The Synergizer service for translating gene, protein and other biological identifiers.
Ontology highlight
ABSTRACT: Unlabelled
The Synergizer is a database and web service that provides translations of biological database identifiers. It is accessible both programmatically and interactively.Availability
The Synergizer is freely available to all users inter-actively via a web application (http://llama.med.harvard.edu/synergizer/translate) and programmatically via a web service. Clients implementing the Synergizer application programming interface (API) are also freely available. Please visit http://llama.med.harvard.edu/synergizer/doc for details.
SUBMITTER: Berriz GF
PROVIDER: S-EPMC2553440 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundEach major protein database uses its own conventions when assigning protein identifiers. Resolving the various, potentially unstable, identifiers that refer to identical proteins is a major challenge. This is a common problem when attempting to unify datasets that have been annotated with proteins from multiple data sources or querying data providers with one flavour of protein identifiers when the source database uses another. Partial solutions for protein identifier mapping exist but they are limited to specific species or techniques and to a very small number of databases. As a result, we have not found a solution that is generic enough and broad enough in mapping scope to suit our needs.ResultsWe have created the Protein Identifier Cross-Reference (PICR) service, a web application that provides interactive and programmatic (SOAP and REST) access to a mapping algorithm that uses the UniProt Archive (UniParc) as a data warehouse to offer protein cross-references based on 100% sequence identity to proteins from over 70 distinct source databases loaded into UniParc. Mappings can be limited by source database, taxonomic ID and activity status in the source database. Users can copy/paste or upload files containing protein identifiers or sequences in FASTA format to obtain mappings using the interactive interface. Search results can be viewed in simple or detailed HTML tables or downloaded as comma-separated values (CSV) or Microsoft Excel (XLS) files suitable for use in a local database or a spreadsheet. Alternatively, a SOAP interface is available to integrate PICR functionality in other applications, as is a lightweight REST interface.ConclusionWe offer a publicly available service that can interactively map protein identifiers and protein sequences to the majority of commonly used protein databases. Programmatic access is available through a standards-compliant SOAP interface or a lightweight REST interface. The PICR interface, documentation and code examples are available at http://www.ebi.ac.uk/Tools/picr.
| S-EPMC2151082 | biostudies-literature
Project description: Not available
| S-EPMC10836943 | biostudies-literature
Project description:BackgroundThe automated retrieval and integration of information about protein point mutations in combination with structure, domain and interaction data from literature and databases promises to be a valuable approach to study structure-function relationships in biomedical data sets.ResultsWe developed a rule- and regular expression-based protein point mutation retrieval pipeline for PubMed abstracts, which shows an F-measure of 87% for the mutation retrieval task on a benchmark dataset. In order to link mutations to their proteins, we utilize a named entity recognition algorithm for the identification of gene names co-occurring in the abstract, and establish links based on sequence checks. Vice versa, we could show that gene recognition improved from 77% to 91% F-measure when considering mutation information given in the text. To demonstrate practical relevance, we utilize mutation information from text to evaluate a novel solvation energy based model for the prediction of stabilizing regions in membrane proteins. For five G protein-coupled receptors we identified 35 relevant single mutations and associated phenotypes, of which none had been annotated in the UniProt or PDB database. In 71% reported phenotypes were in compliance with the model predictions, supporting a relation between mutations and stability issues in membrane proteins.ConclusionWe present a reliable approach for the retrieval of protein mutations from PubMed abstracts for any set of genes or proteins of interest. We further demonstrate how amino acid substitution information from text can be utilized for protein structure stability studies on the basis of a novel energy model.
