GO terms as well as more comprehensive annotation of these proteins in many other processes. Furthermore, data describing the microRNAs that regulate the expression of these priority proteins have also been curated. Thus, this project has increased both the breadth and depth of annotation for these prioritized BBB proteins. Database URLhttps://www.ebi.ac.uk/QuickGO/., (© The Author(s) 2021. Published by Oxford University Press.)

The domain of transcription regulation has been notoriously difficult to annotate in the Gene Ontology, partly because of the intricacies of gene regulation which involve molecular interactions with DNA as well as amongst protein complexes. The molecular function 'transcription coregulator activity' is a part of the biological process 'regulation of transcription, DNA-templated' that occurs in the cellular component 'chromatin'. It can mechanistically link sequence-specific DNA-binding transcription factor (dbTF) regulatory DNA target sites to coactivator and corepressor target sites through the molecular function 'cis-regulatory region sequence-specific DNA binding'. Many questions arise about transcription coregulators (coTF). Here, we asked how many unannotated, putative coregulators can be identified in protein complexes? Therefore, we mined the CORUM and hu.MAP protein complex databases with known and strongly presumed human transcription coregulators. In addition, we trawled the BioGRID and IntAct molecular interaction databases for interactors of the known 1457 human dbTFs annotated by the GREEKC and GO consortia. This yielded 1093 putative transcription factor coregulator complex subunits, of which 954 interact directly with a dbTF. This substantially expands the set of coTFs that could be annotated to 'transcription coregulator activity' and sets the stage for renewed annotation and wet-lab research efforts. To this end, we devised a prioritisation score based on existing GO annotations of already curated transcription coregulators as well as interactome representation. Since all the proteins that we mined are parts of protein complexes, we propose to concomitantly engage in annotation of the putative transcription coregulator-containing complexes in the Complex Portal database., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Fine root endophytes (FRE) were traditionally considered a morphotype of arbuscular mycorrhizal fungi (AMF), but recent genetic studies demonstrate that FRE belong within the subphylum Mucoromycotina, rather than in the subphylum Glomeromycotina with the AMF. These findings prompt enquiry into the fundamental ecology of FRE and AMF. We sampled FRE and AMF in roots of Trifolium subterraneum from 58 sites across temperate southern Australia. We investigated the environmental drivers of composition, richness, and root colonization of FRE and AMF by using structural equation modelling and canonical correspondence analyses. Root colonization by FRE increased with increasing temperature and rainfall but decreased with increasing phosphorus (P). Root colonization by AMF increased with increasing soil organic carbon but decreased with increasing P. Richness of FRE decreased with increasing temperature and soil pH. Richness of AMF increased with increasing temperature and rainfall but decreased with increasing soil aluminium (Al) and pH. Aluminium, soil pH, and rainfall were, in decreasing order, the strongest drivers of community composition of FRE; they were also important drivers of community composition of AMF, along with temperature, in decreasing order: rainfall, Al, temperature, and soil pH. Thus, FRE and AMF showed the same responses to some (e.g. soil P, soil pH) and different responses to other (e.g. temperature) key environmental factors. Overall, our data are evidence for niche differentiation among these co-occurring mycorrhizal associates.

