A short guide to long non-coding RNA gene nomenclature

The HUGO Gene Nomenclature Committee (HGNC) is the only organisation authorised to assign standardised nomenclature to human genes. Of the 38,000 approved gene symbols in our database (http://www.genenames.org), the majority represent protein-coding (pc) genes; however, we also name pseudogenes, phenotypic loci, some genomic features, and to date have named more than 8,500 human non-protein coding RNA (ncRNA) genes and ncRNA pseudogenes. We have already established unique names for most of the small ncRNA genes by working with experts for each class. Small ncRNAs can be defined into their respective classes by their shared homology and common function. In contrast, long non-coding RNA (lncRNA) genes represent a disparate set of loci related only by their size, more than 200 bases in length, share no conserved sequence homology, and have variable functions. As with pc genes, wherever possible, lncRNAs are named based on the known function of their product; a short guide is presented herein to help authors when developing novel gene symbols for lncRNAs with characterised function. Researchers must contact the HGNC with their suggestions prior to publication, to check whether the proposed gene symbol can be approved. Although thousands of lncRNAs have been predicted in the human genome, for the vast majority their function remains unresolved. lncRNA genes with no known function are named based on their genomic context. Working with lncRNA researchers, the HGNC aims to provide unique and, wherever possible, meaningful gene symbols to all lncRNA genes.     

<Emphasis Type="Italic">Vive la différence</Emphasis>: naming structural variants in the human reference genome

The HUGO Gene Nomenclature Committee has approved gene symbols for the majority of protein-coding genes on the human reference genome. To adequately represent regions of complex structural variation, the Genome Reference Consortium now includes alternative representations of some of these regions as part of the reference genome. Here, we describe examples of how we name novel genes in these regions and how this nomenclature is displayed on our website, http://genenames.org.     

Genew: the Human Gene Nomenclature Database

Genew, the Human Gene Nomenclature Database, is the only resource that provides data for all human genes which have approved symbols. It is managed by the HUGO Gene Nomenclature Committee (HGNC) as a confidential database, containing over 16 000 records, 80% of which are represented on the Web by searchable text files. The data in Genew are highly curated by HGNC editors and gene records can be searched on the Web by symbol or name to directly retrieve information on gene symbol, gene name, cytogenetic location, OMIM number and PubMed ID. Data are integrated with other human gene databases, e.g. GDB, LocusLink and SWISS-PROT, and approved gene symbols are carefully co-ordinated with the Mouse Genome Database (MGD). Approved gene symbols are available for querying and browsing at http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl.     

Proposed nomenclature for microhaplotypes

Microhaplotypes are a new type of genetic marker in forensics and population genetics. A standardized nomenclature is desirable. A simple approach that does not require a central authority for approval is proposed. The nomenclature proposed follows the recommendation of the HUGO Gene Nomenclature Committee (http://www.genenames.org): “We strongly encourage naming families and groups of genes related by sequence and/or function using a “root” symbol. This is an efficient and informative way to name related genes, and already works well for a number of established gene families…” The proposal involves a simple root consisting of “mh” followed by the two-digit chromosome number and unique characters established by the authors in the initial publication. We suggest the unique symbol be an indication of the laboratory followed by characters unique to the chromosome and laboratory. For instance, the microhaplotype symbol mh01KK-001 refers to a locus on chromosome 1 published by the Kidd Lab (KK-) as their #001. Publication defines mh01KK-001 as comprised of four single nucleotide polymorphisms (SNPs), rs4648344, rs6663840, rs58111155, and rs6688969.     

HCOP: a searchable database of human orthology predictions

The HUGO Gene Nomenclature Committee (HGNC) Comparison of Orthology Predictions (HCOP) search tool combines the human, mouse, rat and chicken orthology assertions made by PhIGs, HomoloGene, Ensembl, Inparanoid, Mouse Genome Informatics (MGI) and HGNC, enabling users to identify predicted ortholog pairs for a specified gene or genes. The HCOP resource provides a useful method to integrate, compare and access a variety of disparate sources of human orthology data. The HCOP search tool, data and documentation are available at http://www.gene.ucl.ac.uk/hcop.     

Domain-enhanced analysis of microarray data using GO annotations

MOTIVATION: New biological systems technologies give scientists the ability to measure thousands of bio-molecules including genes, proteins, lipids and metabolites. We use domain knowledge, e.g. the Gene Ontology, to guide analysis of such data. By focusing on domain-aggregated results at, say the molecular function level, increased interpretability is available to biological scientists beyond what is possible if results are presented at the gene level. RESULTS: We use a 'top-down' approach to perform domain aggregation by first combining gene expressions before testing for differentially expressed patterns. This is in contrast to the more standard 'bottom-up' approach, where genes are first tested individually then aggregated by domain knowledge. The benefits are greater sensitivity for detecting signals. Our method, domain-enhanced analysis (DEA) is assessed and compared to other methods using simulation studies and analysis of two publicly available leukemia data sets. AVAILABILITY: Our DEA method uses functions available in R (http://www.r-project.org/) and SAS (http://www.sas.com/). The two experimental data sets used in our analysis are available in R as Bioconductor packages, 'ALL' and 'golubEsets' (http://www.bioconductor.org/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

