[Toward virtual life from bioinformatics].
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Animals↗
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Results for “Bioinformatics”
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Confocal microscopy and cellular bioinformatics.
An exhaustive description of most biological data requires, besides a set of analytic information, an eidetic representation of the data itself. In this paper, two topics are presented. The first one is confocal microscopy, an advanced technique to produce multidimensional cellular and subcellular structures images. The second one is a software application, named BIOCELL, based on a relational database management system, developed to provide the biologist with an integrated tool to handle simultaneously cell biology linguistic and eidetic information.
Biotechnology↗
Bioinformatics and biotechnology information--some moves in Europe.
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Biotechnology↗
Bioinformatics and molecular medicine--introduction and call for papers.
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Computational Biology↗
The European Bioinformatics Institute. Submission and updating of sequence databases.
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Computational Biology↗
A programming course in bioinformatics for computer and information science students.
We have created a course entitled "Representations and Algorithms for Computational Molecular Biology" with three specific goals in mind. First, we want to provide a technical introduction for computer science and medical information science students to the challenges of computing with molecular biology data, particularly the advantages of having easy access to real-world data sets. Second, we want to equip the students with the skills required of productive research assistants in molecular biology computing research projects. Finally, we want to provide a showcase for local investigators to describe their work in the context of a course that provide adequate background information. In order to achieve these goals, we have created a programming course, in which three major projects and six smaller assignments are assigned during the quarter. We stress fundamental representations and algorithms during the first part of the course in lectures given by the core faculty, and then have more focused lectures in which faculty research interests are highlighted. The course stressed issues of structural molecular biology, in order to better motivate the critical issues in sequence analysis. The culmination of the course was a challenge to the students to use a version of protein threading to predict which members of a set of unknown sequences were globins. The course was well received, and has been made a core requirement in the Medical Information Sciences program.
Algorithms↗
[Novel chemokines identified through bioinformatics].
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Amino Acid Sequence↗
TAMBIS--Transparent Access to Multiple Bioinformatics Information Sources.
The TAMBIS project aims to provide transparent access to disparate biological databases and analysis tools, enabling users to utilize a wide range of resources with the minimum of effort. A prototype system has been developed that includes a knowledge base of biological terminology (the biological Concept Model), a model of the underlying data sources (the Source Model) and a 'knowledge-driven' user interface. Biological concepts are captured in the knowledge base using a description logic called GRAIL. The Concept Model provides the user with the concepts necessary to construct a wide range of multiple-source queries, and the user interface provides a flexible means of constructing and manipulating those queries. The Source Model provides a description of the underlying sources and mappings between terms used in the sources and terms in the biological Concept Model. The Concept Model and Source Model provide a level of indirection that shields the user from source details, providing a high level of source transparency. Source independent, declarative queries formed from terms in the Concept Model are transformed into a set of source dependent, executable procedures. Query formulation, translation and execution is demonstrated using a working example.
Artificial Intelligence↗
AnaBench: a Web/CORBA-based workbench for biomolecular sequence analysis.
BACKGROUND: Sequence data analyses such as gene identification, structure modeling or phylogenetic tree inference involve a variety of bioinformatics software tools. Due to the heterogeneity of bioinformatics tools in usage and data requirements, scientists spend much effort on technical issues including data format, storage and management of input and output, and memorization of numerous parameters and multi-step analysis procedures. RESULTS: In this paper, we present the design and implementation of AnaBench, an interactive, Web-based bioinformatics Analysis workBench allowing streamlined data analysis. Our philosophy was to minimize the technical effort not only for the scientist who uses this environment to analyze data, but also for the administrator who manages and maintains the workbench. With new bioinformatics tools published daily, AnaBench permits easy incorporation of additional tools. This flexibility is achieved by employing a three-tier distributed architecture and recent technologies including CORBA middleware, Java, JDBC, and JSP. A CORBA server permits transparent access to a workbench management database, which stores information about the users, their data, as well as the description of all bioinformatics applications that can be launched from the workbench. CONCLUSION: AnaBench is an efficient and intuitive interactive bioinformatics environment, which offers scientists application-driven, data-driven and protocol-driven analysis approaches. The prototype of AnaBench, managed by a team at the Université de Montréal, is accessible on-line at: http://malawimonas.bcm.umontreal.ca:8091/anabench. Please contact the authors for details about setting up a local-network AnaBench site elsewhere.
