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 1,225 records · Page 68Linked to original sources
PubMed2004

Semia: semi-automatic interactive graphic editing tool to annotate ambulatory ECG records.

We designed and developed a special purpose interactive graphic editing tool semi-automatic (Semia) to annotate transient ischaemic ST segment episodes and other non-ischaemic ST segment events in 24h ambulatory electrocardiogram (ECG) records. The tool allows representation and viewing of the data, interaction with the data globally and locally at different resolutions, examining data at any point, manual adjustment of heart-beat fiducial points, and manual and automatic editing of annotations. Efficient and fast display of ambulatory ECG signal waveforms, display of diagnostic and morphology feature-vector time-series, dynamic interface controls, and automated procedures to help annotate, made the tool efficient, user friendly and usable. Human expert annotators used the Semia tool to successfully annotate the Long-Term ST database (LTST DB), a result of a multinational effort. The tool supported paperless editing of annotations at dislocated geographical sites. We present design, characteristic "look and feel", functionality, and development of Semia annotating tool.

Ambulatory Care Information Systems↗
PubMed2006

MitoP2: the mitochondrial proteome database--now including mouse data.

The MitoP2 database (http://www.mitop.de) integrates information on mitochondrial proteins, their molecular functions and associated diseases. The central database features are manually annotated reference proteins localized or functionally associated with mitochondria supplied for yeast, human and mouse. MitoP2 enables (i) the identification of putative orthologous proteins between these species to study evolutionarily conserved functions and pathways; (ii) the integration of data from systematic genome-wide studies such as proteomics and deletion phenotype screening; (iii) the prediction of novel mitochondrial proteins using data integration and the assignment of evidence scores; and (iv) systematic searches that aim to find the genes that underlie common and rare mitochondrial diseases. The data and analysis files are referenced to data sources in PubMed and other online databases and can be easily downloaded. MitoP2 users can explore the relationship between mitochondrial dysfunctions and disease and utilize this information to conduct systems biology approaches on mitochondria.

Animals↗
PubMed2006

A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments: systematically incorporating validated biological knowledge.

MOTIVATION: Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into 'active regions' (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. METHODOLOGY: In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). RESULTS: For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that the HMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments.

Artificial Intelligence↗
PubMed2006

Saccharomyces cerevisiae S288C genome annotation: a working hypothesis.

The S. cerevisiae genome is the most well-characterized eukaryotic genome and one of the simplest in terms of identifying open reading frames (ORFs), yet its primary annotation has been updated continually in the decade since its initial release in 1996 (Goffeau et al., 1996). The Saccharomyces Genome Database (SGD; www.yeastgenome.org) (Hirschman et al., 2006), the community-designated repository for this reference genome, strives to ensure that the S. cerevisiae annotation is as accurate and useful as possible. At SGD, the S. cerevisiae genome sequence and annotation are treated as a working hypothesis, which must be repeatedly tested and refined. In this paper, in celebration of the tenth anniversary of the completion of the S. cerevisiae genome sequence, we discuss the ways in which the S. cerevisiae sequence and annotation have changed, consider the multiple sources of experimental and comparative data on which these changes are based, and describe our methods for evaluating, incorporating and documenting these new data.

Base Sequence↗
PubMed2025

Colorectal Liver Metastasis Pathomics Model: Integrating Single-Cell and Spatial Transcriptome Analysis With Pathomics for Predicting Liver Metastasis in Colorectal Cancer.

