Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Data annotation”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,045 records · Page 58Linked to original sources
PubMed2005

Data mining for protein-protein interactions in invertebrate model organisms.

Well-annotated genome databases are available for many invertebrate species, notably the fruitfly, Drosophila melanogaster, and the nematode, Caenorhabditis elegans. An adequate interpretation of this information at the biological level requires the exploration of the interactions between the gene products. Knowledge of protein interactions and the components of cell signalling pathways in the fly and worm are particularly valuable as hypotheses can be rapidly tested using the powerful genetic toolkits available. Invertebrates offer additional experimental advantages when attempting to characterise protein-protein interactions (PPIs). Their relatively small genome size compared to mammals helps to reduce missed interactions due to redundancy, and their function can be addressed using forward (mutants) and reverse (RNA interference) genetics. However, the researcher looking for evidence of PPIs for a protein of interest is faced with the challenge of extracting interaction data from sources that are highly varied, such as the results of microarray experiments in the unstructured text of research papers. This challenge is greatly reduced by a range of public databases of curated information, as well as publicly available, enhanced search engines, which can provide either direct experimental evidence for a PPI, or valuable clues for generating new hypotheses.

Animals↗
PubMed2005

Effective function annotation through catalytic residue conservation.

Because of the extreme impact of genome sequencing projects, protein sequences without accompanying experimental data now dominate public databases. Homology searches, by providing an opportunity to transfer functional information between related proteins, have become the de facto way to address this. Although a single, well annotated, close relationship will often facilitate sufficient annotation, this situation is not always the case, particularly if mutations are present in important functional residues. When only distant relationships are available, the transfer of function information is more tenuous, and the likelihood of encountering several well annotated proteins with different functions is increased. The consequence for a researcher is a range of candidate functions with little way of knowing which, if any, are correct. Here, we address the problem directly by introducing a computational approach to accurately identify and segregate related proteins into those with a functional similarity and those where function differs. This approach should find a wide range of applications, including the interpretation of genomics/proteomics data and the prioritization of targets for high-throughput structure determination. The method is generic, but here we concentrate on enzymes and apply high-quality catalytic site data. In addition to providing a series of comprehensive benchmarks to show the overall performance of our approach, we illustrate its utility with specific examples that include the correct identification of haptoglobin as a nonenzymatic relative of trypsin, discrimination of acid-d-amino acid ligases from a much larger ligase pool, and the successful annotation of BioH, a structural genomics target.

Amino Acid Sequence↗
PubMed2026

CeLLTra: aligning cell names with gene expression via a pathway-informed transformer.

MOTIVATION: Single-cell RNA sequencing (scRNA-Seq) technology enables detailed exploration of gene expression at the individual cell level, crucial for annotating cell types and understanding cellular diversity. Traditional methods for cell type annotation often rely on marker genes and manual labeling, posing challenges due to low data quality and incomplete reference datasets. RESULTS: We developed CeLLTra, a novel contrastive learning framework that leverages a Transformer-based model integrating biological pathway information to group genes into super tokens, effectively capturing comprehensive gene expression from scRNA-Seq data. By combining this pathway-informed Transformer with a pretrained domain-specific language model, CeLLTra accurately aligns cell-type annotations with gene expression profiles. Evaluations on a large-scale human scRNA-Seq dataset showed that CeLLTra significantly outperformed state-of-the-art methods in supervised and zero-shot cell-type prediction. Additionally, CeLLTra generalized well to external datasets, improving clustering performance and enabling better characterization of cancerous cell states in tumor-infiltrating myeloid cells from non-small cell lung cancer patients. AVAILABILITY AND IMPLEMENTATION: CeLLTra is freely available on GitHub (https://github.com/WJZheng-group/CeLLTra) and Zenodo (https://doi.org/10.5281/zenodo.17666735). The datasets underlying this article are the following: GSE201333 and GSE127465. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus repository.

Humans↗
PubMed2002

[Computer-assisted system for analysis of heart rate variability in clinical studies].

