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At least 1,009 records · Page 56Linked to original sources
PubMed2004

Genome SEGE: a database for 'intronless' genes in eukaryotic genomes.

BACKGROUND: A number of completely sequenced eukaryotic genome data are available in the public domain. Eukaryotic genes are either 'intron containing' or 'intronless'. Eukaryotic 'intronless' genes are interesting datasets for comparative genomics and evolutionary studies. The SEGE database containing a collection of eukaryotic single exon genes is available. However, SEGE is derived using GenBank. The redundant, incomplete and heterogeneous qualities of GenBank data are a bottleneck for biological investigation in comparative genomics and evolutionary studies. Such studies often require representative gene sets from each genome and this is possible only by deriving specific datasets from completely sequenced genome data. Thus Genome SEGE, a database for 'intronless' genes in completely sequenced eukaryotic genomes, has been constructed. AVAILABILITY: http://sege.ntu.edu.sg/wester/intronless DESCRIPTION: Eukaryotic 'intronless' genes are extracted from nine completely sequenced genomes (four of which are unicellular and five of which are multi-cellular). The complete dataset is available for download. Data subsets are also available for 'intronless' pseudo-genes. The database provides information on the distribution of 'intronless' genes in different genomes together with their length distributions in each genome. Additionally, the search tool provides pre-computed PROSITE motifs for each sequence in the database with appropriate hyperlinks to InterPro. A search facility is also available through the web server. CONCLUSIONS: The unique features that distinguish Genome SEGE from SEGE is the service providing representative 'intronless' datasets for completely sequenced genomes. 'Intronless' gene sets available in this database will be of use for subsequent bio-computational analysis in comparative genomics and evolutionary studies. Such analysis may help to revisit the original genome data for re-examination and re-annotation.

Databases, Genetic↗
PubMed2004

Genome-wide mapping of the cohesin complex in the yeast Saccharomyces cerevisiae.

In eukaryotic cells, cohesin holds sister chromatids together until they separate into daughter cells during mitosis. We have used chromatin immunoprecipitation coupled with microarray analysis (ChIP chip) to produce a genome-wide description of cohesin binding to meiotic and mitotic chromosomes of Saccharomyces cerevisiae. A computer program, PeakFinder, enables flexible, automated identification and annotation of cohesin binding peaks in ChIP chip data. Cohesin sites are highly conserved in meiosis and mitosis, suggesting that chromosomes share a common underlying structure during different developmental programs. These sites occur with a semiperiodic spacing of 11 kb that correlates with AT content. The number of sites correlates with chromosome size; however, binding to neighboring sites does not appear to be cooperative. We observed a very strong correlation between cohesin sites and regions between convergent transcription units. The apparent incompatibility between transcription and cohesin binding exists in both meiosis and mitosis. Further experiments reveal that transcript elongation into a cohesin-binding site removes cohesin. A negative correlation between cohesin sites and meiotic recombination sites suggests meiotic exchange is sensitive to the chromosome structure provided by cohesin. The genome-wide view of mitotic and meiotic cohesin binding provides an important framework for the exploration of cohesins and cohesion in other genomes.

Binding Sites↗
PubMed2004

A sequence alignment-independent method for protein classification.

Annotation of the rapidly accumulating body of sequence data relies heavily on the detection of remote homologues and functional motifs in protein families. The most popular methods rely on sequence alignment. These include programs that use a scoring matrix to compare the probability of a potential alignment with random chance and programs that use curated multiple alignments to train profile hidden Markov models (HMMs). Related approaches depend on bootstrapping multiple alignments from a single sequence. However, alignment-based programs have limitations. They make the assumption that contiguity is conserved between homologous segments, which may not be true in genetic recombination or horizontal transfer. Alignments also become ambiguous when sequence similarity drops below 40%. This has kindled interest in classification methods that do not rely on alignment. An approach to classification without alignment based on the distribution of contiguous sequences of four amino acids (4-grams) was developed. Interest in 4-grams stemmed from the observation that almost all theoretically possible 4-grams (20(4)) occur in natural sequences and the majority of 4-grams are uniformly distributed. This implies that the probability of finding identical 4-grams by random chance in unrelated sequences is low. A Bayesian probabilistic model was developed to test this hypothesis. For each protein family in Pfam-A and PIR-PSD, a feature vector called a probe was constructed from the set of 4-grams that best characterised the family. In rigorous jackknife tests, unknown sequences from Pfam-A and PIR-PSD were compared with the probes for each family. A classification result was deemed a true positive if the probe match with the highest probability was in first place in a rank-ordered list. This was achieved in 70% of cases. Analysis of false positives suggested that the precision might approach 85% if selected families were clustered into subsets. Case studies indicated that the 4-grams in common between an unknown and the best matching probe correlated with functional motifs from PRINTS. The results showed that remote homologues and functional motifs could be identified from an analysis of 4-gram patterns.

