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At least 775 records · Page 43Linked to original sources
PubMed2005

A semantic analysis of the annotations of the human genome.

The correct interpretation of any biological experiment depends in an essential way on the accuracy and consistency of the existing annotation databases. Such databases are ubiquitous and used by all life scientists in most experiments. However, it is well known that such databases are incomplete and many annotations may also be incorrect. In this paper we describe a technique that can be used to analyze the semantic content of such annotation databases. Our approach is able to extract implicit semantic relationships between genes and functions. This ability allows us to discover novel functions for known genes. This approach is able to identify missing and inaccurate annotations in existing annotation databases, and thus help improve their accuracy. We used our technique to analyze the current annotations of the human genome. From this body of annotations, we were able to predict 212 additional gene-function assignments. A subsequent literature search found that 138 of these gene-functions assignments are supported by existing peer-reviewed papers. An additional 23 assignments have been confirmed in the meantime by the addition of the respective annotations in later releases of the Gene Ontology database. Overall, the 161 confirmed assignments represent 75.95% of the proposed gene-function assignments. Only one of our predictions (0.4%) was contradicted by the existing literature. We could not find any relevant articles for 50 of our predictions (23.58%). The method is independent of the organism and can be used to analyze and improve the quality of the data of any public or private annotation database.

Chromosome Mapping↗
PubMed2006

Genomic and proteomic approaches to vaccine candidate identification for Anaplasma marginale.

Outer membrane protein preparations have been demonstrated to elicit protective immunity for Anaplasma marginale. Attempts to recreate this protective immunity using known surface proteins have been unsuccessful; therefore, novel outer membrane proteins have been searched for using a proteomic/genomic approach. Annotation of the whole genome sequence identified 62 outer membrane protein candidates based on sequence similarity to known surface proteins. In a proteomics approach for the identification of immunostimulatory outer membrane proteins, outer membrane preparations that were separated on 2D gels were used to immunize calves . Antisera from the calves were used to detect immunoreactive proteins, which were then selected and subjected to mass spectrometric analyses. These data were mapped back to the annotated A. marginale genome and have identified several new outer membrane proteins that are vaccine candidates.

Anaplasma marginale↗
PubMed2004

Genomic data visualization on the Web.

UNLABELLED: Many types of genomic data can be represented in matrix format, with rows corresponding to genes and columns corresponding to gene features. The heat map is a popular technique for visualizing such data, plotting the data on a two-dimensional grid and using a color scale to represent the magnitude of each matrix entry. Prism is a Web-based software tool for generating annotated heat map visualizations of genome-wide data quickly. The tool provides a selection of genome-specific annotation catalogs as well as a catalog upload capability. The heat maps generated are clickable, allowing the user to drill down to examine specific matrix entries, and gene annotations are linked to relevant genomic databases. AVAILABILITY: http://noble.gs.washington.edu/prism

Computer Graphics↗
PubMed1996

Extensible biosignal (EBS) file format: simple method for EEG data exchange.

Increasing use of computer technology in EEG research requires the creation of standardized data formats to transmit, exchange, analyze or modify mainly EEG/MEG as well as mere general polygraphic data. The extensible biosignal file format (EBS) has been designed for easy use. The concept of the EBS format is a simple structure of variable size, consisting of one fixed and two variable headers and a data section. In the variable header, any information can be stored in attributes. The data are archived in one of 3 organizational forms: channel order, temporal order, or compressed. The format supports various data types, multiple biosignals (ECG, EEG, MEG, polygraph), annotations, processing history, location diagrams (CGM), 16 hit ISO 10646 character set, random access to large amounts of data, global or private extensions, self-identification, and multiple tools for conversion, modification and visualization which are freely available in source code.

Brain↗
PubMed1998

[ACOnet (Austrian Academic Computer Network) as data carrier for teleradiologic consultations].

PURPOSE: To assess the feasibility of image transfer for teleradiologic consultations using the Austrian Academic Computer Network (ACOnet). The ACOnet corresponds between the main universities to a MAN (Metropolitan Area Network) with a transfer rate of 4 Mbps. Its use is free of charge for university institutions. MATERIALS AND METHODS: 1740 test image data sets and 620 image data sets for teleradiological consultations were exchanged without annotations between the Departments of Diagnostic Radiology of the universities of Innsbruck and Graz, using the ACOnet. RESULTS: Data transmission was reliable and fast with an average transfer capacity of 170.2 kBytes/s (94-341 kBytes/s). There were no major problems with image transfer during the test phase. CONCLUSION: Due to its high transfer capacity, the ACOnet is considered a reasonable alternative to the ISDN service.

