Augmenting the National Nutrition Data System to Promote Diet Sustainability Analyses.

Research on sustainable diets has become an important and growing area of the nutrition field, but recent studies have pointed to a lack of sustainability metrics and methods that are hindering research and policy progress. To fill this gap, the White House National Strategy on Hunger, Nutrition, and Health calls for increased funding to improve metrics, data collection, and research to address all domains of sustainability, which include nutrition/health, economic, environmental, and social domains. Commodity recipe databases, such as the Food Commodity Intake Database (FCID), are important tools for conducting diet sustainability analyses because they translate mixed dishes from dietary surveys, such as the National Health and Nutrition Examination Survey (NHANES), into commodity ingredients. These ingredients have been linked to data on environmental impacts and economic costs from other databases, thus facilitating collaboration between nutrition researchers, environmental scientists, economists, and others. These linkages cannot be made with other components of the national nutrition data system, such as the Food Patterns Equivalents Database (FPED), because the disaggregated food groups from them are not relevant for examining environmental impacts. Although the NHANES is conducted on an ongoing basis, and FPED is continually updated, the FCID has not been officially updated since 2010. This severely limits advancements in sustainability research and related policy analyses. In this commentary, we argue that the federal government should promote this diet sustainability work by integrating a commodity recipe database into the national nutrition data system, and updating it on a regular basis, as it does with other component databases.

Increased Risk of Severe Cardiovascular Events Following Exacerbations of Chronic Obstructive Pulmonary Disease: Results of the EXACOS-CV Study in Japan.

INTRODUCTION: Severe exacerbations of chronic obstructive pulmonary disease (COPD) are known to increase the risk of cardiovascular events. However, this association has not been investigated specifically in patients with COPD in Japan, whose characteristics may differ from those of Western patients (i.e., western Europe, the US, and Canada). METHODS: This longitudinal retrospective cohort study analyzed secondary claims data and included patients aged ≥ 40 years with COPD (International Classification of Diseases-10 codes J41-J44). All exacerbations occurring during follow-up were measured. Time-dependent Cox models were used to estimate hazard ratios (HRs) for the association between time periods following an exacerbation of COPD (vs. time prior to a first exacerbation) and occurrence of a first hospitalization for a severe fatal or non-fatal cardiovascular event. RESULTS: The analysis included 152,712 patients with COPD with a mean age of 73.8 years and 37.6% of whom were female. During a median follow-up of 37 months, 63,182 (41.4%) patients experienced ≥ 1 exacerbation and 13,314 (8.7%) patients experienced ≥ 1 severe cardiovascular event. Following an exacerbation of COPD, the risk of a severe cardiovascular event was increased in the first 30 days [adjusted HR (aHR) 1.44, 95% confidence interval (CI) 1.33-1.55] and remained elevated for 365 days post-exacerbation (aHR 1.13, 95% CI 1.04-1.23). Specifically, the risks of acute coronary syndrome or arrhythmias remained significantly increased for up to 180 days, and the risk of decompensated heart failure for 1 year. CONCLUSION: Among Japanese patients with COPD, the risk of experiencing a severe cardiovascular event increased following a COPD exacerbation and remained elevated for 365 days, emphasizing the need to prevent exacerbations.

A novel method to monitor rheumatoid arthritis prevalence using hospital and medication databases.

