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 1,261 records · Page 70Linked to original sources

Genome prediction of PhoB regulated promoters in Sinorhizobium meliloti and twelve proteobacteria.

In proteobacteria, genes whose expression is modulated in response to the external concentration of inorganic phosphate are often regulated by the PhoB protein which binds to a conserved motif (Pho box) within their promoter regions. Using a position weight matrix algorithm derived from known Pho box sequences, we identified 96 putative Pho regulon members whose promoter regions contained one or more Pho boxs in the Sinorhizobium meliloti genome. Expression of these genes was examined through assays of reporter gene fusions and through comparison with published microarray data. Of 96 genes, 31 were induced and 3 were repressed by Pi starvation in a PhoB dependent manner. Novel Pho regulon members included several genes of unknown function. Comparative analysis across 12 proteobacterial genomes revealed highly conserved Pho regulon members including genes involved in Pi metabolism (pstS, phnC and ppdK). Genes with no obvious association with Pi metabolism were predicted to be Pho regulon members in S.meliloti and multiple organisms. These included smc01605 and smc04317 which are annotated as substrate binding proteins of iron transporters and katA encoding catalase. This data suggests that the Pho regulon overlaps and interacts with several other control circuits, such as the oxidative stress response and iron homeostasis.

Algorithms↗

Integrated functional visualization of eukaryotic genomes.

BACKGROUND: Increasing amounts of data from large scale whole genome analysis efforts demands convenient tools for manipulation, visualization and investigation. Whole genome plots offer an intuitive window to the analysis. We describe two applications that enable users to easily plot and explore whole genome data from their own or other researchers' experiments. RESULTS: STRIPE and GFFtool (General Feature Format Tool) are softwares designed to support integration, visualization and exploration of whole genome data from eukaryotic genomes. STRIPE, in addition to providing a highly customizable and interactive data plot, provides access to numerous well-selected databases with updated information on all genes of a genome. GFFtool provides a user-friendly solution to integrating experimental data with the genomic information available in public databases. They also obviate the need for users to maintain large annotation resources, as they link to well-known resources using standard gene and protein identifiers. CONCLUSION: The programs provide the user with broad genomic overviews of data distribution, fast access to data of interest, and the ability to navigate speedily from one resource to another, and gain a better understanding of result of whole genome analysis experiments.

Algorithms↗

Has the yo-yo stopped? An assessment of human protein-coding gene number.

Since the identification of approximately 25,000 proteins from the draft human genome assembly in 2001, estimates of the total have oscillated between 30,000 and 70,000. The recently announced genome closure has not generated a consensus gene count despite this being a key parameter for many areas of biology including drug target discovery and characterization of the human proteome. Contrary to earlier predictions of constitutive under-detection for eukaryotic genes, the latest model organism updates have produced minor increases in the worm but fly and yeast gene numbers have decreased. The postdraft, precompletion interval has produced large increases in human transcript coverage, continuous improvements in genome assembly and refinements in automated genomic annotation. Notably these enhancements have resulted in an Ensembl human protein-coding gene number of 22,184, a decrease of 1862 since the first release. Longitudinal database surveys indicate that redundancy-reduced human mRNA and protein collections are flattening out at approximately 28,000, although Ensembl maps approximately 20,000 known sequences. Observations suggest high-throughput cloning projects are predominantly extending known genes or sampling new splice forms and novel protein discovery has slowed to a trickle. The hypothesis that substantial numbers of short proteins remain experimentally and computationally undetected in mammalian genomes is neither supported by sequence data nor by the extensive homology between mouse and human proteins. Aggregating the independent annotations for complete transcripts from seven completed human chromosomes extrapolates to approximately 25,000 genes. The inclusion of partial putative genes would increase this to above 30,000 but recent data suggest these represent predominantly nonprotein-coding transcripts. Mass spectrometry-based proteomics has already verified more than 10% of human genes but has not identified significant numbers of unpredicted proteins. The available data are thus converging to a basal protein-coding gene number well below 30,000, which could even be as low as 25,000.

Chromosomes, Human↗

Experimental validation of data mined single nucleotide polymorphisms from several databases and consecutive dbSNP builds.

