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At least 163 records · Page 9Linked to original sources
PubMed2004

GOblet: a platform for Gene Ontology annotation of anonymous sequence data.

GOblet is a comprehensive web server application providing the annotation of anonymous sequence data with Gene Ontology (GO) terms. It uses a variety of different protein databases (human, murines, invertebrates, plants, sp-trembl) and their respective GO mappings. The user selects the appropriate database and alignment threshold and thereafter submits single or multiple nucleotide or protein sequences. Results are shown in different ways, e.g. as survey statistics for the main GO categories for all sequences or as detailed results for each single sequence that has been submitted. In its newest version, GOblet allows the batch submission of sequences and provides an improved display of results with the aid of Java applets. All output data, together with the Java applet, are packed to a downloadable archive for local installation and analysis. GOblet can be accessed freely at http://goblet.molgen.mpg.de.

Animals↗
PubMed2005

Design of a system for combined analysis of microarray-based gene expression and FlyBase-derived annotation in Drosophila.

In recent years, researchers began to utilize both the experimental microarray data and the annotation data from FlyBase to identify Drosophila genes that might encode certain functions. So far, they have to manually combine data from both the microarray experiment and the FlyBase, which is a slow and tedious process. The goal of this research is to construct a flexible relational database system that integrates the microarray data with annotation information from FlyBase.

Animals↗
PubMed2005

Who tangos with GOA?-Use of Gene Ontology Annotation (GOA) for biological interpretation of '-omics' data and for validation of automatic annotation tools.

The number of large-scale experimental datasets generated from high-throughput technologies has grown rapidly. Biological knowledge resources such as the Gene Ontology Annotation (GOA) database, which provides high-quality functional annotation to proteins within the UniProt Knowledgebase, can play an important role in the analysis of such data. The integration of GOA with analytical tools has proved to aid the clustering, annotation and biological interpretation of such large expression datasets. GOA is also useful in the development and validation of automated annotation tools, in particular text-mining systems. The increasing interest in GOA highlights the great potential of this freely available resource to assist both the biological research and bioinformatics communities.

Animals↗
PubMed2002

Assessing the impact of comparative genomic sequence data on the functional annotation of the Drosophila genome.

BACKGROUND: It is widely accepted that comparative sequence data can aid the functional annotation of genome sequences; however, the most informative species and features of genome evolution for comparison remain to be determined. RESULTS: We analyzed conservation in eight genomic regions (apterous, even-skipped, fushi tarazu, twist, and Rhodopsins 1, 2, 3 and 4) from four Drosophila species (D. erecta, D. pseudoobscura, D. willistoni, and D. littoralis) covering more than 500 kb of the D. melanogaster genome. All D. melanogaster genes (and 78-82% of coding exons) identified in divergent species such as D. pseudoobscura show evidence of functional constraint. Addition of a third species can reveal functional constraint in otherwise non-significant pairwise exon comparisons. Microsynteny is largely conserved, with rearrangement breakpoints, novel transposable element insertions, and gene transpositions occurring in similar numbers. Rates of amino-acid substitution are higher in uncharacterized genes relative to genes that have previously been studied. Conserved non-coding sequences (CNCSs) tend to be spatially clustered with conserved spacing between CNCSs, and clusters of CNCSs can be used to predict enhancer sequences. CONCLUSIONS: Our results provide the basis for choosing species whose genome sequences would be most useful in aiding the functional annotation of coding and cis-regulatory sequences in Drosophila. Furthermore, this work shows how decoding the spatial organization of conserved sequences, such as the clustering of CNCSs, can complement efforts to annotate eukaryotic genomes on the basis of sequence conservation alone.

Animals↗
PubMed2004

An XML standard for the dissemination of annotated 2D gel electrophoresis data complemented with mass spectrometry results.

BACKGROUND: Many proteomics initiatives require a seamless bioinformatics integration of a range of analytical steps between sample collection and systems modeling immediately assessable to the participants involved in the process. Proteomics profiling by 2D gel electrophoresis to the putative identification of differentially expressed proteins by comparison of mass spectrometry results with reference databases, includes many components of sample processing, not just analysis and interpretation, are regularly revisited and updated. In order for such updates and dissemination of data, a suitable data structure is needed. However, there are no such data structures currently available for the storing of data for multiple gels generated through a single proteomic experiments in a single XML file. This paper proposes a data structure based on XML standards to fill the void that exists between data generated by proteomics experiments and storing of data. RESULTS: In order to address the resulting procedural fluidity we have adopted and implemented a data model centered on the concept of annotated gel (AG) as the format for delivery and management of 2D Gel electrophoresis results. An eXtensible Markup Language (XML) schema is proposed to manage, analyze and disseminate annotated 2D Gel electrophoresis results. The structure of AG objects is formally represented using XML, resulting in the definition of the AGML syntax presented here. CONCLUSION: The proposed schema accommodates data on the electrophoresis results as well as the mass-spectrometry analysis of selected gel spots. A web-based software library is being developed to handle data storage, analysis and graphic representation. Computational tools described will be made available at http://bioinformatics.musc.edu/agml. Our development of AGML provides a simple data structure for storing 2D gel electrophoresis data.