BACKGROUND: Most estimates of rheumatoid arthritis (RA) prevalence, including all official figures in Australia and many other countries, are based on self-report. Self-report has been shown to overestimate RA, but the 'gold standard' of reviewing individual medical records is costly, time-consuming and impractical for large-scale research and population monitoring. This study provides an algorithm to estimate RA cases using administrative data that can be adjusted for use in multiple contexts to provide the first approximate RA cohort in Australia that does not rely on self-report. METHODS: Survey data on self-reported RA and medications from 25 467 respondents of the Australian Longitudinal Study on Women's Health (ALSWH) were linked with data from the national medication reimbursement database, hospital and emergency department (ED) episodes, and Medicare Benefits codes. RA prevalence was calculated for self-reported RA, self-reported RA medications, dispensed RA medications, and hospital/ED RA presentations. Linked data were used to exclude individuals with confounding autoimmune conditions. RESULTS: Of 25 467 survey respondents, 1367 (5·4%) women self-reported disease. Of the 26 840 women with hospital or ED presentations, 292 (1·1%) received ICD-10 codes for RA. There were 1038 (2·8%) cases by the medication database definition, and 294 cases (1·5%) by the self-reported medication definition. After excluding individuals with other rheumatic conditions, prevalence was 3·9% for self-reported RA, 1·9% based on the medication database definition and 0·5% by self-reported medication definition. This confirms the overestimation of RA based on self-reporting. CONCLUSIONS: We provide an algorithm for identifying individuals with RA, which could be used for population studies and monitoring RA in Australia and, with adjustments, internationally. Its balance of accuracy and practicality will be useful for health service planning using relatively easily accessible input data.

Correspondence on "Beyond the Dimer and Trimer: Tetraspiro[2.1.25.1.29.1.213.13]hexadecane-1,3,5,7-tetraone - the Cyclic Tetramer of Carbonylcyclopropane".

The International Union of Pure and Applied Chemistry (IUPAC) name given in the title is incorrect. The correct IUPAC name for this molecule is tetraspiro[2.1.25.1.29.1.213.13]hexadecane-4,8,12,16-tetraone. The incorrect name given in the title, unfortunately, makes the carbon atom hexavalent at two different (3 and 5) positions. In addition, the two other keto groups (at positions 1 and 7) would appear on two of the cyclopropane rings if one adopts to the incorrect name. Nevertheless, this wrong name is a good example to discuss the importance of IUPAC nomenclature in the classroom with students.

Clinical safety of daridorexant in insomnia treatment: Analysis of FDA adverse event reports.

OBJECTIVE: Daridorexant, a novel dual orexin receptor antagonist, was approved by the FDA in 2022 for the treatment of insomnia in adults. The aim of this study is to delve into the adverse events (AEs) of daridorexant by analyzing data from the FAERS database, to assess its safety and effectiveness in clinical applications. METHODS: This study selected data from the FAERS database from the first quarter of 2022 to the third quarter of 2023. Various data analysis methods were used, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM), to assess AEs related to daridorexant. RESULTS: The study analyzed a total of 2,624,030 AE reports, of which 1318 were related to daridorexant. It identified 59 preferred terms (PTs) involving 23 system organ classes (SOCs). Signal mining identified new potential AEs related to daridorexant, including sleep-related psychiatric symptoms (nightmare, abnormal dreams, sleep terror, etc.), emotional and perceptual abnormalities (hallucination, depression, agitation), physiological and behavioral responses (palpitations, dry mouth, energy increased, etc.), suicide risk (suicidal ideation, intentional overdose), and other special concern AEs (tachyphrenia, sleep-related eating disorder, hypersensitivity). CONCLUSION: Although some new potential AEs have been identified, these findings need further verification in broader datasets and long-term studies due to limitations in data sources and analysis methods. Future research should comprehensively assess the safety and effectiveness of daridorexant, providing more accurate guidance for medical professionals in the treatment of insomnia.

Mechanism of mangiferin in the treatment of oral submucous fibrosis based on Gene Expression Omnibus database chip mining combined with network pharmacology and molecular docking.

