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At least 811 records · Page 45Linked to original sources
PubMed2005

Complete reannotation of the Arabidopsis genome: methods, tools, protocols and the final release.

BACKGROUND: Since the initial publication of its complete genome sequence, Arabidopsis thaliana has become more important than ever as a model for plant research. However, the initial genome annotation was submitted by multiple centers using inconsistent methods, making the data difficult to use for many applications. RESULTS: Over the course of three years, TIGR has completed its effort to standardize the structural and functional annotation of the Arabidopsis genome. Using both manual and automated methods, Arabidopsis gene structures were refined and gene products were renamed and assigned to Gene Ontology categories. We present an overview of the methods employed, tools developed, and protocols followed, summarizing the contents of each data release with special emphasis on our final annotation release (version 5). CONCLUSION: Over the entire period, several thousand new genes and pseudogenes were added to the annotation. Approximately one third of the originally annotated gene models were significantly refined yielding improved gene structure annotations, and every protein-coding gene was manually inspected and classified using Gene Ontology terms.

Alternative Splicing↗
PubMed2005

Atlas - a data warehouse for integrative bioinformatics.

BACKGROUND: We present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development. DESCRIPTION: The Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations. CONCLUSION: The Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data models, enforcing the relationships between data types. Second, integration is achieved through a combination of APIs, ontology, and tools. The Atlas software is freely available under the GNU General Public License at: http://bioinformatics.ubc.ca/atlas/

Computational Biology↗
PubMed2005

Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites.

Protein phosphorylation, which is an important mechanism in posttranslational modification, affects essential cellular processes such as metabolism, cell signaling, differentiation, and membrane transportation. Proteins are phosphorylated by a variety of protein kinases. In this investigation, we develop a novel tool to computationally predict catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the concepts of profile Hidden Markov Models (HMM), computational models are trained from the kinase-specific groups of phosphorylation sites. After evaluating the trained models, we select the model with highest accuracy in each kinase-specific group and provide a Web-based prediction tool for identifying protein phosphorylation sites. The main contribution here is that we have developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity.

Computational Biology↗
PubMed2002

Annotation of human chromosome 21 for relevance to Down syndrome: gene structure and expression analysis.

Down syndrome is caused by an extra copy of human chromosome 21 and the resultant dosage-related overexpression of genes contained within it. To efficiently direct experiments to determine specific gene-phenotype correlations, it is necessary to identify all genes within 21q and assess their functional associations and expression patterns. Analysis of the complete finished sequence of 21q resulted in annotated 225 genes and gene models, most of which were incomplete and/or had little or no experimental verification. Here we correct or complete the genomic structures of 16 genes, 4 of which were not reported in the annotation of the complete sequence. Our data include the identification of six genes encoding short or ambiguous open reading frames; the identification of three cases in which alternative splicing produces two structurally unrelated protein sequences; and the identification of six genes encoding proteins with functional motifs, two genes with unusually low similarity to their orthologous mouse proteins, and four genes with significant conservation in Drosophila melanogaster. We further demonstrate that an additional nine gene models represent bona fide transcripts and develop expression patterns for these genes plus nine additional novel chromosome 21 genes and four paralogous genes mapping elsewhere in the human genome. These data have implications for generating complete transcript maps of chromosome 21 and for the entire human genome, and for defining expression abnormalities in Down syndrome and mouse models.

Animals↗
PubMed2001

Mining functional information associated with expression arrays.

Deciphering the networks of interactions between molecules in biological systems has gained momentum with the monitoring of gene expression patterns at the genomic scale. Expression array experiments provide vast amounts of experimental data about these networks, the analysis of which requires new computational methods. In particular, issues related to the extraction of biological information are key for the end users. We propose here a strategy, implemented in a system called GEISHA (gene expression information system for human analysis) and able to detect biological terms significantly associated to different gene expression clusters by mining collections of Medline abstracts. GEISHA is based on a comparison of the frequency of abstracts linked to different gene clusters and containing a given term. Interpretation by the end user of the biological meaning of the terms is facilitated by embedding them in the corresponding significant sentences and abstracts and by establishing relations with other, equally significant terms. The information provided by GEISHA for the available yeast expression data compares favorably with the functional annotations provided by human experts, demonstrating the potential value of GEISHA as an assistant for the analysis of expression array experiments.

Animals↗
PubMed2026

CanVar-UK: A collaborative platform for germline interpretation in cancer susceptibility genes.