| S-EPMC2745585 | biostudies-literature
Project description:UNLABELLED:Redundancy among sequence identifiers is a recurring problem in bioinformatics. Here, we present a rapid and efficient method of fingerprinting identifiers to ascertain whether two or more aliases are identical. A number of tools and approaches have been developed to resolve differing names for the same genes and proteins, however, these methods each have their own limitations associated with their various goals. We have taken a different approach to the aliasing problem by simplifying the way aliases are stored and curated with the objective of simultaneously achieving speed and flexibility. Our approach (Booly-hashing) is to link identifiers with their corresponding hash keys derived from unique fingerprints such as gene or protein sequences. This tool has proven invaluable for designing a new data integration platform known as Booly, and has wide applicability to situations in which a dedicated efficient aliasing system is required. Compared with other aliasing techniques, Booly-hashing methodology provides 1) reduced run time complexity, 2) increased flexibility (aliasing of other data types, e.g. pharmaceutical drugs), 3) no required assumptions regarding gene clusters or hierarchies, and 4) simplicity in data addition, updating, and maintenance. The new Booly-hashing aliasing model has been incorporated as a central component of the Booly data integration platform we have recently developed and shoud be broadly applicable to other situations in which an efficient streamlined aliasing systems is required. This aliasing tool and database, which allows users to quickly group the same genes and proteins together can be accessed at: http://booly.ucsd.edu/alias. AVAILABILITY:The database is available for free at http://booly.ucsd.edu/alias.
| S-EPMC3082858 | biostudies-other
Project description:We take a social-ecological systems perspective to investigate the linkages between ecosystem services and human well-being in South Africa. A recent paper identified different types of social-ecological systems in the country, based on distinct bundles of ecosystem service use. These system types were found to represent increasingly weak direct feedbacks between nature and people, from rural "green-loop" communities to urban "red-loop" societies. Here we construct human well-being bundles and explore whether the well-being bundles can be used to identify the same social-ecological system types that were identified using bundles of ecosystem service use. Based on national census data, we found three distinct well-being bundle types that are mainly characterized by differences in income, unemployment and property ownership. The distribution of these well-being bundles approximates the distribution of ecosystem service use bundles to a substantial degree: High levels of income and education generally coincided with areas characterised by low levels of direct ecosystem service use (or red-loop systems), while the majority of low well-being areas coincided with medium and high levels of direct ecosystem service use (or transition and green-loop systems). However, our results indicate that transformations from green-loop to red-loop systems do not always entail an immediate improvement in well-being, which we suggest may be due to a time lag between changes in the different system components. Using human well-being bundles as an indicator of social-ecological dynamics may be useful in other contexts since it is based on socio-economic data commonly collected by governments, and provides important insights into the connections between ecosystem services and human well-being at policy-relevant sub-national scales.
| S-EPMC5047452 | biostudies-literature
Project description:When working on an ongoing genome sequencing and assembly project, it is rather inconvenient when gene identifiers change from one build of the assembly to the next. The gene labelling system described here, UniqTag, addresses this common challenge. UniqTag assigns a unique identifier to each gene that is a representative k-mer, a string of length k, selected from the sequence of that gene. Unlike serial numbers, these identifiers are stable between different assemblies and annotations of the same data without requiring that previous annotations be lifted over by sequence alignment. We assign UniqTag identifiers to ten builds of the Ensembl human genome spanning eight years to demonstrate this stability. The implementation of UniqTag in Ruby and an R package are available at https://github.com/sjackman/uniqtag sjackman/uniqtag. The R package is also available from CRAN: install.packages ("uniqtag"). Supplementary material and code to reproduce it is available at https://github.com/sjackman/uniqtag-paper.
| S-EPMC4447347 | biostudies-literature
Project description:BackgroundOne step in the model organism database curation process is to find, for each article, the identifier of every gene discussed in the article. We consider a relaxation of this problem suitable for semi-automated systems, in which each article is associated with a ranked list of possible gene identifiers, and experimentally compare methods for solving this geneId ranking problem. In addition to baseline approaches based on combining named entity recognition (NER) systems with a "soft dictionary" of gene synonyms, we evaluate a graph-based method which combines the outputs of multiple NER systems, as well as other sources of information, and a learning method for reranking the output of the graph-based method.ResultsWe show that named entity recognition (NER) systems with similar F-measure performance can have significantly different performance when used with a soft dictionary for geneId-ranking. The graph-based approach can outperform any of its component NER systems, even without learning, and learning can further improve the performance of the graph-based ranking approach.ConclusionThe utility of a named entity recognition (NER) system for geneId-finding may not be accurately predicted by its entity-level F1 performance, the most common performance measure. GeneId-ranking systems are best implemented by combining several NER systems. With appropriate combination methods, usefully accurate geneId-ranking systems can be constructed based on easily-available resources, without resorting to problem-specific, engineered components.
