Yi Ling Tam, Sarah Cameron, Andrew Preston, Lauren Cowley
{"title":"<i>GWarrange</i>: a pre- and post- genome-wide association studies pipeline for detecting phenotype-associated genome rearrangement events.","authors":"Yi Ling Tam, Sarah Cameron, Andrew Preston, Lauren Cowley","doi":"10.1099/mgen.0.001268","DOIUrl":null,"url":null,"abstract":"<p><p>The use of <i>k</i>-mers to capture genetic variation in bacterial genome-wide association studies (bGWAS) has demonstrated its effectiveness in overcoming the plasticity of bacterial genomes by providing a comprehensive array of genetic variants in a genome set that is not confined to a single reference genome. However, little attempt has been made to interpret <i>k</i>-mers in the context of genome rearrangements, partly due to challenges in the exhaustive and high-throughput identification of genome structure and individual rearrangement events. Here, we present <i>GWarrange</i>, a pre- and post-bGWAS processing methodology that leverages the unique properties of <i>k</i>-mers to facilitate bGWAS for genome rearrangements. Repeat sequences are common instigators of genome rearrangements through intragenomic homologous recombination, and they are commonly found at rearrangement boundaries. Using whole-genome sequences, repeat sequences are replaced by short placeholder sequences, allowing the regions flanking repeats to be incorporated into relatively short <i>k</i>-mers. Then, locations of flanking regions in significant <i>k</i>-mers are mapped back to complete genome sequences to visualise genome rearrangements. Four case studies based on two bacterial species (<i>Bordetella pertussis</i> and <i>Enterococcus faecium</i>) and a simulated genome set are presented to demonstrate the ability to identify phenotype-associated rearrangements. <i>GWarrange</i> is available at https://github.com/DorothyTamYiLing/GWarrange.</p>","PeriodicalId":18487,"journal":{"name":"Microbial Genomics","volume":"10 7","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316554/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microbial Genomics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1099/mgen.0.001268","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
The use of k-mers to capture genetic variation in bacterial genome-wide association studies (bGWAS) has demonstrated its effectiveness in overcoming the plasticity of bacterial genomes by providing a comprehensive array of genetic variants in a genome set that is not confined to a single reference genome. However, little attempt has been made to interpret k-mers in the context of genome rearrangements, partly due to challenges in the exhaustive and high-throughput identification of genome structure and individual rearrangement events. Here, we present GWarrange, a pre- and post-bGWAS processing methodology that leverages the unique properties of k-mers to facilitate bGWAS for genome rearrangements. Repeat sequences are common instigators of genome rearrangements through intragenomic homologous recombination, and they are commonly found at rearrangement boundaries. Using whole-genome sequences, repeat sequences are replaced by short placeholder sequences, allowing the regions flanking repeats to be incorporated into relatively short k-mers. Then, locations of flanking regions in significant k-mers are mapped back to complete genome sequences to visualise genome rearrangements. Four case studies based on two bacterial species (Bordetella pertussis and Enterococcus faecium) and a simulated genome set are presented to demonstrate the ability to identify phenotype-associated rearrangements. GWarrange is available at https://github.com/DorothyTamYiLing/GWarrange.
期刊介绍:
Microbial Genomics (MGen) is a fully open access, mandatory open data and peer-reviewed journal publishing high-profile original research on archaea, bacteria, microbial eukaryotes and viruses.