Austria↗
PubMed2006

Determination of strongly overlapping signaling activity from microarray data.

BACKGROUND: As numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups. RESULTS: Here we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly coupled signaling pathways and, in some cases, activity of specific signaling proteins. We demonstrate the technique using the Rosetta yeast deletion mutant data set, decompositional analysis by Bayesian Decomposition, and annotation analysis using ClutrFree. We determined from measurements of gene persistence in patterns across multiple potential dimensionalities that 15 basis vectors provides the correct dimensionality for interpreting the data. Using gene ontology and data on gene regulation in the Saccharomyces Genome Database, we identified the transcriptional signatures of several cellular processes in yeast, including cell wall creation, ribosomal disruption, chemical blocking of protein synthesis, and, critically, individual signatures of the strongly coupled mating and filamentation pathways. CONCLUSION: This works demonstrates that microarray data can provide downstream indicators of pathway activity either through use of gene ontology or transcription factor databases. This can be used to investigate the specificity and success of targeted therapeutics as well as to elucidate signaling activity in normal and disease processes.

Algorithms↗
PubMed2005

Global profiling of Shewanella oneidensis MR-1: expression of hypothetical genes and improved functional annotations.

The gamma-proteobacterium Shewanella oneidensis strain MR-1 is a metabolically versatile organism that can reduce a wide range of organic compounds, metal ions, and radionuclides. Similar to most other sequenced organisms, approximately 40% of the predicted ORFs in the S. oneidensis genome were annotated as uncharacterized "hypothetical" genes. We implemented an integrative approach by using experimental and computational analyses to provide more detailed insight into gene function. Global expression profiles were determined for cells after UV irradiation and under aerobic and suboxic growth conditions. Transcriptomic and proteomic analyses confidently identified 538 hypothetical genes as expressed in S. oneidensis cells both as mRNAs and proteins (33% of all predicted hypothetical proteins). Publicly available analysis tools and databases and the expression data were applied to improve the annotation of these genes. The annotation results were scored by using a seven-category schema that ranked both confidence and precision of the functional assignment. We were able to identify homologs for nearly all of these hypothetical proteins (97%), but could confidently assign exact biochemical functions for only 16 proteins (category 1; 3%). Altogether, computational and experimental evidence provided functional assignments or insights for 240 more genes (categories 2-5; 45%). These functional annotations advance our understanding of genes involved in vital cellular processes, including energy conversion, ion transport, secondary metabolism, and signal transduction. We propose that this integrative approach offers a valuable means to undertake the enormous challenge of characterizing the rapidly growing number of hypothetical proteins with each newly sequenced genome.

Gene Expression Profiling↗
PubMed2026

GICPIdb: an archival repository of multimodal data focusing on pathological images for gastrointestinal cancers.

INTRODUCTION: Deep learning (DL) shows great potential for predicting biomarkers from routine histopathological slides of gastrointestinal (GI) cancers. Yet most existing models are validated on limited patient cohorts, while pathological image annotation and molecular marker standardization demand substantial professional expertise. To address these gaps, we constructed the Gastrointestinal Cancer Pathological Image Archive (GICPIdb, gicpidb.shubuzuo.top), a dedicated database and web platform covering seven major GI cancer types. METHODS: High-quality hematoxylin and eosin (H&E)-stained whole-slide images were collected from multiple sources and uniformly processed. Image annotations were performed by board-certified pathologists following standardized protocols. GICPIdb offers five interactive web modules for data uploading, quality control, feature extraction, online annotation and AI-based prediction. Its intuitive interface supports data browsing, retrieval, visualization and downloading. RESULTS: The database houses 2,863 pathologist-annotated, uniformly processed, high-quality H&E stained images collected from 2,655 patients. Of these, 1,699 patients were sourced from The Cancer Genome Atlas (TCGA), 182 from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 424 from China-Japan Friendship Hospital and 350 from Chifeng Municipal Hospital in Inner Mongolia, China. It also integrates data on over 50 key molecular markers (e.g., MSI, TMB) and prognostic labels related to survival, recurrence and metastasis. DISCUSSION: GICPIdb aims to promote the development of DL-driven AI tools for cancer research and clinical translation. The multi-institutional data collection and standardized annotation pipeline are expected to enhance the generalizability and reproducibility of AI-based prediction models across diverse patient populations.

deep learning↗
PubMed2006

Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis.

Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. In this study, we use Machine Learning and Exploratory Data Analysis (EDA) techniques to examine and characterize amino acid sequences of human proteins localized in nine cellular compartments. A dataset of 3,749 protein sequences representing human proteins was extracted from the SWISS-PROT database. Feature vectors were created to capture specific amino acid sequence characteristics. Relative to a Support Vector Machine, a Multi-layer Perceptron, and a Naive Bayes classifier, the C4.5 Decision Tree algorithm was the most consistent performer across all nine compartments in reliably predicting the subcellular localization of proteins based on their amino acid sequences (average Precision=0.88; average Sensitivity=0.86). Furthermore, EDA graphics characterized essential features of proteins in each compartment. As examples, proteins localized on the plasma membrane had higher proportions of hydrophobic amino acids; cytoplasmic proteins had higher proportions of neutral amino acids; and mitochondrial proteins had higher proportions of neutral amino acids and lower proportions of polar amino acids. These data showed that the C4.5 classifier and EDA tools can be effective for characterizing and predicting the subcellular localization of human proteins based on their amino acid sequences.

Algorithms↗
PubMed2005

GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists.

Phenotype analysis is commonly recognized to be of great importance for gaining insight into genetic interaction underlying inherited diseases. However, few computational contributions have been proposed for this purpose, mainly owing to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. We developed and made available through GFINDer web server an original approach for the analysis of genetic disorder related genes by exploiting the information on genetic diseases and their clinical phenotypes present in textual form within the Online Mendelian Inheritance in Man (OMIM) database. Because several synonyms for the same name and different names for overlapping concepts are often used in OMIM, we first normalized phenotype location descriptions reducing them to a list of unique controlled terms representing phenotype location categories. Then, we hierarchically structured them and the correspondent genetic diseases according to their topology and granularity of description, respectively. Thus, in GFINDer we could implement specific Genetic Disorders modules for the analysis of these structured data. Such modules allow to automatically annotate user-classified gene lists with updated disease and clinical information, classify them according to the genetic syndrome and the phenotypic location categories, and statistically identify the most relevant categories in each gene class. GFINDer is available for non-profit use at http://www.bioinformatics.polimi.it/GFINDer/.

Data Interpretation, Statistical↗
PubMed2005

CASCAD: a database of annotated candidate single nucleotide polymorphisms associated with expressed sequences.

BACKGROUND: With the recent progress made in large-scale genome sequencing projects a vast amount of novel data is becoming available. A comparative sequence analysis, exploiting sequence information from various resources, can be used to uncover hidden information, such as genetic variation. Although there are enormous amounts of SNPs for a wide variety of organisms submitted to NCBI dbSNP and annotated in most genome assembly viewers like Ensembl and the UCSC Genome Browser, these platforms do not easily allow for extensive annotation and incorporation of experimental data supporting the polymorphism. However, such information is very important for selecting the most promising and useful candidate polymorphisms for use in experimental setups. DESCRIPTION: The CASCAD database is designed for presentation and query of candidate SNPs that are retrieved by in silico mining of high-throughput sequencing data. Currently, the database provides collections of laboratory rat (Rattus norvegicus) and zebrafish (Danio rerio) candidate SNPs. The database stores detailed information about raw data supporting the candidate, extensive annotation and links to external databases (e.g. GenBank, Ensembl, UniGene, and LocusLink), verification information, and predictions of a potential effect for non-synonymous polymorphisms in coding regions. The CASCAD website allows search based on an arbitrary combination of 27 different parameters related to characteristics like candidate SNP quality, genomic localization, and sequence data source or strain. In addition, the database can be queried with any custom nucleotide sequences of interest. The interface is crosslinked to other public databases and tightly coupled with primer design and local genome assembly interfaces in order to facilitate experimental verification of candidates. CONCLUSIONS: The CASCAD database discloses detailed information on rat and zebrafish candidate SNPs, including the raw data underlying its discovery. An advanced web-based search interface http://cascad.niob.knaw.nl allows universal access to the database content and allows various queries supporting many types of research utilizing single nucleotide polymorphisms.

Animals↗
PubMed2006

Chemical structure indexing of toxicity data on the internet: moving toward a flat world.