The liver is the primary target organ for hematologic metastasis of colorectal cancer (CRC), and CRC liver metastasis (CRLM) often precludes radical resection, making it the leading cause of death in patients with CRC. To improve the identification and prediction of liver metastasis risk, we identified a cell type of liver metastasis--triggering malignant cells (LMTMCs) through integrating single-cell RNA sequencing and spatial transcriptome analysis. Multiomics cell communication analysis indicated that the interaction between fibroblasts and LMTMCs through the COL1A1-CD44/SDC4 and LAMA4-CD44 signaling axes could promote CRLM. By applying the one-class logistic regression algorithm, we developed a CRLM scoring system in the bulk RNA-sequencing data according to the abundance of LMTMCs in each individual. Using the grouping labels derived from the CRLM scoring system in the bulk data and the corresponding whole-slide images without any manual annotations at the region or pixel level, processed via slide-level weakly supervised learning, a deep-learning model based on the ResNet18 architecture, called Colorectal Liver Metastasis Pathomics Model, was developed to predict the risk of liver metastasis in patients with CRC. The Colorectal Liver Metastasis Pathomics Model achieved an area under the curve of 0.84 at the internal test set of The Cancer Genome Atlas-CRC histology images. In the external independent validation sets, namely the Affiliated Hospital of Southwest Medical University and the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University cohorts, the areas under the curve were 0.89 and 0.72, respectively, indicating effective classification performances. This study provided new insights and tools for the early identification of CRLM and demonstrated the potential of combining multiomics with deep learning-based pathomics in cancer research.

Humans↗
PubMed2006

Literature mining for the biologist: from information retrieval to biological discovery.

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.

Computational Biology↗
PubMed2000

Getting started with Acedb.

Acedb is one of the more venerable pieces of Genomics software. Acedb was originally created in 1992 by Richard Durbin and Jean Thierry-Mieg to manage the data from the Caenorhabditis elegans mapping project and subsequently the C. elegans sequencing project. From beginnings as a C. elegans-specific tool, it has been continuously developed into a flexible suite of data management, display and scripting tools providing facilities for managing and annotation mapping information and DNA and peptide sequences. This paper gives a basic overview of the Acedb suite, and step-by-step guidance on how to download and install Acedb. It is intended to take an Acedb novice to stage where they can begin to experiment and explore the facilities that are available.

Animals↗
PubMed2025

FootprintCharter: unsupervised detection and quantification of footprints in single molecule footprinting data.

SUMMARY: Single molecule footprinting profiles the heterogeneity of TF occupancy at cis-regulatory elements across cell populations at unprecedented resolution. The single molecule nature of the data in principle allows for observing the footprint of individual transcription factors and nucleosomes. However, we currently lack algorithms to quantify these occupancy patterns of chromatin binding factors in an automated way and without prior assumptions on their genomic location. Here we present FootprintCharter, an unsupervised tool to detect and quantify footprints for transcription factors (TFs) and nucleosomes from single molecule footprinting data. After detection, TF footprints can be labeled with orthogonal motif annotations provided by the user. FootprintCharter allows for the quantification of complex molecular states such as positioning of unphased nucleosomes and combinatorial co-binding of multiple TFs. AVAILABILITY AND IMPLEMENTATION: FootprintCharter is freely available on Bioconductor with version 2.2.0 of https://bioconductor.org/packages/SingleMoleculeFootprinting through the functions FootprintCharter, PlotFootprints, and Plot_FootprintCharter_SM.

Transcription Factors↗
PubMed2003

Talisman--rapid application development for the grid.

In order to make use of the emerging grid and network services offered by various institutes and mandated by many current research projects, some kind of user accessible client is required. In contrast with attempts to build generic workbenches, Talisman is designed to allow a bioinformatics expert to rapidly build custom applications, immediately visible using standard web technology, for users who wish to concentrate on the biology of their problem rather than the informatics aspects. As a component of the MyGrid project, it is intended to allow access to arbitrary resources, including but not limited to relational, object and flat file data sources, analysis programs and grid based storage, tracking and distributed annotation systems.

Computational Biology↗
PubMed2005

Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues.

