Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhou Y
PROVIDER: S-EPMC6447622 | biostudies-literature | 2019 Apr
REPOSITORIES: biostudies-literature
Publications
Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.
Zhou Yingyao Y Zhou Bin B Pache Lars L Chang Max M Khodabakhshi Alireza Hadj AH Tanaseichuk Olga O Benner Christopher C Chanda Sumit K SK
Nature communications 20190403 1
A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In ...[more]
Similar Datasets
Project description:The rich content of nutrients in human waste provides an outlook for turning it from pollutants to potential resources. The pilot-scale resource-oriented toilet with forward osmosis technology was demonstrated to have advantages to recover clean water, nitrogen, phosphorus, potassium, biogas, and heat from urine and feces. For the possibility of further full-scale implementation in different scenarios, six resource-oriented toilet systems and one conventional toilet system were designed in this study. The methodology of cost-benefit analysis and life cycle assessment were applied to analyze the life cycle economic feasibility and environmental sustainability of these systems. As results indicated, resource-oriented toilets with forward osmosis technology concentrating urine proved to have both economic and environmental benefit. The economic net present value results of new resource-oriented toilets were much better than conventional toilet. The energy consumption in resource-oriented toilets contributes a lot to the environmental impacts while resource recovery such as the fertilizer production and fresh water harvest in resource-oriented toilet systems offsets a lot. Taking both life cycle economic feasibility and environmental sustainability into consideration, the partial resource-oriented toilet (only recovering nutrients from urine) is the best choice, and the totally independent resource-oriented toilet could be applied to replace conventional toilets in areas without any external facilities such as sewer and water supply system etc.
| S-EPMC6106690 | biostudies-literature
Project description:In the era of life-omics, huge amounts of multi-omics data have been generated and widely used in biomedical research. It is challenging for biologists with limited programming skills to obtain biological insights from multi-omics data. Thus, a biologist-oriented platform containing visualization functions is needed to make complex omics data digestible. Here, we propose an easy-to-use, interactive web server named ExpressVis. In ExpressVis, users can prepare datasets; perform differential expression analysis, clustering analysis, and survival analysis; and integrate expression data with protein-protein interaction networks and pathway maps. These analyses are organized into six modules. Users can use each module independently or use several modules interactively. ExpressVis displays analysis results in interactive figures and tables, and provides comprehensive interactive operations in each figure and table, between figures or tables in each module, and among different modules. It is freely accessible at https://omicsmining.ncpsb.org.cn/ExpressVis and does not require login. To test the performance of ExpressVis for multi-omics studies of clinical cohorts, we re-analyzed a published hepatocellular carcinoma dataset and reproduced their main findings, suggesting that ExpressVis is convenient enough to analyze multi-omics data. Based on its complete analysis processes and unique interactive operations, ExpressVis provides an easy-to-use solution for exploring multi-omics data.
| S-EPMC9252728 | biostudies-literature
Project description:Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern 'omic profiling techniques and computational modeling to promote rapid and transformative advances in vaccinology and vaccine discovery. The NIH/NIAID Human Immunology Project Consortium (HIPC) has leveraged systems immunology approaches to identify molecular signatures associated with the immunogenicity of many vaccines. However, comparative analyses have been limited by the distributed nature of some data, potential batch effects across studies, and the absence of multiple relevant studies from non-HIPC groups in ImmPort. To support comparative analyses across different vaccines, we have created the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets. This data resource is available through ImmuneSpace, along with code to reproduce the processing and batch normalization starting from the underlying study data in ImmPort and the Gene Expression Omnibus (GEO). The current release comprises 1405 participants from 53 cohorts profiling the response to 24 different vaccines. This novel systems vaccinology data release represents a valuable resource for comparative and meta-analyses that will accelerate our understanding of mechanisms underlying vaccine responses.
| S-EPMC9584267 | biostudies-literature
Project description:We here describe the development of a freely available online database resource, GeneSpeed Beta Cell, which has been created for the pancreatic islet and pancreatic developmental biology investigator community.We have developed GeneSpeed Beta Cell as a separate component of the GeneSpeed database, providing a genomics-type data repository of pancreas and islet-relevant datasets interlinked with the domain-oriented GeneSpeed database.GeneSpeed Beta Cell allows the query of multiple published and unpublished select genomics datasets in a simultaneous fashion (multiexperiment viewing) and is capable of defining intersection results from precomputed analysis of such datasets (multidimensional querying). Combined with the protein-domain categorization/assembly toolbox provided by the GeneSpeed database, the user is able to define spatial expression constraints of select gene lists in a relatively rigid fashion within the pancreatic expression space. We provide several demonstration case studies of relevance to islet cell biology and development of the pancreas that provide novel insight into islet biology.The combination of an exhaustive domain-based compilation of the transcriptome with gene array data of interest to the islet biologist affords novel methods for multidimensional querying between individual datasets in a rapid fashion, presently not available elsewhere.
| S-EPMC2532782 | biostudies-literature
Project description:Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.