Computational Biology↗
Longitudinal characterization of mixed-genotype SARS-CoV-2 infections in a military cohort reveals compartmentalized viral populations.
UNLABELLED: Mixed-genotype severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections are a concern due to the potential generation of novel recombinants that give rise to new variants. To better understand intra-host viral dynamics, we analyzed specimens from 24 participants from the U.S. Military Health System's Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential COVID-19 cohort with suspected mixed-genotype SARS-CoV-2 infections. From an initial 24 suspected cases, we confirmed 17 as genuine coinfections and graded them by evidence: 7 were "strong"; 4 were "moderate"; 6 were "weak"; and 7 were deemed unlikely to be true mixed-genotype infections. Access to swabs from multiple body sites across the course of infection allowed us to observe compartmentalization and shifts in variant dominance that would have been missed by a single-timepoint analysis, as well as one recombinant Omicron BA.1/BA.2 genome. By using an evidence-based bioinformatic framework to assess sequencing data from well-characterized clinical cases, we distinguished genuine coinfections from bioinformatic artifacts. Our findings emphasize the importance of both extensive specimen collection and careful bioinformatic approaches in ascertaining dual genotype infections. IMPORTANCE: Novel recombinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) arise from coinfections with different lineages, but mixed infections are not screened for despite risk to public health, and most surveillance relies on single swabs. We analyzed a longitudinal data set with specimens from multiple body sites, providing an opportunity to assess intra-host dynamics. To distinguish true coinfection from bioinformatic artifacts with confidence, we applied a framework that grades evidence for mixed genotypes by incorporating lineage and clade with manually validated variant calls. This allowed investigation beyond abundance levels of mixed genotypes within a single specimen, including observations of compartmentalization and a recombinant virus. This work enables further study of evolutionary, immunological, and clinical implications of mixed SARS-CoV-2 genotypes. Detecting dual-genotype infections and discriminating between true dual-genotype infection vs potential bioinformatics-based artifacts support public health and military readiness. These efforts provide evidence to bolster decision-making in molecular epidemiological studies to track transmission and for the choice of effective countermeasures.
SARS-CoV-2↗
The power of an integrated informatic and molecular approach to type 1 diabetes research.
Recent years have witnessed an explosive growth in available biological data. This includes a tremendous quantity of sequence data (e.g., biological structures, genetic and physical maps, pathways) generated by genome and transcriptome projects focused on humans, mice, and a multitude of other species. Diabetes research stands to greatly benefit from this data, which is distributed across public and private databases and the scientific literature. The increasing quantity and complexity of this biological data necessitates use of novel bioinformatics strategies for its efficient retrieval, analysis, and interpretation. Bioinformatic capability is becoming increasingly indispensable for fast and comprehensive analysis of biological data by diabetes researchers. There is great potential for diabetes scientists and clinicians to take advantage of recent bioinformatics and knowledge discovery developments to radically transform and advance this field of research. This paper will review advances in the field of bioinformatics relevant to diabetes research and preview a new specialty diabetes database, Diabetaeta, that we are creating to serve as a central bioinformatic portal for type 1 diabetes research, as well as serving as a public repository for beta cell gene and protein expression data.
Animals↗
GNARE: automated system for high-throughput genome analysis with grid computational backend.
Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational analysis in public genomics databases. However, applying the potentially enormous scientific value of this information to the understanding of biological systems requires computing and data storage technology of an unprecedented scale. The Grid, with its aggregated and distributed computational and storage infrastructure, offers an ideal platform for high-throughput bioinformatics analysis. To leverage this we have developed the Genome Analysis Research Environment (GNARE)--a scalable computational system for the high-throughput analysis of genomes, which provides an integrated database and computational backend for data-driven bioinformatics applications. GNARE efficiently automates the major steps of genome analysis including acquisition of data from multiple genomic databases; data analysis by a diverse set of bioinformatics tools; and storage of results and annotations. High-throughput computations in GNARE are performed using distributed heterogeneous Grid computing resources such as Grid2003, TeraGrid, and the DOE Science Grid. Multi-step genome analysis workflows involving massive data processing, the use of application-specific tools and algorithms and updating of an integrated database to provide interactive web access to results are all expressed and controlled by a "virtual data" model which transparently maps computational workflows to distributed Grid resources. This paper describes how Grid technologies such as Globus, Condor, and the Gryphyn Virtual Data System were applied in the development of GNARE. It focuses on our approach to Grid resource allocation and to the use of GNARE as a computational framework for the development of bioinformatics applications.