Algorithms↗
PubMed1990

[Incidence of exertion hypertension in normotensive individuals].

Exercise hypertension is the blood pressure response to dynamic exercise stress which is considered excessive as compared to what is normally observed in a healthy population. It is a useful indicator to assist in detecting those persons who may develop sustained hypertension. Research has been conducted on a large number of patients, carefully selected using as criteria: arterial pressure, presence of other diseases and age. The examination has been conducted by observing the stressor test on the cycloergometer and annotating the Pa with the traditional method. The data was then analyzed using statistical methods. We conclude that at the moment of maximum effort the patients can be divided into two groups based upon PaD values superior or inferior to 20% of base values or to 95 mmHg. Those who exceed these values (43% of all patients studied) are considered as effort hypertensive subjects. The work's originality derives from the statistically proven consideration that the evaluation of PaD at the 8th minute of the test with cardiac rate between 70 and 90% of the maximum theoretical cardiac rate is predictive of effort hypertension. Hence continuation of the test can be avoided with advantages in terms of time and reduction of the generic risks inherent in carrying out a maximal test. Follow up on the patients is in progress.

Adolescent↗
PubMed2025

Ulmus minor response to Dutch elm disease: de novo transcriptome assembly and annotation.

Dutch elm disease (DED), caused by Ophiostoma novo-ulmi (ONU), has devastated elm populations across Europe and North America since the 20th century. In this work, a de novo transcriptome assembly of Ulmus minor in response to ONU is presented. We used two DED-resistant genotypes, MDV2.3 and VAD2, and one DED-susceptible genotype, MDV1, to capture responses to ONU at four time points post-inoculation (6, 24, 72, and 144 hours). RNA from collected samples was isolated and sequenced producing 60.88 M 100 bp paired-end reads per sample. We performed a de novo transcriptome assembly combining data from the three genotypes. The assembly was functionally annotated and validated through differential gene expression analysis of the response. This dataset provides a valuable resource for studying molecular mechanisms of DED resistance in elms, contributing to broadening our understanding of tree immunity and facilitating potential applications in functional annotation of future genome assemblies.

Transcriptome↗
PubMed2006

Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure.

MOTIVATION: Drawing inferences from large, heterogeneous sets of biological data requires a theoretical framework that is capable of representing, e.g. DNA and protein sequences, protein structures, microarray expression data, various types of interaction networks, etc. Recently, a class of algorithms known as kernel methods has emerged as a powerful framework for combining diverse types of data. The support vector machine (SVM) algorithm is the most popular kernel method, due to its theoretical underpinnings and strong empirical performance on a wide variety of classification tasks. Furthermore, several recently described extensions allow the SVM to assign relative weights to various datasets, depending upon their utilities in performing a given classification task. RESULTS: In this work, we empirically investigate the performance of the SVM on the task of inferring gene functional annotations from a combination of protein sequence and structure data. Our results suggest that the SVM is quite robust to noise in the input datasets. Consequently, in the presence of only two types of data, an SVM trained from an unweighted combination of datasets performs as well or better than a more sophisticated algorithm that assigns weights to individual data types. Indeed, for this simple case, we can demonstrate empirically that no solution is significantly better than the naive, unweighted average of the two datasets. On the other hand, when multiple noisy datasets are included in the experiment, then the naive approach fares worse than the weighted approach. Our results suggest that for many applications, a naive unweighted sum of kernels may be sufficient. AVAILABILITY: http://noble.gs.washington.edu/proj/seqstruct

Algorithms↗
PubMed2001

RESOURCERER: a database for annotating and linking microarray resources within and across species.

Microarray expression analysis is providing unprecedented data on gene expression in humans and mammalian model systems. Although such studies provide a tremendous resource for understanding human disease states, one of the significant challenges is cross-referencing the data derived from different species, across diverse expression analysis platforms, in order to properly derive inferences regarding gene expression and disease state. To address this problem, we have developed RESOURCERER, a microarray-resource annotation and cross-reference database built using the analysis of expressed sequence tags (ESTs) and gene sequences provided by the TIGR Gene Index (TGI) and TIGR Orthologous Gene Alignment (TOGA) databases [now called Eukaryotic Gene Orthologs (EGO)].

Animals↗
PubMed2002

Genome-wide analysis of the odorant-binding protein gene family in Drosophila melanogaster.