Germline variants in cancer susceptibility genes (CSGs) are typically inherited rather than arising de novo. Hence, wide cascade testing of families across geographies is common, meaning consistency in variant classification is particularly critical. Variant interpretation requires collation of variant-level data from diverse sources, as well as assembly of comprehensive clinical data, often necessitating sharing of information between genomic testing centers. Here, we describe CanVar-UK, a freely accessible web platform bespoke designed to support interpretation of germline CSG variants. CanVar-UK contains variant-level data for over 1.1 million single-nucleotide variants (SNVs), comprising all possible coding SNVs in 116 established CSGs. The data sources with which variants are annotated include in silico scores from 11 clinically relevant tools, population allele frequencies from gnomAD v4.1, case counts from multiple cohorts, including National Health Service (NHS) clinical laboratory testing, variant-level readouts from 47 selected functional and splicing datasets across 19 CSGs, genetic epidemiology studies, and live linkage to existing consensus classifications in the ClinVar database. The diagnostic discussion forum is only available to registered diagnostic scientist users. Through this, a variant-tagged email message can be dispatched in real time across the diagnostic forum community of >1,500 users, with all exchanges and classifications captured and stored in the platform. Already widely used by NHS diagnostic clinical scientists in the UK, CanVar-UK has a rapidly growing international diagnostic user base (>800 UK and >600 non-UK registered users). Survey of the NHS diagnostic user community illustrates the wide-ranging utility of CanVar-UK within their clinical workflows for interpretation of germline CSG variants.

Journal Article↗
PubMed2005

Assessing team performance in the operating room: development and use of a "black-box" recorder and other tools for the intraoperative environment.

BACKGROUND: The objective of this research was to develop a digital system to archive the complete operative environment along with the assessment tools for analysis of this data, allowing prospective studies of operative performance, intraoperative errors, team performance, and communication. Ability to study this environment will yield new insights, allowing design of systems to avoid preventable errors that contribute to perioperative complications. STUDY DESIGN: A multitrack, synchronized, digital audio-visual recording system (RATE tool) was developed to monitor intraoperative performance, including software to synchronize data and allow assignment of independent observational scores. Cases were scored for technical performance, participants' situational awareness (knowledge of critical information), and their comfort and satisfaction with the conduct of the procedure. RESULTS: Laparoscopic cholecystectomy (n = 10) was studied. Technical performance of the RATE tool was excellent. The RATE tool allowed real time, multitrack data collection of all aspects of the operative environment, while permitting digital recording of the objective assessment data in a time synchronized and annotated fashion during the procedure. The mean technical performance score was 73% +/- 28% of maximum (perfect) performance. Situational awareness varied widely among team members, with the attending surgeon typically the only team member having comprehensive knowledge of critical case information. CONCLUSIONS: The RATE tool allows prospective analysis of performance measures such as technical judgments, team performance, and communication patterns, offers the opportunity to conduct prospective intraoperative studies of human performance, and allows for postoperative discussion, review, and teaching. This study also suggests that gaps in situational awareness might be an underappreciated source of operative adverse events. Future uses of this system will aid teaching, failure or adverse event analysis, and intervention research.

Cholecystectomy, Laparoscopic↗
PubMed2004

A description scheme of biological processes based on elementary bricks of action.

With the fast growth of high-throughput strategies in Biology, there is a strong need to accelerate knowledge acquisition and organization of molecular functions. Unfortunately, although we know that there is a correlation between protein molecules and their functions, we are unable to clearly identify this link. Here, we revisit the current views of protein functions as well as their annotation, and we show that they are incompatible with unambiguous interpretations and the use of this knowledge. We describe herein a description scheme for biological processes based on elementary bricks of action that may be associated with biological molecules. To retrieve the descriptive quality found in annotations of other kinds of biological data, it was decided to develop a scheme involving four levels of abstraction: Basic Elements of Action, Biological Activities, Biological Functionalities and Biological Roles. This multi-level organization is a generic method; it allows for a description of biological processes by using a limited number of elementary bricks of action. Moreover, by using this description of biological processes, it should now be possible to clearly identify unambiguous relationships between the organization of biological processes and the structural or functional organizations of biological molecules.

Algorithms↗
PubMed2006

Does everything now make (anti)sense?

The data generated by the FANTOM (Functional Annotation of Mouse) consortium, Compugen and Affymetrix have collectively provided evidence that most of the mammalian genomes are actively transcribed. The emergence of an antisense RNA world brings new practical complexities to the study and detection of gene expression. However, we also need to address the fundamental questions regarding the functional importance of these molecules. In this brief paper, we focus on non-coding natural antisense transcription, as it appears to be a potentially powerful mechanism for extending the complexity of the protein coding genome, which is currently unable to explain inter-species diversification.