OBJECTIVES: This study aims to investigate the primary target and potential mechanism of mangiferin (MF) in treating oral submucous fibrosis (OSF) through Gene Expression Omnibus (GEO) database chip mining, network pharmacology, and molecular docking techniques. METHODS: Potential therapeutic targets for OSF were identified using GEO chip data. The potential targets of MF were predicted, and disease-related targets for OSF were collected from databases. A Venn diagram was created using the EVenn platform to identify overlapping targets. The protein-protein interaction (PPI) network was constructed using the STRING database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the DAVID platform. Cytoscape 3.10.1 software was used to visualize a drug-target-pathway-disease network, while AutoDocktools 1.5.6 software was employed for molecular docking analysis. RESULTS: A total of 356 potential targets for MF and 360 disease-related targets for OSF were obtained from multiple databases. The top 15 key target proteins in the PPI network were selected as significant candidates. GO function and KEGG pathway enrichment analyses revealed that MF treatment primarily involved advanced glycation end products-receptor (AGE-RAGE), epidermal growth factor receptor (EGFR), and other signaling pathways associated with OSF pathogenesis. Molecular docking analysis demonstrated that MF exhibited a strong binding activity toward AKT serine kinase 1 (AKT1), tumor necrosis factor (TNF), and other core targets. CONCLUSIONS: These findings suggest that MF may exert its therapeutic effects on OSF through a multitarget approach involving various signaling pathways.

Efficacy of chemoradiotherapy in elderly patients with stage IV inoperable head and neck cancer.

OBJECTIVES: This study aimed to compare the efficacy of chemoradiotherapy (CRT) with radiotherapy (RT) alone for elderly patients (≥ 65 years) with stage IV inoperable head and neck cancer (IV-HNC). METHODS: Elderly patients diagnosed with inoperable IV-HNC from 2010 to 2015 were identified using the SEER database. Then, we performed a 1:1 propensity-score matched (PSM) analysis to reduce treatment selection bias, and the prognostic role of CRT was investigated using Kaplan-Meier analysis, log-rank test, and Cox proportional hazard models. The main outcome was overall survival (OS), and the secondary outcome was cancer-specific survival (CSS). RESULTS: A total of 3318 patients were enrolled, of whom 601 received RT alone and 2717 received CRT. Through PSM, 526 patients were successfully matched, and balances between the two treatment groups were reached. In the matched dataset, multivariable Cox analysis revealed that CRT was associated with better OS (HR = 0.580, P < 0.001) and CSS (HR = 0.586, P < 0.001). Meanwhile, subgroups of patients with IV-HNC (younger age, male sex, being married, black race, grade I-II, oral cavity site, T3-T4 stage, N0-N1 stage, M1 stage) were inclined to benefit more from CRT treatment. Furthermore, the survival benefit of CRT was more pronounced in patients aged 65 to 80 years, but was absent in patients aged 80 years or older. CONCLUSIONS: This study indicated that CRT resulted in better survival than RT alone in elderly patients with inoperable IV-HNC, especially for those subpopulations that benefit more from CRT treatment.

Association between blood urea nitrogen to serum albumin ratio and in-hospital mortality in critical patients with diabetic ketoacidosis: a retrospective analysis of the eICU database.

Background: This study aimed to investigate the association between blood urea nitrogen to serum albumin ratio (BAR) and the risk of in-hospital mortality in patients with diabetic ketoacidosis. Methods: A total of 3,962 diabetic ketoacidosis patients from the eICU Collaborative Research Database were included in this analysis. The primary outcome was in-hospital death. Results: Over a median length of hospital stay of 3.1 days, 86 in-hospital deaths were identified. One unit increase in LnBAR was positively associated with the risk of in-hospital death (hazard ratio [HR], 1.82 [95% CI, 1.42-2.34]). Furthermore, a nonlinear, consistently increasing correlation between elevated BAR and in-hospital mortality was observed (P for trend =0.005 after multiple-adjusted). When BAR was categorized into quartiles, the higher risk of in-hospital death (multiple-adjusted HR, 1.99 [95% CI, (1.1-3.6)]) was found in participants in quartiles 3 to 4 (BAR≥6.28) compared with those in quartiles 1 to 2 (BAR<6.28). In the subgroup analysis, the LnBAR-hospital death association was significantly stronger in participants without kidney insufficiency (yes versus no, P-interaction=0.023). Conclusion: There was a significant and positive association between BAR and the risk of in-hospital death in patients with diabetic ketoacidosis. Notably, the strength of this association was intensified among those without kidney insufficiency.

