Suppr超能文献

CoMutPlotter:一个用于癌症队列中突变可视化汇总的网络工具。

CoMutPlotter: a web tool for visual summary of mutations in cancer cohorts.

机构信息

Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.

Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.

出版信息

BMC Med Genomics. 2019 Jul 11;12(Suppl 5):99. doi: 10.1186/s12920-019-0510-y.

Abstract

BACKGROUND

CoMut plot is widely used in cancer research publications as a visual summary of mutational landscapes in cancer cohorts. This summary plot can inspect gene mutation rate and sample mutation burden with their relevant clinical details, which is a common first step for analyzing the recurrence and co-occurrence of gene mutations across samples. The cBioPortal and iCoMut are two web-based tools that allow users to create intricate visualizations from pre-loaded TCGA and ICGC data. For custom data analysis, only limited command-line packages are available now, making the production of CoMut plots difficult to achieve, especially for researchers without advanced bioinformatics skills. To address the needs for custom data and TCGA/ICGC data comparison, we have created CoMutPlotter, a web-based tool for the production of publication-quality graphs in an easy-of-use and automatic manner.

RESULTS

We introduce a web-based tool named CoMutPlotter to lower the barriers between complex cancer genomic data and researchers, providing intuitive access to mutational profiles from TCGA/ICGC projects as well as custom cohort studies. A wide variety of file formats are supported by CoMutPlotter to translate cancer mutation profiles into biological insights and clinical applications, which include Mutation Annotation Format (MAF), Tab-separated values (TSV) and Variant Call Format (VCF) files.

CONCLUSIONS

In summary, CoMutPlotter is the first tool of its kind that supports VCF file, the most widely used file format, as its input material. CoMutPlotter also provides the most-wanted function for comparing mutation patterns between custom cohort and TCGA/ICGC project. Contributions of COSMIC mutational signatures in individual samples are also included in the summary plot, which is a unique feature of our tool. CoMutPlotter is freely available at http://tardis.cgu.edu.tw/comutplotter .

摘要

背景

CoMut 图在癌症研究出版物中被广泛用作癌症队列突变景观的可视化摘要。该汇总图可以检查基因的突变率和样本的突变负担及其相关的临床细节,这是分析样本间基因突变的重现和共发生的常见第一步。cBioPortal 和 iCoMut 是两个基于网络的工具,允许用户从预加载的 TCGA 和 ICGC 数据中创建复杂的可视化效果。对于自定义数据分析,现在仅提供有限的命令行包,使得难以生成 CoMut 图,特别是对于没有高级生物信息学技能的研究人员来说。为了满足自定义数据和 TCGA/ICGC 数据比较的需求,我们创建了 CoMutPlotter,这是一个基于网络的工具,用于以易用和自动化的方式生成出版质量的图形。

结果

我们引入了一个名为 CoMutPlotter 的基于网络的工具,以降低复杂的癌症基因组数据与研究人员之间的障碍,为 TCGA/ICGC 项目以及自定义队列研究提供直观的突变谱访问。CoMutPlotter 支持多种文件格式,可将癌症突变谱转化为生物学见解和临床应用,包括突变注释格式 (MAF)、制表符分隔值 (TSV) 和变体调用格式 (VCF) 文件。

结论

总之,CoMutPlotter 是第一个支持 VCF 文件的此类工具,VCF 文件是最广泛使用的文件格式,作为其输入材料。CoMutPlotter 还提供了比较自定义队列和 TCGA/ICGC 项目之间突变模式的最需要的功能。个体样本中 COSMIC 突变特征的贡献也包含在汇总图中,这是我们工具的独特功能。CoMutPlotter 可在 http://tardis.cgu.edu.tw/comutplotter 免费获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b38/6624176/a2c031e97b8e/12920_2019_510_Fig1_HTML.jpg