The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data are freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are more multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Motivation: A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called 'causal interaction' takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g. gene regulation) need to be described with a careful appreciation of the underlying molecular reactions. A proper description of this information enables archiving, sharing and reuse by humans and for automated computational processing. Various representations of causal relationships between biological components are currently used in a variety of resources., Results: Here, we propose a checklist that accommodates current representations, called the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while fostering uniformity and interoperability of the data across resources., Availability and Implementation: The checklist together with examples is accessible at https://github.com/MI2CAST/MI2CAST., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Older cancer survivors may experience ageing at an accelerated rate when compared to their similar-aged, cancer-free counterparts. Ageing is undoubtedly a significant risk factor for cancer. There is evidence to suggest, however, that this relationship may in fact be bidirectional, with cancer and its treatments contributing to the ageing process. In this review, we outline the current literature linking cancer and anti-cancer therapy to adverse ageing outcomes and explore what additional research is needed in order to conclusively define cancer and its treatment as an accelerator of ageing., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to report., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Vegetated coastal ecosystems (VCEs) are in global decline and sensitive to climate change; yet may also assist its mitigation through high rates of 'blue' carbon sequestration and storage. Alterations of relative sea-level (RSL) are pervasive drivers of change that reflect the interaction between tidal inundation regimes and ground surface elevation. Although many studies have investigated sediment accretion within VCEs, relatively few have addressed spatiotemporal patterns of resilience in response to RSL change. In this study, we used high resolution elevation models and field surveys to identify RSL changes and socio-ecological responses in a tidal lagoon system following earthquakes in New Zealand. We expected that vegetation changes would result from RSL effects caused by surface-elevation changes in intertidal zones. Elevation measurements showed a sequence of vertical displacements resulting from major earthquakes in 2011 and 2012, and additional surface-elevation loss since. VCE losses were recorded over an 8 year period post-2011 in response to high rates of RSL rise (up to 41 mm yr -1 ). Anthropogenic factors influenced the pattern of losses and illustrate opportunities for managing risks to other VCEs facing RSL rise. Four key principles for building VCE resilience were identified: i) anthropogenic encroachment results in resilience loss due to the need for landward migration when changes exceed the tolerance thresholds of VCEs at their lower elevational limits; ii) connectivity losses exacerbate encroachment effects, and conversely, are a practical focus for management; iii) landscape-scale risk exposure is disproportionately influenced by the largest wetland remnants illustrating the importance of site-specific vulnerabilities and their assessment; and iv) establishing new protected areas to accommodate the movement of VCEs is needed, and requires a combination of land tenure rearrangements and connectivity conservation. Embracing these concepts offers promise for improving whole-system resilience to help address the challenge of global climate change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.

Objectives: The AHA/ACC-2017 hypertension guideline recommends an age-independent target blood pressure (BP) of less than 130/80 mmHg. In an elderly cohort without established cardiovascular disease (CVD) at baseline, we determined the impact of this guideline on the prevalence of hypertension and associated CVD risk., Methods: Nineteen thousand, one hundred and fourteen participants aged at least 65 years from the ASPirin in Reducing Events in the Elderly (ASPREE) study were grouped by baseline BP: 'pre-2017 hypertensive' (BP ≥140/90 mmHg and/or on antihypertensive drugs); 'reclassified hypertensive' (normotensive by pre-2017 guidelines; hypertensive by AHA/ACC-2017 guideline), and 'normotensive' (BP <130 and <80 mmHg). For each group, we evaluated CVD risk factors, predicted 10-year CVD risk using the Atherosclerotic Cardiovascular Disease (ASCVD) risk equation, and reported observed CVD event rates during a median 4.7-year follow-up., Results: Overall, 74.4% (14 213/19 114) were 'pre-2017 hypertensive'; an additional 12.3% (2354/19 114) were 'reclassified hypertensive' by the AHA/ACC-2017 guideline. Of those 'reclassified hypertensive', the majority (94.5%) met criteria for antihypertensive treatment although 29% had no other traditional CVD risk factors other than age. Further, a relatively lower mean 10-year predicted CVD risk (18% versus 26%, P < 0.001) and lower CVD rates (8.9 versus 12.1/1000 person-years, P = 0.01) were observed in 'reclassified hypertensive' compared with 'pre-2017 hypertensive'. Compared with 'normotensive', a hazard ratio (95% confidence interval) for CVD events of 1.60 (1.26-2.02) for 'pre-2017 hypertensive' and 1.26 (0.93-1.71) for 'reclassified hypertensive' was observed., Conclusion: Applying current CVD risk calculators in the elderly 'reclassified hypertensive', as a result of shifting the BP threshold lower, increases eligibility for antihypertensive treatment but documented CVD rates remain lower than hypertensive patients defined by pre2017 BP thresholds.