Animals↗
PubMed2002

Identification of unstable transcripts in Arabidopsis by cDNA microarray analysis: rapid decay is associated with a group of touch- and specific clock-controlled genes.

mRNA degradation provides a powerful means for controlling gene expression during growth, development, and many physiological transitions in plants and other systems. Rates of decay help define the steady state levels to which transcripts accumulate in the cytoplasm and determine the speed with which these levels change in response to the appropriate signals. When fast responses are to be achieved, rapid decay of mRNAs is necessary. Accordingly, genes with unstable transcripts often encode proteins that play important regulatory roles. Although detailed studies have been carried out on individual genes with unstable transcripts, there is limited knowledge regarding their nature and associations from a genomic perspective, or the physiological significance of rapid mRNA turnover in intact organisms. To address these problems, we have applied cDNA microarray analysis to identify and characterize genes with unstable transcripts in Arabidopsis thaliana (AtGUTs). Our studies showed that at least 1% of the 11,521 clones represented on Arabidopsis Functional Genomics Consortium microarrays correspond to transcripts that are rapidly degraded, with estimated half-lives of less than 60 min. AtGUTs encode proteins that are predicted to participate in a broad range of cellular processes, with transcriptional functions being over-represented relative to the whole Arabidopsis genome annotation. Analysis of public microarray expression data for these genes argues that mRNA instability is of high significance during plant responses to mechanical stimulation and is associated with specific genes controlled by the circadian clock.

Arabidopsis↗
PubMed1999

Ontology for immunogenetics: the IMGT-ONTOLOGY.

MOTIVATION: IMGT, the international ImMunoGeneTics database (http:@imgt.cines.fr:8104), created by M.-P. Lefranc, is an integrated database specializing in antigen receptors (immunoglobulins and T-cell receptors) and major histocompatibility complex (MHC) of all vertebrate species. IMGT accurate immunogenetics data are based on the standardization of the biological knowledge provided by the 'ImMunoGeneTics' IMGT-ONTOLOGY. The IMGT-ONTOLOGY describes the classification and specification of terms needed for immunogenetics and bioinformatics. IMGT-ONTOLOGY covers four main concepts: 'IDENTIFICATION', 'DESCRIPTION', 'CLASSIFICATION' and 'OBTENTION'. These concepts allow an extensive and standardized description and characterization of immunoglobulin and T-cell receptor data. The controlled vocabulary and the annotation rules are indispensable to ensure accuracy, consistency and coherence in IMGT. IMGT-ONTOLOGY allows scientists and clinicians to use, for the first time, identical terms with the same meaning in immunogenetics. It provides a semantic repository that will improve interoperability between specialist and generalist databases.

Animals↗
PubMed2002

Biological information: making it accessible and integrated (and trying to make sense of it).

The availability of the genome sequences of human and mouse, human sequence variation data and other large genetic data sets will lead to a revolution in understanding of the human machine and the treatment of its diseases. The success of the international genome sequencing consortiums shows what can be achieved by well coordinated large scale public domain projects and the benefits of data access to all. It is already clear that the availability of this sequence is having a huge impact on research worldwide. Complete genome sequences provide a framework to pull all biological data together such that each piece has the potential to say something about biology as a whole. Biology is too complex for any organisation to have a monopoly of ideas or data, so the collection, analysis and access to this data can be contributed to by research institutes around the world. However, although it is possible for all this data to be accessible to all through the internet, the more organisations provide data or analysis separately, the harder it becomes for anyone to collect and integrate the results. To address these problems of intergration of data, open standards for biological data exchange, such as the 'Distributed Annotation System' (DAS) are being developed and bioinformatics (Dowell et al., 2001) as a whole is now being strongly driven by the open source software (OSS) model for collaborative software development (Hubbard and Birney, 1999). The leading provider of human genome annotation, the Ensembl project (http://www.ensembl.org), is entirely an OSS project and has been widely adopted by academic and commerical organisations alike (Hubbard et al., 2002). Accurate automatic annotation of features such as genes in vertebrate genomes currently relies on supporting evidence in the form of homologies to mRNAs, ESTs or protein. However, it appears that sufficient high quality experimentally curated annotation now exists to be used as a substrate for machine learning algorithms to create effective models of biological signal sequences (Down and Hubbard, 2002). Is there hope for ab initio prediction methods after all?

Chromosome Mapping↗
PubMed2025

OpTiles: an R package for adaptive tiling and methylation variability profiling.

SUMMARY: OpTiles is an R package that dynamically defines tiling windows based on the distribution of sequenced CpGs, addressing the limitations of traditional fixed-tiling approaches in targeted methylation datasets. By integrating CpG density with intra-region methylation variability, it provides a reliability metric and extended functionality for annotating, prioritizing, and interpreting complex methylation data. AVAILABILITY AND IMPLEMENTATION: OpTiles is implemented in R and source code is freely available at https://github.com/fhaive/OpTiles. Data are available on Zenodo at https://doi.org/10.5281/zenodo.16961292.