The Cancer Clinical Library Database (CCLD) from the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) Project.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea's cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

The DRB3 gene of the bovine major histocompatibility complex: discovery, diversity and distribution of alleles in commercial breeds of cattle and applications for development of vaccines.

The bovine Major Histocompatibility Complex (MHC), also known as the Bovine Leucocyte Antigen (BoLA) complex, is the genomic region that encodes the most important molecules for antigen presentation to initiate immune responses. The first evidence of MHC in bovines pointed to a locus containing 2 antigens, one detected by cytotoxic antiserum (MHC class I) and another studied by mixed lymphocyte culture tests (MHC class II). The most studied gene in the BoLA region is the highly polymorphic BoLA-DRB3, which encodes a ß chain with a peptide groove domain involved in antigen presentation for T cells that will develop and co-stimulate cellular and humoral effector responses. BoLA-DRB3 alleles have been associated with outcomes in infectious diseases such as mastitis, trypanosomiasis, and tick loads, and with production traits. To catalog these alleles, 2 nomenclature methods were proposed, and the current use of both systems makes it difficult to list, comprehend and apply these data effectively. In this review we have organized the knowledge available in all of the reports on the frequencies of BoLA-DRB3 alleles. It covers information from studies made in at least 26 countries on more than 30 breeds; studies are lacking in countries that are important producers of cattle livestock. We highlight practical applications of BoLA studies for identification of markers associated with resistance to infectious and parasitic diseases, increased production traits and T cell epitope mapping, in addition to genetic diversity and conservation studies of commercial and creole and locally adapted breeds. Finally, we provide support for the need of studies to discover new BoLA alleles and uncover unknown roles of this locus in production traits.

DNA profiling in India: Addressing issues of sample preservation, databasing, marker selection, & statistical approaches.

DNA technology is the gold standard with respect to the identification of individuals from biological evidence. The technology offers the convenience of a universally similar approach and methodology for analysis across the globe. However, the technology has not realised its full potential in India due to the lack of a DNA database and lacunae in sample collection and preservation from the scene of crime and victims (especially those of sexual assault). Further, statistical interpretation of DNA results is non-existent in the majority of cases. Though the latest technologies and developments in the field of DNA analysis are being adopted and implemented,very little has been enacted practically to improve optimise sample collection and preservation. This article discusses current casework scenarios that highlight the pitfalls and ambiguous areas in the field of DNA analysis, especially with respect DNA databases, sampling, andstatistical approaches to genetic data analysis. Possible solutions and mitigation measures are suggested.

Enhancing maritime transportation security: A data-driven Bayesian network analysis of terrorist attack risks.

Maritime terrorist accidents have a significant low-frequency-high-consequence feature and, thus, require new research to address the associated inherent uncertainty and the scarce literature in the field. This article aims to develop a novel method for maritime security risk analysis. It employs real accident data from maritime terrorist attacks over the past two decades to train a data-driven Bayesian network (DDBN) model. The findings help pinpoint key contributing factors, scrutinize their interdependencies, ascertain the probability of different terrorist scenarios, and describe their impact on different manifestations of maritime terrorism. The established DDBN model undergoes a thorough verification and validation process employing various techniques, such as sensitivity, metrics, and comparative analyses. Additionally, it is tested against recent real-world cases to demonstrate its effectiveness in both retrospective and prospective risk propagation, encompassing both diagnostic and predictive capabilities. These findings provide valuable insights for the various stakeholders, including companies and government bodies, fostering comprehension of maritime terrorism and potentially fortifying preventive measures and emergency management.

Overall survival associated with surgery, radiotherapy, and chemotherapy in metastatic vulvar cancer: A retrospective cohort study based on the SEER database.

