{"title":"GenoSee: a novel visualization tool for graphical genotypes.","authors":"Shumpei Hashimoto","doi":"10.1270/jsbbs.24041","DOIUrl":null,"url":null,"abstract":"<p><p>Visualizing genotypic data is essential in genetic research and breeding programs as it offers clear representations of genomic information, enhancing understanding of genetic architecture. This becomes especially critical with the emergence of next-generation sequencing (NGS) technologies, which generate vast datasets necessitating effective visualization tools. While traditional tools for graphical genotypes have been groundbreaking, they often lack flexibility and universal applicability. These tools encounter limitations such as user-customized visualization and compatibility issues across different operating systems. In this study, I introduce GenoSee, a novel visualization tool designed to address these shortcomings. GenoSee can handle phased and non-phased variant calling data, offering extensive customization to suit diverse research requirements. It operates seamlessly across multiple platforms, ensuring compatibility, and provides high-quality graphical genotypes. GenoSee facilitates deeper insights into genomic structures, thereby advancing genetic and genomic research, and breeding programs by enhancing accessibility to genetic data visualization.</p>","PeriodicalId":9258,"journal":{"name":"Breeding Science","volume":"74 5","pages":"454-461"},"PeriodicalIF":2.0000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11780333/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Breeding Science","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1270/jsbbs.24041","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/23 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
Abstract
Visualizing genotypic data is essential in genetic research and breeding programs as it offers clear representations of genomic information, enhancing understanding of genetic architecture. This becomes especially critical with the emergence of next-generation sequencing (NGS) technologies, which generate vast datasets necessitating effective visualization tools. While traditional tools for graphical genotypes have been groundbreaking, they often lack flexibility and universal applicability. These tools encounter limitations such as user-customized visualization and compatibility issues across different operating systems. In this study, I introduce GenoSee, a novel visualization tool designed to address these shortcomings. GenoSee can handle phased and non-phased variant calling data, offering extensive customization to suit diverse research requirements. It operates seamlessly across multiple platforms, ensuring compatibility, and provides high-quality graphical genotypes. GenoSee facilitates deeper insights into genomic structures, thereby advancing genetic and genomic research, and breeding programs by enhancing accessibility to genetic data visualization.
期刊介绍:
Breeding Science is published by the Japanese Society of Breeding. Breeding Science publishes research papers, notes and reviews
related to breeding. Research Papers are standard original articles.
Notes report new cultivars, breeding lines, germplasms, genetic
stocks, mapping populations, database, software, and techniques
significant and useful for breeding. Reviews summarize recent and
historical events related breeding.
Manuscripts should be submitted by corresponding author. Corresponding author must have obtained permission from all authors
prior to submission. Correspondence, proofs, and charges of excess page and color figures should be handled by the corresponding author.