Computational Biology↗
PubMed2007

Internet-enabled high-resolution brain mapping and virtual microscopy.

Virtual microscopy involves the conversion of histological sections mounted on glass microscope slides to high-resolution digital images. Virtual microscopy offers several advantages over traditional microscopy, including remote viewing and data sharing, annotation, and various forms of data mining. We describe a method utilizing virtual microscopy for generation of internet-enabled, high-resolution brain maps and atlases. Virtual microscopy-based digital brain atlases have resolutions approaching 100,000 dpi, which exceeds by three or more orders of magnitude resolutions obtainable in conventional print atlases, MRI, and flat-bed scanning. Virtual microscopy-based digital brain atlases are superior to conventional print atlases in five respects: (1) resolution, (2) annotation, (3) interaction, (4) data integration, and (5) data mining. Implementation of virtual microscopy-based digital brain atlases is located at BrainMaps.org, which is based on more than 10 million megapixels (35 terabytes) of scanned images of serial sections of primate and non-primate brains with a resolution of 0.46 microm/pixel (55,000 dpi). The method can be replicated by labs seeking to increase accessibility and sharing of neuroanatomical data. Online tools offer the possibility of visualizing and exploring completely digitized sections of brains at a sub-neuronal level and can facilitate large-scale connectional tracing, histochemical, and stereological analyses.

Animals↗
PubMed2006

Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles.

Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Because the Mass Deviance metric is derived from the natural distribution of peptide masses, it is machine- and proteome-independent and enables assessment of feature detection tools in the absence of completely annotated data sets. We validate the use of Mass Deviance with a second, independent metric that is based on isotopic distributions, demonstrating that we can use Mass Deviance to identify aberrant features with high accuracy. We then demonstrate the use of independent metrics in tandem as a robust way to evaluate the performance of peptide feature detection algorithms. This work is done on complex LC-MS profiles of Saccharomyces cerevisiae which present a significant challenge to peptide feature detection algorithms.

Algorithms↗
PubMed2005

Modularized learning of genetic interaction networks from biological annotations and mRNA expression data.

MOTIVATION: Inferring the genetic interaction mechanism using Bayesian networks has recently drawn increasing attention due to its well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. RESULTS: We propose a novel method to infer genetic networks by alleviating the shortage of available mRNA expression data with prior knowledge. We call the proposed method 'modularized network learning' (MONET). Firstly, the proposed method divides a whole gene set to overlapped modules considering biological annotations and expression data together. Secondly, it infers a Bayesian network for each module, and integrates the learned subnetworks to a global network. An algorithm that measures a similarity between genes based on hierarchy, specificity and multiplicity of biological annotations is presented. The proposed method draws a global picture of inter-module relationships as well as a detailed look of intra-module interactions. We applied the proposed method to analyze Saccharomyces cerevisiae stress data, and found several hypotheses to suggest putative functions of unclassified genes. We also compared the proposed method with a whole-set-based approach and two expression-based clustering approaches.

Algorithms↗
PubMed2004

MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource for plant genomics.

Arabidopsis thaliana is the most widely studied model plant. Functional genomics is intensively underway in many laboratories worldwide. Beyond the basic annotation of the primary sequence data, the annotated genetic elements of Arabidopsis must be linked to diverse biological data and higher order information such as metabolic or regulatory pathways. The MIPS Arabidopsis thaliana database MAtDB aims to provide a comprehensive resource for Arabidopsis as a genome model that serves as a primary reference for research in plants and is suitable for transfer of knowledge to other plants, especially crops. The genome sequence as a common backbone serves as a scaffold for the integration of data, while, in a complementary effort, these data are enhanced through the application of state-of-the-art bioinformatics tools. This information is visualized on a genome-wide and a gene-by-gene basis with access both for web users and applications. This report updates the information given in a previous report and provides an outlook on further developments. The MAtDB web interface can be accessed at http://mips.gsf.de/proj/thal/db.