Motivation: The number of protein records in the UniProt Knowledgebase (UniProtKB: https://www.uniprot.org) continues to grow rapidly as a result of genome sequencing and the prediction of protein-coding genes. Providing functional annotation for these proteins presents a significant and continuing challenge., Results: In response to this challenge, UniProt has developed a method of annotation, known as UniRule, based on expertly curated rules, which integrates related systems (RuleBase, HAMAP, PIRSR, PIRNR) developed by the members of the UniProt consortium. UniRule uses protein family signatures from InterPro, combined with taxonomic and other constraints, to select sets of reviewed proteins which have common functional properties supported by experimental evidence. This annotation is propagated to unreviewed records in UniProtKB that meet the same selection criteria, most of which do not have (and are never likely to have) experimentally verified functional annotation. Release 2020&#95;01 of UniProtKB contains 6496 UniRule rules which provide annotation for 53 million proteins, accounting for 30% of the 178 million records in UniProtKB. UniRule provides scalable enrichment of annotation in UniProtKB., Availability and Implementation: UniRule rules are integrated into UniProtKB and can be viewed at https://www.uniprot.org/unirule/. UniRule rules and the code required to run the rules, are publicly available for researchers who wish to annotate their own sequences. The implementation used to run the rules is known as UniFIRE and is available at https://gitlab.ebi.ac.uk/uniprot-public/unifire., (© The Author(s) 2020. Published by Oxford University Press.)

The universal protein knowledgebase (UniProtKB) collects and centralises functional information on proteins across a wide range of species. In addition to the functional information added to all protein entries, for enzymes, which represent 20-40% of most proteomes, UniProtKB provides additional information about Enzyme Commission classification, catalytic activity, cofactors, enzyme regulation, kinetics and pathways, all based on critical assessment of published experimental data. Computer-based analysis and structural data are used to enrich the annotation of the sequence through the identification of active sites and binding sites. While the annotation of enzymes is well-defined, the curation of pseudoenzymes in UniProtKB has highlighted some challenges: how to identify them, how to assess their lack of catalytic activity, how to annotate their lack of catalytic activity in a consistent way and how much can be inferred and propagated from experimental data obtained from other species. Through various examples, we illustrate some of these issues and discuss some of the changes we propose to enhance the annotation and discovery of pseudoenzymes. Ultimately, improving the curation of pseudoenzymes will provide the scientific community with a comprehensive resource for pseudoenzymes, which in turn will lead to a better understanding of the evolution of these molecules, the aetiology of related diseases and the development of drugs., (© 2019 European Bioinformatics Institute. The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Aims: Arbuscule-producing fine root endophytes (FRE) (previously incorrectly Glomus tenue ) were recently placed within subphylum Mucoromycotina; the first report of arbuscules outside subphylum Glomeromycotina. Here, we aimed to estimate nutrient concentrations in plant and fungal structures of FRE and to test the utility of cryo-scanning electron microscopy (cryoSEM) for studying these fungi., Methods: We used replicated cryoSEM and X-ray microanalysis of heavily colonized roots of Trifolium subterraneum ., Results: Intercellular hyphae and hyphae in developed arbuscules were consistently very thin; 1.35 ± 0.03 μm and 0.99 ± 0.03 μm in diameter, respectively (mean ± SE). Several intercellular hyphae were often adjacent to each other forming "hyphal ropes." Developed arbuscules showed higher phosphorus concentrations than senesced arbuscules and non-colonized structures. Senesced arbuscules showed greatly elevated concentrations of calcium and magnesium., Conclusion: While uniformly thin hyphae and hyphal ropes are distinct features of FRE, the morphology of fully developed arbuscules, elevated phosphorus in fungal structures, and accumulation of calcium with loss of structural integrity in senesced arbuscules are similar to glomeromycotinian fungi. Thus, we provide evidence that FRE may respond to similar host-plant signals or that the host plant may employ a similar mechanism of association with FRE and AMF., (Copyright © 2020 Albornoz, Hayes, Orchard, Clode, Nazeri, Standish, Bending, Hilton and Ryan.)