DNA Methylation↗
PubMed2005

WebACT--an online companion for the Artemis Comparison Tool.

UNLABELLED: WebACT is an online resource which enables the rapid provision of simultaneous BLAST comparisons between up to five genomic sequences in a format amenable for visualization with the well-known Artemis Comparison Tool (ACT). Comparisons can be generated on-the-fly using sequences directly retrieved via EMBL database queries, or by entering or uploading user sequences. Furthermore, pre-computed comparisons are available between all publicly available, completed prokaryotic genomes and plasmids currently contained within the Genome Reviews database (372 sequences, representing 175 different species). The system is designed to minimize the volume of downloaded data and maximize performance. Genome sequences, annotation and pre-computed comparisons are stored in a relational database allowing flexible querying based on user-defined sequence regions, from whole genome to a defined region flanking a specified gene. Comparison and sequence files, whether computed online or retrieved from the database of pre-computed genome comparisons, can be viewed online using ACT and are available for download. AVAILABILITY: Freely accessible at http://www.webact.org. SUPPLEMENTARY INFORMATION: User guide and worked examples are available at http://www.webact.org/WebACT/docs.

Computational Biology↗
PubMed2006

A lock-and-key model for protein-protein interactions.

MOTIVATION: Protein-protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism's proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs, and also to identify potentially missing or spurious interactions. RESULTS: Our new graph model explains observed interactions between proteins by an underlying interaction of complementary binding domains (lock-and-key model). This leads to a novel graph-theoretical algorithm to identify bipartite subgraphs within protein-protein interaction networks where the underlying data are taken from yeast two-hybrid experimental results. By testing on synthetic data, we demonstrate that under certain modelling assumptions, the algorithm will return correct domain information about each protein in the network. Tests on data from various model organisms show that the local and global patterns predicted by the model are indeed found in experimental data. Using functional and protein structure annotations, we show that bipartite subnetworks can be identified that correspond to biologically relevant interaction motifs. Some of these are novel and we discuss an example involving SH3 domains from the Saccharomyces cerevisiae interactome. AVAILABILITY: The algorithm (in Matlab format) is available (see http://www.maths.strath.ac.uk/~aas96106/lock_key.html).

Algorithms↗
PubMed2006

Detecting functional modules in the yeast protein-protein interaction network.

MOTIVATION: Identification of functional modules in protein interaction networks is a first step in understanding the organization and dynamics of cell functions. To ensure that the identified modules are biologically meaningful, network-partitioning algorithms should take into account not only topological features but also functional relationships, and identified modules should be rigorously validated. RESULTS: In this study we first integrate proteomics and microarray datasets and represent the yeast protein-protein interaction network as a weighted graph. We then extend a betweenness-based partition algorithm, and use it to identify 266 functional modules in the yeast proteome network. For validation we show that the functional modules are indeed densely connected subgraphs. In addition, genes in the same functional module confer a similar phenotype. Furthermore, known protein complexes are largely contained in the functional modules in their entirety. We also analyze an example of a functional module and show that functional modules can be useful for gene annotation. CONTACT: yuan.33@osu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Algorithms↗
PubMed2004

MitoProteome: mitochondrial protein sequence database and annotation system.

MitoProteome is an object-relational mitochondrial protein sequence database and annotation system. The initial release contains 847 human mitochondrial protein sequences, derived from public sequence databases and mass spectrometric analysis of highly purified human heart mitochondria. Each sequence is manually annotated with primary function, subfunction and subcellular location, and extensively annotated in an automated process with data extracted from external databases, including gene information from LocusLink and Ensembl; disease information from OMIM; protein-protein interaction data from MINT and DIP; functional domain information from Pfam; protein fingerprints from PRINTS; protein family and family-specific signatures from InterPro; structure data from PDB; mutation data from PMD; BLAST homology data from NCBI NR; and proteins found to be related based on LocusLink and SWISS-PROT references and sequence and taxonomy data. By highly automating the processes of maintaining the MitoProteome Protein List and extracting relevant data from external databases, we are able to present a dynamic database, updated frequently to reflect changes in public resources. The MitoProteome database is publicly available at http://www. mitoproteome.org/. Users may browse and search MitoProteome, and access a complete compilation of data relevant to each protein of interest, cross-linked to external databases.

Computational Biology↗
PubMed2005

KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites.

KinasePhos is a novel web server for computationally identifying catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the profile hidden Markov model, computational models are learned from the kinase-specific groups of the phosphorylation sites. After evaluating the learned models, the model with highest accuracy was selected from each kinase-specific group, for use in a web-based prediction tool for identifying protein phosphorylation sites. Therefore, this work developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. The prediction tool is freely available at http://KinasePhos.mbc.nctu.edu.tw/.

Computational Biology↗