Gene expression profiling of cancer tissues is expected to contribute to our understanding of cancer biology as well as developments of new methods of diagnosis and therapy. Our collaborative efforts in Japan have been mainly focused on solid tumors such as breast, colorectal and hepatocellular cancers. The expression data are obtained by a high-throughput RT-PCR technique, and patients are recruited mainly from a single hospital. In the cancer gene expression database (CGED), the expression and clinical data are presented in a way useful for scientists interested in specific genes or biological functions. The data can be retrieved either by gene identifiers or by functional categories defined by Gene Ontology terms or the Swiss-Prot annotation. Expression patterns of multiple genes, selected by names or similarity search of the patterns, can be compared. Visual presentation of the data with sorting function enables users to easily recognize of relationships between gene expression and clinical parameters. Data for other cancers such as lung and thyroid cancers will be added in the near future. The URL of CGED is http://cged.hgc.jp.

Databases, Genetic↗
PubMed2006

GRSDB: a database of quadruplex forming G-rich sequences in alternatively processed mammalian pre-mRNA sequences.

Guanine-rich nucleic acids are known to form highly stable G-quadruplex structures, also known as G-quartets. Recently, there has been a tremendous amount of interest in studying G-quadruplexes owing to the realization of their biological importance. G-rich sequences (GRSs) capable of forming G-quadruplexes are found in the vicinity of polyadenylation regions and are involved in regulating 3' end processing of mammalian pre-mRNAs. G-rich motifs are also known to play an important role in alternative, tissue-specific splicing by interacting with hnRNP H protein subfamily. Whether quadruplex structure directly plays a role in regulating RNA processing events requires further investigation. To date there has not been a comprehensive effort to study G-quadruplexes near RNA processing sites. We have applied a computational approach to map putative Quadruplex forming GRSs within the transcribed regions of a large number of alternatively processed human and mouse gene sequences that were obtained as fully annotated entries from GenBank and RefSeq. We have used the computed data to build the GRSDB database that provides a unique avenue for studying G-quadruplexes in the context of RNA processing sites. GRSDB website offers visual comparison of G-quadruplex distribution patterns among all the alternative RNA products of a gene with the help of dynamic graphics. At present, GRSDB contains data from 1310 human and mouse genes, of which 1188 are alternatively processed. It has a total of 379,223 predicted G-quadruplexes, of which 54,252 are near RNA processing sites. GRSDB is a good resource for researchers interested in investigating the functional relevance of G-quadruplexes, especially in the context of alternative RNA processing. It can be accessed at http://bioinformatics.ramapo.edu/grsdb/.

Alternative Splicing↗
PubMed2006

Organelle DB: an updated resource of eukaryotic protein localization and function.

Organelle DB (http://organelledb.lsi.umich.edu) is a web-accessible relational database presenting a supplemented catalog of organelle-localized proteins and major protein complexes. Since its release in 2004, Organelle DB has grown by 20% to encompass over 30,000 proteins from 138 eukaryotic organisms. Each protein in Organelle DB is presented with its subcellular localization, primary sequence and a detailed description of its function, as available. All records in Organelle DB have been annotated using controlled vocabulary from the Gene Ontology consortium. Protein localization data are inherently visual, and Organelle DB is a significant repository of biological images, housing 1500 micrographs of yeast cells carrying stained proteins. Furthermore, we report here the development of Organelle View, an extension of Organelle DB for the interactive visualization of organelles and subcellular structures in the budding yeast Saccharomyces cerevisiae. Organelle View offers a dimensional representation of a yeast cell; users can search Organelle View for proteins of interest, and the organelles housing these proteins will be highlighted in the cell image. Among other applications, Organelle View may serve as an educational aid engaging introductory biology students through a visually 'fun' interface. Organelle View can be accessed from the Organelle DB home page or directly at http://organelleview.lsi.umich.edu.

Animals↗
PubMed2006

WormBase: new content and better access.