Phosphatases play an essential role in the regulation of protein phosphorylation. Less abundant than kinases, many phosphatases are components of one or more macromolecular complexes with different substrate specificities and specific functionalities. The expert scientific curation of phosphatase complexes for the UniProt and Complex Portal databases supports the whole scientific community by collating and organising small- and large-scale experimental data from the scientific literature into context-specific central resources, where the data can be freely accessed and used to further academic and translational research. In this review, we discuss how the diverse biological functions of phosphatase complexes are presented in UniProt and the Complex Portal, and how understanding the biological significance of phosphatase complexes in Caenorhabditis elegans offers insight into the mechanisms of substrate diversity in a variety of cellular and molecular processes., (© 2020 European Bioinformatics Institute. The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

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.

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide association studies, applied universally across biological fields, including Alzheimer's disease (AD) research., Objective: We aim to demonstrate the applicability of GO for interpretation of AD datasets to improve the understanding of the underlying molecular disease mechanisms, including the involvement of inflammatory pathways and dysregulated microRNAs (miRs)., Methods: We have undertaken a systematic full article GO annotation approach focused on microglial proteins implicated in AD and the miRs regulating their expression. PANTHER was used for enrichment analysis of previously published AD data. Cytoscape was used for visualizing and analyzing miR-target interactions captured from published experimental evidence., Results: We contributed 3,084 new annotations for 494 entities, i.e., on average six new annotations per entity. This included a total of 1,352 annotations for 40 prioritized microglial proteins implicated in AD and 66 miRs regulating their expression, yielding an average of twelve annotations per prioritized entity. The updated GO resource was then used to re-analyze previously published data. The re-analysis showed novel processes associated with AD-related genes, not identified in the original study, such as 'gliogenesis', 'regulation of neuron projection development', or 'response to cytokine', demonstrating enhanced applicability of GO for neuroscience research., Conclusions: This study highlights ongoing development of the neurobiological aspects of GO and demonstrates the value of biocuration activities in the area, thus helping to delineate the molecular bases of AD to aid the development of diagnostic tools and treatments.

Background: The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality., Objective: To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer's disease research., Methods: We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community., Results: Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset., Conclusion: This represents a significant enhancement in the expert curated data pertinent to Alzheimer's disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

The Human Proteome Organization's (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome.

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are now recognised as major determinants in cellular regulation. This white paper presents a roadmap for future e-infrastructure developments in the field of IDP research within the ELIXIR framework. The goal of these developments is to drive the creation of high-quality tools and resources to support the identification, analysis and functional characterisation of IDPs. The roadmap is the result of a workshop titled "An intrinsically disordered protein user community proposal for ELIXIR" held at the University of Padua. The workshop, and further consultation with the members of the wider IDP community, identified the key priority areas for the roadmap including the development of standards for data annotation, storage and dissemination; integration of IDP data into the ELIXIR Core Data Resources; and the creation of benchmarking criteria for IDP-related software. Here, we discuss these areas of priority, how they can be implemented in cooperation with the ELIXIR platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for an IDP Community in ELIXIR and is an appeal to identify and involve new stakeholders., Competing Interests: No competing interests were disclosed., (Copyright: © 2019 Davey NE et al.)

