Incorporating intergenic regions into reversal and transposition distances with indels.

IF 0.7 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Bioinformatics and Computational Biology Pub Date : 2021-12-01 Epub Date: 2021-11-13 DOI:10.1142/S0219720021400114
Alexsandro Oliveira Alexandrino, Andre Rodrigues Oliveira, Ulisses Dias, Zanoni Dias
{"title":"Incorporating intergenic regions into reversal and transposition distances with indels.","authors":"Alexsandro Oliveira Alexandrino,&nbsp;Andre Rodrigues Oliveira,&nbsp;Ulisses Dias,&nbsp;Zanoni Dias","doi":"10.1142/S0219720021400114","DOIUrl":null,"url":null,"abstract":"<p><p>Problems in the genome rearrangement field are often formulated in terms of pairwise genome comparison: given two genomes [Formula: see text] and [Formula: see text], find the minimum number of genome rearrangements that may have occurred during the evolutionary process. This broad definition lacks at least two important considerations: the first being which features are extracted from genomes to create a useful mathematical model, and the second being which types of genome rearrangement events should be represented. Regarding the first consideration, seminal works in the genome rearrangement field solely used gene order to represent genomes as permutations of integer numbers, neglecting many important aspects like gene duplication, intergenic regions, and complex interactions between genes. Regarding the second consideration, some rearrangement events are widely studied such as reversals and transpositions. In this paper, we shed light on the first consideration and created a model that takes into account gene order and the number of nucleotides in intergenic regions. In addition, we consider events of reversals, transpositions, and indels (insertions and deletions) of genomic material. We present a 4-approximation algorithm for reversals and indels, a [Formula: see text]-approximation algorithm for transpositions and indels, and a 6-approximation for reversals, transpositions, and indels.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"19 6","pages":"2140011"},"PeriodicalIF":0.7000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Bioinformatics and Computational Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1142/S0219720021400114","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/11/13 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 3

Abstract

Problems in the genome rearrangement field are often formulated in terms of pairwise genome comparison: given two genomes [Formula: see text] and [Formula: see text], find the minimum number of genome rearrangements that may have occurred during the evolutionary process. This broad definition lacks at least two important considerations: the first being which features are extracted from genomes to create a useful mathematical model, and the second being which types of genome rearrangement events should be represented. Regarding the first consideration, seminal works in the genome rearrangement field solely used gene order to represent genomes as permutations of integer numbers, neglecting many important aspects like gene duplication, intergenic regions, and complex interactions between genes. Regarding the second consideration, some rearrangement events are widely studied such as reversals and transpositions. In this paper, we shed light on the first consideration and created a model that takes into account gene order and the number of nucleotides in intergenic regions. In addition, we consider events of reversals, transpositions, and indels (insertions and deletions) of genomic material. We present a 4-approximation algorithm for reversals and indels, a [Formula: see text]-approximation algorithm for transpositions and indels, and a 6-approximation for reversals, transpositions, and indels.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
将基因间区与索引结合到反转和转位距离中。
基因组重排领域的问题通常用成对基因组比较的方式来表述:给定两个基因组[公式:见文]和[公式:见文],找出在进化过程中可能发生的基因组重排的最小数量。这个宽泛的定义至少缺少两个重要的考虑:第一是从基因组中提取哪些特征来创建有用的数学模型,第二是应该表示哪些类型的基因组重排事件。关于第一个考虑,基因组重排领域的开创性工作仅仅使用基因顺序将基因组表示为整数排列,而忽略了许多重要方面,如基因复制、基因间区域和基因之间复杂的相互作用。关于第二个考虑因素,一些重排事件被广泛研究,如反转和移位。在本文中,我们阐明了第一个考虑因素,并创建了一个考虑基因顺序和基因间区域核苷酸数量的模型。此外,我们还考虑了基因组物质的反转、转位和缺失(插入和缺失)事件。我们提出了一个用于反转和索引的4-近似算法,一个用于换位和索引的[公式:见文本]-近似算法,以及一个用于反转、换位和索引的6-近似算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Bioinformatics and Computational Biology
Journal of Bioinformatics and Computational Biology MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
2.10
自引率
0.00%
发文量
57
期刊介绍: The Journal of Bioinformatics and Computational Biology aims to publish high quality, original research articles, expository tutorial papers and review papers as well as short, critical comments on technical issues associated with the analysis of cellular information. The research papers will be technical presentations of new assertions, discoveries and tools, intended for a narrower specialist community. The tutorials, reviews and critical commentary will be targeted at a broader readership of biologists who are interested in using computers but are not knowledgeable about scientific computing, and equally, computer scientists who have an interest in biology but are not familiar with current thrusts nor the language of biology. Such carefully chosen tutorials and articles should greatly accelerate the rate of entry of these new creative scientists into the field.
期刊最新文献
Predicting ncRNA-Protein interactions with a graph attention model exploiting personalized subgraphs. Early lifespan prediction in Caenorhabditis elegans via contrastive learning and channel attention. Study of the mechanism of step-by-step interaction of viral proteins during replication and transcription. Mendelian randomization and AlphaFold3 analysis suggest putative causal plasma proteins in graves' disease. PLMABFW: A deep learning framework for predicting Antibody-Antigen interactions using protein language model.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1