WormBase (http://wormbase.org), a model organism database for Caenorhabditis elegans and other related nematodes, continues to evolve and expand. Over the past year WormBase has added new data on C.elegans, including data on classical genetics, cell biology and functional genomics; expanded the annotation of closely related nematodes with a new genome browser for Caenorhabditis remanei; and deployed new hardware for stronger performance. Several existing datasets including phenotype descriptions and RNAi experiments have seen a large increase in new content. New datasets such as the C.remanei draft assembly and annotations, the Vancouver Fosmid library and TEC-RED 5' end sites are now available as well. Access to and searching WormBase has become more dependable and flexible via multiple mirror sites and indexing through Google.

Animals↗
PubMed2006

VectorBase: a home for invertebrate vectors of human pathogens.

VectorBase (http://www.vectorbase.org/) is a web-accessible data repository for information about invertebrate vectors of human pathogens. VectorBase annotates and maintains vector genomes providing an integrated resource for the research community. Currently, VectorBase contains genome information for two organisms: Anopheles gambiae, a vector for the Plasmodium protozoan agent causing malaria, and Aedes aegypti, a vector for the flaviviral agents causing Yellow fever and Dengue fever.

Aedes↗
PubMed2000

Minisatellites: mutability and genome architecture.

Minisatellites have been found in association with important features of human genome biology such as gene regulation, chromosomal fragile sites, and imprinting. Our knowledge of minisatellite biology has greatly increased in the past 10 years owing to the identification and careful analysis of human hypermutable minisatellites, experimental models in yeast, and recent in vitro studies of minisatellite recombination properties. In parallel, minisatellites have been put forward as potential biomarkers for the monitoring of genotoxic agents such as ionizing radiation. We summarize and discuss recent observations on minisatellites. In addition we take advantage of recent whole chromosome sequence data releases to provide a unifying view which may facilitate the annotation of tandem repeat sequences.

Animals↗
PubMed2005

Profile and analysis of gene expression changes during early development in germinating spores of Ceratopteris richardii.

Analysis of an expressed sequence tag library with more than 5,000 sequences from spores of the fern Ceratopteris richardii reveals that more than 3,900 of them represent distinct genes, and almost 70% of these have significant similarity to Arabidopsis (Arabidopsis thaliana) genes. Eight genes are common between three very different dormant plant systems, Ceratopteris spores, Arabidopsis seeds, and Arabidopsis pollen. We evaluated the pattern of mRNA abundance over the first 48 h of spore development using a microarray of cDNAs representing 3,207 distinct genes of C. richardii and determined the relative levels of RNA abundance for 3,143 of these genes using a Bayesian method of statistical analysis. More than 900 of them (29%) show a significant change between any of the five time points analyzed, and these have been annotated based on their sequence similarity with the Arabidopsis proteome. Novel data arising from these analyses identify genes likely to be critical for the germination and subsequent early development of diverse cells and tissues emerging from dormancy.

Arabidopsis↗
PubMed2005

Structure and architecture of the maize genome.

Maize (Zea mays or corn) plays many varied and important roles in society. It is not only an important experimental model plant, but also a major livestock feed crop and a significant source of industrial products such as sweeteners and ethanol. In this study we report the systematic analysis of contiguous sequences of the maize genome. We selected 100 random regions averaging 144 kb in size, representing about 0.6% of the genome, and generated a high-quality dataset for sequence analysis. This sampling contains 330 annotated genes, 91% of which are supported by expressed sequence tag data from maize and other cereal species. Genes averaged 4 kb in size with five exons, although the largest was over 59 kb with 31 exons. Gene density varied over a wide range from 0.5 to 10.7 genes per 100 kb and genes did not appear to cluster significantly. The total repetitive element content we observed (66%) was slightly higher than previous whole-genome estimates (58%-63%) and consisted almost exclusively of retroelements. The vast majority of genes can be aligned to at least one sequence read derived from gene-enrichment procedures, but only about 30% are fully covered. Our results indicate that much of the increase in genome size of maize relative to rice (Oryza sativa) and Arabidopsis (Arabidopsis thaliana) is attributable to an increase in number of both repetitive elements and genes.

Base Composition↗
PubMed2003

Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms.

The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.

Algorithms↗