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., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The 21st century is witnessing an explosive surge in our understanding of pseudoenzyme-driven regulatory mechanisms in biology. Pseudoenzymes are proteins that have sequence homology with enzyme families but that are proven or predicted to lack enzyme activity due to mutations in otherwise conserved catalytic amino acids. The best-studied pseudoenzymes are pseudokinases, although examples from other families are emerging at a rapid rate as experimental approaches catch up with an avalanche of freely available informatics data. Kingdom-wide analysis in prokaryotes, archaea and eukaryotes reveals that between 5 and 10% of proteins that make up enzyme families are pseudoenzymes, with notable expansions and contractions seemingly associated with specific signaling niches. Pseudoenzymes can allosterically activate canonical enzymes, act as scaffolds to control assembly of signaling complexes and their localization, serve as molecular switches, or regulate signaling networks through substrate or enzyme sequestration. Molecular analysis of pseudoenzymes is rapidly advancing knowledge of how they perform noncatalytic functions and is enabling the discovery of unexpected, and previously unappreciated, functions of their intensively studied enzyme counterparts. Notably, upon further examination, some pseudoenzymes have previously unknown enzymatic activities that could not have been predicted a priori. Pseudoenzymes can be targeted and manipulated by small molecules and therefore represent new therapeutic targets (or anti-targets, where intervention should be avoided) in various diseases. In this review, which brings together broad bioinformatics and cell signaling approaches in the field, we highlight a selection of findings relevant to a contemporary understanding of pseudoenzyme-based biology., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated, encyclopaedic database that collates and summarizes information on stable, macromolecular complexes of known function. It captures complex composition, topology and function and links out to a large range of domain-specific resources that hold more detailed data, such as PDB or Reactome. We have made several significant improvements since our last update, including improving compliance to the FAIR data principles by providing complex-specific, stable identifiers that include versioning. Protein complexes are now available from 20 species for download in standards-compliant formats such as PSI-XML, MI-JSON and ComplexTAB or can be accessed via an improved REST API. A component-based JS front-end framework has been implemented to drive a new website and this has allowed the use of APIs from linked services to import and visualize information such as the 3D structure of protein complexes, its role in reactions and pathways and the co-expression of complex components in the tissues of multi-cellular organisms. A first draft of the complete complexome of Saccharomyces cerevisiae is now available to browse and download.

The analysis and interpretation of high-throughput datasets relies on access to high-quality bioinformatics resources, as well as processing pipelines and analysis tools. Gene Ontology (GO, geneontology.org) is a major resource for gene enrichment analysis. The aim of this project, funded by the Alzheimer's Research United Kingdom (ARUK) foundation and led by the University College London (UCL) biocuration team, was to enhance the GO resource by developing new neurological GO terms, and use GO terms to annotate gene products associated with dementia. Specifically, proteins and protein complexes relevant to processes involving amyloid-beta and tau have been annotated and the resulting annotations are denoted in GO databases as 'ARUK-UCL'. Biological knowledge presented in the scientific literature was captured through the association of GO terms with dementia-relevant protein records; GO itself was revised, and new GO terms were added. This literature biocuration increased the number of Alzheimer's-relevant gene products that were being associated with neurological GO terms, such as 'amyloid-beta clearance' or 'learning or memory', as well as neuronal structures and their compartments. Of the total 2055 annotations that we contributed for the prioritised gene products, 526 have associated proteins and complexes with neurological GO terms. To ensure that these descriptive annotations could be provided for Alzheimer's-relevant gene products, over 70 new GO terms were created. Here, we describe how the improvements in ontology development and biocuration resulting from this initiative can benefit the scientific community and enhance the interpretation of dementia data.

