轨迹图:一个灵活的基因组数据组合分析工具包。

IF 4.3 2区 生物学 PLoS Computational Biology Pub Date : 2023-09-05 eCollection Date: 2023-09-01 DOI:10.1371/journal.pcbi.1011477
Yiming Zhang, Ran Zhou, Lunxu Liu, Lu Chen, Yuan Wang
{"title":"轨迹图:一个灵活的基因组数据组合分析工具包。","authors":"Yiming Zhang,&nbsp;Ran Zhou,&nbsp;Lunxu Liu,&nbsp;Lu Chen,&nbsp;Yuan Wang","doi":"10.1371/journal.pcbi.1011477","DOIUrl":null,"url":null,"abstract":"<p><p>Here, we introduce Trackplot, a Python package for generating publication-quality visualization by a programmable and interactive web-based approach. Compared to the existing versions of programs generating sashimi plots, Trackplot offers a versatile platform for visually interpreting genomic data from a wide variety of sources, including gene annotation with functional domain mapping, isoform expression, isoform structures identified by scRNA-seq and long-read sequencing, as well as chromatin accessibility and architecture without any preprocessing, and also offers a broad degree of flexibility for formats of output files that satisfy the requirements of major journals. The Trackplot package is an open-source software which is freely available on Bioconda (https://anaconda.org/bioconda/trackplot), Docker (https://hub.docker.com/r/ygidtu/trackplot), PyPI (https://pypi.org/project/trackplot/) and GitHub (https://github.com/ygidtu/trackplot), and a built-in web server for local deployment is also provided.</p>","PeriodicalId":49688,"journal":{"name":"PLoS Computational Biology","volume":"19 9","pages":"e1011477"},"PeriodicalIF":4.3000,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503704/pdf/","citationCount":"0","resultStr":"{\"title\":\"Trackplot: A flexible toolkit for combinatorial analysis of genomic data.\",\"authors\":\"Yiming Zhang,&nbsp;Ran Zhou,&nbsp;Lunxu Liu,&nbsp;Lu Chen,&nbsp;Yuan Wang\",\"doi\":\"10.1371/journal.pcbi.1011477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Here, we introduce Trackplot, a Python package for generating publication-quality visualization by a programmable and interactive web-based approach. Compared to the existing versions of programs generating sashimi plots, Trackplot offers a versatile platform for visually interpreting genomic data from a wide variety of sources, including gene annotation with functional domain mapping, isoform expression, isoform structures identified by scRNA-seq and long-read sequencing, as well as chromatin accessibility and architecture without any preprocessing, and also offers a broad degree of flexibility for formats of output files that satisfy the requirements of major journals. The Trackplot package is an open-source software which is freely available on Bioconda (https://anaconda.org/bioconda/trackplot), Docker (https://hub.docker.com/r/ygidtu/trackplot), PyPI (https://pypi.org/project/trackplot/) and GitHub (https://github.com/ygidtu/trackplot), and a built-in web server for local deployment is also provided.</p>\",\"PeriodicalId\":49688,\"journal\":{\"name\":\"PLoS Computational Biology\",\"volume\":\"19 9\",\"pages\":\"e1011477\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2023-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503704/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PLoS Computational Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1371/journal.pcbi.1011477\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/9/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PLoS Computational Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1371/journal.pcbi.1011477","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在这里,我们介绍Trackplot,这是一个Python包,用于通过可编程和交互式的基于web的方法生成出版物质量的可视化。与现有版本的生鱼片图生成程序相比,Trackplot提供了一个多功能平台,用于直观解释来自各种来源的基因组数据,包括功能域映射的基因注释、异构体表达、scRNA-seq鉴定的异构体结构和长读测序,以及染色质的可访问性和架构,无需任何预处理,还为满足主要期刊要求的输出文件格式提供了广泛的灵活性。Trackplot软件包是一款开源软件,可在Bioconda上免费获得(https://anaconda.org/bioconda/trackplot),Docker(https://hub.docker.com/r/ygidtu/trackplot),PyPI(https://pypi.org/project/trackplot/)和GitHub(https://github.com/ygidtu/trackplot),并且还提供了用于本地部署的内置web服务器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Trackplot: A flexible toolkit for combinatorial analysis of genomic data.

Here, we introduce Trackplot, a Python package for generating publication-quality visualization by a programmable and interactive web-based approach. Compared to the existing versions of programs generating sashimi plots, Trackplot offers a versatile platform for visually interpreting genomic data from a wide variety of sources, including gene annotation with functional domain mapping, isoform expression, isoform structures identified by scRNA-seq and long-read sequencing, as well as chromatin accessibility and architecture without any preprocessing, and also offers a broad degree of flexibility for formats of output files that satisfy the requirements of major journals. The Trackplot package is an open-source software which is freely available on Bioconda (https://anaconda.org/bioconda/trackplot), Docker (https://hub.docker.com/r/ygidtu/trackplot), PyPI (https://pypi.org/project/trackplot/) and GitHub (https://github.com/ygidtu/trackplot), and a built-in web server for local deployment is also provided.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
期刊最新文献
Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model. Ten simple rules for teaching an introduction to R Evolutionary analyses of intrinsically disordered regions reveal widespread signals of conservation. A weak coupling mechanism for the early steps of the recovery stroke of myosin VI: A free energy simulation and string method analysis. Validity conditions of approximations for a target-mediated drug disposition model: A novel first-order approximation and its comparison to other approximations.
×
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:481959085
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