HGNC: The Why and How of Standardised Gene Nomenclature

The HUGO Gene Nomenclature Committee (HGNC) aims to approve a unique gene symbol and gene name for every human gene. Standardisation of gene symbols is necessary to allow researchers and curators to refer to the same gene without ambiguity. Consistent use of gene symbols in publications and across different websites makes it easy for researchers to find all relevant information for a particular gene and facilitates data mining and retrieval. For each gene that we name we curate relevant information including symbol aliases, chromosomal location, locus type, sequence accessions and links to relevant databases. Therefore, our website is a central resource for human genetics. 
 
We endeavour to approve gene symbols that are acceptable to researchers to encourage widespread use of our symbols. In order to achieve this, we contact researchers that work on particular genes for advice before approving symbols and allow researchers to submit gene symbols to us directly for our consideration. We attend conferences to discuss difficult nomenclature matters and to gain community agreement. We interact with annotators of genes and proteins to provide symbols and names that accurately reflect the nature of each gene and its products. We also work with the gene nomenclature committees for other organisms, and aim to approve equivalent gene symbols for orthologous genes in human and other vertebrate species, especially mouse and rat. 
 
We will demonstrate the steps that are required to name a gene, and will show how and where the nomenclature of a particular gene is used. We will also explain the nature of our collaborations with particular journals and other databases in striving to achieve the use of a common gene nomenclature by all

Molecular Biogeography: Towards an Integrated Framework for Conserving Pan-African Biodiversity

BACKGROUND: Biogeographic models partition ecologically similar species assemblages into discrete ecoregions. However, the history, relationship and interactions between these regions and their assemblages have rarely been explored. METHODOLOGY/PRINCIPAL FINDINGS: Here we develop a taxon-based approach that explicitly utilises molecular information to compare ecoregion history and status, which we exemplify using a continentally distributed mammalian species: the African bushbuck (Tragelaphus scriptus). We reveal unprecedented levels of genetic diversity and structure in this species and show that ecoregion biogeographic history better explains the distribution of molecular variation than phenotypic similarity or geography. We extend these data to explore ecoregion connectivity, identify core habitats and infer ecological affinities from them. CONCLUSIONS/SIGNIFICANCE: This analysis defines 28 key biogeographic regions for sub-Saharan Africa, and provides a valuable framework for the incorporation of genetic and biogeographic information into a more widely applicable model for the conservation of continental biodiversity

A test of reproductive skew models in a field population of a multiple-queen ant

Determining the evolutionary basis of variation in reproductive skew (degree of sharing of reproduction among coexisting individuals) is an important task both because skew varies widely across social taxa and because testing models of skew evolution permits tests of kin selection theory. Using parentage analyses based on microsatellite markers, we measured skew among female eggs (n=32.3 eggs per colony, range=20-68) in 17 polygynous colonies from a UK field population of the ant Leptothorax acervorum. We used skew among eggs as our principal measure of skew because of the high degree of queen turnover in the study population. Queens within colonies did not make significantly unequal contributions to queen and worker adult or pupal offspring, indicating that skew among female eggs reflected skew among daughter queens. On average, both skew among female eggs (measured by the B index) and queen-queen relatedness proved to be low (means±SE=0.06±0.02 and 0.28±0.08, respectively). However, contrary to current skew models, there was no significant association of skew with either relatedness or worker number (used as a measure of productivity). In L. acervorum, predictions of the concession model of skew may hold between but not within populations because queens are unable to assess their relatedness to other queens within colonies. Additional phenomena that may help maintain low skew in the study population include indiscriminate infanticide in the form of egg cannibalism and split sex ratios that penalize reproductive monopoly by single queens within polygynous colonie

High performance computation of landscape genomic models integrating local indices of spatial association