It has long been postulated that symbiotic fungi facilitated plant migrations onto land through enhancing the scavenging of mineral nutrients and exchanging these for photosynthetically fixed organic carbon. Today, land plant-fungal symbioses are both widespread and diverse. Recent discoveries show that a variety of potential fungal associates were likely available to the earliest land plants, and that these early partnerships were probably affected by changing atmospheric CO 2 concentrations. Here, we evaluate current hypotheses and knowledge gaps regarding early plant-fungal partnerships in the context of newly discovered fungal mutualists of early and more recently evolved land plants and the rapidly changing views on the roles of plant-fungal symbioses in the evolution and ecology of the terrestrial biosphere., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The Complex Portal ( www.ebi.ac.uk/complexportal ) is an encyclopedia of macromolecular complexes. Complexes are assigned unique, stable IDs, are species specific, and list all participating members with links to an appropriate reference database (UniProtKB, ChEBI, RNAcentral). Each complex is annotated extensively with its functions, properties, structure, stoichiometry, tissue expression profile, and subcellular location. Links to domain-specific databases allow the user to access additional information and enable data searching and filtering. Complexes can be saved and downloaded in PSI-MI XML, MI-JSON, and tab-delimited formats.

The Proteomics Standards Initiative (PSI) of the Human Proteome Organization (HUPO) has now been developing and promoting open community standards and software tools in the field of proteomics for 15 years. Under the guidance of the chair, cochairs, and other leadership positions, the PSI working groups are tasked with the development and maintenance of community standards via special workshops and ongoing work. Among the existing ratified standards, the PSI working groups continue to update PSI-MI XML, MITAB, mzML, mzIdentML, mzQuantML, mzTab, and the MIAPE (Minimum Information About a Proteomics Experiment) guidelines with the advance of new technologies and techniques. Furthermore, new standards are currently either in the final stages of completion (proBed and proBAM for proteogenomics results as well as PEFF) or in early stages of design (a spectral library standard format, a universal spectrum identifier, the qcML quality control format, and the Protein Expression Interface (PROXI) web services Application Programming Interface). In this work we review the current status of all of these aspects of the PSI, describe synergies with other efforts such as the ProteomeXchange Consortium, the Human Proteome Project, and the metabolomics community, and provide a look at future directions of the PSI.

Summary: Proteins frequently function as parts of complexes, assemblages of multiple proteins and other biomolecules, yet network visualizations usually only show proteins as parts of binary interactions. ComplexViewer visualizes interactions with more than two participants and thereby avoids the need to first expand these into multiple binary interactions. Furthermore, if binding regions between molecules are known then these can be displayed in the context of the larger complex., Availability and Implementation: freely available under Apache version 2 license; EMBL-EBI Complex Portal: http://www.ebi.ac.uk/complexportal; Source code: https://github.com/MICommunity/ComplexViewer; Package: https://www.npmjs.com/package/complexviewer; http://biojs.io/d/complexviewer. Language: JavaScript; Web technology: Scalable Vector Graphics; Libraries: D3.js., Contact: colin.combe@ed.ac.uk or juri.rappsilber@ed.ac.uk., (© The Author 2017. Published by Oxford University Press.)

Fine root endophytes (FRE) are arbuscule-forming fungi presently considered as a single species-Glomus tenue in the Glomeromycota (Glomeromycotina)-but probably belong within the Mucoromycotina. Thus, FRE are the only known arbuscule-forming fungi not within the arbuscular mycorrhizal fungi (AMF; Glomeromycotina) as currently understood. Phylogenetic differences between FRE and AMF could reflect ecological differences. To synthesize current ecological knowledge, we reviewed the literature on FRE and identified 108 papers that noted the presence of FRE and, in some, the colonization levels for FRE or AMF (or both). We categorized these records by geographic region, host-plant family and environment (agriculture, moderate-natural, low-temperature, high-altitude and other) and determined their influence on the percentage of root length colonized by FRE in a meta-analysis. We found that FRE are globally distributed, with many observations from Poaceae, perhaps due to grasses being widely distributed. In agricultural environments, colonization by FRE often equalled or exceeded that of AMF, particularly in Australasia. In moderate-natural and high-altitude environments, average colonization by FRE (~10%) was lower than that of AMF (~35%), whereas in low-temperature environments, colonization was similar (~20%). Several studies suggested that FRE can enhance host-plant phosphorus uptake and growth, and may be more resilient than AMF to environmental stress in some host plants. Further research is required on the functioning of FRE in relation to the environment, host plant and co-occurring AMF and, in particular, to examine whether FRE are important for plant growth in stressful environments. Targeted molecular primers are urgently needed for further research on FRE.