BioGPS and GXD: mouse gene expression data—the benefits and challenges of data integration

Mouse gene expression data are complex and voluminous. To maximize the utility of these data, they must be made readily accessible through databases, and those resources need to place the expression data in the larger biological context. Here we describe two community resources that approach these problems in different but complementary ways: BioGPS and the Mouse Gene Expression Database (GXD). BioGPS connects its large and homogeneous microarray gene expression reference data sets via plugins with a heterogeneous collection of external gene centric resources, thus casting a wide but loose net. GXD acquires different types of expression data from many sources and integrates these data tightly with other types of data in the Mouse Genome Informatics (MGI) resource, with a strong emphasis on consistency checks and manual curation. We describe and contrast the “loose” and “tight” data integration strategies employed by BioGPS and GXD, respectively, and discuss the challenges and benefits of data integration. BioGPS is freely available at http://biogps.org. GXD is freely available through the MGI web site (www.informatics.jax.org) or directly at www.informatics.jax.org/expression.shtml.     

TSGDB: a database system for tumor suppressor genes

A Web-based database system was constructed and implemented that contains 174 tumor suppressor genes. The database homepage was created to accommodate these genes in a pull-down window so that each gene can be viewed individually in a separate Web page. Information displayed on each page includes gene name, aliases, source organism, chromosome location, expression cells/tissues, gene structure, protein size, gene functions and major reference sources. Queries to the database can be conducted through a user-friendly interface, and query results are returned in the HTML format on dynamically generated web pages. AVAILABILITY: The database is available at http://www.cise.ufl.edu/~yy1/HTML-TSGDB/Homepage.html (data files also at http://www.patcar.org/Databases/Tumor_Suppressor_Genes)     

The Mouse Gene Expression Database (GXD)

The Gene Expression Database (GXD) is a community resource of gene expression information for the laboratory mouse. By combining the different types of expression data, GXD aims to provide increasingly complete information about the expression profiles of genes in different mouse strains and mutants, thus enabling valuable insights into the molecular networks that underlie normal development and disease. GXD is integrated with the Mouse Genome Database (MGD). Extensive interconnections with sequence databases and with databases from other species, and the development and use of shared controlled vocabularies extend GXD's utility for the analysis of gene expression information. GXD is accessible through the Mouse Genome Informatics web site at http://www.informatics.jax.org/ or directly at http://www.informatics.jax.org/menus/expression_menu. shtml.     

ESTminer: a suite of programs for gene and allele identification

SUMMARY: ESTminer is a collection of programs that use expressed sequence tag (EST) data from inbred genomes to identify unique genes within gene families. The algorithm utilizes Cap3 to perform an initial clustering of related EST sequences to produce a consensus sequence of a gene family. These consensus sequences are then used to collect all ESTs in the original EST library that are related using BLAST. A redundancy based criterion is applied to each EST to identify reliable unique gene-sequences. Using a highly inbred genome as a source of ESTs eliminates the necessity of computing covariance on each polymorphism to identify alleles of the same gene, thus making this algorithm more streamlined than other alternatives which must computationally attempt to distinguish genes from alleles. AVAILABILITY: The programs were written in PERL and are freely available at http://www.soybase.org/publication_data/Nelson/ESTminer/ESTminer.html CONTACT: nelsonrt@iastate.edu SUPPLEMENTARY INFORMATION: Figures and dataset can be obtained from: http://www.soybase.org/publication_data/Nelson/ESTminer/ESTminer.html.     

PGMapper: a web-based tool linking phenotype to genes

SUMMARY: With the availability of whole genome sequence in many species, linkage analysis, positional cloning and microarray are gradually becoming powerful tools for investigating the links between phenotype and genotype or genes. However, in these methods, causative genes underlying a quantitative trait locus, or a disease, are usually located within a large genomic region or a large set of genes. Examining the function of every gene is very time consuming and needs to retrieve and integrate the information from multiple databases or genome resources. PGMapper is a software tool for automatically matching phenotype to genes from a defined genome region or a group of given genes by combining the mapping information from the Ensembl database and gene function information from the OMIM and PubMed databases. PGMapper is currently available for candidate gene search of human, mouse, rat, zebrafish and 12 other species. AVAILABILITY: Available online at http://www.genediscovery.org/pgmapper/index.jsp.     

It's a knockout!

'It's a Knockout!' provides an update of some of the latest mouse knockouts in TBASE (http://www.jax.org/tbase/ and Ref. 1). The column provides a concise phenotypic profile of novel mutants and renders their complete characterization directly accessible to Web users, via unique and unchanging accession numbers (TBASE identities). Where possible, interesting knockouts will be grouped according to gene families, application or phenotypic similarities.