Olfaction is of considerable importance to many insects in behaviors critical for survival and reproduction, including location of food sources, selection of mates, recognition of colony con-specifics, and determination of oviposition sites. An ubiquitous, but poorly understood, component of the insect's olfactory system is a group of odorant-binding proteins (OBPs) that are present at high concentrations in the aqueous lymph surrounding the dendrites of olfactory receptor neurons. OBPs are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Here we show that the Drosophila genome carries 51 potential OBP genes, a number comparable to that of its odorant-receptor genes. We find that the majority (73%) of these OBP-like genes occur in clusters of as many as nine genes, in contrast to what has been observed for the Drosophila odorant-receptor genes. Two of the presumptive OBP gene clusters each carries an odorant-receptor gene. We also report an intriguing subfamily of 12 putative OBPs that share a unique C-terminal structure with three conserved cysteines and a conserved proline. Members of this subfamily have not previously been described for any insect. We have performed phylogenetic analyses of the OBP-related proteins in Drosophila as well as other insects, and we discuss the duplication and divergence of the genes for this large family. [The sequence data from this study have been submitted to FlyBase. Annotations for these sequences are available as supplementary material at http://www.genome.org.]

Amino Acid Sequence↗
PubMed2002

Transcriptional response of Pasteurella multocida to defined iron sources.

Pasteurella multocida was grown in iron-free chemically defined medium supplemented with hemoglobin, transferrin, ferritin, and ferric citrate as iron sources. Whole-genome DNA microarrays were used to monitor global gene expression over seven time points after the addition of the defined iron source to the medium. This resulted in a set of data containing over 338,000 gene expression observations. On average, 12% of P. multocida genes were differentially expressed under any single condition. A majority of these genes encoded P. multocida proteins that were involved in either transport and binding or were annotated as hypothetical proteins. Several trends are evident when the data from different iron sources are compared. In general, only two genes (ptsN and sapD) were expressed at elevated levels under all of the conditions tested. The results also show that genes with increased expression in the presence of hemoglobin did not respond to transferrin or ferritin as an iron source. Correspondingly, genes with increased expression in the transferrin and ferritin experiments were expressed at reduced levels when hemoglobin was supplied as the sole iron source. Finally, the data show that genes that were most responsive to the presence of ferric citrate did not follow a trend similar to that of the other iron sources, suggesting that different pathways respond to inorganic or organic sources of iron in P. multocida. Taken together, our results demonstrate that unique subsets of P. multocida genes are expressed in response to different iron sources and that many of these genes have yet to be functionally characterized.

Bacterial Proteins↗
PubMed2005

Whole genome shotgun sequencing of Brassica oleracea and its application to gene discovery and annotation in Arabidopsis.

Through comparative studies of the model organism Arabidopsis thaliana and its close relative Brassica oleracea, we have identified conserved regions that represent potentially functional sequences overlooked by previous Arabidopsis genome annotation methods. A total of 454,274 whole genome shotgun sequences covering 283 Mb (0.44 x) of the estimated 650 Mb Brassica genome were searched against the Arabidopsis genome, and conserved Arabidopsis genome sequences (CAGSs) were identified. Of these 229,735 conserved regions, 167,357 fell within or intersected existing gene models, while 60,378 were located in previously unannotated regions. After removal of sequences matching known proteins, CAGSs that were close to one another were chained together as potentially comprising portions of the same functional unit. This resulted in 27,347 chains of which 15,686 were sufficiently distant from existing gene annotations to be considered a novel conserved unit. Of 192 conserved regions examined, 58 were found to be expressed in our cDNA populations. Rapid amplification of cDNA ends (RACE) was used to obtain potentially full-length transcripts from these 58 regions. The resulting sequences led to the creation of 21 gene models at 17 new Arabidopsis loci and the addition of splice variants or updates to another 19 gene structures. In addition, CAGSs overlapping already annotated genes in Arabidopsis can provide guidance for manual improvement of existing gene models. Published genome-wide expression data based on whole genome tiling arrays and massively parallel signature sequencing were overlaid on the Brassica-Arabidopsis conserved sequences, and 1399 regions of intersection were identified. Collectively our results and these data sets suggest that several thousand new Arabidopsis genes remain to be identified and annotated.

Arabidopsis↗
PubMed2005

Biological master games: using biologists' reasoning to guide algorithm development for integrated functional genomics.

