Symbol Nomenclature Research Articles

Symbol Nomenclature for Graphical Representations of Glycans.

Author(s): Varki, Ajit; Cummings, Richard D; Aebi, Markus; Packer, Nicole H; Seeberger, Peter H; Esko, Jeffrey D; Stanley, Pamela; Hart, Gerald; Darvill, Alan; Kinoshita, Taroh; Prestegard, James J; Schnaar, Ronald L; Freeze, Hudson H; Marth, Jamey D; Bertozzi, Carolyn R; Etzler, Marilynn E; Frank, Martin; Vliegenthart, Johannes Fg; Lutteke, Thomas; Perez, Serge; Bolton, Evan; Rudd, Pauline; Paulson, James; Kanehisa, Minoru; Toukach, Philip; Aoki-Kinoshita, Kiyoko F; Dell, Anne; Narimatsu, Hisashi; York, William; Taniguchi, Naoyuki; Kornfeld, Stuart

Symbol nomenclature for representing glycan structures: Extension to cover different carbohydrate types

This Viewpoint article addresses comments made on our original article describing a symbolic system for the depiction of N- and O-linked carbohydrate structures and proposes a method for extending the symbol set to include monosaccharides commonly found in carbohydrates present in bacteria and plants. As before, basic monosaccharides are shown by shape with one or more additions such as solid fill or additions of lines, crosses or dots to represent functional groups. The use of colour to differentiate constituent monosaccharides is avoided, thus enabling the system to be used in a variety of formats. Linkage and anomericity are shown by the angle and type of line connecting the symbols. In this extended version, new symbols are proposed for additional hexoses and it is proposed that pyranose and furanose forms of the monosaccharides could be shown by solid or broken outlines to the symbols. Conventions for depicting the presence of multiple functional groups such as deoxy-(NH(2))(2) are also discussed. It is hoped that these proposals will stimulate discussion so that a consensus can be reached as to how the glycobiology community can best convey complex information in a simple manner.

Impact of Biological and Environmental Variabilities on Biological Monitoring—An Approach Using Toxicokinetic Models

Biological monitoring of occupational exposure is characterized by important variability, due both to variability in the environment and to biological differences between workers. A quantitative description and understanding of this variability is important for a dependable application of biological monitoring. This work describes this variability, using a toxicokinetic model, for a large range of chemicals for which reference biological reference values exist. A toxicokinetic compartmental model describing both the parent compound and its metabolites was used. For each chemical, compartments were given physiological meaning. Models were elaborated based on physiological, physicochemical, and biochemical data when available, and on half-lives and central compartment concentrations when not available. Fourteen chemicals were studied (arsenic, cadmium, carbon monoxide, chromium, cobalt, ethylbenzene, ethyleneglycol monomethylether, fluorides, lead, mercury, methyl isobutyl ketone, penthachlorophenol, phenol, and toluene), representing 20 biological indicators. Occupational exposures were simulated using Monte Carlo techniques with realistic distributions of both individual physiological parameters and exposure conditions. Resulting biological indicator levels were then analyzed to identify the contribution of environmental and biological variability to total variability. Comparison of predicted biological indicator levels with biological exposure limits showed a high correlation with the model for 19 out of 20 indicators. Variability associated with changes in exposure levels (GSD of 1.5 and 2.0) is shown to be mainly influenced by the kinetics of the biological indicator. Thus, with regard to variability, we can conclude that, for the 14 chemicals modeled, biological monitoring would be preferable to air monitoring. For short half-lives (less than 7 hr), this is very similar to the environmental variability. However, for longer half-lives, estimated variability decreased. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource: tables detailing the CBTK models for all 14 chemicals and the symbol nomenclature that was used.]

Symbol nomenclature for glycan representation

The glycan symbol nomenclature proposed by Harvey et al. in these pages has relative advantages and disadvantages. The use of symbols to depict glycans originated from Kornfeld in 1978, was systematized in the First Edition of "Essentials of Glycobiology" and updated for the second edition, with input from relevant organizations such as the Consortium for Functional Glycomics. We also note that >200 illustrations in the second edition have already been published using our nomenclature and are available for download at PubMed.