Background: Large cancer registries help analyze the prognosis of rare malignancies, such as advanced vulvar cancer. This study aimed to compare the overall survival (OS) rates of patients with metastatic vulvar cancer who had undergone chemoradiotherapy and radiotherapy alone and identify prognostic factors using data from the Surveillance, Epidemiology, and End Results (SEER) registry. Methods: In this retrospective cohort study, we used the SEER database to identify patients with metastatic vulvar cancer diagnosed between 2000 and 2019. Propensity score matching was performed to balance the covariates. Kaplan-Meier curves and Cox models were used to analyze OS. Results: A total of 685 patients were included and divided into chemoradiotherapy and radiotherapy groups, and 400 patients were included after propensity score matching. The chemoradiotherapy group had higher OS in the matched cohort (hazard ratio [HR] = 0.7367; 95% confidence interval [CI]: 0.5906-0.9190; P = 0.0049) than the radiotherapy group, which was similar to that in the pre-matched cohort (P < 0.0001). Patients who had undergone surgery + radiotherapy with or without chemotherapy showed higher OS rates than those who had received radiotherapy with or without chemotherapy for patients aged <75 years and local tumor excision/destruction or surgical removal of the primary site was the recommended surgical choice (P < 0.05). Chemoradiotherapy is sufficient for patients ≥75 years of age. Conclusions: Patients with metastatic vulvar cancer should undergo surgery if they can tolerate it. Adjuvant chemoradiotherapy should be encouraged because this treatment modality was associated with higher OS than radiotherapy alone.

CANTATAdb 3.0: an updated repository of plant long noncoding RNAs.

CANTATAdb 3.0 is an updated database of plant long non-coding RNAs (lncRNAs), containing 571,688 lncRNAs identified across 108 species, including 100 Magnoliopsida (flowering plants), a significant expansion from the previous version. A notable feature is the inclusion of 112,980 lncRNAs that are expressed specifically in certain plant organs or embryos, indicating their potential role in development and organ-specific processes. In addition, CANTATAdb 3.0 includes 74,886 pairs of evolutionarily conserved lncRNAs found across 47 species and inferred from genome-genome alignments as well as conserved lncRNAs obtained with a similarity-search approach in 5,479 species pairs, which would further aid in the selection of lncRNAs for functional studies. Interestingly, we find that conserved lncRNAs with tissue specific expression patterns tend to occupy the same plant organ across different species, pointing toward conserved biological roles. The database now offers extended search capabilities, and downloadable data in popular formats, further facilitating research on plant lncRNAs.

EstroGene2.0: A multi-omic database of response to estrogens, ER-modulators, and resistance to endocrine therapies in breast cancer.

Endocrine therapies targeting the estrogen receptor (ER/ESR1) are the cornerstone to treat ER-positive breast cancers patients, but resistance often limits their effectiveness. Understanding the molecular mechanisms is thus key to optimize the existing drugs and to develop new ER-modulators. Notable progress has been made although the fragmented way data is reported has reduced their potential impact. Here, we introduce EstroGene2.0, an expanded database of its precursor 1.0 version. EstroGene2.0 focusses on response and resistance to endocrine therapies in breast cancer models. Incorporating multi-omic profiling of 361 experiments from 212 studies across 28 cell lines, a user-friendly browser offers comprehensive data visualization and metadata mining capabilities (https://estrogeneii.web.app/). Taking advantage of the harmonized data collection, our follow-up meta-analysis revealed substantial diversity in response to different classes of ER-modulators including SERMs, SERDs, SERCA and LDD/PROTAC. Notably, endocrine resistant models exhibit a spectrum of transcriptomic alterations including a contra-directional shift in ER and interferon signaling, which is recapitulated clinically. Furthermore, dissecting multiple ESR1-mutant cell models revealed the different clinical relevance of genome-edited versus ectopic overexpression model engineering and identified high-confidence mutant-ER targets, such as NPY1R. These examples demonstrate how EstroGene2.0 helps investigate breast cancer's response to endocrine therapies and explore resistance mechanisms.

Enhancing Sepsis prognosis: Integrating social determinants and demographic variables into a comprehensive model for critically ill patients.