This article describes the creation of the first expert manually curated noncoding RNA interaction networks for S. cerevisiae The RNA-RNA and RNA-protein interaction networks have been carefully extracted from the experimental literature and made available through the IntAct database (www.ebi.ac.uk/intact). We provide an initial network analysis and compare their properties to the much larger protein-protein interaction network. We find that the proteins that bind to ncRNAs in the network contain only a small proportion of classical RNA binding domains. We also see an enrichment of WD40 domains suggesting their direct involvement in ncRNA interactions. We discuss the challenges in collecting noncoding RNA interaction data and the opportunities for worldwide collaboration to fill the unmet need for this data., (© 2017 Panni et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.)

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices., Competing Interests: Competing interests: No competing interests were disclosed.

Purpose: Although aspirin therapy is used widely in older adults for prevention of cardiovascular disease, its impact on the incidence, progression and severity of age-related macular degeneration (AMD) is uncertain. The effect of low-dose aspirin on the course of AMD will be evaluated in this clinical trial., Design: A sub-study of the 'ASPirin in Reducing Events in the Elderly' (ASPREE) trial, ASPREE-AMD is a 5-year follow-up double-blind, placebo-controlled, randomized trial of the effect of 100 mg daily aspirin on the course of AMD in 5000 subjects aged 70 years or older, with normal cognitive function and without cardiovascular disease at baseline. Non-mydriatic fundus photography will be performed at baseline, 3-year and 5-year follow-up to determine AMD status., Primary Outcome Measures: The incidence and progression of AMD. Exploratory analyses will determine whether aspirin affects the risk of retinal hemorrhage in late AMD, and whether other factors, such as genotype, systemic disease, inflammatory biomarkers, influence the effect of aspirin on AMD., Conclusion: The study findings will be of significant clinical and public interest due to a potential to identify a possible low cost therapy for preventing AMD worldwide and to determine risk/benefit balance of the aspirin usage by the AMD-affected elderly. The ASPREE-AMD study provides a unique opportunity to determine the effect of aspirin on AMD incidence and progression, by adding retinal imaging to an ongoing, large-scale primary prevention randomized clinical trial.

Fully automated text mining (TM) systems promote efficient literature searching, retrieval, and review but are not sufficient to produce ready-to-consume curated documents. These systems are not meant to replace biocurators, but instead to assist them in one or more literature curation steps. To do so, the user interface is an important aspect that needs to be considered for tool adoption. The BioCreative Interactive task (IAT) is a track designed for exploring user-system interactions, promoting development of useful TM tools, and providing a communication channel between the biocuration and the TM communities. In BioCreative V, the IAT track followed a format similar to previous interactive tracks, where the utility and usability of TM tools, as well as the generation of use cases, have been the focal points. The proposed curation tasks are user-centric and formally evaluated by biocurators. In BioCreative V IAT, seven TM systems and 43 biocurators participated. Two levels of user participation were offered to broaden curator involvement and obtain more feedback on usability aspects. The full level participation involved training on the system, curation of a set of documents with and without TM assistance, tracking of time-on-task, and completion of a user survey. The partial level participation was designed to focus on usability aspects of the interface and not the performance per se In this case, biocurators navigated the system by performing pre-designed tasks and then were asked whether they were able to achieve the task and the level of difficulty in completing the task. In this manuscript, we describe the development of the interactive task, from planning to execution and discuss major findings for the systems tested.Database URL: http://www.biocreative.org., (Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.)

