Suppr超能文献

svpluscnv:复杂结构变异数据的分析和可视化。

svpluscnv: analysis and visualization of complex structural variation data.

机构信息

Department of Genetics and Genomics Sciences, Icahn School of Medicine, New York, NY, 10029, USA.

Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.

出版信息

Bioinformatics. 2021 Jul 27;37(13):1912-1914. doi: 10.1093/bioinformatics/btaa878.

Abstract

MOTIVATION

Despite widespread prevalence of somatic structural variations (SVs) across most tumor types, understanding of their molecular implications often remains poor. SVs are extremely heterogeneous in size and complexity, hindering the interpretation of their pathogenic role. Tools integrating large SV datasets across platforms are required to fully characterize the cancer's somatic landscape.

RESULTS

svpluscnv R package is a swiss army knife for the integration and interpretation of orthogonal datasets including copy number variant segmentation profiles and sequencing-based structural variant calls. The package implements analysis and visualization tools to evaluate chromosomal instability and ploidy, identify genes harboring recurrent SVs and detects complex rearrangements such as chromothripsis and chromoplexia. Further, it allows systematic identification of hot-spot shattered genomic regions, showing reproducibility across alternative detection methods and datasets.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ccbiolab/svpluscnv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

尽管大多数肿瘤类型中普遍存在体细胞结构变异 (SVs),但对其分子影响的理解往往仍然很差。SVs 在大小和复杂性上非常多样,这阻碍了对其致病作用的解释。需要整合来自不同平台的大型 SV 数据集的工具来全面描述癌症的体细胞景观。

结果

svpluscnv R 包是整合和解释正交数据集的瑞士军刀,包括拷贝数变异分割谱和基于测序的结构变异调用。该软件包实现了分析和可视化工具,用于评估染色体不稳定性和倍性,识别包含反复出现 SVs 的基因,并检测复杂重排,如染色体重排和染色体片段融合。此外,它允许系统地识别热点破碎的基因组区域,在不同的检测方法和数据集之间具有可重复性。

可用性和实现

https://github.com/ccbiolab/svpluscnv。

补充信息

补充数据可在生物信息学在线获得。

相似文献

10
Population-based structural variation discovery with Hydra-Multi.使用Hydra-Multi进行基于人群的结构变异发现。
Bioinformatics. 2015 Apr 15;31(8):1286-9. doi: 10.1093/bioinformatics/btu771. Epub 2014 Dec 2.

引用本文的文献

7
Metabolism-associated molecular classification of endometrial carcinoma.子宫内膜癌的代谢相关分子分类
Front Genet. 2023 Jan 16;14:955466. doi: 10.3389/fgene.2023.955466. eCollection 2023.

本文引用的文献

2
Pan-cancer analysis of whole genomes.泛癌症全基因组分析。
Nature. 2020 Feb;578(7793):82-93. doi: 10.1038/s41586-020-1969-6. Epub 2020 Feb 5.
5
Next-generation characterization of the Cancer Cell Line Encyclopedia.下一代癌症细胞系百科全书的特征描述。
Nature. 2019 May;569(7757):503-508. doi: 10.1038/s41586-019-1186-3. Epub 2019 May 8.
7
The landscape of genomic alterations across childhood cancers.儿童癌症中基因组改变的全景。
Nature. 2018 Mar 15;555(7696):321-327. doi: 10.1038/nature25480. Epub 2018 Feb 28.
8
circlize Implements and enhances circular visualization in R.circlize在R语言中实现并增强了圆形可视化。
Bioinformatics. 2014 Oct;30(19):2811-2. doi: 10.1093/bioinformatics/btu393. Epub 2014 Jun 14.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验