Rapid development in the annotation of human genetic variation has increased the numbers of single nucleotide polymorphisms (SNPs) in candidate genes by several orders of magnitude. The selection of both useful target SNPs for disease-gene association studies and SNPs associated with the treatment response is therefore an increasingly challenging task. We describe a workflow for selecting SNPs based on their putative function and frequency in candidate genes extracted from PubMed resources. The annotation of each SNP and its frequency in a Caucasian population was assessed in several databases. Approximately 4000 SNPs were identified from an initial 233 candidate genes. In a case study, we performed actual genotyping of 1030 of these SNPs in 213 genes and obtained 710 successfully genotyped SNPs. Using the flow-chart outlined here, only 87 SNPs were monomorphic (approximately 12%). This study reports the frequency of SNPs in a Caucasian population, selected in silico, using a candidate gene approach and validated by actually genotyping 193 individuals. The selected genotypes represent a valuable set of verified candidate SNPs for pharmacogenetic studies in Caucasian populations.

Breast Neoplasms↗

Systematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons.

Recent advances in microarray technology have opened new ways for functional annotation of previously uncharacterised genes on a genomic scale. This has been demonstrated by unsupervised clustering of co-expressed genes and, more importantly, by supervised learning algorithms. Using prior knowledge, these algorithms can assign functional annotations based on more complex expression signatures found in existing functional classes. Previously, support vector machines (SVMs) and other machine-learning methods have been applied to a limited number of functional classes for this purpose. Here we present, for the first time, the comprehensive application of supervised neural networks (SNNs) for functional annotation. Our study is novel in that we report systematic results for ~100 classes in the Munich Information Center for Protein Sequences (MIPS) functional catalog. We found that only ~10% of these are learnable (based on the rate of false negatives). A closer analysis reveals that false positives (and negatives) in a machine-learning context are not necessarily "false" in a biological sense. We show that the high degree of interconnections among functional classes confounds the signatures that ought to be learned for a unique class. We term this the "Borges effect" and introduce two new numerical indices for its quantification. Our analysis indicates that classification systems with a lower Borges effect are better suitable for machine learning. Furthermore, we introduce a learning procedure for combining false positives with the original class. We show that in a few iterations this process converges to a gene set that is learnable with considerably low rates of false positives and negatives and contains genes that are biologically related to the original class, allowing for a coarse reconstruction of the interactions between associated biological pathways. We exemplify this methodology using the well-studied tricarboxylic acid cycle.

Algorithms↗

Differentiation of regions with atypical oligonucleotide composition in bacterial genomes.

BACKGROUND: Complete sequencing of bacterial genomes has become a common technique of present day microbiology. Thereafter, data mining in the complete sequence is an essential step. New in silico methods are needed that rapidly identify the major features of genome organization and facilitate the prediction of the functional class of ORFs. We tested the usefulness of local oligonucleotide usage (OU) patterns to recognize and differentiate types of atypical oligonucleotide composition in DNA sequences of bacterial genomes. RESULTS: A total of 163 bacterial genomes of eubacteria and archaea published in the NCBI database were analyzed. Local OU patterns exhibit substantial intrachromosomal variation in bacteria. Loci with alternative OU patterns were parts of horizontally acquired gene islands or ancient regions such as genes for ribosomal proteins and RNAs. OU statistical parameters, such as local pattern deviation (D), pattern skew (PS) and OU variance (OUV) enabled the detection and visualization of gene islands of different functional classes. CONCLUSION: A set of approaches has been designed for the statistical analysis of nucleotide sequences of bacterial genomes. These methods are useful for the visualization and differentiation of regions with atypical oligonucleotide composition prior to or accompanying gene annotation.

DNA, Bacterial↗

Using metabolic flux data to further constrain the metabolic solution space and predict internal flux patterns: the Escherichia coli spectrum.

Constraint-based metabolic modeling has been used to capture the genome-scale, systems properties of an organism's metabolism. The first generation of these models has been built on annotated gene sequence. To further this field, we now need to develop methods to incorporate additional "omic" data types including transcriptomics, metabolomics, and fluxomics to further facilitate the construction, validation, and predictive capabilities of these models. The work herein combines metabolic flux data with an in silico model of central metabolism of Escherichia coli for model centric integration of the flux data. The extreme pathways for this network, which define the allowable solution space for all possible flux distributions, are analyzed using the alpha-spectrum. The alpha-spectrum determines which extreme pathways can and cannot contribute to the metabolic flux distribution for a given condition and gives the allowable range of weightings on each extreme pathway that can contribute. Since many extreme pathways cannot be used under certain conditions, the result is a "condition-specific" solution space that is a subset of the original solution space. The alpha-spectrum results are used to create a "condition-specific" extreme pathway matrix that can be analyzed using singular value decomposition (SVD). The first mode of the SVD analysis characterizes the solution space for a given condition. We show that SVD analysis of the alpha-spectrum extreme pathway matrix that incorporates measured uptake and byproduct secretion rates, can predict internal flux trends for different experimental conditions. These predicted internal flux trends are, in general, consistent with the flux trends measured using experimental metabolic flux analysis techniques.