A large gap remains between the amount of knowledge in scientific literature and the fraction that gets curated into standardized databases, despite many curation initiatives. Yet the availability of comprehensive knowledge in databases is crucial for exploiting existing background knowledge, both for designing follow-up experiments and for interpreting new experimental data. Structured resources also underpin the computational integration and modeling of regulatory pathways, which further aids our understanding of regulatory dynamics. We argue how cooperation between the scientific community and professional curators can increase the capacity of capturing precise knowledge from literature. We demonstrate this with a project in which we mobilize biological domain experts who curate large amounts of DNA binding transcription factors, and show that they, although new to the field of curation, can make valuable contributions by harvesting reported knowledge from scientific papers. Such community curation can enhance the scientific epistemic process.Database URL: http://www.tfcheckpoint.org., (© The Author(s) 2016. Published by Oxford University Press.)

Molecular interaction databases collect, organize, and enable the analysis of the increasing amounts of molecular interaction data being produced and published as we move towards a more complete understanding of the interactomes of key model organisms. The organization of these data in a structured format supports analyses such as the modeling of pairwise relationships between interactors into interaction networks and is a powerful tool for understanding the complex molecular machinery of the cell. This chapter gives an overview of the principal molecular interaction databases, in particular the IMEx databases, and their curation policies, use of standardized data formats and quality control rules. Special attention is given to the MIntAct project, in which IntAct and MINT joined forces to create a single resource to improve curation and software development efforts. This is exemplified as a model for the future of molecular interaction data collation and dissemination.

Biocuration has become a cornerstone for analyses in biology, and to meet needs, the amount of annotations has considerably grown in recent years. However, the reliability of these annotations varies; it has thus become necessary to be able to assess the confidence in annotations. Although several resources already provide confidence information about the annotations that they produce, a standard way of providing such information has yet to be defined. This lack of standardization undermines the propagation of knowledge across resources, as well as the credibility of results from high-throughput analyses. Seeded at a workshop during the Biocuration 2012 conference, a working group has been created to address this problem. We present here the elements that were identified as essential for assessing confidence in annotations, as well as a draft ontology--the Confidence Information Ontology--to illustrate how the problems identified could be addressed. We hope that this effort will provide a home for discussing this major issue among the biocuration community. Tracker URL: https://github.com/BgeeDB/confidence-information-ontology Ontology URL: https://raw.githubusercontent.com/BgeeDB/confidence-information-ontology/master/src/ontology/cio-simple.obo, (© The Author(s) 2015. Published by Oxford University Press.)

Objective: To describe the goals of the Proteomics Standards Initiative (PSI) of the Human Proteome Organization, the methods that the PSI has employed to create data standards, the resulting output of the PSI, lessons learned from the PSI's evolution, and future directions and synergies for the group., Materials and Methods: The PSI has 5 categories of deliverables that have guided the group. These are minimum information guidelines, data formats, controlled vocabularies, resources and software tools, and dissemination activities. These deliverables are produced via the leadership and working group organization of the initiative, driven by frequent workshops and ongoing communication within the working groups. Official standards are subjected to a rigorous document process that includes several levels of peer review prior to release., Results: We have produced and published minimum information guidelines describing what information should be provided when making data public, either via public repositories or other means. The PSI has produced a series of standard formats covering mass spectrometer input, mass spectrometer output, results of informatics analysis (both qualitative and quantitative analyses), reports of molecular interaction data, and gel electrophoresis analyses. We have produced controlled vocabularies that ensure that concepts are uniformly annotated in the formats and engaged in extensive software development and dissemination efforts so that the standards can efficiently be used by the community.Conclusion In its first dozen years of operation, the PSI has produced many standards that have accelerated the field of proteomics by facilitating data exchange and deposition to data repositories. We look to the future to continue developing standards for new proteomics technologies and workflows and mechanisms for integration with other omics data types. Our products facilitate the translation of genomics and proteomics findings to clinical and biological phenotypes. The PSI website can be accessed at http://www.psidev.info., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.)