| S-EPMC1617121 | biostudies-literature
Project description:BackgroundThe ability to locate publicly available gene expression microarray datasets effectively and efficiently facilitates the reuse of these potentially valuable resources. Centralized biomedical databases allow users to query dataset metadata descriptions, but these annotations are often too sparse and diverse to allow complex and accurate queries. In this study we examined the ability of PubMed article identifiers to locate publicly available gene expression microarray datasets, and investigated whether the retrieved datasets were representative of publicly available datasets found through statements of data sharing in the associated research articles.ResultsIn a recent article, Ochsner and colleagues identified 397 studies that had generated gene expression microarray data. Their search of the full text of each publication for statements of data sharing revealed 203 publicly available datasets, including 179 in the Gene Expression Omnibus (GEO) or ArrayExpress databases. Our scripted search of GEO and ArrayExpress for PubMed identifiers of the same 397 studies returned 160 datasets, including six not found by the original search for data sharing statements. As a proportion of datasets found by either method, the search for data sharing statements identified 91.4% of the 209 publicly available datasets, compared to only 76.6% found by our search carried out using PubMed identifiers. Searching GEO or ArrayExpress alone retrieved 63.2% and 46.9% of all available datasets, respectively. There was no difference in the type of datasets found by PubMed identifier searches in terms of research theme or the technology used. However, the studies identified were more likely to have larger sample sizes, were more frequently cited, and published in higher impact journals.ConclusionsSearching database entries using PubMed identifiers can identify the majority of publicly available datasets, but caution is required when this method is used to collect data for policy evaluation since studies in low impact journals are disproportionately excluded. We urge authors of all datasets to complete the citation fields for their dataset submissions once publication details are known, thereby ensuring their work has maximum visibility and can contribute to subsequent studies.
| S-EPMC2990274 | biostudies-literature
Project description:Transcriptomic signatures based on cellular mRNA expression profiles can be used to categorize cell types and states. Yet whether different functional groups of genes perform better or worse in this process remains largely unexplored. Here we test the core matrisome - that is, all genes coding for structural proteins of the extracellular matrix - for its ability to delineate distinct cell types in embryonic single-cell RNA-sequencing (scRNA-seq) data. We show that even though expressed core matrisome genes correspond to less than 2% of an entire cellular transcriptome, their RNA expression levels suffice to recapitulate essential aspects of cell type-specific clustering. Notably, using scRNA-seq data from the embryonic limb, we demonstrate that core matrisome gene expression outperforms random gene subsets of similar sizes and can match and exceed the predictive power of transcription factors. While transcription factor signatures generally perform better in predicting cell types at early stages of chicken and mouse limb development, i.e., when cells are less differentiated, the information content of the core matrisome signature increases in more differentiated cells. Moreover, using cross-species analyses, we show that these cell type-specific signatures are evolutionarily conserved. Our findings suggest that each cell type produces its own unique extracellular matrix, or matreotype, which becomes progressively more refined and cell type-specific as embryonic tissues mature.
| S-EPMC8233473 | biostudies-literature
Project description:Cancer-associated fibroblasts (CAFs), a prominent component of the tumor microenvironment, play an important role in tumor development, invasion, and drug resistance. The expression of distinct "CAF-markers" which separates CAFs from normal fibroblasts and epithelial cells, have traditionally been used to identify them. These commonly used CAF-markers have been reported to differ greatly across different CAF subpopulations, even within a cancer type. Using an unbiased -omic approach from public data and in-house RNAseq data from patient derived novel CAF cells, TIMP-1, SPARC, COL1A2, COL3A1 and COL1A1 were identified as potential CAF-markers by differential gene expression analysis using publicly available single cell sequencing data and in-house RNAseq data to distinguish CAF populations from tumor epithelia and normal oral fibroblasts. Experimental validation using qPCR and immunofluorescence revealed CAF-specific higher expression of TIMP-1 and COL1A2 as compared to other markers in 5 novel CAF cells, derived from patients of diverse gender, habits and different locations of head and neck squamous cell carcinoma (HNSC). Upon immunohistochemical (IHC) analysis of FFPE blocks however, COL1A2 showed better differential staining between tumor epithelia and tumor stroma. Similar data science driven approach utilizing single cell sequencing and RNAseq data from stabilized CAFs can be employed to identify CAF-markers in various cancers.
| S-EPMC10457345 | biostudies-literature