Ammonia↗

A functional profile of gene expression in ARPE-19 cells.

BACKGROUND: Retinal pigment epithelium cells play an important role in the pathogenesis of age related macular degeneration. Their morphological, molecular and functional phenotype changes in response to various stresses. Functional profiling of genes can provide useful information about the physiological state of cells and how this state changes in response to disease or treatment. In this study, we have constructed a functional profile of the genes expressed by the ARPE-19 cell line of retinal pigment epithelium. METHODS: Using Affymetrix MAS 5.0 microarray analysis, genes expressed by ARPE-19 cells were identified. Using GeneChip annotations, these genes were classified according to their known functions to generate a functional gene expression profile. RESULTS: We have determined that of approximately 19,044 unique gene sequences represented on the HG-U133A GeneChip, 6,438 were expressed in ARPE-19 cells irrespective of the substrate on which they were grown (plastic, fibronectin, collagen, or Matrigel). Rather than focus our subsequent analysis on the identity or level of expression of each individual gene in this large data set, we examined the number of genes expressed within 130 functional categories. These categories were selected from a library of HG-U133A GeneChip annotations linked to the Affymetrix MAS 5.0 data sets. Using this functional classification scheme, we were able to categorize about 70% of the expressed genes and condense the original data set of over 6,000 data points into a format with 130 data points. The resulting ARPE-19 Functional Gene Expression Profile is displayed as a percentage of ARPE-19-expressed genes. CONCLUSION: The Profile can readily be compared with equivalent microarray data from other appropriate samples in order to highlight cell-specific attributes or treatment-induced changes in gene expression. The usefulness of these analyses is based on the assumption that the numbers of genes expressed within a functional category provide an indicator of the overall level of activity within that particular functional pathway.

Cell Line↗

BotDB: A database resource for the clostridial neurotoxins.

BotDB is a database designed to encapsulate the rapidly expanding amount of information about the structure and function of the botulinum (BoNT) and tetanus neurotoxins and to track a variety of basic and applied research efforts. The AceDB management system was chosen for this project because of its flexibility in manipulating semistructured data sets and for its information retrieval query languages. In addition to storing amino and nucleic acid sequences of the clostridial neurotoxin genes and proteins, BotDB provides sequence data for new classes of objects, including neurotoxin mutants, substrates and their mutants, associated nontoxic proteins, and C-fragment vaccine candidates. New data types provide information on detection assays for the neurotoxins and on structural data from X-ray crystallographic and circular dichroism spectroscopic studies. Kinetic parameters from biochemical experiments include reaction rates for substrate cleavage and block of neurotransmission. The structures and kinetic characteristics of presently known chemical inhibitors are also being archived. All of these data are associated with citations of the relevant literature for on-line annotation. Graphics viewer programs are provided to display stored images and three-dimensional representations of protein structures. BotDB is in the alpha test phase of development and will become a publicly available Web site.

Amino Acid Sequence↗

Genolevures complete genomes provide data and tools for comparative genomics of hemiascomycetous yeasts.

The Génolevures online database (http://cbi.labri.fr/Genolevures/) provides tools and data relative to 4 complete and 10 partial genome sequences determined and manually annotated by the Génolevures Consortium, to facilitate comparative genomic studies of hemiascomycetous yeasts. With their relatively small and compact genomes, yeasts offer a unique opportunity for exploring eukaryotic genome evolution. The new version of the Génolevures database provides truly complete (subtelomere to subtelomere) chromosome sequences, 25 000 protein-coding and tRNA genes, and in silico analyses for each gene element. A new feature of the database is a novel collection of conserved multi-species protein families and their mapping to metabolic pathways, coupled with an advanced search feature. Data are presented with a focus on relations between genes and genomes: conservation of genes and gene families, speciation, chromosomal reorganization and synteny. The Génolevures site includes an area for specific studies by members of its international community.

Databases, Genetic↗

Identification of FGF-dependent genes in the Drosophila tracheal system.

The embryonic development of the tracheal system of the fruit fly Drosophila provides a paradigm for genetic studies of branching morphogenesis. Efforts of many laboratories have identified Branchless (Bnl, a fibroblast growth factor homologue) and Breathless (Btl, the receptor homologue) as crucial factors at many stages of tracheal system development. The downstream targets of the Bnl/Btl signalling cascade, however, remain mostly unknown. Misexpression of the bnl gene results in specific tracheal phenotypes that lead to larval death. We characterised the transcriptional profiles of targeted over-expression of bnl in the embryonic trachea and of loss-of-function bnl(P1) mutant embryos. Gene expression data was mapped to high-throughput in situ hybridisation based ImaGO-annotation. Thus, we identified and confirmed by quantitative PCR 13 Bnl-dependent genes that are expressed in cells within and outside of the tracheal system.