Standardized chemical structure annotation of public toxicity databases and information resources is playing an increasingly important role in the 'flattening' and integration of diverse sets of biological activity data on the Internet. This review discusses public initiatives that are accelerating the pace of this transformation, with particular reference to toxicology-related chemical information. Chemical content annotators, structure locator services, large structure/data aggregator web sites, structure browsers, International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) codes, toxicity data models and public chemical/biological activity profiling initiatives are all playing a role in overcoming barriers to the integration of toxicity data, and are bringing researchers closer to the reality of a mineable chemical Semantic Web. An example of this integration of data is provided by the collaboration among researchers involved with the Distributed Structure-Searchable Toxicity (DSSTox) project, the Carcinogenic Potency Project, projects at the National Cancer Institute and the PubChem database.

Abstracting and Indexing↗
PubMed2026

Scalable near-real-time Bayesian phylogenetics for outbreaks with Delphy.

Pathogen genomic analysis is central to tracking, understanding and containing outbreaks1-13, but the complexity and cost of state-of-the-art phylogenetic tools limit global access and impact. Here we introduce Delphy, an exact reformulation of Bayesian phylogenetics14-17 designed to transform its speed, scalability and accessibility while retaining Bayesian state-of-the-art accuracy. Delphy's central data structure, an explicit mutation-annotated tree, takes advantage of the high sequence similarity of large-scale epidemic datasets18-20 for efficient tree exploration and convergence. By reproducing key analyses from recent major epidemics, including Ebola1,21, Zika2, SARS-CoV-2 (ref. 22), mpox3,4 and H5N1 (refs. 23,24), we demonstrate state-of-the-art accuracy with up to 2-3 orders of magnitude improvements in speed. Assessing Delphy's scalability, we show that a simulated dataset of 100,000 sequences can be analysed within a day. We distribute Delphy as a client-side web application that enables local, interactive analysis of raw data on the user's machine. Delphy automatically identifies key viral lineages and mutations, as well as their emergence and prevalence through time, with quantified uncertainties grounded in Bayesian theory. Delphy establishes Bayesian phylogenetics as a fast, accessible frontline tool for future outbreak response.

Journal Article↗
PubMed2004

Transposable element annotation of the rice genome.

MOTIVATION: The high content of repetitive sequences in the genomes of many higher eukaryotes renders the task of annotating them computationally intensive. Presently, the only widely accepted method of searching and annotating transposable elements (TEs) in large genomic sequences is the use of the RepeatMasker program, which identifies new copies of TEs by pairwise sequence comparisons with a library of known TEs. Profile hidden Markov models (HMMs) have been used successfully in discovering distant homologs of known proteins in large protein databases, but this approach has only rarely been applied to known model TE families in genomic DNA. RESULTS: We used a combination of computational approaches to annotate the TEs in the finished genome of Oryza sativa ssp. japonica. In this paper, we discuss the strengths and the weaknesses of the annotation methods used. These approaches included: the default configuration of RepeatMasker using cross_match, an implementation of the Smith-Waterman-Gotoh algorithm; RepeatMasker using WU-BLAST for similarity searching; and the HMMER package, used to search for TEs with profile HMMs. All the results were converted into GFF format and post-processed using a set of Perl scripts. RepeatMasker was used in the case of most TE families. The WU-BLAST implementation of RepeatMasker was found to be manifold faster than cross_match with only a slight loss in sensitivity and was thus used to obtain the final set of data. HMMER was used in the annotation of the Mutator-like element (MULE) superfamily and the miniature inverted-repeat transposable element (MITE) polyphyletic group of families, for which large libraries of elements were available and which could be divided into well-defined families. The HMMER search algorithm was extremely slow for models over 1000 bp in length, so MULE families with members over 1000 bp long were processed with RepeatMasker instead. The main disadvantage of HMMER in this application is that, since it was developed with protein sequences in mind, it does not search the negative DNA strand. With the exception of TE families with essentially palindromic sequences, reverse complement models had to be created and run to compensate for this shortcoming. We conclude that a modification of RepeatMasker to incorporate libraries of profile HMMs in searches could improve the ability to detect degenerated copies of TEs. AVAILABILITY: The Perl scripts and TE sequences used in construction of the RepeatMasker library and the profile HMMs are available upon request.

Algorithms↗
PubMed1999

Biosequence exegesis.

Annotation of large-scale gene sequence data will benefit from comprehensive and consistent application of well-documented, standard analysis methods and from progressive and vigilant efforts to ensure quality and utility and to keep the annotation up to date. However, it is imperative to learn how to apply information derived from functional genomics and proteomics technologies to conceptualize and explain the behaviors of biological systems. Quantitative and dynamical models of systems behaviors will supersede the limited and static forms of single-gene annotation that are now the norm. Molecular biological epistemology will increasingly encompass both teleological and causal explanations.