Arabidopsis↗
PubMed1988

The Annotator's Assistant: an expert system for direct submission of genetic sequence data.

As DNA sequencing technology improves and more rapid techniques become routine in molecular biology labs, researchers need to expedite the incorporation of information into genetic sequence databases, such as GenBank, by directly submitting sequence data. The Annotator's Assistant is an expert system that runs on an IBM PC and helps the molecular biologist, who may have little knowledge of the structure or content of a GenBank entry, to construct a complete and valid sequence submission file. This expert system uses a simple molecular biology knowledge base and a selection of customized screen entry forms to guide the user through the entry and annotation of a sequence and its biological features. The system compiles information about the contributor, journal references, physical and functional characteristics of the nucleic acid, source organism and features, and checks it to eliminate incomplete answers and simple errors. Users supply input by answering direct and multiple-choice questions, selecting menu items and completing entry forms; on-line help is available. Users may also enter new or unusual information using generic forms. Several modules of the expert system were converted into Prolog programs and compiled, decreasing the running time significantly. The expert system rules and the data entry forms are easy to modify, update and customize for specific sequence classes.

Base Sequence↗
PubMed1992

Finding errors in DNA sequences.

An algorithm is described that can detect certain errors within coding regions of DNA sequences. The algorithm is based on the idea that an insertion or deletion error within a coding sequence would interrupt the reading frame and cause the correct translation of a DNA sequence to require one or more frameshifts. If the coding sequence shows similarity to a known protein sequence then such errors can be detected by comparing the conceptual translations of DNA sequences in all six reading frames with every sequence in a protein sequence data base. We have incorporated these ideas into a computer program, called DETECT, that can serve as an aid to the experimentalist who is determining new DNA sequences so that obvious errors may be located and corrected. The program has been tested using raw experimental data and against sequences from the European Molecular Biology Laboratory data base, annotated as containing frameshifts. We have also tested it using unidentified open reading frames that flank known, annotated genes in the GenBank data base. Many potential errors are apparent and in some cases functions can be suggested for the "corrected" versions of these reading frames leading to the identification of new genes. As more sequences are determined the power of this method will increase substantially.

Adenylyl Cyclases↗
PubMed2006

Combining gene annotations and gene expression data in model-based clustering: weighted method.

It has been increasingly recognized that incorporating prior knowledge into cluster analysis can result in more reliable and meaningful clusters. In contrast to the standard modelbased clustering with a global mixture model, which does not use any prior information, a stratified mixture model was recently proposed to incorporate gene functions or biological pathways as priors in model-based clustering of gene expression profiles: various gene functional groups form the strata in a stratified mixture model. Albeit useful, the stratified method may be less efficient than the global analysis if the strata are non-informative to clustering. We propose a weighted method that aims to strike a balance between a stratified analysis and a global analysis: it weights between the clustering results of the stratified analysis and that of the global analysis; the weight is determined by data. More generally, the weighted method can take advantage of the hierarchical structure of most existing gene functional annotation systems, such as MIPS and Gene Ontology (GO), and facilitate choosing appropriate gene functional groups as priors. We use simulated data and real data to demonstrate the feasibility and advantages of the proposed method.

Algorithms↗
PubMed2006

The MGED Ontology: a resource for semantics-based description of microarray experiments.

MOTIVATION: The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. RESULTS: Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. AVAILABILITY: The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). CONTACT: Stoeckrt@pcbi.upenn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational Biology↗
PubMed2006

Aetiology-specific patterns in end-stage heart failure patients identified by functional annotation and classification of microarray data.

BACKGROUND: The objective of the present study was to use gene expression profiling, functional annotations and classification to identify aetiology-specific biological processes and potential molecular markers for different aetiologies of end-stage heart failure. METHODS AND RESULTS: Individual left ventricular myocardial samples from eleven coronary artery disease and nine dilated cardiomyopathy transplant patients were co-hybridized with pooled RNA from four non-failing hearts on custom-made arrays of 7000 human genes. Significance analysis identified differential expression of 153 and 147 genes, respectively, in coronary artery disease or dilated cardiomyopathy versus non-failing hearts. Analysis of Gene Ontology biological process annotations indicated aetiology-specific patterns, primarily related to genes involved in catabolism and in regulation of protein kinase activity. Gene expression classifiers were obtained and used for class prediction of random samples of coronary artery diseased and dilated cardiomyopathic hearts. Best classifiers frequently included matrix metalloproteinase 3, fibulin 1, ATP-binding cassette, sub-family B member 1 and iroquois homeobox protein 5. CONCLUSION: Combining functional annotation from microarray data and classification analysis constitutes a potent strategy to identify disease-specific biological processes and gene expression markers in e.g. end-stage coronary artery disease and dilated cardiomyopathy.