Amino Acid Sequence↗

GBuilder--an application for the visualization and integration of EST cluster data.

This paper presents a network-centric DNA sequence visualization and analysis tool called GBuilder. The tool is an easy-to-use Java application that can be used to analyze DNA sequence clusters and assemblies. The emphasis is on the analysis of EST data, where these highly redundant collections of low-quality and often alternatively spliced or chimeric sequence data are difficult to explore. The tool has the capacity to visualize similarities or dissimilarities between sequences at the level of the nucleotide base or annotation in many ways. Sequences may also be edited manually. The novel feature of GBuilder is its ability to access different data sources and analysis applications available on the Internet and to integrate these results and functionality back into itself. External resources such as EST cluster databases and conventional command-line analysis applications are integrated and accessed using CORBA (Common Object Request Broker Architecture), which provides a standard implementation independent protocol for integration. New CORBA services can be integrated immediately if they use a known interface described using the Interface Definition Language.

Algorithms↗

Genome Information Broker (GIB): data retrieval and comparative analysis system for completed microbial genomes and more.

Genome Information Broker (GIB) is a powerful tool for the study of comparative genomics. GIB allows users to retrieve and display partial and/or whole genome sequences together with the relevant biological annotation. GIB has accumulated all the completed microbial genome and has recently been expanded to include Arabidopsis thaliana genome data from DDBJ/EMBL/GenBank. In the near future, hundreds of genome sequences will be determined. In order to handle such huge data, we have enhanced the GIB architecture by using XML, CORBA and distributed RDBs. We introduce the new GIB here. GIB is freely accessible at http://gib.genes.nig.ac.jp/.

Arabidopsis↗

A modular class-aware workflow for small RNA sequencing analysis using mouse sperm as a case study.

BACKGROUND: Small RNA sequencing analysis is challenging because RNA classes differ in biogenesis, sequence redundancy, genomic organization, and annotation reliability. Integrated workflows accommodating these constraints remain limited, particularly for fragment-level and cluster-level analysis. METHODS: We present a reproducible, containerized, class-aware workflow for small RNA sequencing analysis, using mouse sperm as a case study. The workflow combines standardized preprocessing with complementary annotation and quantification strategies for microRNAs (miRNAs), transfer RNA-derived small RNAs (tsRNAs), ribosomal RNA-derived small RNAs (rsRNAs), and PIWI-interacting RNA (piRNA)-enriched genomic clusters. Using sperm small RNA data from offspring of lipopolysaccharide (LPS)-exposed male mice, we compared integrated-reference mapping, multi-class annotation, fragment-level tsRNA profiling, and genome-based piRNA cluster analysis, with custom modules for locus-aware harmonization and condition-specific cluster analysis. RESULTS: Integrated-reference mapping aligned 88.17% of reads and retained 690 features after filtering. It identified 11 differentially expressed miRNAs between LPS and controls, while other classes showed limited signal. Fragment-level profiling improved tsRNA resolution. piRNA cluster analysis identified 958 control and 940 LPS clusters, with 18 control-specific and no LPS-specific clusters. CONCLUSION: This workflow supports transparent, reproducible, class-aware interpretation of small RNA sequencing data while emphasizing cautious interpretation of piRNA-enriched signals from total small RNA sequencing.

Small non-coding RNA analysis↗

PROMPT: a protein mapping and comparison tool.