| S-EPMC5539544 | biostudies-other
Project description:Building mechanistic models of kinase-driven signaling pathways requires quantitative measurements of protein phosphorylation across physiologically relevant conditions, but this is rarely done because of the insensitivity of traditional technologies. By using a multiplexed deep phosphoproteome profiling workflow, we were able to generate a deep phosphoproteomics dataset of the EGFR-MAPK pathway in non-transformed MCF10A cells across physiological ligand concentrations with a time resolution of <12 min and in the presence and absence of multiple kinase inhibitors. An improved phosphosite mapping technique allowed us to reliably identify >46,000 phosphorylation sites on >6600 proteins, of which >4500 sites from 2110 proteins displayed a >2-fold increase in phosphorylation in response to EGF. This data was then placed into a cellular context by linking it to 15 previously published protein databases. We found that our results were consistent with much, but not all previously reported data regarding the activation and negative feedback phosphorylation of core EGFR-ERK pathway proteins. We also found that EGFR signaling is biphasic with substrates downstream of RAS/MAPK activation showing a maximum response at <3ng/ml EGF while direct substrates, such as HGS and STAT5B, showing no saturation. We found that RAS activation is mediated by at least 3 parallel pathways, two of which depend on PTPN11. There appears to be an approximately 4-minute delay in pathway activation at the step between RAS and RAF, but subsequent pathway phosphorylation was extremely rapid. Approximately 80 proteins showed a >2-fold increase in phosphorylation across all experiments and these proteins had a significantly higher median number of phosphorylation sites (~18) relative to total cellular phosphoproteins (~4). Over 60% of EGF-stimulated phosphoproteins were downstream of MAPK and included mediators of cellular processes such as gene transcription, transport, signal transduction and cytoskeletal arrangement. Their phosphorylation was either linear with respect to MAPK activation or biphasic, corresponding to the biphasic signaling seen at the level of the EGFR. This deep, integrated phosphoproteomics data resource should be useful in building mechanistic models of EGFR and MAPK signaling and for understanding how downstream responses are regulated.
| S-EPMC10418157 | biostudies-literature
Project description:Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardtii. The model, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting for 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat-grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic pathway-level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth-associated pathways to increased light intensity. Thus, iCre1355 represents an experimentally validated genome-scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae.
| S-EPMC4715634 | biostudies-literature
Project description:ObjectiveOur aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D).MethodsStructural and practical identifiability analysis were performed on three published control-oriented models for glucose control in patients with type 1 diabetes (T1D): the subcutaneous oral glucose minimal model (SOGMM), the intensive control insulin-nutrition-glucose (ICING) model, and the minimal model control-oriented (MMC). Structural identifiability was addressed with a combination of the generating series (GS) approach and identifiability tableaus whereas practical identifiability was studied by means of (1) global ranking of parameters via sensitivity analysis together with the Latin hypercube sampling method (LHS) and (2) collinearity analysis among parameters. For practical identifiability and model identification, continuous glucose monitor (CGM), insulin pump, and meal records were selected from a set of patients (n = 5) on continuous subcutaneous insulin infusion (CSII) that underwent a clinical trial in an outpatient setting. The performance of the identified models was analyzed by means of the root mean square (RMS) criterion.ResultsA reliable set of identifiable parameters was found for every studied model after analyzing the possible identifiability issues of the original parameter sets. According to an importance factor ([Formula: see text]), it was shown that insulin sensitivity is not the most influential parameter from the dynamical point of view, that is, is not the parameter impacting the outputs the most of the three models, contrary to what is assumed in the literature. For the test data, the models demonstrated similar performance with most RMS values around 20 mg/dl (min: 15.64 mg/dl, max: 51.32 mg/dl). However, MMC failed to identify the model for patient 4. Also, considering the three models, the MMC model showed the higher parameter variability when reidentified every 6 hours.ConclusionThis study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.
| S-EPMC6134618 | biostudies-literature
Project description:BackgroundFrom 1999 to 2010, annual disbursements of development assistance for health for vaccinations increased from $0.5 billion to $2.0 billion (all financial values USD 2010). In its 2012 Global Vaccine Action Plan (GVAP), the World Health Assembly recommended establishing a comprehensive vaccination resource tracking system to better understand the source and recipients of these funds, and ultimately their impact on outcomes. This systematic review aims to respond to the GVAP recommendation in reviewing and assessing the state of the data and literature on vaccination resource tracking.MethodsWe scrutinized all relevant vaccination resource tracking systems identified in the literature and by practitioners in the field. We examined schemes used elsewhere in the health sector and by other sectors. Informant interviews were also conducted to determine what data exists and how it might be utilized. With this information, we completed a qualitative assessment of existing approaches to vaccination resources tracking.ResultsTracking systems provide information about some vaccine-related activity in the majority of low- and middle-income countries. Data are generally available for the period of 2006-2010. Levels of granularity vary. Interviewees were concerned about the degree of rigor used to validate the data and the lack of verification. Data are often presented in tabular form, which may be unwieldy for non-technical audiences.ConclusionsThe schemes currently in place to track the resources available for vaccinations were fairly advanced relative to other mechanisms in the health sector. Nonetheless, the coverage, validity, and accessibility of vaccination resource tracking data could be ameliorated. Establishing improved feedback loops and verification mechanisms that connect country-level administrators and the international organizations that support reporting efforts would enhance data quality.
| S-EPMC4263052 | biostudies-literature
Project description:In silico network-based methods have shown promising results in the field of drug development. Yet, most of networks used in the previous research have not included context information even though biological associations actually do appear in the specific contexts. Here, we reconstruct an anatomical context-specific network by assigning contexts to biological associations using protein expression data and scientific literature. Furthermore, we employ the context-specific network for the analysis of drug effects with a proximity measure between drug targets and diseases. Distinct from previous context-specific networks, intercellular associations and phenomic level entities such as biological processes are included in our network to represent the human body. It is observed that performances in inferring drug-disease associations are increased by adding context information and phenomic level entities. In particular, hypertension, a disease related to multiple organs and associated with several phenomic level entities, is analyzed in detail to investigate how our network facilitates the inference of drug-disease associations. Our results indicate that the inclusion of context information, intercellular associations, and phenomic level entities can contribute towards a better prediction of drug-disease associations and provide detailed insight into understanding of how drugs affect diseases in the human body.
| S-EPMC5548804 | biostudies-literature