Since its introduction, landscape genomics has developed quickly with the increasing availability of both molecular and topo-climatic data. The current challenges of the field mainly involve processing large numbers of models and disentangling selection from demography. Several methods address the latter, either by estimating a neutral model from population structure or by inferring simultaneously environmental and demographic effects. Here we present Sam$\beta$ada, an integrated approach to study signatures of local adaptation, providing rapid processing of whole genome data and enabling assessment of spatial association using molecular markers. Specifically, candidate loci to adaptation are identified by automatically assessing genome-environment associations. In complement, measuring the Local Indicators of Spatial Association (LISA) for these candidate loci allows to detect whether similar genotypes tend to gather in space, which constitutes a useful indication of the possible kinship relationship between individuals. In this paper, we also analyze SNP data from Ugandan cattle to detect signatures of local adaptation with Sam$\beta$ada, BayEnv, LFMM and an outlier method (FDIST approach in Arlequin) and compare their results. Sam$\beta$ada is an open source software for Windows, Linux and MacOS X available at \url{http://lasig.epfl.ch/sambada}Comment: 1 figure in text, 1 figure in supplementary material The structure of the article was modified and some explanations were updated. The methods and results presented are the same as in the previous versio

Monitoring changes in genetic diversity

DNA is the most elemental level of biodiversity, drives the process of speciation, and underpins other levels of biodiversity, including functional traits, species and ecosystems. Until recently biodiversity indicators have largely overlooked data from the molecular tools that are available for measuring variation at the DNA level. More direct analysis of trends in genetic diversity are now feasible and are ready to be incorporated into biodiversity monitoring. This chapter explores the current state-of-the-art in genetic monitoring, with an emphasis on new molecular tools and the richness of data they provide to supplement existing approaches. We also briefly consider proxy approaches that may be useful for many-species, global scale monitoring cases

Intelligent Tools for Drum Loop Retrieval and Generation

Large libraries of musical data are an increasingly common feature of contemporary computer-based music production practice, with producers often relying heavily on large, curated libraries of data such as loops and samples when making tracks. Drum loop libraries are a particularly common type of library in this context. However, their typically large size, coupled with often poor user interfaces means navigating and exploring them in a fast, easy and enjoyable way is not always possible. Additionally, writing a drum part for a whole track out of many drum loops can be a laborious process, requiring manually editing of many drum loops. The aim of this thesis is to contribute novel techniques based on Music Information Retrieval (MIR) and machine learning that make the process of writing drum tracks using drum loops faster, easier and more enjoyable. We primarily focus on tools for drum loop library navigation and exploration, with additional work on assistive generation of drum loops. We contribute proof-of-concept and prototype tools, Groove Explorer and Groove Explorer 2, for drum loop library exploration based on an interface applying similarity-based visual arrangement of drum loops. Work on Groove Explorer suggested that there were limitations in the existing state-of-the-art approaches to drum loop similarity modelling that must be addressed for tools such as ours to be successful. This was verified via a perceptual study, which identified possible areas of improvement in similarity modelling. Following this, we develop and evaluate a set of novel models for drum loop analysis that capture rhythmic structure and the perceptually relevant qualities of microtiming. Drawing from this, a new approach to drum loop similarity modelling was verified in context as part of Groove Explorer 2, which we evaluated via a user study. The results indicated that our approach could make drum loop library exploration faster, easier and more enjoyable. We finally present an automatic drum loop generation system, jaki, that uses a novel approach for drum loop generation according to user constraints, that could extend Groove Explorer 2 as a drum loop editing and composition tool. Combined, these two systems could offer an end-to-end solution to improved writing of drum tracks

SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data

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genenames.org: the HGNC resources in 2011

The HUGO Gene Nomenclature Committee (HGNC) aims to assign a unique gene symbol and name to every human gene. The HGNC database currently contains almost 30 000 approved gene symbols, over 19 000 of which represent protein-coding genes. The public website, www.genenames.org, displays all approved nomenclature within Symbol Reports that contain data curated by HGNC editors and links to related genomic, phenotypic and proteomic information. Here we describe improvements to our resources, including a new Quick Gene Search, a new List Search, an integrated HGNC BioMart and a new Statistics and Downloads facility