BACKGROUND: Comparison of large protein datasets has become a standard task in bioinformatics. Typically researchers wish to know whether one group of proteins is significantly enriched in certain annotation attributes or sequence properties compared to another group, and whether this enrichment is statistically significant. In order to conduct such comparisons it is often required to integrate molecular sequence data and experimental information from disparate incompatible sources. While many specialized programs exist for comparisons of this kind in individual problem domains, such as expression data analysis, no generic software solution capable of addressing a wide spectrum of routine tasks in comparative proteomics is currently available. RESULTS: PROMPT is a comprehensive bioinformatics software environment which enables the user to compare arbitrary protein sequence sets, revealing statistically significant differences in their annotation features. It allows automatic retrieval and integration of data from a multitude of molecular biological databases as well as from a custom XML format. Similarity-based mapping of sequence IDs makes it possible to link experimental information obtained from different sources despite discrepancies in gene identifiers and minor sequence variation. PROMPT provides a full set of statistical procedures to address the following four use cases: i) comparison of the frequencies of categorical annotations between two sets, ii) enrichment of nominal features in one set with respect to another one, iii) comparison of numeric distributions, and iv) correlation of numeric variables. Analysis results can be visualized in the form of plots and spreadsheets and exported in various formats, including Microsoft Excel. CONCLUSION: PROMPT is a versatile, platform-independent, easily expandable, stand-alone application designed to be a practical workhorse in analysing and mining protein sequences and associated annotation. The availability of the Java Application Programming Interface and scripting capabilities on one hand, and the intuitive Graphical User Interface with context-sensitive help system on the other, make it equally accessible to professional bioinformaticians and biologically-oriented users. PROMPT is freely available for academic users from http://webclu.bio.wzw.tum.de/prompt/.

Computational Biology↗

ViewGene: a graphical tool for polymorphism visualization and characterization.

The human genome project is producing an enormous amount of sequence data, based on which single base changes between individuals can be identified. Unfortunately, computer tools that were adequate for sequence assembly are less than ideal for the characterization of polymorphism data [single nucleotide (snp) or insertion/deletion (indel)] and other sequence features, and their relationship to each other. We have developed viewGene as a flexible tool that takes input from a number of sequence formats and analysis programs (Genbank, FASTA, RepeatMasker, Cross match, BLAST, user-defined data) to construct a sequence reference scaffold that can be viewed through a simple graphical interface. polymorphisms generated from many sources can be added to this scaffold through the same sequence formats, with a variety of options to control what is displayed. Large amounts of polymorphism data can be organized so that patterns and haplotypes can be readily discerned. In our laboratory, viewGene has been used to view annotated genbank records, find nonrepetitive sequence fragments for polymorphism detection, and visualize similarity search results. Manipulation, cross-referencing, and haplotype viewing of snp data are essential for quality assessment and identification of variants associated with genetic disease, and viewGene provides all three of these important functions.

Amino Acid Sequence↗

Functional Annotation Routines Used by ABRF Bioinformatics Core Facilities - Observations, Comparisons, and Considerations.

The functional annotation of gene lists is a common analysis routine required for most genomics experiments, and bioinformatics core facilities must support these analyses. In contrast to methods such as the quantitation of RNA-Seq reads or differential expression analysis, our research group noted a lack of consensus in our preferred approaches to functional annotation. To investigate this observation, we selected 4 experiments that represent a range of experimental designs encountered by our cores and analyzed those data with 6 tools used by members of the Association of Biomolecular Resource Facilities (ABRF) Genomic Bioinformatics Research Group (GBIRG). To facilitate comparisons between tools, we focused on a single biological result for each experiment. These results were represented by a gene set, and we analyzed these gene sets with each tool considered in our study to map the result to the annotation categories presented by each tool. In most cases, each tool produces data that would facilitate identification of the selected biological result for each experiment. For the exceptions, Fisher's exact test parameters could be adjusted to detect the result. Because Fisher's exact test is used by many functional annotation tools, we investigated input parameters and demonstrate that, while background set size is unlikely to have a significant impact on the results, the numbers of differentially expressed genes in an annotation category and the total number of differentially expressed genes under consideration are both critical parameters that may need to be modified during analyses. In addition, we note that differences in the annotation categories tested by each tool, as well as the composition of those categories, can have a significant impact on results.

Computational Biology↗

Large-scale analysis of the yeast genome by transposon tagging and gene disruption.

Economical methods by which gene function may be analysed on a genomic scale are relatively scarce. To fill this need, we have developed a transposon-tagging strategy for the genome-wide analysis of disruption phenotypes, gene expression and protein localization, and have applied this method to the large-scale analysis of gene function in the budding yeast Saccharomyces cerevisiae. Here we present the largest collection of defined yeast mutants ever generated within a single genetic background--a collection of over 11,000 strains, each carrying a transposon inserted within a region of the genome expressed during vegetative growth and/or sporulation. These insertions affect nearly 2,000 annotated genes, representing about one-third of the 6,200 predicted genes in the yeast genome. We have used this collection to determine disruption phenotypes for nearly 8,000 strains using 20 different growth conditions; the resulting data sets were clustered to identify groups of functionally related genes. We have also identified over 300 previously non-annotated open reading frames and analysed by indirect immunofluorescence over 1,300 transposon-tagged proteins. In total, our study encompasses over 260,000 data points, constituting the largest functional analysis of the yeast genome ever undertaken.

Algorithms↗