Animals↗
PubMed2005

Overview of BioCreAtIvE: critical assessment of information extraction for biology.

BACKGROUND: The goal of the first BioCreAtIvE challenge (Critical Assessment of Information Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of the art for text mining applied to biological problems. The results were presented in a workshop held in Granada, Spain March 28-31, 2004. The articles collected in this BMC Bioinformatics supplement entitled "A critical assessment of text mining methods in molecular biology" describe the BioCreAtIvE tasks, systems, results and their independent evaluation. RESULTS: BioCreAtIvE focused on two tasks. The first dealt with extraction of gene or protein names from text, and their mapping into standardized gene identifiers for three model organism databases (fly, mouse, yeast). The second task addressed issues of functional annotation, requiring systems to identify specific text passages that supported Gene Ontology annotations for specific proteins, given full text articles. CONCLUSION: The first BioCreAtIvE assessment achieved a high level of international participation (27 groups from 10 countries). The assessment provided state-of-the-art performance results for a basic task (gene name finding and normalization), where the best systems achieved a balanced 80% precision / recall or better, which potentially makes them suitable for real applications in biology. The results for the advanced task (functional annotation from free text) were significantly lower, demonstrating the current limitations of text-mining approaches where knowledge extrapolation and interpretation are required. In addition, an important contribution of BioCreAtIvE has been the creation and release of training and test data sets for both tasks. There are 22 articles in this special issue, including six that provide analyses of results or data quality for the data sets, including a novel inter-annotator consistency assessment for the test set used in task 2.

Computational Biology↗
PubMed2006

PlantQTL-GE: a database system for identifying candidate genes in rice and Arabidopsis by gene expression and QTL information.

We have designed and implemented a web-based database system, called PlantQTL-GE, to facilitate quantitatine traits locus (QTL) based candidate gene identification and gene function analysis. We collected a large number of genes, gene expression information in microarray data and expressed sequence tags (ESTs) and genetic markers from multiple sources of Oryza sativa and Arabidopsis thaliana. The system integrates these diverse data sources and has a uniform web interface for easy access. It supports QTL queries specifying QTL marker intervals or genomic loci, and displays, on rice or Arabidopsis genome, known genes, microarray data, ESTs and candidate genes and similar putative genes in the other plant. Candidate genes in QTL intervals are further annotated based on matching ESTs, microarray gene expression data and cis-elements in regulatory sequences. The system is freely available at http://www.scbit.org/qtl2gene/new/.

Arabidopsis↗
PubMed2006

Tools for integrated sequence-structure analysis with UCSF Chimera.

BACKGROUND: Comparing related structures and viewing the structures in the context of sequence alignments are important tasks in protein structure-function research. While many programs exist for individual aspects of such work, there is a need for interactive visualization tools that: (a) provide a deep integration of sequence and structure, far beyond mapping where a sequence region falls in the structure and vice versa; (b) facilitate changing data of one type based on the other (for example, using only sequence-conserved residues to match structures, or adjusting a sequence alignment based on spatial fit); (c) can be used with a researcher's own data, including arbitrary sequence alignments and annotations, closely or distantly related sets of proteins, etc.; and (d) interoperate with each other and with a full complement of molecular graphics features. We describe enhancements to UCSF Chimera to achieve these goals. RESULTS: The molecular graphics program UCSF Chimera includes a suite of tools for interactive analyses of sequences and structures. Structures automatically associate with sequences in imported alignments, allowing many kinds of crosstalk. A novel method is provided to superimpose structures in the absence of a pre-existing sequence alignment. The method uses both sequence and secondary structure, and can match even structures with very low sequence identity. Another tool constructs structure-based sequence alignments from superpositions of two or more proteins. Chimera is designed to be extensible, and mechanisms for incorporating user-specific data without Chimera code development are also provided. CONCLUSION: The tools described here apply to many problems involving comparison and analysis of protein structures and their sequences. Chimera includes complete documentation and is intended for use by a wide range of scientists, not just those in the computational disciplines. UCSF Chimera is free for non-commercial use and is available for Microsoft Windows, Apple Mac OS X, Linux, and other platforms from http://www.cgl.ucsf.edu/chimera.

Computer Graphics↗