BACKGROUND: The Sequential Organ Failure Assessment (SOFA) score monitors organ failure and defines sepsis but may not fully capture factors influencing sepsis mortality. Socioeconomic and demographic impacts on sepsis outcomes have been highlighted recently. OBJECTIVE: To evaluate the prognostic value of SOFA scores against demographic and social health determinants for predicting sepsis mortality in critically ill patients, and to assess if a combined model increases predictive accuracy. METHODS: The study utilized retrospective data from the MIMIC-IV database and prospective external validation from the Penn State Health cohort. A Random Forest model incorporating SOFA scores, demographic/social data, and the Charlson Comorbidity Index was trained and validated. FINDINGS: In the MIMIC-IV dataset of 32,970 sepsis patients, 6,824 (20.7%) died within 30 days. A model including demographic, socioeconomic, and comorbidity data with SOFA scores improved predictive accuracy beyond SOFA scores alone. Day 2 SOFA, age, weight, and comorbidities were significant predictors. External validation showed consistent performance, highlighting the importance of delta SOFA between days 1 and 3. CONCLUSION: Adding patient-specific demographic and socioeconomic information to clinical metrics significantly improves sepsis mortality prediction. This suggests a more comprehensive, multidimensional prognostic approach is needed for accurate sepsis outcome predictions.

Association between lactate/albumin ratio and prognosis in critically ill patients with acute kidney injury undergoing continuous renal replacement therapy.

BACKGROUND: The primary objective was to examine the association between the lactate/albumin ratio (LAR) and the prognosis of patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT). METHODS: Utilizing the Medical Information Mart for Intensive Care IV (MIMIC-IV, v2.0) database, we categorized 703 adult AKI patients undergoing CRRT into survival and non-survival groups based on 28-day mortality. Patients were further grouped by LAR tertiles: low (< 0.692), moderate (0.692-1.641), and high (> 1.641). Restricted cubic splines (RCS), Least Absolute Shrinkage and Selection Operator (LASSO) regression, inverse probability treatment weighting (IPTW), and Kaplan-Meier curves were employed. RESULTS: In our study, the patients had a mortality rate of 50.07% within 28 days and 62.87% within 360 days. RCS analysis revealed a non-linear correlation between LAR and the risk of mortality at both 28 and 360 days. Cox regression analysis, which was adjusted for nine variables identified by LASSO, confirmed that a high LAR (>1.641) served as an independent predictor of mortality at these specific time points (p < 0.05) in AKI patients who were receiving CRRT. These findings remained consistent even after IPTW adjustment, thereby ensuring a reliable and robust outcome. Kaplan-Meier survival curves exhibited a gradual decline in cumulative survival rates at both 28 and 360 days as the LAR values increased (log-rank test, χ2 = 48.630, p < 0.001; χ2 = 33.530, p < 0.001). CONCLUSION: A high LAR (>1.641) was found to be an autonomous predictor of mortality at both 28 and 360 days in critically ill patients with AKI undergoing CRRT.

MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions.

From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell-cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein-protein cell-cell communication interactions are routinely predicted from transcriptomic data. However, inferring metabolite-mediated intercellular signaling remains challenging, partially due to the limited size of intercellular prior knowledge resources focused on metabolites. Here, we leverage knowledge-graph infrastructure to integrate generalistic metabolite-protein with curated metabolite-receptor resources to create MetalinksDB. MetalinksDB is an order of magnitude larger than existing metabolite-receptor resources and can be tailored to specific biological contexts, such as diseases, pathways, or tissue/cellular locations. We demonstrate MetalinksDB's utility in identifying deregulated processes in renal cancer using multi-omics bulk data. Furthermore, we infer metabolite-driven intercellular signaling in acute kidney injury using spatial transcriptomics data. MetalinksDB is a comprehensive and customizable database of intercellular metabolite-protein interactions, accessible via a web interface (https://metalinks.omnipathdb.org/) and programmatically as a knowledge graph (https://github.com/biocypher/metalinks). We anticipate that by enabling diverse analyses tailored to specific biological contexts, MetalinksDB will facilitate the discovery of disease-relevant metabolite-mediated intercellular signaling processes.

A large-scale assessment of sequence database search tools for homology-based protein function prediction.

Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND-one of the most popular tools for function prediction-under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.