We review some powerful new algorithms that build on the intuitive biological interpretation techniques for statistical analysis of functional genomics experiments. Although they were originally designed for transcriptomics, we argue that these algorithms are applicable to any type of -omics study (transcriptomics, proteomics, metabolomics). Rank Products (RP), a strictly non-parametric test statistic to detect differentially regulated elements (genes, proteins, metabolites) in genome-wide screens. RP is particularly powerful for noisy data and low numbers of replicates and makes full use of the availability of a large number of parallel measurements that is typical of modern large-scale experiments. Iterative Group Analysis (iGA), a statistical method that makes the transition from regulated single elements to significant classes of elements, and thus provides an automatic functional annotation of an experiment. Graph-based iGA (GiGA), an extension of iGA that combines experimental data with a broad variety of biological annotations to highlight physiologically relevant regions in a given "evidence graph" (e.g., metabolic networks, signaling pathway diagrams, protein interaction maps). The sequential application of these techniques yields an increasingly abstract interpretation of experimental data that is at the same time quantitative, statistically rigorous, and biologically significant. The results can be used either as helpful tools to guide data visualization and exploration, or as the input for downstream computational applications in a systems biology framework.

Algorithms↗
PubMed2026

AEGIS: an annotation extraction and genomic integration resource.

MOTIVATION: Genome annotation files (GFF3/GTF) are the standard for storing genomic feature data, yet their flexibility often results in formatting inconsistencies that create bottlenecks for downstream bioinformatics analyses. A robust, unified framework is required to parse, standardise, and validate these files to ensure interoperability and facilitate complex comparative genomic tasks. RESULTS: We present AEGIS (Annotation Extraction and Genomic Integration Suite), a comprehensive toolkit designed to parse, correct, and standardise genome annotations. Beyond quality control, AEGIS provides advanced modules for flexible feature extraction (e.g., coding sequences, promoters) and comparative genomic analysis. Uniquely, it integrates multiple lines of evidence, including sequence homology, synteny, and coordinate-based lift-overs, to assess gene model correspondence and infer orthology. We demonstrate the utility of AEGIS by quantifying complex structural changes between Arabidopsis annotation versions and identifying high-confidence orthologues across diverse plant genomes. AVAILABILITY: AEGIS is implemented in Python. Source code and documentation are freely available under the GPL-3 license at https://github.com/Tomsbiolab/aegis and as a Docker container at https://hub.docker.com/r/tomsbiolab/aegis. The package is also available on PyPI (pip install aegis-bio).

Software↗
PubMed2004

Predicting metal-binding site residues in low-resolution structural models.

The accurate prediction of the biochemical function of a protein is becoming increasingly important, given the unprecedented growth of both structural and sequence databanks. Consequently, computational methods are required to analyse such data in an automated manner to ensure genomes are annotated accurately. Protein structure prediction methods, for example, are capable of generating approximate structural models on a genome-wide scale. However, the detection of functionally important regions in such crude models, as well as structural genomics targets, remains an extremely important problem. The method described in the current study, MetSite, represents a fully automatic approach for the detection of metal-binding residue clusters applicable to protein models of moderate quality. The method involves using sequence profile information in combination with approximate structural data. Several neural network classifiers are shown to be able to distinguish metal sites from non-sites with a mean accuracy of 94.5%. The method was demonstrated to identify metal-binding sites correctly in LiveBench targets where no obvious metal-binding sequence motifs were detectable using InterPro. Accurate detection of metal sites was shown to be feasible for low-resolution predicted structures generated using mGenTHREADER where no side-chain information was available. High-scoring predictions were observed for a recently solved hypothetical protein from Haemophilus influenzae, indicating a putative metal-binding site.

Binding Sites↗
PubMed2002

Yeast genomic expression studies using DNA microarrays.

The exploration and characterization of yeast genomic expression programs is providing a wealth of information about yeast biology, as well as other organisms. The intriguing biology of yeast species invites characterization of genomic expression patterns to illuminate the details of cellular physiology. In addition to its value as an interesting organism, yeast maintains its role as an excellent model in which to characterize genomic expression programs. Microarray studies are quickly spreading to plant, animal, and microbial organisms that remain in the early stages of characterization. The extensive knowledge of yeast biology, as well as the relative ease with which yeast studies can be performed and controlled, facilitates interpretation of the genomic expression data. Importantly, existing information about yeast biology, including functional annotations for each gene, is captured and efficiently presented in databases such as the Saccharomyces Genome Database (SGD), the Munich Information Center Yeast Genome Database (MIPS), the Yeast and Pombe Protein Databases (YPD and PPD, respectively), and others. A number of databases also allow the exploration of published genomic expression studies, including the "Expression Connection" at SGD and the Microarray Global Viewer (yMGV) organized by Marc et al. Consulting these databases to retrieve known details about gene function and regulation vastly facilitates interpretation of the genomic expression data, allowing biological hypotheses to be formulated and tested. These hypotheses can be applied to other organisms that may execute genomic expression programs similar to those seen in yeast. Furthermore, as more genomic expression studies in multiple organisms emerge, large-scale data comparisons can be conducted, within and across organisms. Incorporating the results of yeast studies into such comparisons is certain to increase our understanding about the function, regulation, and evolution of genomic expression programs.