This paper describes an adaptable framework that facilitates exploratory analysis, interpretation and classification of beat-to-beat data extracted from the electrocardiogram (ECG). The system supports a variety of user-defined annotations and allows the definition of analysis programs. Special care is taken on the correct treatment of corrupted and missing data, ubiquitously found in real world problems. Besides the computation, the performance of single features can be inspected using different kinds of diagrams provided by the system. Combinations of features can be evaluated using a polynomial classifier. Both the computation and combination of features are defined as tasks that can be dispatched by a server to various clients. The framework is easily adaptable to different problem structures and has been used successfully in three studies.

Atrial Fibrillation↗
PubMed2006

The LIFEdb database in 2006.

LIFEdb (http://www.LIFEdb.de) integrates data from large-scale functional genomics assays and manual cDNA annotation with bioinformatics gene expression and protein analysis. New features of LIFEdb include (i) an updated user interface with enhanced query capabilities, (ii) a configurable output table and the option to download search results in XML, (iii) the integration of data from cell-based screening assays addressing the influence of protein-overexpression on cell proliferation and (iv) the display of the relative expression ('Electronic Northern') of the genes under investigation using curated gene expression ontology information. LIFEdb enables researchers to systematically select and characterize genes and proteins of interest, and presents data and information via its user-friendly web-based interface.

Cell Proliferation↗
PubMed2004

Genome annotation of a 1.5 Mb region of human chromosome 6q23 encompassing a quantitative trait locus for fetal hemoglobin expression in adults.

BACKGROUND: Heterocellular hereditary persistence of fetal hemoglobin (HPFH) is a common multifactorial trait characterized by a modest increase of fetal hemoglobin levels in adults. We previously localized a Quantitative Trait Locus for HPFH in an extensive Asian-Indian kindred to chromosome 6q23. As part of the strategy of positional cloning and a means towards identification of the specific genetic alteration in this family, a thorough annotation of the candidate interval based on a strategy of in silico / wet biology approach with comparative genomics was conducted. RESULTS: The ~1.5 Mb candidate region was shown to contain five protein-coding genes. We discovered a very large uncharacterized gene containing WD40 and SH3 domains (AHI1), and extended the annotation of four previously characterized genes (MYB, ALDH8A1, HBS1L and PDE7B). We also identified several genes that do not appear to be protein coding, and generated 17 kb of novel transcript sequence data from re-sequencing 97 EST clones. CONCLUSION: Detailed and thorough annotation of this 1.5 Mb interval in 6q confirms a high level of aberrant transcripts in testicular tissue. The candidate interval was shown to exhibit an extraordinary level of alternate splicing - 19 transcripts were identified for the 5 protein coding genes, but it appears that a significant portion (14/19) of these alternate transcripts did not have an open reading frame, hence their functional role is questionable. These transcripts may result from aberrant rather than regulated splicing.

3',5'-Cyclic-AMP Phosphodiesterases↗
PubMed2003

InterDom: a database of putative interacting protein domains for validating predicted protein interactions and complexes.

Advances in proteomics technology have enabled new proteins to be discovered at an unprecedented speed, and high throughput experimental methods have been developed to detect protein interactions and complexes en masse. Such bottom-up, data-driven approach has resulted in data that may be uninformative or potentially errorful, requiring further validation and annotation. The InterDom database focuses on providing supporting evidence for the detected protein interactions based on putative protein domain interactions. Using an integrative approach, InterDom derives potential domain interactions by combining data from multiple sources, ranging from domain fusions, protein interactions and complexes, to scientific literature. The InterDom database is available at http://InterDom.lit.org.sg.

Databases, Protein↗
PubMed2005

Correlation between gene expression profiles and protein-protein interactions within and across genomes.