Computational Biology↗
The construction of meaning in computational integrative biology.
It is the contention of this paper that current data mining work in bioinformatics tends to emphasize data representation to the neglect of another essential aspect of biological systems, namely dynamics. This results in a divorce of both the enterprise and the teaching of bioinformatics from its central aim of meaning-construction. The paper argues that this neglect of dynamics is rooted in an information-processing view of cognitive psychology, and needs to be complemented by a more narrative perspective which emphasizes the explanation, rather than the mere description, of observed patterns in data. This increased emphasis on explanatory narrative in the form of dynamical modeling leads to both a deeper understanding of biological information and a more invigorating approach to the teaching of bioinformatics. The paper presents a cross-curricular teaching framework for a first-year undergraduate course in bioinformatic dynamical modeling which is based around the use of narrative plots.
Communication↗
Bayesian inference on biopolymer models.
MOTIVATION: Most existing bioinformatics methods are limited to making point estimates of one variable, e.g. the optimal alignment, with fixed input values for all other variables, e.g. gap penalties and scoring matrices. While the requirement to specify parameters remains one of the more vexing issues in bioinformatics, it is a reflection of a larger issue: the need to broaden the view on statistical inference in bioinformatics. RESULTS: The assignment of probabilities for all possible values of all unknown variables in a problem in the form of a posterior distribution is the goal of Bayesian inference. Here we show how this goal can be achieved for most bioinformatics methods that use dynamic programming. Specifically, a tutorial style description of a Bayesian inference procedure for segmentation of a sequence based on the heterogeneity in its composition is given. In addition, full Bayesian inference algorithms for sequence alignment are described. AVAILABILITY: Software and a set of transparencies for a tutorial describing these ideas are available at http://www.wadsworth.org/res&res/bioinfo/
Bayes Theorem↗
An ontology for biological function based on molecular interactions.
MOTIVATIONS: A number of important bioinformatics computations involve computing with function: executing computational operations whose inputs or outputs are descriptions of the functions of biomolecules. Examples include performing functional queries to sequence and pathway databases, and determining functional equality to evaluate algorithms that predict function from sequence. A prerequisite to computing with function is the existence of an ontology that provides a structured semantic encoding of function. Functional bioinformatics is an emerging subfield of bioinformatics that is concerned with developing ontologies and algorithms for computing with biological function. RESULTS: The article explores the notion of computing with function, and explains the importance of ontologies of function to bioinformatics. The functional ontology developed for the EcoCyc database is presented. This ontology can encode a diverse array of biochemical processes, including enzymatic reactions involving small-molecule substrates and macromolecular substrates, signal-transduction processes, transport events, and mechanisms of regulation of gene expression. The ontology is validated through its use to express complex functional queries for the EcoCyc DB. CONTACT: pkarp@ai.sri.com
Computational Biology↗
Universal DNA primers amplify bacterial DNA from human fetal membranes and link Fusobacterium nucleatum with prolonged preterm membrane rupture.
