Genetic Nomenclature

Genetics includes the study of genotypes, phenotypes and the mechanisms of genetic control between them. Genetic terms describe the processes, genes, alleles and traits with which genetic phenomena are described and examined. In this chapter we will concentrate on the discussions of genetic term standardizations and, at the end of the chapter, we will list some terms relevant to genetic processes and concepts in a Genetic Glossary

Standard Genetic Nomenglature of the Pig, with Glossaries

Genetics includes the study of genotypes, phenotypes and the mechanisms of genetic control between them. Genetic terms describe the processes, genes and traits with which genetic phenomena are described and examined. The genetic process terminologies are thoroughly discussed in the previous chapters. Therefore, in this chapter, we will only list the terms for genetic processes and concepts in Appendix I (a general genetic glossary), and concentrate the discussion on pig gene and trait terminologies (Appendix 10; a glossary for pig diseases and defects is also included (Appendix III)

Ontology Development and its Utility in QTL Data Annotation

Two ontologies have been developed to characterize traits in vertebrate species. The Vertebrate Trait Ontology (VT) is a vocabulary of terms describing measurable or observable characteristics related to the morphology, physiology, or development of vertebrates. The livestock Product Trait Ontology (PT) defines those characteristics relevant to products produced by or obtained from animals maintained for use and/or profit. Both ontologies are being used to annotate data in the Animal QTL Database, providing a common basis for comparisons across databases or between species. The VT and PT will benefit the livestock production industry by implementing standardized trait nomenclature to enhance animal improvement accuracy and to unambiguously utilize research outcomes

A Quantitative Trait Loci Resource and Comparison Tool for Pigs: PigQTLDB

The Pig Quantitative Trait Loci (QTL) database has gathered all pig QTL data published during the past 10 years. The database and its peripheral tools make it possible to compare, confirm and locate on pig chromosomes the most feasible location for a gene responsible for quantitative trait important to pig production

Standard Genetic Nomenclature

Genetics includes the study of genotypes and phenotypes, the mechanisms of genetic control between them, and information transfer between generations. Genetic terms describe processes, genes and traits with which genetic phenomena are examined and described. While the genetic terminologies are extensively discussed in this book and elsewhere, the standardization of their names has been an ongoing process. Therefore, this chapter will only concentrate on discussions about the issues involved in the standardization of gene and trait terminologies

Rapid Communication: The Progesterone Receptor (PGR) Gene Maps to Porcine Chromosome 9p13-p11 by a Rodent-Porcine Somatic Cell Hybrid Panel

Genus and Species. Sus scrofa. Locus. Pig Progesterone Receptor ( PGR) gene. Source and Description of Primers. Oligonucleotide primers were designed from a partial pig PGR cDNA sequence (Genbank accession no. S49016) with inferred intron-exon boundary information from humans (Misrahi et al., 1993). The primers were designed to span the intron between exons 7 and 8 of the PGR gene

Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs

Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in knowledge graphs. Existing approaches to KGET focus on how to better encode the knowledge provided by the neighbors and types of an entity into its representation. However, they ignore the semantic knowledge provided by the way in which types can be clustered together. In this paper, we propose a novel method called Multi-view Contrastive Learning for knowledge graph Entity Typing (MCLET), which effectively encodes the coarse-grained knowledge provided by clusters into entity and type embeddings. MCLET is composed of three modules: i) Multi-view Generation and Encoder module, which encodes structured information from entity-type, entity-cluster and cluster-type views; ii) Cross-view Contrastive Learning module, which encourages different views to collaboratively improve view-specific representations of entities and types; iii) Entity Typing Prediction module, which integrates multi-head attention and a Mixture-of-Experts strategy to infer missing entity types. Extensive experiments show the strong performance of MCLET compared to the state-of-the-artComment: Accepted at EMNLP 2023 Mai