相似文献

1
CoMutPlotter: a web tool for visual summary of mutations in cancer cohorts.
BMC Med Genomics. 2019 Jul 11;12(Suppl 5):99. doi: 10.1186/s12920-019-0510-y.
2
mSignatureDB: a database for deciphering mutational signatures in human cancers.
Nucleic Acids Res. 2018 Jan 4;46(D1):D964-D970. doi: 10.1093/nar/gkx1133.
3
vcfr: a package to manipulate and visualize variant call format data in R.
Mol Ecol Resour. 2017 Jan;17(1):44-53. doi: 10.1111/1755-0998.12549. Epub 2016 Jul 12.
4
Web-TCGA: an online platform for integrated analysis of molecular cancer data sets.
BMC Bioinformatics. 2016 Feb 6;17:72. doi: 10.1186/s12859-016-0917-9.
6
: a web application to identify the most similar mutational signature using shiny.
F1000Res. 2020 Jun 10;9:586. doi: 10.12688/f1000research.24435.2. eCollection 2020.

引用本文的文献

1
Comut-viz: efficiently creating and browsing comutation plots online.
BMC Bioinformatics. 2023 Jun 1;24(1):226. doi: 10.1186/s12859-023-05351-8.
2
A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care.
IEEE Pac Vis Symp. 2022 Apr;2022:101-110. doi: 10.1109/pacificvis53943.2022.00019. Epub 2022 Jun 8.
3
MutScape: an analytical toolkit for probing the mutational landscape in cancer genomics.
NAR Genom Bioinform. 2021 Nov 1;3(4):lqab099. doi: 10.1093/nargab/lqab099. eCollection 2021 Dec.
4
Integration of Online Omics-Data Resources for Cancer Research.
Front Genet. 2020 Oct 23;11:578345. doi: 10.3389/fgene.2020.578345. eCollection 2020.
5
Tumor mutational burden predicts survival in patients with low-grade gliomas expressing mutated IDH1.
Neurooncol Adv. 2020 Mar 27;2(1):vdaa042. doi: 10.1093/noajnl/vdaa042. eCollection 2020 Jan-Dec.
6
CoMut: visualizing integrated molecular information with comutation plots.
Bioinformatics. 2020 Aug 1;36(15):4348-4349. doi: 10.1093/bioinformatics/btaa554.

本文引用的文献

1
VAReporter: variant reporter for cancer research of massive parallel sequencing.
BMC Genomics. 2018 May 9;19(Suppl 2):86. doi: 10.1186/s12864-018-4468-5.
2
mSignatureDB: a database for deciphering mutational signatures in human cancers.
Nucleic Acids Res. 2018 Jan 4;46(D1):D964-D970. doi: 10.1093/nar/gkx1133.
3
Clinical Identification of Oncogenic Drivers and Copy-Number Alterations in Pituitary Tumors.
Endocrinology. 2017 Jul 1;158(7):2284-2291. doi: 10.1210/en.2016-1967.
4
Survival of Del17p CLL Depends on Genomic Complexity and Somatic Mutation.
Clin Cancer Res. 2017 Feb 1;23(3):735-745. doi: 10.1158/1078-0432.CCR-16-0594. Epub 2016 Aug 8.
5
The Ensembl Variant Effect Predictor.
Genome Biol. 2016 Jun 6;17(1):122. doi: 10.1186/s13059-016-0974-4.
6
Understanding mutagenesis through delineation of mutational signatures in human cancer.
Carcinogenesis. 2016 Jun;37(6):531-40. doi: 10.1093/carcin/bgw055. Epub 2016 May 4.
7
Landscape of somatic mutations in 560 breast cancer whole-genome sequences.
Nature. 2016 Jun 2;534(7605):47-54. doi: 10.1038/nature17676. Epub 2016 May 2.
9
Oncotator: cancer variant annotation tool.
Hum Mutat. 2015 Apr;36(4):E2423-9. doi: 10.1002/humu.22771. Epub 2015 Mar 16.
10
Choice of transcripts and software has a large effect on variant annotation.
Genome Med. 2014 Mar 31;6(3):26. doi: 10.1186/gm543. eCollection 2014.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验