MOTIVATION: Function annotation of an unclassified protein on the basis of its interaction partners is well documented in the literature. Reliable predictions of interactions from other data sources such as gene expression measurements would provide a useful route to function annotation. We investigate the global relationship of protein-protein interactions with gene expression. This relationship is studied in four evolutionarily diverse species, for which substantial information regarding their interactions and expression is available: human, mouse, yeast and Escherichia coli. RESULTS: In E.coli the expression of interacting pairs is highly correlated in comparison to random pairs, while in the other three species, the correlation of expression of interacting pairs is only slightly stronger than that of random pairs. To strengthen the correlation, we developed a protocol to integrate ortholog information into the interaction and expression datasets. In all four genomes, the likelihood of predicting protein interactions from highly correlated expression data is increased using our protocol. In yeast, for example, the likelihood of predicting a true interaction, when the correlation is > 0.9, increases from 1.4 to 9.4. The improvement demonstrates that protein interactions are reflected in gene expression and the correlation between the two is strengthened by evolution information. The results establish that co-expression of interacting protein pairs is more conserved than that of random ones.

Animals↗
PubMed2002

Microarray data quality analysis: lessons from the AFGC project. Arabidopsis Functional Genomics Consortium.

Genome-wide expression profiling with DNA microarrays has and will provide a great deal of data to the plant scientific community. However, reliability concerns have required the development data quality tests for common systematic biases. Fortunately, most large-scale systematic biases are detectable and some are correctable by normalization. Technical replication experiments and statistical surveys indicate that these biases vary widely in severity and appearance. As a result, no single normalization or correction method currently available is able to address all the issues. However, careful sequence selection, array design, experimental design and experimental annotation can substantially improve the quality and biological of microarray data. In this review, we discuss these issues with reference to examples from the Arabidopsis Functional Genomics Consortium (AFGC) microarray project.

Arabidopsis↗
PubMed2003

The MUSC DNA Microarray Database.

SUMMARY: The Medical University of South Carolina (MUSC) DNA Microarray Database is a web-accessible archive of DNA microarray data. The database was developed using the DNA microarray project/data management system, micro ArrayDB. Annotations for each DNA microarray project and associated cRNA target information are stored in a MySQL relational database and linked to array hybridization data (raw and normalized). At the discretion of investigators, data are placed into the public domain where they can be interrogated and downloaded through a web browser. In addition to serving as an online resource of gene expression data, the MUSC DNA Microarray Database is a model for other academic DNA microarray data repositories. AVAILABILITY: Browsing and downloading of MUSC DNA Microarray Database information can be done after registration at http://proteogenomics.musc.edu/pss/home.php.

Academic Medical Centers↗
PubMed2002

Surviving in a sea of data: a survey of plant genome data resources and issues in building data management systems.

Exponential growth of data, largely from whole-genome analyses, has changed the way biologists think about and handle data. Optimal use of these data requires effective methods to analyze and manage these data sets. Computers, software and the World Wide Web are now integral components of biological discovery. Understanding how information is obtained, processed and annotated in public databases allows researchers to effectively organize, analyze and export their own data into these databases. In this review we focus largely on two areas related to management of genomic data. We cite examples of resources available in the public domain and describe some of the software for data management systems currently available for plant research. In addition, we discuss a few concepts of data management from the perspective of an individual or group that wishes to provide data to the public databases, to use the information in the public databases more efficiently, or to develop a database to manage large data sets internally or for public access. These concepts include data descriptions, exchange format, curation, attribution, and database implementation.

Databases, Nucleic Acid↗
PubMed2006

NeuroScholar's electronic laboratory notebook and its application to neuroendocrinology.

Scientists continually relate information from the published literature to their current research. The challenge of this essential and time-consuming activity increases as the body of scientific literature continues to grow. In an attempt to lessen the challenge, we have developed an Electronic Laboratory Notebook (ELN) application. Our ELN functions as a component of another application we have developed, an open-source knowledge management system for the neuroscientific literature called NeuroScholar (http://www. neuroscholar. org/). Scanned notebook pages, images, and data files are entered into the ELN, where they can be annotated, organized, and linked to similarly annotated excerpts from the published literature within Neuroscholar. Associations between these knowledge constructs are created within a dynamic node-and-edge user interface. To produce an interactive, adaptable knowledge base. We demonstrate the ELN's utility by using it to organize data and literature related to our studies of the neuroendocrine hypothalamic paraventricular nucleus (PVH). We also discuss how the ELN could be applied to model other neuroendocrine systems; as an example we look at the role of PVH stressor-responsive neurons in the context of their involvement in the suppression of reproductive function. We present this application to the community as open-source software and invite contributions to its development.