Carbocyanines↗
PubMed2003

A revised annotation and comparative analysis of Helicobacter pylori genomes.

Huge amounts of genomic information are currently being generated. Therefore, biologists require structured, exhaustive and comparative databases. The PyloriGene database (http://genolist.pasteur.fr/PyloriGene) was developed to respond to these needs, by integrating and connecting the information generated during the sequencing of two distinct strains of Helicobacter pylori. This led to the need for a general annotation consensus, as the physical and functional annotations of the two strains differed significantly in some cases. A revised functional classification system was created to accommodate the existing data and to make it possible to classify coding sequences (CDS) into several functional categories to harmonize CDS classification. The annotation of the two complete genomes was revised in the light of new data, allowing us to reduce the percentage of hypothetical proteins from approximately 40 to 33%. This resulted in the reassignment of functions for 108 CDS (approximately 7% of all CDS). Interestingly, the functions of only approximately 13% of CDS (222 out of 1658 CDS) were annotated as a result of work done directly on H.pylori genes. Finally, comparison of the two published genomes revealed a significant amount of size variation between corresponding (orthologous) CDS. Most of these size variations were due to natural polymorphisms, although other sources of variation were identified, such as pseudogenes, new genes potentially regulated by slipped-strand mispairing mechanism, or frame-shifts. 113 of these differences were due to different start codon assignments, a common problem when constructing physical annotations.

Databases, Nucleic Acid↗
PubMed2006

BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities.

BindingDB (http://www.bindingdb.org) is a publicly accessible database currently containing approximately 20,000 experimentally determined binding affinities of protein-ligand complexes, for 110 protein targets including isoforms and mutational variants, and approximately 11,000 small molecule ligands. The data are extracted from the scientific literature, data collection focusing on proteins that are drug-targets or candidate drug-targets and for which structural data are present in the Protein Data Bank. The BindingDB website supports a range of query types, including searches by chemical structure, substructure and similarity; protein sequence; ligand and protein names; affinity ranges and molecular weight. Data sets generated by BindingDB queries can be downloaded in the form of annotated SDfiles for further analysis, or used as the basis for virtual screening of a compound database uploaded by the user. The data in BindingDB are linked both to structural data in the PDB via PDB IDs and chemical and sequence searches, and to the literature in PubMed via PubMed IDs.

Databases, Protein↗
PubMed2006

IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model.

BACKGROUND: Although protein-protein interaction (PPI) networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. RESULTS: By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB) online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics) format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections) algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. CONCLUSION: IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of IntNetDB is freely accessible at http://hanlab.genetics.ac.cn/IntNetDB.htm. Visualization requires Mozilla version 1.8 (or higher) or Internet Explorer with installation of SVGviewer.

Algorithms↗
PubMedDate not supplied

Detailed peptide characterization using PEPTIDEMASS--a World-Wide-Web-accessible tool.

In peptide mass fingerprinting, there are frequently peptides whose masses cannot be explained. These are usually attributed to either a missed cleavage site during the chemical or enzymatic cutting process, the lack of reduction and alkylation of a protein, protein modifications like the oxidation of methionine, or the presence of protein post-translational modifications. However, they could equally be due to database errors, unusual splicing events, variants of a protein in a population, or artifactual protein modifications. Unfortunately the verification of each of these possibilities can be tedious and time-consuming. To better utilize annotated protein databases for the understanding of peptide mass fingerprinting data, we have written the program "PEPTIDEMASS". This program generates the theoretical peptide masses of any protein in the SWISS-PROT database, or of any sequence specified by the user. If the sequence is derived from the SWISS-PROT database, the program takes into account any annotations for that protein in order to generate the peptide masses. In this manner, the user can obtain the predicted masses of peptides from proteins which are known to have signal sequences, propeptides, transit peptides, simple post-translational modifications, and disulfide bonds. Users are also warned if any peptide masses are subject to change from protein isoforms, database conflicts, or an mRNA splicing variation. The program is freely accessible to the scientific community via the ExPASy World Wide Web server, at the URL address: http://www.expasy.ch/www/tools.html.

Amino Acid Sequence↗