A large number of bacterial species have been identified in fetal membranes after preterm labour (PTL) associated with intrauterine infection by microbiological culture. In this study, we have investigated a molecular and bioinformatic approach to organism identification which surmounts the need for specific and diverse microbiological culture conditions required by conventional methods. Samples of fetal membranes were taken from 37 preterm infants, and 6 normal term controls delivered by caesarean section, in which bacteria had been detected by in situ hybridization of 16S ribosomal RNA using a generic probe. Degenerate primers were designed to amplify bacterial 16S ribosomal DNA by PCR and used to amplify bacterial DNA from human fetal membranes. Amplicons were cloned, sequenced and bacteria were identified bioinformatically by comparison of sequences with known bacterial DNA genomes. In situ hybridization using an organism specific probe was then used to confirm the presence of the commonest identified organism in tissue samples. Bacterial DNA amplified from 15/43 samples, all from preterm deliveries, and the bioinformatic approach identified organisms in all cases. Multiple bacteria were identified including Mycoplasma hominis, Pasturella multocida, Pseudomonas PH1, Escherichia coli and Prevotella bivia. The commonest organism Fusobacterium nucleatum was found in 9/15 (60%) of samples. Ten of the 12 samples obtained after prolonged membrane rupture were positive for bacterial DNA, and 7 of these (70%) contained DNA from F. nucleatum. Bacteria from fetal membranes may be identified by molecular and bioinformatic methods. Further work is warranted to investigate the apparent linkage between F. nucleatum, fetal membrane rupture and preterm delivery.
Bacterial Typing Techniques↗
Synthetic community Hi-C benchmarking provides a baseline for virus-host inferences.
Microbiomes influence diverse ecosystems, and viruses increasingly appear to impose key constraints. While viromics has expanded genomic catalogs, host identification for these viruses remains challenging due to the limitations in scaling cultivation-based approaches and the uncertain reliability and relative low resolution of in silico predictions - particularly for understudied viral taxa. Towards this, Hi-C proximity ligation uses sequenced, cross-linked virus and host genomic fragments to infer virus-host linkages and has now been applied in at least ten studies. However, its accuracy remains unknown. Here we assess Hi-C performance in recovering virus-host interactions using synthetic communities (SynComs) composed of four marine bacterial strains and nine phages with known interactions and then apply optimized bioinformatic protocols to natural soil samples. In SynComs, standard Hi-C sample preparations and analyses showed poor normalized contact score performance (26% specificity, 100% sensitivity, incorrect matches up to class level) that could be dramatically improved by Z-score filtering (Z ≥ 0.5, 99% specificity), though at reduced sensitivity (62% down from 100%). Detection limits were established as reproducibility was poor below minimal phage abundances of 105 PFU/mL. Applying optimized bioinformatic protocols to natural soil samples, we compared virus-host linkages inferred from proximity-ligated Hi-C sequencing with predictions generated by in silico homology-based and machine learning-based bioinformatic approaches. Prior to Z-score thresholding, agreement was relatively high at the phylum to family levels (72%), but not at the genus (43%) or species (15%) levels. Z-score thresholding reduced sensitivity (only 34% of predictions were retained), with only modest improvements in congruence with bioinformatic methods (48% or 18% at genus or species levels, respectively). Regardless, this led to 79 genus-level-congruent virus-host linkages and 293 new ones revealed by Hi-C alone - i.e., providing many new virus-host interactions to explore in already well-studied climate-critical soils. Overall, these findings provide empirical benchmarks and methodological guidelines to improve the accuracy and reliability of Hi-C for virus-host linkage studies in complex microbial communities.
Genomics↗
Genome-wide analysis of gene transcription in the hypothalamus.
As the genomic regions containing loci predisposing to obesity-related traits are mapped in human population screens and mouse genetic studies, identification of susceptibility genes will increasingly be facilitated by bioinformatic methods. We hypothesized that candidate genes can be prioritized by their expression levels in tissues of central importance in obesity. Our objective was to develop a combined bioinformatics and molecular paradigm to identify novel genes as candidates for murine or human obesity genetic modifiers based on their differential expression patterns in the hypothalamus compared with other murine tissues. We used bioinformatics tools to search publicly available gene expression databases using criteria designed to identify novel genes differentially expressed in the hypothalamus. We used RNA methods to determine their expression sites and levels of expression in the hypothalamus of the murine brain. We identified the chromosomal location of the novel genes in mice and in humans and compared these locations with those of genetic loci predisposing to obesity-related traits. We developed a search strategy that correctly identified a set of genes known to be important in hypothalamic function as well as a candidate gene for Prader-Willi syndrome that was not previously identified as differentially expressed in the hypothalamus. Using this same strategy, we identified and characterized a set of 11 genes not previously known to be differentially expressed in the murine hypothalamus. Our results demonstrate the feasibility of combined bioinformatics and molecular approaches to the identification of genes that are candidates for obesity-related disorders in humans and mice.
Animals↗