Animals↗
PubMed2026

TCRspec: A Recognition Interface-Informed Multimodal Method for TCR-pMHC Specificity Prediction.

Specific recognition between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to adaptive immunity, yet accurate prediction of TCR-pMHC specificity remains challenging. Existing models mainly rely on sequence features or isolated molecular structures, limiting their ability to capture interface-level determinants within the ternary recognition complex. Here, we constructed the multimodal TCR-pMHC ternary complex (MM-TCR) data set, integrating paired TCR-pMHC sequences, V/J gene annotations, and modeled TCR-pMHC complex structures refined by short molecular dynamics-based relaxation. Based on MM-TCR, we developed TCRspec, an interpretable multimodal framework combining sequence embeddings, gene-usage features, and complex-level structural representations. Under a stringent CD-HIT TCR-cluster-disjoint split, TCRspec achieved an average AUROC of 0.896 and AUPRC of 0.882 across seven antigen-specific test data sets, outperforming representative baseline models. Cross-validation and ablation analyses confirmed the contribution of ternary complex structural information and MD-refined structures. In independent OOD peptide-TCR systems, TCRspec retained discriminative performance and identified model-inferred peptide positions associated with TCR recognition, providing a structure-informed framework for TCR specificity prediction.

Receptors, Antigen, T-Cell↗
PubMed2006

PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments.

BACKGROUND: Microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis. RESULTS: Here we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs.PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis.PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is freely available at http://mapman.mpimp-golm.mpg.de/pageman/. CONCLUSION: PageMan allows multiple microarray experiments to be efficiently condensed into a single page graphical display. The flexible interface allows data to be quickly and easily visualized, facilitating comparisons within experiments and to published experiments, thus enabling researchers to gain a rapid overview of the biological responses in the experiments.

Database Management Systems↗
PubMedDate not supplied

Understanding biology through bioinformatics.

During the journey from the discovery of DNA to be the source of genetic information and elucidation of double-helical nature of DNA molecule to the assembly of human genome sequence and there after, bioinformatics has become an integral part of modern biology. Bioinformatics relies substantially on significant contributions made by scientists in various fields, including but not limited to, linguistics, biology, mathematics, computer science, and statistics. There is an ever increasing amount of data to elucidate toxic mechanisms and/or adverse effects of xenobiotics in the field of toxicogenomics. Annotation in combination with various bioinformatics analytical tools can play a crucial role in the understanding of genes and proteins, and can potentially help draw meaningful conclusions from various data sources. This article attempts to present a simple overview of bioinformatics, and an effort is made to discuss annotation.

Computational Biology↗
PubMed2017

Functional mapping and annotation of genetic associations with FUMA.

A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.

Chromatin↗
PubMed2000

The Mouse Genome Database (MGD): expanding genetic and genomic resources for the laboratory mouse. The Mouse Genome Database Group.

The Mouse Genome Database (MGD) is a comprehensive public database of mouse genomic, genetic and phenotypic information (http://www. informatics.jax.org). This community database provides information about genes, serves as a mapping resource of the mouse genome, details mammalian orthologs, integrates experimental data, represents standardized mouse nomenclature for genes and alleles, incorporates links to other genomic resources such as sequence data, and includes a variety of additional information about the laboratory mouse. MGD scientists and annotators work cooperatively with the research community to provide an integrated, consensus view of the mouse genome while also providing experimental data including data conflicting with the consensus representation. Recent improvements focus on the representation of phenotypic information and the enhancement of gene and allele descriptions.

Animals↗
PubMed2005

Galaxy: a platform for interactive large-scale genome analysis.

Accessing and analyzing the exponentially expanding genomic sequence and functional data pose a challenge for biomedical researchers. Here we describe an interactive system, Galaxy, that combines the power of existing genome annotation databases with a simple Web portal to enable users to search remote resources, combine data from independent queries, and visualize the results. The heart of Galaxy is a flexible history system that stores the queries from each user; performs operations such as intersections, unions, and subtractions; and links to other computational tools. Galaxy can be accessed at http://g2.bx.psu.edu.

Biological Evolution↗