Adult↗
PubMed2003

Automated Gene Ontology annotation for anonymous sequence data.

Gene Ontology (GO) is the most widely accepted attempt to construct a unified and structured vocabulary for the description of genes and their products in any organism. Annotation by GO terms is performed in most of the current genome projects, which besides generality has the advantage of being very convenient for computer based classification methods. However, direct use of GO in small sequencing projects is not easy, especially for species not commonly represented in public databases. We present a software package (GOblet), which performs annotation based on GO terms for anonymous cDNA or protein sequences. It uses the species independent GO structure and vocabulary together with a series of protein databases collected from various sites, to perform a detailed GO annotation by sequence similarity searches. The sensitivity and the reference protein sets can be selected by the user. GOblet runs automatically and is available as a public service on our web server. The paper also addresses the reliability of automated GO annotations by using a reference set of more than 6000 human proteins. The GOblet server is accessible at http://goblet.molgen.mpg.de.

Animals↗
PubMed2002

Protein interaction verification and functional annotation by integrated analysis of genome-scale data.

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.

Animals↗
PubMed1995

BEAUTY: an enhanced BLAST-based search tool that integrates multiple biological information resources into sequence similarity search results.

BEAUTY (BLAST enhanced alignment utility) is an enhanced version of the NCBI's BLAST data base search tool that facilitates identification of the functions of matched sequences. We have created new data bases of conserved regions and functional domains for protein sequences in NCBI's Entrez data base, and BEAUTY allows this information to be incorporated directly into BLAST search results. A Conserved Regions Data Base, containing the locations of conserved regions within Entrez protein sequences, was constructed by (1) clustering the entire data base into families, (2) aligning each family using our PIMA multiple sequence alignment program, and (3) scanning the multiple alignments to locate the conserved regions within each aligned sequence. A separate Annotated Domains Data Base was constructed by extracting the locations of all annotated domains and sites from sequences represented in the Entrez, PROSITE, BLOCKS, and PRINTS data bases. BEAUTY performs a BLAST search of those Entrez sequences with conserved regions and/or annotated domains. BEAUTY then uses the information from the Conserved Regions and Annotated Domains data bases to generate, for each matched sequence, a schematic display that allows one to directly compare the relative locations of (1) the conserved regions, (2) annotated domains and sites, and (3) the locally aligned regions matched in the BLAST search. In addition, BEAUTY search results include World-Wide Web hypertext links to a number of external data bases that provide a variety of additional types of information on the function of matched sequences. This convenient integration of protein families, conserved regions, annotated domains, alignment displays, and World-Wide Web resources greatly enhances the biological informativeness of sequence similarity searches. BEAUTY searches can be performed remotely on our system using the "BCM Search Launcher" World-Wide Web pages (URL is < http:/ /gc.bcm.tmc.edu:8088/ search-launcher/launcher.html > ).

Amino Acid Sequence↗
PubMed2005

A mathematical and computational framework for quantitative comparison and integration of large-scale gene expression data.

Analysis of large-scale gene expression studies usually begins with gene clustering. A ubiquitous problem is that different algorithms applied to the same data inevitably give different results, and the differences are often substantial, involving a quarter or more of the genes analyzed. This raises a series of important but nettlesome questions: How are different clustering results related to each other and to the underlying data structure? Is one clustering objectively superior to another? Which differences, if any, are likely candidates to be biologically important? A systematic and quantitative way to address these questions is needed, together with an effective way to integrate and leverage expression results with other kinds of large-scale data and annotations. We developed a mathematical and computational framework to help quantify, compare, visualize and interactively mine clusterings. We show that by coupling confusion matrices with appropriate metrics (linear assignment and normalized mutual information scores), one can quantify and map differences between clusterings. A version of receiver operator characteristic analysis proved effective for quantifying and visualizing cluster quality and overlap. These methods, plus a flexible library of clustering algorithms, can be called from a new expandable set of software tools called CompClust 1.0 (http://woldlab.caltech.edu/compClust/). CompClust also makes it possible to relate expression clustering patterns to DNA sequence motif occurrences, protein-DNA interaction measurements and various kinds of functional annotations. Test analyses used yeast cell cycle data and revealed data structure not obvious under all algorithms. These results were then integrated with transcription motif and global protein-DNA interaction data to identify G1 regulatory modules.

Algorithms↗