An essential glycobiology resource for developmental biologists

![Figure][1] Essentials of Glycobiology, Second Edition Edited by Ajit Varki, Richard D. Cummings, Jeffrey D. Esko, Hudson H. Freeze, Pamela Stanley, Carolyn R. Bertozzi, Gerald W. Hart and Marilynn E. Etzler Cold Spring Harbor Laboratory Press (2009) 784 pages, ISBN 978-087969770-9, $158

RGST - Rat Gene Symbol Tracker, a database for defining official rat gene symbols

BackgroundThe names of genes are central in describing their function and relationship. However, gene symbols are often a subject of controversy. In addition, the discovery of mammalian genes is now so rapid that a proper use of gene symbol nomenclature rules tends to be overlooked. This is currently the situation in the rat and there is a need for a cohesive and unifying overview of all rat gene symbols in use. Based on the experiences in rat gene symbol curation that we have gained from running the "Ratmap" rat genome database, we have now developed a database that unifies different rat gene naming attempts with the accepted rat gene symbol nomenclature rules.DescriptionThis paper presents a newly developed database known as RGST (Rat Gene Symbol Tracker). The database contains rat gene symbols from three major sources: the Rat Genome Database (RGD), Ensembl, and NCBI-Gene. All rat symbols are compared with official symbols from orthologous human genes as specified by the Human Gene Nomenclature Committee (HGNC). Based on the outcome of the comparisons, a rat gene symbol may be selected. Rat symbols that do not match a human ortholog undergo a strict procedure of comparisons between the different rat gene sources as well as with the Mouse Genome Database (MGD). For each rat gene this procedure results in an unambiguous gene designation. The designation is presented as a status level that accompanies every rat gene symbol suggested in the database. The status level describes both how a rat symbol was selected, and its validity.ConclusionThis database fulfils the important need of unifying rat gene symbols into an automatic and cohesive nomenclature system. The RGST database is available directly from the RatMap home page: .

Identification and characterization of genomic DNA sequences of the S‐ribonuclease gene associated with self‐incompatibility allelesS1toS5in European pear

AbstractThe stylar products of the S‐locus for the gametophytic self‐incompatibility (GSI) system in theRosaceaeare ribonucleases (S‐RNases). Recently, sequences for 13 pearS‐RNase alleles have been published and named following a letter–symbol nomenclature (SatoSdandShtoSp). To establish the correspondence between these sequences and the self‐incompatibility alleles we have described previously (S1toS5), we have amplified genomic DNA with consensus primers from the cultivars, ‘Williams’ (S1S2), ‘Coscia’ (S3S4), ‘Butirra Precoce Morettini’ (S1S3), ‘Santa Maria Morettini’ (S2S3) and ‘Doyenne du Comice’ (S4S5) and identified PCR products specifically associated with eachSallele. Cloning and sequencing of the amplification products has revealed that they correspond to European pear sequences already deposited in the database. This allowed us to linkS‐RNase sequences withSallele phenotypes and to determine a correspondence between the symbol–letter nomenclature used to nameS‐RNase sequences and the number‐based nomenclature used to nameSalleles. Based on this result the prediction of new cross‐incompatibilities among pear cultivars is discussed. Finally, we propose a unified number‐based nomenclature to avoid future confusion denominatingSalleles in pear.

Estimating Truncation Error in Image Well Theory

Assume that groundwater production from wells located beyond a distance, say r1, that is large relative to the spacing between adjacent image wells may be treated as an equivalent, continuous, constant production rate per unit of ground surface area. By means of the Theis [1935] equation and the usual symbol nomenclature, the result is s (r1, ∞) = QNw (t/S) [e−u1 − u1 W(u1)[, where s(r1, ∞) is the drawdown at the observation point due to all image wells beyond distance r1, and Nw is the density of wells per unit area. This equation is useful for calculating the minimum radius r1 min of the image well model in which image wells must be included for a given pumping time t to keep the truncation error due to omitted image wells less than some predesignated value s(r1, ∞)max. On the other hand, if an image well model exists and is composed of a fixed number of image wells for approximating an areally infinite system, then the equation will give the maximum allowable pumping time for a given allowable truncation error s(r1, ∞)max.